update(hexbin-basic): altair — comprehensive quality review

[MarkusNeusinger]

Summary

Updated altair implementation for hexbin-basic.

Changes: Comprehensive quality review

Changes

• Enhanced hexbin transform approach
• Better Vega-Lite idioms
• Improved color encoding and sizing

Test Plan

• Preview images uploaded to GCS staging
• Implementation file passes ruff format/check
• Metadata YAML updated with current versions
• Automated review triggered

---

Generated with Claude Code /update command

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a hexagonal binning visualization of GPS traffic density in a metropolitan area (Seattle, based on coordinates ~-122.3°W, ~47.6°N). Hexagons are rendered using the viridis colormap, ranging from dark purple (low vehicle count ~0) to bright yellow (high count ~120+). Three distinct density clusters are visible: a large downtown core cluster in the center-left area showing moderate density (teal/green), a compact shopping district hotspot on the right side with the highest peak density (bright yellow hexagons), and a smaller industrial zone cluster in the upper-left with moderate density. The x-axis is labeled "Longitude (°W)" with values from -122.37 to -122.30, and the y-axis is labeled "Latitude (°N)" from 47.586 to 47.644. A gradient color bar legend on the right shows "Vehicle Count" from 0 to 120+. The title "hexbin-basic · altair · pyplots.ai" is centered at the top. Hexagons have thin white strokes providing clean separation. The y-axis has very dense tick labels (every 0.002°).

Score: 83/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	13	20
Spec Compliance	14	15
Data Quality	14	15
Code Quality	8	10
Library Mastery	7	10
Total	83	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 28pt, axis titles 22pt, ticks 18pt, legend title 20pt, legend labels 16pt). All meet thresholds. Deducting 1 point because the y-axis has ~30 tick labels packed tightly every 0.002°, creating visual noise.
• VQ-02: No Overlap (5/6) — No text physically overlaps, but the y-axis tick labels are extremely dense (30 labels from 47.586 to 47.644). Near-overlap condition reduces readability.
• VQ-03: Element Visibility (6/6) — Hexagons are perfectly sized and tile correctly with computed hex_area. White stroke edges provide clean separation. Color gradient makes density differences immediately visible.
• VQ-04: Color Accessibility (4/4) — Viridis colormap is perceptually uniform and colorblind-safe. Good contrast between density levels.
• VQ-05: Layout & Canvas (3/4) — Plot fills canvas well with 1600×900 dimensions. Minor wasted space due to irregular data shape, and the legend could be slightly better integrated.
• VQ-06: Axis Labels & Title (2/2) — "Longitude (°W)" and "Latitude (°N)" — descriptive with units. Title follows correct format.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Above defaults: custom SVG hexagon shapes, viridis colormap, white stroke edges creating a polished tiled appearance, and thoughtful axis formatting. Not yet publication-ready — would benefit from a more refined overall composition.
• DE-02: Visual Refinement (4/6) — Good refinement: view stroke removed (strokeWidth=0), grid opacity at 0.15 (subtle), domain color customized to feat(seaborn): implement chord-basic #888. White hex strokes add visual polish. Could go further with background color and axis styling.
• DE-03: Data Storytelling (4/6) — Multiple density clusters create a clear narrative about urban traffic patterns. The viridis color gradient naturally draws the eye to hotspots. Three distinct zones (downtown, shopping, industrial) at different densities tell a coherent spatial story.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct hexagonal binning plot with points aggregated into hexagonal bins colored by density count.
• SC-02: Required Features (3/4) — Color bar present ✓, viridis colormap ✓, gridsize controllable via hex_radius ✓. Spec suggests "consider log scale for color mapping when density varies widely" — not implemented despite ~120x density variation (1 to 120+).
• SC-03: Data Mapping (3/3) — X=longitude, Y=latitude, color=count. All correctly mapped. Axes show full data range.
• SC-04: Title & Legend (3/3) — Title "hexbin-basic · altair · pyplots.ai" in correct format. Legend "Vehicle Count" is descriptive and appropriate.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows multiple density clusters at different concentration levels demonstrating the key hexbin feature of revealing density patterns. Could show even more dramatic density contrast.
• DQ-02: Realistic Context (5/5) — GPS coordinates for Seattle metropolitan traffic density. Real coordinates, plausible scenario (urban traffic analytics), neutral topic.
• DQ-03: Appropriate Scale (4/4) — Latitude/longitude realistic for Seattle. Vehicle counts 0-120+ reasonable for hex bin aggregation. 5000 total points within spec's recommended range.

Code Quality (8/10)

• CQ-01: KISS Structure (2/3) — Follows Import→Data→Plot→Save structure without functions/classes. However, the manual hexagonal binning computation (lines 32-63) is verbose — 30+ lines of manual hex grid math that could potentially be simplified.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — altair, numpy, pandas — all used, no unused imports.
• CQ-04: Code Elegance (1/2) — The manual hex coordinate computation is functional but verbose. The hex_area calculation with magic constant 1.15 reduces clarity. Understandable given Altair's lack of native hexbin.
• CQ-05: Output & API (1/1) — Saves as plot.png with scale_factor=3.0 and plot.html. Current Altair 6.0 API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of Altair's declarative grammar: mark_point with custom SVG shape, encoding channels, alt.Scale/Axis/Legend/Title/Tooltip. The manual hexbin computation outside Altair is necessary since Altair lacks native hexbin, but it means the library's built-in aggregation transforms aren't utilized.
• LM-02: Distinctive Features (3/5) — Custom SVG path for hexagon mark shapes is a distinctive Altair technique. Tooltip encoding with formatted fields leverages Altair's interactive capabilities. HTML export alongside PNG. configure_view/configure_axis for styling.

Score Caps Applied

• None — no cap conditions triggered (DE-01=5 > 2, all other checks pass)

Strengths

• Creative use of custom SVG hexagon path with mark_point — a clever workaround for Altair's lack of native hexbin
• Realistic GPS coordinate data with three distinct density clusters creating meaningful spatial patterns
• Good visual polish: viridis colormap, white hex strokes, subtle grid, and explicitly set font sizes
• Proper title format and descriptive axis labels with units

Weaknesses

• Y-axis tick labels are too dense (every 0.002°, ~30 labels) creating visual noise — reduce tick frequency
• Manual hexbin computation is verbose (30+ lines) — could be streamlined
• No log scale considered for color mapping despite ~120x density variation
• Background and overall composition could be more refined for publication quality

Issues Found

1. VQ-02 MODERATE: Y-axis ticks too dense — 30 tick labels packed every 0.002° reduces readability
   • Fix: Increase y-axis tick step to 0.005° or use tickCount to limit to ~12-15 labels
2. SC-02 MINOR: Log scale for color not considered despite density ranging 1-120+
   • Fix: Add scale=alt.Scale(scheme="viridis", type="log") or type="symlog" on color encoding
3. CQ-01 MODERATE: Hex binning computation is verbose at ~30 lines
   • Fix: Streamline the hex coordinate computation, remove intermediate variables where possible
4. DE-01 MODERATE: Design is good but not yet at publication level
   • Fix: Consider adding a subtle background color, refine axis tick formatting, and ensure visual hierarchy is maximized

AI Feedback for Next Attempt

Reduce y-axis tick density (tickCount or larger step) to eliminate visual crowding. Consider log or symlog color scale for better density differentiation given the 120x range. Streamline the manual hex binning code — the computation is correct but could be more concise. For design excellence, consider a subtle background tint, further axis refinement, and ensuring the color gradient maximally differentiates between density levels.

Verdict: REJECTED

[Copilot]

Pull request overview

Updates the Altair implementation for the hexbin-basic plot in plots/, aiming to improve the hexbin approach and bring the implementation/metadata up to date.

Changes:

• Replaced the prior rectangular binning approach with a manual hexagonal bin aggregation and a custom hexagon mark.
• Updated Altair implementation styling/encodings (color, size, grid).
• Updated Altair metadata (timestamps, generator, Python version, quality score field).

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
plots/hexbin-basic/implementations/altair.py	Switches to manual hexbin aggregation and renders hexagons via a custom point shape; updates chart encodings and export.
plots/hexbin-basic/metadata/altair.yaml	Updates generation metadata (updated time, generator, Python version) and modifies quality_score.

[Copilot]

quality_score was changed from a numeric value to null. In this repo, plot metadata almost always keeps quality_score as a 0–100 number, and setting it to null may break consumers that expect a score (and it no longer matches the header). If the score is not yet known, consider keeping the previous value or updating it to the new reviewed score instead of null.

Suggested change

	quality_score: null
	quality_score: 60

[Copilot]

col_idx is computed but never used. Please remove it (or use it) to avoid dead code and keep the binning logic easier to follow.

Suggested change

col_idx = np.round(longitude / dx).astype(int)

[Copilot]

The plot header is malformed: the Quality: line is missing the numeric score ("Quality: /100"), and the date label changed from the usual Created:/Updated: pattern used elsewhere. This breaks the standard 4-line header format used across plot implementations and can also confuse any tooling that parses the header.

Suggested change

	Quality: /100 | Updated: 2026-02-21
	Quality: 90/100 | Created: 2026-02-21

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a hexagonal binning visualization of GPS-like traffic density data in the Seattle metropolitan area. Hexagons are colored using the viridis colormap, ranging from dark purple (low vehicle count ~5-10) to bright yellow (high count ~100+). Two primary density hotspots are visible: a downtown core cluster around longitude -122.34 to -122.35 / latitude 47.60-47.61, and a shopping district cluster near longitude -122.31 / latitude 47.62. A sparser industrial zone cluster appears in the upper-left. The x-axis is labeled "Longitude (°W)" and the y-axis "Latitude (°N)". A color legend titled "Vehicle Count" sits in the top-right corner showing the symlog scale. The background is a subtle off-white (#f9f9fb) with white hexagon stroke edges providing definition. The title reads "hexbin-basic · altair · pyplots.ai" at top center.

Score: 84/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	6	10
Total	84	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 28, axes 22, ticks 18, legend title 20, legend labels 16). However, the x-axis tick labels have what appears to be a duplicate value (-122.35 appearing twice) due to the .2f format combined with auto tick placement, which is a minor formatting flaw.
• VQ-02: No Overlap (5/6) — Text elements are clean with no overlap. However, hexagonal markers show slight overlap/layering at edges where hexagons don't tile perfectly due to the mark_point approach, creating a subtle 3D-stacking artifact.
• VQ-03: Element Visibility (5/6) — Hexagons are clearly visible with good color differentiation. Density patterns are easy to read. Minor inconsistencies in hexagon sizing at the edges of the data range.
• VQ-04: Color Accessibility (4/4) — Viridis colormap is perceptually uniform and colorblind-safe. Excellent contrast between low-density (dark purple) and high-density (yellow) regions.
• VQ-05: Layout & Canvas (3/4) — The hex pattern fills a substantial portion of the canvas. Some empty space in the upper-right and lower-left corners due to the natural data shape. Legend is well-positioned near the data.
• VQ-06: Axis Labels & Title (2/2) — "Longitude (°W)" and "Latitude (°N)" are descriptive with units.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Above defaults with custom off-white background (#f9f9fb), configured axis domain/tick/label colors ([bullet-basic] Basic Bullet Chart #999, feat(plotnine): implement scatter-basic #444, [area-basic] plotnine implementation #333), white hexagon strokes for definition, and viridis with symlog scale. Thoughtful but not yet publication-ready.
• DE-02: Visual Refinement (4/6) — View stroke removed, subtle grid (opacity 0.12), custom background, configured axis styling. Good refinement but could push further with more whitespace or cleaner grid treatment.
• DE-03: Data Storytelling (4/6) — The data clearly shows two distinct density hotspots with varying density levels, creating a natural visual hierarchy through the viridis colormap. The viewer can immediately identify the downtown and shopping district clusters. Could be elevated with annotations labeling the clusters.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct hexagonal binning plot. Creative approach using mark_point with custom SVG hexagon path since Altair lacks built-in hexbin.
• SC-02: Required Features (4/4) — Color bar/legend showing density scale ✓, perceptually uniform colormap (viridis) ✓, gridsize control via hex_radius ✓, symlog scale for wide density variation ✓.
• SC-03: Data Mapping (3/3) — X=longitude (numeric), Y=latitude (numeric), Color=count (density). All axes show full data range.
• SC-04: Title & Legend (3/3) — Title format "hexbin-basic · altair · pyplots.ai" is correct. Legend label "Vehicle Count" is descriptive and matches the data context.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows multiple density clusters (downtown, shopping district, industrial zone) with good variation from sparse to dense. Demonstrates the key hexbin advantage of revealing density patterns. Could have slightly wider count range for more dramatic effect.
• DQ-02: Realistic Context (5/5) — GPS coordinates match Seattle area (lat ~47.59-47.64, lon ~-122.30 to -122.37). Traffic density is a real-world application listed in the spec. "Vehicle Count" is a neutral, comprehensible metric.
• DQ-03: Appropriate Scale (4/4) — Coordinates are geographically accurate for Seattle. Vehicle counts (5-100+) are plausible per-hexbin values. 5000 data points is appropriate for this visualization type.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → hex binning computation → plot → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at the start.
• CQ-03: Clean Imports (2/2) — altair, numpy, pandas — all used.
• CQ-04: Code Elegance (2/2) — The manual hex binning is necessarily complex since Altair lacks built-in hexbin, but the implementation is clean and well-structured. The SVG hexagon path approach is clever. No fake UI elements.
• CQ-05: Output & API (1/1) — Saves as plot.png with scale_factor=3.0 and plot.html. Uses current Altair API.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (3/5) — The Altair portion (Chart, mark_point, encode, properties, configure) is idiomatic. However, a significant portion of the code is numpy/pandas hex binning computation rather than leveraging Altair's declarative grammar. Altair's strength is its declarative API, and having extensive imperative data processing somewhat dilutes this.
• LM-02: Distinctive Features (3/5) — Uses custom SVG path in mark_point (distinctive Vega-Lite/Altair feature), interactive tooltips, symlog scale type, configure_view/configure_axis for styling, and HTML export. The custom shape approach is a creative use of Altair's SVG path support.

Score Caps Applied

• None — no caps triggered

Strengths

• Creative solution to Altair's lack of built-in hexbin using custom SVG hexagon path with mark_point (keep this approach)
• Realistic GPS data context with Seattle coordinates and multi-cluster traffic density pattern (keep this data story)
• Full spec compliance with all required features including colorbar, viridis, symlog scale
• Clean code structure with proper seed, explicit font sizes, and thoughtful axis configuration
• Good design foundation with custom background, subtle grid, configured axis colors, and white hex strokes

Weaknesses

• Hexagon tiling has slight overlap/layering artifacts at edges due to mark_point sizing approach
• X-axis tick labels show potential duplicate values from .2f formatting with auto tick placement
• Design polish not yet at publication level — needs stronger visual identity
• Library mastery constrained by the amount of imperative numpy/pandas code for hex computation

Issues Found

1. VQ-01 MINOR: X-axis tick label formatting creates near-duplicate values with .2f format
   • Fix: Use explicit tick values via axis=alt.Axis(values=[...]) to avoid duplicate-appearing labels, or use .3f format
2. VQ-02 MINOR: Hexagon tiling shows slight overlap/layering at edges
   • Fix: Fine-tune hex_area calculation or add small padding to reduce overlap artifacts
3. DE-01 MODERATE: Design is above defaults but not yet publication-quality
   • Fix: Consider a more sophisticated color scheme, add a subtitle describing the data context, or use a custom font hierarchy
4. LM-01 MODERATE: Heavy reliance on numpy/pandas for hex binning
   • Fix: Consider using Altair's built-in bin transform with mark_rect for a more declarative approach, or use transform_bin to push more computation into the Vega-Lite spec

AI Feedback for Next Attempt

To reach 90+, focus on: (1) Fix x-axis tick formatting to avoid duplicate-looking labels — use explicit tick values or .3f format. (2) Fine-tune hexagon sizing to reduce tiling overlap artifacts. (3) Elevate design excellence — consider adding a subtitle with data context, refine the color legend placement, increase whitespace around the plot. (4) Explore whether Altair's transform_bin or mark_rect could replace some of the manual hex computation for more idiomatic library usage. The data story and spec compliance are strong — the main gaps are in visual polish and library mastery.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a hexagonal binning visualization of Seattle metropolitan traffic density based on 5,000 GPS vehicle observations. The chart uses pointy-top hexagonal shapes rendered via custom SVG paths, colored using the viridis colormap ranging from dark purple (low vehicle counts, ~1-10) through teal/green (medium, ~20-60) to bright yellow (high, ~80-100+). Three distinct geographic clusters are visible: a Downtown Core cluster (bottom-center) with the highest density bright-yellow hexagons, a Shopping District cluster (right side, around lat 47.62) with broad medium-to-high density, and a smaller Industrial Zone cluster (upper-left) that is sparser overall but has a tight bright center. Each cluster is labeled with a text annotation using a white halo effect for readability. The background is light gray (#f9f9fb), grid lines are extremely subtle (0.08 opacity), and the view has no border stroke. A vertical color bar on the right labeled "Vehicle Count" shows the density scale. The title reads "hexbin-basic · altair · pyplots.ai" with a subtitle "Seattle metropolitan traffic density — 5,000 GPS vehicle observations." Axis labels show "Longitude (°W)" and "Latitude (°N)" with formatted tick values.

Score: 92/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	9	10
Total	92	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 28pt, axis titles 22pt, tick labels 18pt, subtitle 18pt, legend title 20pt, legend labels 16pt, annotation text 16pt. All perfectly readable.
• VQ-02: No Overlap (6/6) — No text collisions. Annotations placed outside data clusters at offset positions. Legend well-separated from chart area.
• VQ-03: Element Visibility (5/6) — Hexagons well-sized with dynamically computed hex_area proportional to chart dimensions. White stroke (0.4px) provides clean separation. Minor: some edge hexagons appear slightly inconsistent in apparent size.
• VQ-04: Color Accessibility (4/4) — Viridis colormap is perceptually uniform and colorblind-safe. Excellent contrast from dark purple to bright yellow.
• VQ-05: Layout & Canvas (3/4) — Good proportions at 1600×900 with balanced padding. Data fills ~60% of chart area. Some natural empty space in corners due to data distribution shape.
• VQ-06: Axis Labels & Title (2/2) — "Longitude (°W)" and "Latitude (°N)" are descriptive with units.

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Thoughtful design: viridis + symlog scale for density, light gray background (#f9f9fb), custom axis colors (#aaa domain/tick, feat(plotly): implement donut-labeled #555 labels, [area-basic] plotnine implementation #333 titles), white hexagon borders, text halo effect on annotations. Clearly above defaults.
• DE-02: Visual Refinement (5/6) — View stroke removed, very subtle grid (gridOpacity=0.08, gridColor=#ccc), light background, custom padding, muted axis styling. Nearly all visual noise eliminated.
• DE-03: Data Storytelling (5/6) — Three distinct urban zones create a clear narrative. Text annotations guide the viewer immediately. Subtitle provides context. Computed density levels (High/Medium/Low) in tooltips add depth. Viewer instantly understands the density pattern.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct hexagonal binning plot, manually computed hex grid with proper pointy-top hexagonal shapes.
• SC-02: Required Features (4/4) — Color bar present with gradient legend. Viridis perceptually uniform colormap. Symlog scale for wide density range (spec suggests considering log scale). Hex resolution controlled via hex_radius parameter.
• SC-03: Data Mapping (3/3) — X=Longitude, Y=Latitude correctly mapped. Axes use zero=False with appropriate range showing all data.
• SC-04: Title & Legend (3/3) — Title format "hexbin-basic · altair · pyplots.ai" correct. Legend labeled "Vehicle Count" is descriptive and matches data.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows density variation effectively with three clusters at different densities (high downtown, medium shopping, sparse industrial). Demonstrates hexbin's advantage over scatter for dense data. Minor: Industrial zone's tight cluster produces a bright hotspot that could slightly confuse the density narrative.
• DQ-02: Realistic Context (5/5) — GPS coordinates for Seattle traffic density using actual geographic coordinates (~47.6°N, ~122.3°W). Named zones (Downtown Core, Shopping District, Industrial Zone) are plausible and neutral.
• DQ-03: Appropriate Scale (4/4) — Realistic latitude/longitude values for Seattle. 5,000 GPS observations is appropriate for hex binning. Bin counts (1-100+) are sensible for this data size and grid resolution.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data generation → hex computation → chart layers → composition → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at the start.
• CQ-03: Clean Imports (2/2) — All three imports (altair, numpy, pandas) are used.
• CQ-04: Code Elegance (2/2) — Manual hex binning computation is necessary since Altair lacks built-in hexbin. Code is well-organized with clear comments. Appropriate complexity.
• CQ-05: Output & API (1/1) — Saves plot.png with scale_factor=3.0 and plot.html. Uses current Altair 6.x API.

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) — Expert use of Altair API: alt.layer() for composition, encoding types (Q, N), alt.Scale/Axis/Legend/Title configuration, transform_calculate for computed fields, configure_view and configure_axis for global styling.
• LM-02: Distinctive Features (4/5) — Uses several Altair-specific features: alt.selection_point with pointerover for interactive hover, conditional encoding (strokeWidth varies on hover state), custom SVG shape path for mark_point, rich tooltip encoding with formatting, HTML export leveraging Vega-Lite's web-native architecture.

Score Caps Applied

• None — No cap conditions triggered.

Strengths

• Manual hexagonal binning computation is well-implemented and produces clean, properly tiled hexagons
• Excellent use of Altair-specific features: interactive hover selection, conditional encoding, custom SVG shape paths, and rich tooltips
• Strong data storytelling with three named geographic clusters and contextual subtitle
• High visual refinement: subtle grid, removed view stroke, light background, text halo annotations, custom axis styling
• Symlog color scale handles density variation effectively as suggested by the spec

Weaknesses

• Some edge hexagons appear slightly inconsistent in visual size compared to central ones
• Industrial zone's tight cluster creates a bright hotspot that slightly muddies the density narrative (appears as dense as downtown locally)

Issues Found

None critical. Minor visual inconsistencies at hexagon edges.

AI Feedback for Next Attempt

N/A — Implementation approved.

Verdict: APPROVED