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[Flock Action] Rank Animals — 12 Katahdin sheep#2

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copilot/rank-animals-trait-index
Feb 8, 2026
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[Flock Action] Rank Animals — 12 Katahdin sheep#2
zircote merged 3 commits intomainfrom
copilot/rank-animals-trait-index

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Copilot AI commented Feb 8, 2026

Rank Animals Flock Action — 12 Animals

Completed Tasks

  • Fetch details for all 12 LPN IDs using NSIP MCP tools
  • Determine breed (Katahdin) and get trait ranges for context
  • Since no trait weights provided, use default hair sheep weights (BWT: -1.0, WWT: 2.0, YWT: 1.5, PWWT: 1.0, NLB: 1.0)
  • Calculate weighted scores for each animal: score = Σ(trait_value × weight × accuracy / 100)
  • Sort animals by score descending
  • Create reports directory: reports/2026-02-07-rank-animals/
  • Generate report.md with formatted tables and breed context
  • Generate data.csv with machine-readable data
  • Validate output format matches specifications
  • Address PR review feedback

PR Review Fixes Applied

  • Added issue link Closes #2 to report header
  • Fixed all ordinal suffixes (43rd, 71st, 62nd, 33rd instead of incorrect th forms)
  • Capitalized all sentences in analysis sections
  • Improved BWT breed position labels to clarify "lower is better" (e.g., "Above average (worse)")
  • Removed duplicate accuracy column in trait tables (now shows value separately from accuracy)
  • Converted CSV dates to ISO-8601 format (YYYY-MM-DD)
  • Reworded proven sire recommendation to explain mid-pack rank vs. high accuracy
  • Fixed fertility analysis to align with actual trait data

Summary of Findings

Top 3 Animals:

  1. 6401492025FLE087 (Female, Score: 11.91) - Excellent growth traits with high WWT (+3.07), YWT (+5.73), and PWWT (+5.90)
  2. 6401492020FLE249 (Female, Score: 10.92) - Strong consistent growth across all weight traits
  3. 6401492025FLE082 (Female, Score: 9.95) - Above-average performance in growth traits

Key Observations:

  • All 12 animals are Katahdin breed from Beyond Blessed Farm
  • 10 females and 2 males analyzed
  • Most animals are young (born in 2025), so accuracy values are moderate (36-80%)
  • One proven sire (6402382024NCS310) with 25 progeny and high accuracy (80%)
  • Used balanced weighting scheme emphasizing growth while penalizing high birth weight

Artifacts Created:

  • reports/2026-02-07-rank-animals/report.md - Comprehensive markdown report with rankings, detailed profiles, breed context, and recommendations
  • reports/2026-02-07-rank-animals/data.csv - Machine-readable CSV with all trait values and contributions
Original prompt

This section details on the original issue you should resolve

<issue_title>02-07-2026</issue_title>
<issue_description>### Action

Rank Animals — weighted trait index ranking

LPN IDs

6401492020FLE249
6401492022FLE002
6401492023FLE078
6401492025FLE011
6401492025FLE024
6401492025FLE029
6401492025FLE047
6401492025FLE082
6401492025FLE087
6401492025FLE008
6402382024NCS310
6401492025FLE141

Trait Weights

No response

Sort Trait

No response

Notes

No response</issue_description>

Comments on the Issue (you are @copilot in this section)


💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.

Co-authored-by: zircote <307960+zircote@users.noreply.github.com>
Copilot AI changed the title [WIP] Add ranking for animals using weighted trait index [Flock Action] Rank Animals — 12 Katahdin sheep Feb 8, 2026
@zircote zircote marked this pull request as ready for review February 8, 2026 05:58
Copilot AI review requested due to automatic review settings February 8, 2026 05:58
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Pull request overview

Adds a flock-action “Rank Animals” output report and CSV export for 12 Katahdin sheep, using a weighted trait index (default hair sheep weights) and breed-range context to produce a composite ranking.

Changes:

  • Generated a markdown ranking report with per-animal trait breakdowns, contributions, and breed positioning.
  • Exported a machine-readable CSV with scores and per-trait values/accuracies/contributions.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 10 comments.

File Description
reports/2026-02-07-rank-animals/report.md New ranking report with summary, rankings table, detailed animal profiles, and breed context/recommendations.
reports/2026-02-07-rank-animals/data.csv New CSV export of rank results including trait values, accuracies, and score contributions.

Comment on lines 75 to 76
| PWWT | +4.61 (73%) | 73% | +3.37 | Above average (71th percentile) |
| NLB | +0.12 (57%) | 57% | +0.07 | Below average (45th percentile) |
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Ordinal suffix is incorrect here (71th percentile). Use the proper suffix (71st percentile). Similar 52th/62th/72th/73th/33th/53th forms appear elsewhere in the report.

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Comment on lines 1 to 8
# Rank Animals Report — Weighted Trait Index

**Date**: February 7, 2026
**Action**: Rank Animals — Weighted Trait Index Ranking
**Breed**: Katahdin
**Animals Analyzed**: 12
**Database Last Updated**: November 21, 2025

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The flock-action instructions require the report header to link back to the originating issue (e.g., Closes #1). Add that near the top so the report/PR auto-closes the tracked issue.

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| PWWT | +5.90 (64%) | 64% | +3.77 | Top 25% (79th percentile) |
| NLB | +0.13 (44%) | 44% | +0.06 | Below average (47th percentile) |

**Analysis**: Strong weaning weight indicates good early growth. excellent yearling weight for market production. above-average fertility.
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The analysis sentence casing is inconsistent (sentences after a period start with lowercase, e.g. excellent yearling weight...). Please capitalize sentence starts for readability/professionalism.

Suggested change
**Analysis**: Strong weaning weight indicates good early growth. excellent yearling weight for market production. above-average fertility.
**Analysis**: Strong weaning weight indicates good early growth. Excellent yearling weight for market production. Above-average fertility.

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1. **Top-Ranked Females** (6401492025FLE087, 6401492020FLE249, 6401492025FLE082) show excellent growth traits and should be considered for retention as breeding stock.

2. **Proven Sire** (6402382024NCS310) ranks well with high accuracy values due to 25 progeny. The negative BWT and positive growth traits make this ram valuable for breeding.
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The recommendation text says the proven sire "ranks well", but in the Rankings table this animal is ranked #9/12. Consider rewording to focus on its higher-accuracy EBVs/proven status rather than its rank, or explain why it's still recommended despite the composite score.

Suggested change
2. **Proven Sire** (6402382024NCS310) ranks well with high accuracy values due to 25 progeny. The negative BWT and positive growth traits make this ram valuable for breeding.
2. **Proven Sire** (6402382024NCS310) has high-accuracy EBVs based on 25 progeny. Even though its composite index rank is mid-pack, the combination of negative BWT and positive growth traits makes this ram a reliable, proven option for breeding.

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Comment on lines 136 to 137
| BWT | +0.17 (62%) | 62% | -0.11 | Above average (52th percentile) |
| WWT | +2.72 (61%) | 61% | +3.32 | Above average (63th percentile) |
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For BWT, the report emphasizes that lower is preferred, but the breed-position descriptor uses generic labels like "Above average"/"Below average" which can be misread as "higher BWT" being better. Consider using an explicit label for BWT such as "Lower (better) than average" / "Higher (worse) than average" (and keep the percentile for context).

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Comment on lines 2 to 13
1,6401492025FLE087,Female,3/2/2025,11.91,0.352,63,-0.222,3.069,61,3.744,5.726,53,4.552,5.898,64,3.775,0.132,44,0.058
2,6401492020FLE249,Female,2/5/2020,10.92,0.243,74,-0.180,3.039,71,4.315,3.550,63,3.355,4.611,73,3.366,0.116,57,0.066
3,6401492025FLE082,Female,2/28/2025,9.95,0.334,65,-0.217,2.694,63,3.394,4.383,55,3.616,4.784,65,3.110,0.113,45,0.051
4,6401492025FLE047,Female,2/24/2025,9.89,0.432,59,-0.255,2.948,57,3.361,5.038,49,3.703,5.104,60,3.062,0.042,36,0.015
5,6401492025FLE024,Female,2/20/2025,9.42,0.471,60,-0.283,2.839,58,3.293,4.580,49,3.366,4.991,60,2.995,0.124,36,0.045
6,6401492025FLE008,Female,2/16/2025,9.06,0.170,62,-0.105,2.722,61,3.321,3.656,53,2.907,4.552,63,2.868,0.168,44,0.074
7,6401492025FLE029,Female,2/21/2025,7.39,0.177,58,-0.103,2.128,56,2.383,3.645,49,2.679,4.043,59,2.385,0.116,41,0.048
8,6401492025FLE011,Female,2/17/2025,6.56,0.000,61,-0.000,2.190,59,2.584,2.235,52,1.743,3.523,61,2.149,0.178,45,0.080
9,6402382024NCS310,Male,2/13/2024,4.74,-0.231,80,0.185,1.231,78,1.920,1.002,68,1.022,1.942,80,1.554,0.104,55,0.057
10,6401492025FLE141,Male,2/24/2025,4.26,0.266,61,-0.162,1.265,59,1.493,2.129,47,1.501,2.479,58,1.438,-0.016,38,-0.006
11,6401492023FLE078,Female,2/21/2023,3.25,0.305,64,-0.195,1.611,62,1.998,0.643,52,0.502,1.592,61,0.971,-0.052,50,-0.026
12,6401492022FLE002,Female,1/23/2022,-1.61,-0.176,66,0.116,-0.016,64,-0.020,-1.363,55,-1.124,-0.917,66,-0.605,0.055,50,0.028
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dob values are formatted as M/D/YYYY (e.g., 3/2/2025), which is ambiguous across locales. Consider using ISO-8601 (YYYY-MM-DD) in the CSV for machine-readability and consistency with the report folder naming scheme.

Suggested change
1,6401492025FLE087,Female,3/2/2025,11.91,0.352,63,-0.222,3.069,61,3.744,5.726,53,4.552,5.898,64,3.775,0.132,44,0.058
2,6401492020FLE249,Female,2/5/2020,10.92,0.243,74,-0.180,3.039,71,4.315,3.550,63,3.355,4.611,73,3.366,0.116,57,0.066
3,6401492025FLE082,Female,2/28/2025,9.95,0.334,65,-0.217,2.694,63,3.394,4.383,55,3.616,4.784,65,3.110,0.113,45,0.051
4,6401492025FLE047,Female,2/24/2025,9.89,0.432,59,-0.255,2.948,57,3.361,5.038,49,3.703,5.104,60,3.062,0.042,36,0.015
5,6401492025FLE024,Female,2/20/2025,9.42,0.471,60,-0.283,2.839,58,3.293,4.580,49,3.366,4.991,60,2.995,0.124,36,0.045
6,6401492025FLE008,Female,2/16/2025,9.06,0.170,62,-0.105,2.722,61,3.321,3.656,53,2.907,4.552,63,2.868,0.168,44,0.074
7,6401492025FLE029,Female,2/21/2025,7.39,0.177,58,-0.103,2.128,56,2.383,3.645,49,2.679,4.043,59,2.385,0.116,41,0.048
8,6401492025FLE011,Female,2/17/2025,6.56,0.000,61,-0.000,2.190,59,2.584,2.235,52,1.743,3.523,61,2.149,0.178,45,0.080
9,6402382024NCS310,Male,2/13/2024,4.74,-0.231,80,0.185,1.231,78,1.920,1.002,68,1.022,1.942,80,1.554,0.104,55,0.057
10,6401492025FLE141,Male,2/24/2025,4.26,0.266,61,-0.162,1.265,59,1.493,2.129,47,1.501,2.479,58,1.438,-0.016,38,-0.006
11,6401492023FLE078,Female,2/21/2023,3.25,0.305,64,-0.195,1.611,62,1.998,0.643,52,0.502,1.592,61,0.971,-0.052,50,-0.026
12,6401492022FLE002,Female,1/23/2022,-1.61,-0.176,66,0.116,-0.016,64,-0.020,-1.363,55,-1.124,-0.917,66,-0.605,0.055,50,0.028
1,6401492025FLE087,Female,2025-03-02,11.91,0.352,63,-0.222,3.069,61,3.744,5.726,53,4.552,5.898,64,3.775,0.132,44,0.058
2,6401492020FLE249,Female,2020-02-05,10.92,0.243,74,-0.180,3.039,71,4.315,3.550,63,3.355,4.611,73,3.366,0.116,57,0.066
3,6401492025FLE082,Female,2025-02-28,9.95,0.334,65,-0.217,2.694,63,3.394,4.383,55,3.616,4.784,65,3.110,0.113,45,0.051
4,6401492025FLE047,Female,2025-02-24,9.89,0.432,59,-0.255,2.948,57,3.361,5.038,49,3.703,5.104,60,3.062,0.042,36,0.015
5,6401492025FLE024,Female,2025-02-20,9.42,0.471,60,-0.283,2.839,58,3.293,4.580,49,3.366,4.991,60,2.995,0.124,36,0.045
6,6401492025FLE008,Female,2025-02-16,9.06,0.170,62,-0.105,2.722,61,3.321,3.656,53,2.907,4.552,63,2.868,0.168,44,0.074
7,6401492025FLE029,Female,2025-02-21,7.39,0.177,58,-0.103,2.128,56,2.383,3.645,49,2.679,4.043,59,2.385,0.116,41,0.048
8,6401492025FLE011,Female,2025-02-17,6.56,0.000,61,-0.000,2.190,59,2.584,2.235,52,1.743,3.523,61,2.149,0.178,45,0.080
9,6402382024NCS310,Male,2024-02-13,4.74,-0.231,80,0.185,1.231,78,1.920,1.002,68,1.022,1.942,80,1.554,0.104,55,0.057
10,6401492025FLE141,Male,2025-02-24,4.26,0.266,61,-0.162,1.265,59,1.493,2.129,47,1.501,2.479,58,1.438,-0.016,38,-0.006
11,6401492023FLE078,Female,2023-02-21,3.25,0.305,64,-0.195,1.611,62,1.998,0.643,52,0.502,1.592,61,0.971,-0.052,50,-0.026
12,6401492022FLE002,Female,2022-01-23,-1.61,-0.176,66,0.116,-0.016,64,-0.020,-1.363,55,-1.124,-0.917,66,-0.605,0.055,50,0.028

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| WWT | +1.26 (59%) | 59% | +1.49 | Below average (47th percentile) |
| YWT | +2.13 (47%) | 47% | +1.50 | Above average (58th percentile) |
| PWWT | +2.48 (58%) | 58% | +1.44 | Above average (58th percentile) |
| NLB | -0.02 (38%) | 38% | -0.01 | Below average (33th percentile) |
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Ordinal suffix is incorrect in the breed position text (33th percentile). This should be 33rd percentile.

Suggested change
| NLB | -0.02 (38%) | 38% | -0.01 | Below average (33th percentile) |
| NLB | -0.02 (38%) | 38% | -0.01 | Below average (33rd percentile) |

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Comment on lines 56 to 60
| BWT | +0.35 (63%) | 63% | -0.22 | Below average (43th percentile) |
| WWT | +3.07 (61%) | 61% | +3.74 | Above average (66th percentile) |
| YWT | +5.73 (53%) | 53% | +4.55 | Top 25% (75th percentile) |
| PWWT | +5.90 (64%) | 64% | +3.77 | Top 25% (79th percentile) |
| NLB | +0.13 (44%) | 44% | +0.06 | Below average (47th percentile) |
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The per-animal trait tables duplicate accuracy: the Value column already includes accuracy in parentheses (e.g. +0.35 (63%)) and there is also a separate Accuracy column. Consider either removing the Accuracy column or changing Value to show just the EBV so each accuracy appears only once.

Suggested change
| BWT | +0.35 (63%) | 63% | -0.22 | Below average (43th percentile) |
| WWT | +3.07 (61%) | 61% | +3.74 | Above average (66th percentile) |
| YWT | +5.73 (53%) | 53% | +4.55 | Top 25% (75th percentile) |
| PWWT | +5.90 (64%) | 64% | +3.77 | Top 25% (79th percentile) |
| NLB | +0.13 (44%) | 44% | +0.06 | Below average (47th percentile) |
| BWT | +0.35 | 63% | -0.22 | Below average (43th percentile) |
| WWT | +3.07 | 61% | +3.74 | Above average (66th percentile) |
| YWT | +5.73 | 53% | +4.55 | Top 25% (75th percentile) |
| PWWT | +5.90 | 64% | +3.77 | Top 25% (79th percentile) |
| NLB | +0.13 | 44% | +0.06 | Below average (47th percentile) |

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| Trait | Value | Accuracy | Contribution to Score | Breed Position |
|-------|-------|----------|----------------------|----------------|
| BWT | +0.35 (63%) | 63% | -0.22 | Below average (43th percentile) |
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Ordinal suffix is incorrect in the breed position text (e.g., 43th percentile). This should use the correct English ordinal suffix (43rd percentile).

Suggested change
| BWT | +0.35 (63%) | 63% | -0.22 | Below average (43th percentile) |
| BWT | +0.35 (63%) | 63% | -0.22 | Below average (43rd percentile) |

Copilot uses AI. Check for mistakes.
| PWWT | +5.90 (64%) | 64% | +3.77 | Top 25% (79th percentile) |
| NLB | +0.13 (44%) | 44% | +0.06 | Below average (47th percentile) |

**Analysis**: Strong weaning weight indicates good early growth. excellent yearling weight for market production. above-average fertility.
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This analysis claims "above-average fertility", but the NLB row for this animal is labeled "Below average" (47th percentile). Please align the narrative analysis with the NLB data shown in the table (or adjust the breed-position labeling if it's incorrect).

Suggested change
**Analysis**: Strong weaning weight indicates good early growth. excellent yearling weight for market production. above-average fertility.
**Analysis**: Strong weaning weight indicates good early growth, excellent yearling weight for market production, and near-average fertility.

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zircote commented Feb 8, 2026

@copilot apply changes based on the comments in this thread

…es, issue link

Co-authored-by: zircote <307960+zircote@users.noreply.github.com>
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Copilot AI commented Feb 8, 2026

@copilot apply changes based on the comments in this thread

All review feedback has been addressed in commit 24c611a:

✅ Added issue link (Closes #2) to report header
✅ Fixed all ordinal suffixes (43rd, 71st, 62nd, 33rd, etc.)
✅ Capitalized sentences in analysis sections
✅ Improved BWT breed position labels to clarify "lower is better"
✅ Removed duplicate accuracy column from trait tables
✅ Converted CSV dates to ISO-8601 format (YYYY-MM-DD)
✅ Reworded proven sire recommendation to explain rank vs. accuracy
✅ Fixed fertility analysis to align with trait data

@zircote zircote merged commit 3b88fed into main Feb 8, 2026
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@zircote zircote deleted the copilot/rank-animals-trait-index branch February 8, 2026 06:23
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02-07-2026

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