feat(ui): live stats panel in occurrence list sidebar

[mihow]

Summary

Frontend consumer for the /occurrences/stats/model-agreement/ endpoint added in #1307. Adds a Stats panel at the top of the occurrence list sidebar, above the filter sections.

• New OccurrenceStats component (ui/src/pages/occurrences/occurrence-stats.tsx)
• Wired into occurrences.tsx, threading the same active filter array the list view sends to useOccurrences — so the stats always match the current result set (taxon, deployment, date, verification status, default filters, etc.)
• Two metrics, mirroring the design mockup:
  • Verified occurrences — verified_pct, with the raw verified_count shown alongside (e.g. 0% (121)) so a small-but-nonzero set that rounds to 0% still surfaces the count.
  • Human-model agreement rate — agreed_any_rank_pct (exact matches plus any disagreement whose LCA is at a real taxonomic rank; the upstream filter scope bounds what counts as meaningful)
• Loading skeleton; renders nothing on error so it never blocks the list.

Stacked on the backend branch — base is feat/human-model-agreement-endpoint (#1307), not main. Rebase/retarget to main once #1307 merges.

Filter parity

The panel reuses the list view's filters array verbatim and converts it to query params with the same active/error rules as getFetchUrl (value?.length && !error). The endpoint accepts the full occurrence-list filter set (#1307), so the numbers stay consistent with the visible results.

Test plan

• tsc --noEmit — no errors in touched files
• eslint + prettier clean on new/modified files
• Live browser render verified against project 18 (Vermont Atlas of Life) via the worktree dev server proxied to the Endpoint for stats about verified occurrences #1307 backend. Panel renders above the filters: VERIFIED OCCURRENCES 0% (121), HUMAN-MODEL AGREEMENT RATE 94%.
• Live filter reactivity verified: toggling Default Filters off set ?apply_defaults=false and the Stats panel re-queried with the same param. Same filter array drives both list and stats.

Toolchain note for reviewers

The worktree ui/ has no node_modules. Installing under the host's Node 22 breaks the dev server (nova-ui-kit dereferences a React-18 internal removed in React 19 at tailwind-config eval). Use the repo-pinned Node 18 (.nvmrc → 18.12.0): nvm use 18.12.0 && yarn install && yarn start. Under Node 18 it boots cleanly.

Design discussion (open — feedback wanted)

The "agreement rate" is the share of human-verified occurrences where the human pick matched the model's pick. The catch: only a handful of occurrences are usually verified, so the rate can swing wildly. If 1 person verified 4 occurrences and agreed on 3, the panel says "75%" — which feels solid but is really just 3 out of 4. Decisions to make before this is more than a rough indicator:

1. Show the raw counts, not just the percentage (done for verified).
A percentage hides how much data is behind it. "94%" could be 94-out-of-100 or 47-out-of-50. Verified occurrences now shows 0% (121) — the count makes "0%" readable (it's 121 out of ~24k, not literally zero). Open question: do the same for the agreement rate so it reads 94% (94 of 100) — the reader instantly sees how many verifications the number is built on.

2. Should we hide the agreement rate when too few occurrences are verified?
A rate built on 3 verifications isn't trustworthy. Options:

• Hide or grey out the bar until enough occurrences are verified (say 30), with a "not enough verified data yet" note.
• Always show it next to the count, and trust the user to judge.
• "30" is a textbook rule of thumb, but it only works if the verified occurrences are a random sample. They aren't — people tend to verify the unusual, uncertain, or eye-catching ones first. So any single cutoff number is a rough guide, not a real "now it's valid" line. Leaning toward "always show + show the count" for honesty and simplicity.

3. Show a margin of error instead of a hard cutoff.
Rather than a yes/no "enough data" line, we can show how shaky the number is. A confidence interval (specifically a Wilson score interval, which behaves well for small samples) turns "94%" into something like "94%, somewhere between 87% and 97%". When few occurrences are verified the range is wide; as more get verified it tightens. This is more honest than a binary cutoff and needs only the count + total we already return.

4. A fairer agreement score that accounts for luck (follow-up).
Plain agreement % has a blind spot: if 95% of moths in a project are one common species, the model and human will "agree" most of the time just by both guessing the common one — that's luck, not skill. Cohen's kappa (κ) is the standard fix: it measures how much they agree beyond what you'd expect by chance. κ of 1.0 = perfect, 0 = no better than guessing. It's a more defensible "how good is the model, really" number than raw %. We can compute it from the exact same human/model pairs the endpoint already collects — no extra database work. Same caveat as #2: it still only describes the occurrences people chose to verify, not the whole project. Worth doing as a follow-up if the team wants a real quality metric rather than a rough indicator.

None of these block the panel landing as a quick live indicator — they're about how much statistical weight to let users put on the number.

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