SITE-SPECIFIC OBSERVATION IN THE BREEDING SEASON IMPROVES THE ABILITY OF CHECKLIST DATA TO TRACK POPULATION TRENDS
Bibliographic record
Abstract
Checklist programs that compile birding observations are potentially useful for population monitoring. Previous analyses showed that trends in Quebec checklist data from migration seasons were significantly correlated with trends from the Breeding Bird Survey (BBS) in Quebec, although agreement of trend magnitudes for individual species was low. Here we analyze Quebec checklist data from the breeding season for comparison, using both the full data set and a subset of data collected at frequently visited (“standard”) sites. Checklist trends from the breeding season for standard sites corresponded much more closely to magnitudes of BBS trends than checklist trends based on all sites, although in both cases, checklists accurately reflected direction of BBS trend in >80% of species. Checklist trends from migration seasons for all sites and for standard sites were similar to each other, and did not correspond as well to BBS trends, probably because different populations were sampled in the two seasons. Checklist programs can be improved for population monitoring purposes by encouraging frequent reporting from standard sites, and by collecting recommended ancillary data that allow analysts to select data most appropriate to their research questions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".