Bibliographic record
Abstract
Annual assessment of American woodcock (Scolopax minor; hereafter, woodcock) populations in North America relies primarily on the American Woodcock Singing-Ground Survey (SGS). Ancillary information concerning harvest and hunting effort comes from the Harvest Information Program (HIP), and indices of recruitment come from Wing Collection Surveys (WCS). We report on long-term trends in SGS, HIP, and WCS data in the Eastern and Central Management Regions in the U.S. Analyses of SGS data indicate there have been significant long-term (1968–2017) declines of 1.05% per year in the Eastern Management Region and -0.56 % per year in the Central Management Region. Discontinuance of some routes and their replacement with new routes may have artificially lessened the long-term negative trends in the SGS. Since 2013, total harvest and number of days hunters spent pursuing woodcock have been below the long-term average (1999–2015) in both management regions. Age ratios (number of immatures per adult female) were temporally variable but exhibited no long-term trend in the Eastern Management Region. In the Central Management Region, age ratios were generally higher during the beginning of the study (1963–1987) period versus the latter part (1988–2016).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".