Retrospective Analysis of American Woodcock Population and Harvest Trends in Canada
Bibliographic record
Abstract
We used data from the Canadian component of the annual American Woodcock Singing-ground Survey (SGS) and data from the Canadian National Harvest Survey between 1975 and 2015 to assess temporal fluctuations in the population index, the number of American woodcock (Scolopax minor; hereafter, woodcock) harvested in Canada, and the proportion of successful hunters in Canada. We performed analyses via generalized additive mixed models that allowed us to identify periods when there were significant changes in temporal trends, and years during which there were significant changes in the direction of the temporal trajectory. We included climatic conditions before, during, and after the nesting and brood-rearing seasons (i.e., prior to the hunting season) as explanatory variables in our model. We did not find any effect of climatic variables on the SGS index. The SGS population index showed a slow overall negative decline in Canada, but there were only 2 significant periods of decline (1978–1984 and 1992–1994). Woodcock harvest and the proportion of successful woodcock hunters increased with the size of the SGS population index in the spring. The total harvest and the proportion of successful hunters remained fairly stable during the study period, but both indices showed a period of significant decline that started ca. 2006, and that was followed by a period of significant increase that started ca. 2009.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".