Evaluating occurrence and abundance of displaying male American woodcock ( <i>Scolopax minor</i> ) north of the current Singing‐Ground Survey range
Bibliographic record
Abstract
Abstract Displaying male American woodcock ( Scolopax minor ) are monitored by the American Woodcock Singing‐Ground Survey (SGS), whose findings guide woodcock research and management decisions. However, the SGS may not cover all available woodcock breeding range, particularly in more northern regions. Though there have been frequent recommendations to expand the SGS farther north, occurrence and abundance of woodcock north of the SGS have never been evaluated. To address this issue, we used SGS data collected in Canada between 2000 and 2019 to 1) identify the spatial scale at which landscape covariates had the strongest effect (i.e., scale of effect) and 2) evaluate the effect size of 16 landscape covariates on male woodcock occurrence and abundance index, and 3) develop a predictive map to identify priority areas for SGS expansion in Canada. We found that landscape covariates had the strongest effect on occurrence and the abundance index at a 310‐m radius, suggesting that the most important influence on male woodcock habitat selection and habitat use was the presence of display habitat. Our results also support previous studies showing male woodcock preference for moist areas with young, broadleaf forest intermixed with pasture and grassland clearings for their display. Additionally, we identified sites throughout eastern Canada likely to support relatively high abundances of displaying males during the breeding season. Many of these sites were north and west of the current SGS range, and we identified road‐accessible locations for possible SGS expansion in Manitoba, Ontario, Québec, and Newfoundland. Expanding survey route coverage into areas of predicted woodcock occurrence could improve woodcock population monitoring and guide more effective management and conservation decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".