Habitat Selection, Survival, and Movement Ecology of the American Woodcock (Scolopax minor) in Louisiana USA
Bibliographic record
Abstract
The American Woodcock (Scolopax minor) is a migratory upland game species occurring across much of eastern North America. Across its breeding range in the northern U.S. and southern Canada, this species has experienced significant, pervasive declines over the last half century. As woodcock populations continue to decline, active habitat management for this species has become increasingly important.\nThe state of Louisiana has substantial amounts of woodcock habitat and receives wintering birds from across the woodcock range. However, few data exist regarding survival rates, habitat utilization, and movement patterns across the state.\nIn order to better manage for woodcock habitat in Louisiana, fine scale data on woodcock habitat utilization and spatiotemporal movement patterns are needed. Past studies on woodcock habitat selection have relied predominantly on VHF telemetry, which require an observer to manually track in on woodcock to gather location information.\nOur study employed both GPS and VHF tags on woodcock to gather high resolution movement data in order to evaluate survival, habitat use, and movement patterns of woodcock in Louisiana. We were further able to utilize these data to compare VHF and GPS approaches to habitat sampling.\nOur results suggest that survival rates in Louisiana may be lower than those on the breeding ground, particularly in an area with localized hunting pressure. Mean home range size (Minimum Convex Polygons) was 743 m2 during diurnal periods and 918 m2 during nocturnal periods. Mean hourly movement rate was very similar between day (23 m/hour) and night (26 m/hour). Mean distance traveled between diurnal and nocturnal MCPs was 0.65 km. Simulated random sampling locations which were based on “VHF” points subset from the GPS tags demonstrated that many sampling locations fell within the utilization area of the individual sampling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".