Hunting Success and Northern Bobwhite Density on Tall Timbers Research Station: 1970-2001
Bibliographic record
Abstract
Hunting success, defined as number of coveys found/hr of hunting, has been used as an index of population size of northern bobwhites (Colinus virginianus). However, the relationship between hunting success and bobwhite density has not been documented on individual study areas. We related estimates of bobwhite density on a 445-ha section of Tall Timbers Research Station (TTRS) to the number of coveys flushed/hr of hunting, 1970–2001. To estimate density of bobwhites, we captured bobwhites in baited-funnel traps for a 2–3 week period and recaptured 15–20% of banded birds by systematically hunting the study area using pointing bird dogs. Bobwhite population sizes, calculated using a bias-corrected Peterson estimate, were converted to densities because of changes in study area size over time. Annual density estimates and hunting success ranged from 0.7–4.8 bobwhites/ha and 0.5–2.9 covey finds/hr over the study period, respectively. We assessed the variance in bobwhite abundance explained by year and hunting success using multiple linear regression. There was a significant positive relationship between covey finds/hr and bobwhite density (t25 = 9.070, P = <0.0001). Covey finds/hr explained the greatest amount of variation (r2 = 0.77) in density. Our data suggest that if hunting procedures are standardized over time, hunting success may be used to index bobwhite abundance, and potentially provide crude estimates of population density.
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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 | 0.001 | 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) | 0.003 | 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".