A mobile tool for capturing greater sage‐grouse
Bibliographic record
Abstract
ABSTRACT Capturing greater sage‐grouse ( Centrocercus urophasianus ) using standard approaches can be challenging and inefficient, particularly in areas with relatively small populations and patchy habitat. In areas with low population densities, traditional trapping techniques such as drop‐netting and spotlighting have been largely ineffective. To increase trapping efficiency in such situations, we developed a new method to capture greater sage‐grouse in Wyoming, USA, during spring and autumn 2008–2011. We captured 92 sage‐grouse (30 adult females, 57 yearling females, 3 hatch‐year females, and 2 adult males) using a CODA net launcher modified to mount on a front receiver of a truck or all‐terrain vehicle. We had 71% success (82 successful captures of ≥ 1 grouse in 115 attempts). We captured grouse during spring on the periphery of leks, to reduce disturbance of lekking behavior, and during autumn along gravel roads. Capture mortality was <1.0%. We recorded low mortality (4.6%) up to 2 weeks postcapture that may have been attributed to capture and handling stress. This technique proved effective at capturing greater sage‐grouse and we believe this method can be effective at capturing other lekking species of prairie grouse with similar behavioral traits. © 2018 The Wildlife Society.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".