An experimental assessment of shotgun discharge on aluminum legband retention
Bibliographic record
Abstract
Abstract Although metal legbands have been an important scientific tool, their use for estimation of harvest and survival relies on samples of dead birds harvested by hunters using shotguns. We hypothesized that the force of steel pellets discharged from a shotgun, within the range of conditions normally experienced by goose hunters, was sufficient to reduce probability of band retention. We conducted 8 experimental trials to estimate retention per round fired at aluminum bands normally applied to arctic‐nesting geese in relation to effects of 1) target range (20 m vs. 40 m), 2) steel pellet size (4.57 mm [BB] vs. 3.81 mm [number 2]), 3) cartridge size (76.2 mm [3 in.] vs. 69.9 mm [2.75 in.]), and 4) number of rounds fired (up to 25). There was nearly complete band retention (0.999/round) at 40 m regardless of shot size or shell size used. Retention per round fired at 20 m declined to between 0.984 and 0.987 for number 2 shot and between 0.968 and 0.974 for BB shot. Our conclusions apply to unworn bands, so we recommend further simulations to assess how retention may change with age of bands as they erode or corrode on free‐ranging geese. Bias in estimates associated with loss of older bands from shotgun discharge could be adjusted if bias is estimated as done in this article. © 2011 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".