Effect of neckband color on survival and recovery rates of Ross's geese
Bibliographic record
Abstract
Abstract Colored neckbands are known to reduce survival rates of geese, but the underlying cause for lower survival is unknown. We tested the hypothesis that hunters cause this lower survival rate by actively targeting neckbanded geese. We evaluated this hypothesis by estimating recovery and survival rates of adult Ross's geese (Chen rossii) at both Queen Maud Gulf (QMG) and McConnell River (MCR) Migratory Bird Sanctuaries carrying each of 3 marker combinations: 1) standard legbands (n = 11,321) for basic estimates of recovery and survival rates; 2) standard legbands and colored neckbands (n = 8,587) as the marked sample most detectable and thus most vulnerable to targeting by hunters; and 3) standard legbands and white neckbands (n = 6,501) as the sample exposed to the general risks of carrying neckbands but only minimally detectable by hunters, if at all. Recovery rates (±95% CL) of Ross's geese were lowest for those marked with legbands (0.024 ± 0.004 at MCR and 0.016 ± 0.003 at QMG) and highest for those marked with neckbands, regardless of neckband color (0.042 ± 0.005 at MCR and 0.035 ± 0.005 at QMG). Survival rates (±95% CL) were indistinguishable between geese marked with color and white neckbands (0.54 ± 0.08 at MCR and 0.52 ± 0.08 at QMG), but lower than those marked with standard legbands only (0.72 ± 0.17 at MCR and 0.83 ± 0.23 at QMG). Geese marked with white neckbands were recovered at rates similar to those marked with color neckbands, suggesting that hunter selection of color neckbands did not contribute greatly to lower survival rates in neckbanded geese. Rather, results suggest that neckbanded geese, regardless of neckband visibility, are more vulnerable to hunters than are geese marked only with legbands. © 2012 The Wildlife Society.
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 | 0.001 | 0.002 |
| 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".