Temporal Patterns of Apparent Leg Band Retention in North American Geese
Bibliographic record
Abstract
ABSTRACT An important assumption of mark—recapture studies is that individuals retain their marks, which has not been assessed for goose reward bands. We estimated aluminum leg band retention probabilities and modeled how band retention varied with band type (standard vs. reward band), band age (1–40 months), and goose characteristics (species and size class) for Canada ( Branta canadensis ), cackling ( Branta hutchinsii ), snow ( Chen caerulescens ), and Ross's ( Chen rossii ) geese that field coordinators double‐leg banded during a North American goose reward band study ( N = 40,999 individuals from 15 populations). We conditioned all models in this analysis on geese that were encountered with ≥1 leg band still attached ( n = 5,747 dead recoveries and live recaptures). Retention probabilities for standard aluminum leg bands were high ( = 0.9995, SE < 0.001) and constant over 1–40 months. In contrast, apparent retention probabilities for reward bands demonstrated an interactive relationship between 5 size and species classes (small cackling, medium Canada, large Canada, snow, and Ross's geese). In addition, apparent retention probabilities for each of the 5 classes varied quadratically with time, being lower immediately after banding and at older age classes. The differential retention probabilities among band type (reward vs. standard) that we observed suggests that 1) models estimating reporting probability should incorporate differential band loss if it is nontrivial, 2) goose managers should consider the costs and benefits of double‐banding geese on an operational basis, and 3) the United States Geological Survey Bird Banding Lab should modify protocols for receiving recovery data.
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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 | 0.000 | 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".