Morphological differences and migration patterns of greater and lesser snow geese in New York State
Bibliographic record
Abstract
Abstract Lesser ( Chen caerulescens caerulescens , LSGO) and greater snow goose ( Chen caerulescens atlantica , GSGO) populations have increased substantially in the past 50 years. The light goose conservation order established in 1998 (Canada) and 1999 (U.S.) aimed to increase snow goose harvest and stabilize populations because breeding ground abundance was thought to negatively impact arctic ecosystems. In the Atlantic flyway, where LSGO and GSGO are both available for harvest, techniques to differentiate sub‐species in the field using morphology may be helpful for harvest management because mid‐continent LSGO are ~16 times more abundant than GSGO ( n < 1,000,000). We investigated percentages and spatial distribution of LSGO and GSGO in the spring harvest in NY as this information could be useful for snow goose population and harvest management decisions. We developed a discriminant function analysis (DFA) using heads from snow geese harvested during spring 2016 to 2018 and were able to differentiate between LSGO and GSGO with 95.5% accuracy. Based on the DFA results, we estimated that spring harvest in New York state was 80% GSGO and 20% LSGO. Using band recoveries from autumn and spring harvests, we also identified that GSGO harvest occurred farther west during spring than autumn and in the 2010s than prior two decades. Our results indicate that GSGO comprise most snow goose harvest in New York state and provide evidence for a shift in spring migration patterns of GSGO since the 1990s.
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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 | 0.000 |
| 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 | 0.000 |
| 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".