Geographic variation in migration chronology and winter distribution of midcontinent greater white‐fronted geese
Bibliographic record
Abstract
ABSTRACT We evaluated spatial and temporal differences in migratory behavior among different breeding groups of midcontinent greater white‐fronted geese (Anser albifrons) using band‐recovery data and observations of neck collared geese during migration and winter. Birds from different breeding areas were initially delineated by geographic distance into 6 banding reference areas (BRAs): 1) interior Alaska, 2) North Slope of Alaska, 3) western Northwest Territories (NWT), 4) western Nunavut, 5) central Nunavut, and 6) eastern Nunavut. The banding groups also differed by breeding habitat, with geese from interior Alaska nesting in the boreal forest (taiga), and all other groups breeding in tundra habitats. Geese from interior Alaska migrated earlier during autumn, and were more likely to winter farther south (in Mexico) than geese from other breeding areas. Geese banded in central and eastern Nunavut (Queen Maud Gulf and Inglis River) wintered farther east (in Louisiana) than geese from other breeding areas. Small‐scale (within‐state) geographic segregation of wintering flocks was evidenced by the recent (post‐1990) nearly exclusive use of a new wintering area in north central Texas by geese from interior Alaska. Segregation among BRAs was also apparent in Mexico, where taiga geese were found predominantly in the central Highlands (states of Zacatecas and Durango), whereas tundra geese mostly used states along the Gulf Coast (primarily Tamaulipas). Interior Alaska birds initiated spring migration earlier than geese from other areas, and were more likely than others to stop in the Rainwater Basin of Nebraska, a region where cholera outbreaks periodically kill thousands of geese. Geese from interior Alaska were the first to arrive at spring staging areas in prairie Canada where BRAs exhibited spatial delineation (a longitudinal cline) in relation to breeding areas. Our results show significant geographic and temporal variation among taiga and tundra breeding cohorts during autumn, winter, and spring. Temporal and spatial differences in migratory behavior may allow management practices that accommodate potential demographic differences between taiga and tundra populations. © 2013 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 | 0.000 |
| 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".