Migratory connectivity of Golden-crowned Sparrows from two wintering regions in California
Bibliographic record
Abstract
Abstract Knowledge of migratory connectivity is critical to understanding the consequences of habitat loss and climate change on migratory species. We used light-level geolocators to determine breeding locations and migratory routes of wintering Golden-crowned Sparrows (Zonotrichia atricapilla) in two regions of California, USA. Eight out of 9 birds tagged at coastal-wintering sites in Marin County went to breeding sites along the Gulf Coast of Alaska, while 7 out of 8 inland-wintering birds in Placer County migrated to interior sites in the Yukon, Northwest Territories, and British Columbia, Canada. Our estimate of the strength of migratory connectivity was relatively high (rm = 0.66). Coastal-wintering birds followed a coastal migration route while inland-wintering birds migrated inland. Coastalwintering birds migrated significantly farther than inland birds (3,624 km versus 2,442 km). Coastal birds traveled at a greater rate during spring migration (179 km/d) than did inland birds (118 km/d), but there was no statistical difference in the rate of fall migration (167 km/d and 111 km/d, respectively). Dates of arrival and departure, and duration of spring and fall migration, did not differ between groups, nor did return rates. Rates of return also did not differ between tagged and control birds. The distinct migration routes and breeding areas suggests that there may be more structuring in the migratory geography of the Golden-crowned Sparrow than in a simple panmictic population.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".