Mass dynamics of wintering Pacific Black Brant: body, adipose tissue, organ, and muscle masses vary with location
Bibliographic record
Abstract
We compared body size and mass of the whole body, organs, adipose tissue, and muscles of adult Pacific Black Brant ( Branta bernicla nigricans (Lawrence, 1846)) collected concurrently in Alaska and Baja California during the fall, winter, and spring of 2002–2003. Head and tarsal lengths of males were similar between sites and slightly larger for females in Alaska than in Baja California. Brant appear to operate under similar physiological bounds, but patterns of nutrient allocation differ between sites. Birds wintering in Alaska lost similar amounts of adipose tissue during early winter as birds in Baja California gained during late winter before migration. Masses of the body, adipose tissue, and flight muscles during mid-winter were similar between sites. Seasonal adipose tissue deposition may, therefore, equally favor winter residency or long-distance migration. Gonad and liver masses increased in late winter for birds in Alaska but not for those in Baja California, suggesting birds wintering in Baja may delay reproductive development in favor of allocating reserves needed for migration. Phenotypic flexibility allows Brant to use widely divergent wintering sites. The wintering location of Brant likely depends more upon changes in environmental conditions and food availability, than upon physiological differences between the two wintering populations.
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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".