Determining Seabird Body Condition Using Nonlethal Measures
Bibliographic record
Abstract
Energy stores are critical for successful breeding, and longitudinal studies require nonlethal methods to measure energy stores ("body condition"). Nonlethal techniques for measuring energy reserves are seldom verified independently. We compare body mass, size-corrected mass (SCM), plasma lipids, and isotopic dilution with extracted total body lipid content in three seabird species (thick-billed murres Uria lomvia, all four measures; northern fulmars Fulmarus glacialis, three measures; and black-legged kittiwakes Rissa tridactyla, two measures). SCM and body mass were better predictors of total body lipids for the species with high percent lipids (fulmars; R2 = 0.5-0.6) than for the species with low percent lipids (murres and kittiwakes; R2 = 0.2-0.4). The relationship between SCM and percent body lipids, which we argue is often a better measure of condition, was also poor (R2 < 0.2) for species with low lipids. In a literature comparison of 17 bird species, percent lipids was the only predictor of the strength of the relationship between mass and total body lipids; we suggest that SCM be used as an index of energy stores only when lipids exceed 15% of body mass. Across all three species we measured, SCM based on the ordinary least squares regression of mass on the first principal component outperformed other measures. Isotopic dilution was a better predictor of both total body lipids and percent body lipids than were mass, SCM, or plasma lipids in murres. Total body lipids decreased through the breeding season at both sites, while total and neutral plasma lipid concentrations increased at one site but not another, suggesting mobilization of lipid stores for breeding. A literature review showed substantial variation in the reliability of plasma markers, and we recommend isotopic dilution (oxygen-18, plateau) for determination of energy reserves in birds where lipid content is below 15%.
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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 | 0.001 |
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".