Nutrient Allocation Strategies to Eggs by Lesser Snow Geese (<i>Chen caerulescens</i>) at a Sub-Arctic Colony
Bibliographic record
Abstract
The relative allocation of endogenous- and exogenous-derived nutrients to reproductive investment in Arctic-nesting geese is affected by body size, migration distance, and proximate conditions on the wintering, staging, and breeding grounds prior to clutch initiation. We used δ13C and δ15N measurements of muscle tissue and egg lipid-free yolk and albumen and δ13C analysis of abdominal fat and egg yolk lipids, together with isotopic analyses of foraging plants, to quantify the relative use of endogenous and exogenous reserves in egg production in a breeding population of sub-Arctic Lesser Snow Geese (Chen caerulescens caerulescens) on the Cape Churchill Peninsula, Manitoba, from 2005 to 2008. We used a concentration-dependent, two-isotope, three-source Bayesian (SIAR) mixing model to derive estimates of endogenous reserves to egg macronutrients and a single-isotope (δ13C), two-source (exogenous vs. endogenous) Bayesian model to estimate the source of lipids to eggs. Endogenous protein contributions to eggs were similar to those found using identical Bayesian analytical methods for the larger-bodied Greater Snow Goose (Chen caerulescens atlantica) breeding in the Canadian High Arctic and were on the order of 30%. However, endogenous lipid contributions were considerably greater for the population of Lesser Snow Geese (mean annual contribution of 55.5% vs. 22.3%). This suggests that advantages of larger body size for transport of body lipid reserves for long distances may be countered by the need to use lipids to fuel migration over greater distances. In addition, feeding opportunities of Greater Snow Geese upon arrival at their more distant breeding sites were likely adequate to offset a shorter breeding season and longer development times for offspring than at lower-latitude sites.
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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 | 0.000 | 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) | 0.000 | 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".