Egg size as a predictor of nutrient composition of eggs and neonates of Canada Geese (<i>Branta canadensis interior</i>) and Lesser Snow Geese (<i>Chen caerulescens caerulescens</i>)
Bibliographic record
Abstract
Fresh and pipped eggs were collected to provide data on nutrient composition of eggs and neonates, respectively, of Canada Geese (Branta canadensis interior) and Lesser Snow Geese (Chen caerulescens caerulescens). We sought to determine how well a commonly used and simple index like estimated egg volume or "egg size" predicted egg composition and neonate characteristics including body composition, structural size, and digestive-organ mass. For both species, egg constituents were positively correlated with egg size, but relations for Canada Geese consistently had higher coefficients of determination than did those for Lesser Snow Geese. These differences suggest that there is more among-female variation in nutrient composition of Lesser Snow goose eggs relative to Canada Goose eggs. Most neonatal nutrient constituents were positively correlated with egg size in both species, but the relations between nutrient constituents and egg size were consistently stronger in Lesser Snow Geese than in Canada Geese. Several measures of structural size of neonates were positively correlated with egg size in both species, but egg size was a better predictor of neonate size for Lesser Snow Geese than for Canada Geese. Egg size was a relatively poor predictor of digestive-organ mass for both species. We hypothesize that the stronger relations between neonate quality and egg size in Lesser Snow Geese are a reflection of greater stabilizing selection for embryonic metabolic rates in species that nest at high latitudes and have a short incubation period. The fact that nutrient constituents of eggs were more strongly related to egg size than were the analogous constituents of neonates suggests that variation in metabolic rates of embryos limits the utility of egg size as an accurate and precise predictor of nutrient constituents in the two study species, but especially in Canada Geese.
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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 | 0.000 | 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) | 0.001 | 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".