Factors affecting litter size in Western Gartersnake (<i>Thamnophis elegans</i>) in British Columbia: place, time, and size of mother
Bibliographic record
Abstract
Life-history traits of organisms are influenced by both genetic and environmental factors. We used counts of offspring in captive-born litters to determine how geographic location, year-to-year variation, and body size of mother affected litter size of Western Gartersnake (Thamnophis elegans) in four widely separated populations in British Columbia. Litter size varied significantly among populations, but that variation was largely explained by differences in maternal body size among populations; that is, larger females had larger litters. With maternal size treated as a covariate, there was no further significant effect of location or of different years within sites on litter size. The overall regression, pooled over sites and years, between litter size and size of mother accounted for 55% of the total variation in litter size. Nonetheless, the significant variation in body size among locations calls for explanation and the consequent differences in litter size could be important demographically. Presumably, the large amount of unexplained residual variation reflects other differences, beyond body size, between individual mothers. Such differences among individuals might be determined by genetics or by environmental effects such as foraging success, but our data cannot address this question.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".