Patterns of maternal investment in spotted turtles (<i>Clemmys guttata</i>): Implications of trade-offs, scales of analyses, and incubation substrates
Bibliographic record
Abstract
To maximize potential fitness, reproductive females should invest available resources in either larger propagules (egg and/or hatchling size) or more propagules (clutch size). Females may also enhance offspring performance by selecting nest sites with optimal conditions for the developing eggs. This study examined maternal investment in a population of spotted turtles (Clemmys guttata) in Ontario, Canada over 2 y using radio telemetry, x-ray photography, and indirect assessments of hatchling fitness. Analyses were conducted at 2 scales (clutch and female), utilizing 2 measures of available resources (body size and body condition). Larger females produced wider eggs, and similarity in the slopes of egg width and maternal pelvic aperture on body size may indicate a physical constraint on egg size. However, body size did not explain variation in egg morphometrics (length, width, or mass) when considering the reproductive output of each female over the entire study. Instead, females in better body condition produced more eggs. With respect to nest site selection, no selection for thermal properties was observed, and females exhibited stronger fidelity to nest substrates than to nest locations. Hatchling righting response was not related to hatchling body size or condition, but hatchlings from a clutch performed similarly, indicating maternal genetic effects or an effect of nest conditions. Thus, females in good condition maximize the number of eggs produced over multiple years, and hatchling morphometrics may not directly influence hatchling success.
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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".