Phenotypic effects on survival of neonatal northern watersnakes <i>Nerodia sipedon</i>
Bibliographic record
Abstract
Summary Understanding the trade‐off females make between offspring size and number requires knowing how neonatal size, and traits associated with size, affect survival. We studied neonatal survival in the northern watersnake Nerodia sipedon in outdoor enclosures with artificial hibernation sites. From a total of 950 neonates from 77 litters collected over 3 years, we found a survival rate of 65% between birth and hibernation and 47% during hibernation. Estimated survival from birth to the end of hibernation was 31%, comparable with indirect estimates for free‐living watersnakes. Consistent with the ‘bigger is better’ hypothesis, larger neonates and neonates heavier relative to their body length were more likely to survive both the pre‐hibernation and hibernation periods. Survival in the pre‐hibernation period also decreased with the duration of that period and varied among years. Survival during hibernation was higher in warmer winters. Mass change prior to hibernation did not affect survival during hibernation. These results suggest that an optimal reproductive strategy should exist for female watersnakes, producing a ‘consensus’ among females on the optimal size for offspring. This expectation stands in stark contrast to the pronounced variation in offspring size observed both within and among litters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".