Riverine characteristics and adult demography influence female lake sturgeon (<i>Acipenser fulvescens</i>) spawning behavior, reproductive success, and ovarian quality
Bibliographic record
Abstract
Abiotic conditions and demographic factors can influence the timing of spawning. Behavioral plasticity allows females to select spawning conditions that are conducive to offspring development; however, reproductive costs associated with delaying spawning are not well understood. In this study, factors influencing timing of female spawning, residence time (RT), and reproductive success (RS) during two seasons were determined, and plasma testosterone concentrations were used to quantify atretic rates in a wild, lake sturgeon (Acipenser fulvescens) population. For the 123 females monitored, RT ranged from 1 to 23 days and was influenced by arrival date, temperature, discharge, and male number, with the latter having the largest influence. RS varied due to arrival date, temperature, discharge, male number, male length, and operational sex ratio. Two females had testosterone levels indicative of atresia and RS estimates lower than yearly means; however, most females had normal ovaries, suggesting little reproductive costs of plasticity in spawning ground residency time. Results demonstrate the multitude of factors influencing female reproductive behavior and RS, highlighting the importance of abiotic and demographic conditions to recruitment in wild populations.
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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) | 0.001 | 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".