Lifetime hormone profiles for a long-lived teleost: opercula reveal novel estimates of age-specific reproductive parameters and stress trends in yelloweye rockfish (<i>Sebastes ruberrimus</i>)
Bibliographic record
Abstract
Fisheries management relies on accurate population models for estimating biomass and setting harvest goals; however, physiological data for estimating reproductive parameters in population models are difficult to acquire. Here, lifetime reproductive (progesterone and estradiol) and stress-related (cortisol) hormones were measured in annually deposited growth increments in female yelloweye rockfish ( Sebastes ruberrimus) opercula ( N = 22 females, sampled ages 1–90 years). Analyses of these profiles (∼1-year resolution) provided estimates of physiological (complete puberty) and functional age of sexual maturity (females spawn and contribute larvae to the population) and spawning frequency, with lifetime trends of reproduction and stress. The descriptive mean age of physiological sexual maturity was 11 ± 1 years (standard error (SE)), whereas functional age of maturity was 17 ± 2 years. The estimate of marginal mean spawning frequency was 45.1% ± 5.1%. Stress data (∼15% of females experienced distress events) suggested that females were potentially resilient or not exposed to chronic stressors. Although preliminary, we provide a novel method to estimate age-specific reproductive parameters for proper age-based population modeling of a human-targeted teleost.
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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".