Quantifying maternal reproductive output of chondrichthyan fishes
Bibliographic record
Abstract
For the live-bearing and egg-laying class of chondrichthyan fishes a three parameter logistic ‘maternity’ function with a variable upper asymptote, PMax, can be used to predict the average probability of a female giving birth or laying eggs in a season. Although fundamental to calculating the reproductive capacity of a population, few studies report maternity functions, with maturity functions often used as a proxy. Applying logistic models to simulated and empirical data showed that it was feasible to estimate PMax from maternal data and that accuracy, bias, and confidence interval coverage often improved compared to when a fixed value was used. However, sample sizes of 100–200 maternal females were typically required for accurate estimation of PMax. While maturity parameters could be estimated with greater accuracy, substituting them for maternity parameters overestimated lifetime reproductive output. Greater use of maternity functions has the potential to improve calculation of reproductive output in quantitative populations models. In addition to improvements in parameter estimation, this method involves fewer assumptions and enables statistical inferences to be made on frequency of reproduction.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".