Experimental assessment of the probabilistic maturation reaction norm: condition matters
Bibliographic record
Abstract
The probabilistic maturation reaction norm (PMRN) describes an individual's probability of maturing at a given age as a function of size and other relevant phenotypic traits. Population-level shifts in the PMRN are often interpreted to indicate genetic as opposed to phenotypic changes in maturation in fish. Inferences derived from trends in the PMRN have been challenged, warranting an experimental assessment of the method. This was accomplished in a laboratory experiment using zebrafish (Danio rerio). Fish were reared under different food levels to induce variation in growth and maturation. Plasticity in maturation was not entirely captured by the demographic age- and length-based PMRN. Adding condition to the PMRN captured a greater amount of environmental variation in maturation probability. Nevertheless, significant differences in the PMRNs among the food levels remained after accounting for the influences of age, size and condition on maturation probability indicating plasticity of the PMRN. This was particularly pronounced between fish held on low food levels as compared with fish experiencing abundant resources, with the latter experiencing higher size-specific maturation probabilities. Our analysis emphasizes the need for incorporating salient physiological traits influencing maturation, such as condition, to make accurate inferences about documented shifts observed in the position of PMRNs on maturation trends in wild fish stocks.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".