Patterns of metamorphic age and length in marine fishes, from individuals to taxa
Bibliographic record
Abstract
We present a summary of variability in age and length at metamorphosis for marine fishes. Data from the literature were partitioned into taxonomic, population, and individual levels of resolution to examine the factors affecting the timing of metamorphosis. Temperature appears to be a dominant influence on timing, likely due to its effect on growth rate. Interspecifically, length at metamorphosis correlated poorly with that at hatching but was significantly related to temperature. This pattern was inconsistent for population-level comparisons. Metamorphic age decreased exponentially with increasing temperature in interspecific and population-level comparisons but did not covary with length for either level of resolution. This suggests that age at metamorphosis largely reflects the time required to grow to a given metamorphic length. Within populations, the correlation between metamorphic age and length increases with growth rate, a reflection of variance in age and length. A strong exponential relationship between mean metamorphic age and length and their associated variability (SD) exists, with a slope greater than unity in both cases (i.e., variability increases relative to the mean). With these relationships, we can infer the manner in which individual variability in metamorphic traits is generated throughout ontogeny. These results are considered in light of recruitment variability in marine fishes.
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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 | 0.003 | 0.002 |
| 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".