The biological and statistical significance of life-history invariants in walleye (Sander vitreus)
Bibliographic record
Abstract
Questions: Do life-history invariants exist in walleye (Sander vitreus) and do they differ between the sexes? How does the probability of detecting life-history invariants vary with sample size? How much error is created if invariance is incorrectly assumed when using the relationships to predict one trait from another? Data description: Sex-specific data, obtained from standardized research surveys, on growth, age and size at maturity, and mortality for 435 populations of walleye from Ontario, Canada. Search method: Invariance in four life-history relationships (Lm/Linf, M/k, Tm · M and Tm/Lm) was assessed using linear slopes. We examined sample and effect sizes to determine the extent to which life-history invariants are influenced by statistical power. Errors in estimating traits from predicted invariants were obtained from random samples of 50 populations. Conclusions: Life-history invariants did not exist among populations of walleye. The value of each ratio and the extent of invariance differed between the sexes. The number of populations required to generate variance in Lm/Linf was high for males (200) and females (41), suggesting that this potential life-history invariant may be statistically robust. However, none of the other ratios examined (M/k, Tm · M and Tm/Lm) was invariant at sample sizes of 10 or more populations for either sex. For walleye, Lm can be predicted from Linf if comparisons are from populations ranging widely in Linf. Estimates of either k or Tm are unlikely to yield reliable estimates of M; similarly, Tm cannot be reliably estimated from Lm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".