Predicting Life History Traits of Yellow Perch from Environmental Characteristics of Lakes
Bibliographic record
Abstract
Abstract Sex‐specific life history variation was examined among 72 populations of yellow perchPerca flavescensfrom Ontario, Canada. We sought to determine whether relationships could be applied to other populations to predict parameter values when life history data are not available. Each of the measured traits (early growth rate, maturation size and age, reproductive investment, and maximum size) varied two‐ to threefold among populations. Relationships were developed to predict standard calculations of life history traits from population‐specific data for use in poorly sampled lakes. Associations between life history traits and environmental variables can be used in unsampled lakes. Early growth rate was positively related to lake surface area, while relative density was positively related to total dissolved solids. For both sexes, maximum body size was positively related to lake surface area and negatively related to growing degree‐days. Additional variation in female maximum size was explained by a positive relationship with water hardness. Much variation in yellow perch growth could not be accounted for, despite incorporation of the major hypotheses that appear in the literature relating environmental variation to life history. Although explained variation was too low to generate important management policies, the results indicate types of lakes capable of producing large fish and therefore of interest for future study.
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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 | 0.001 |
| 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".