Spatial and temporal variation of maturation schedules of lake whitefish (Coregonus clupeaformis) in the Great Lakes
Bibliographic record
Abstract
Fish maturation schedules vary greatly among systems and over time, reflecting both plastic and adaptive responses to ecosystem structure, physical habitats, and mortality (natural and fishing). We examined maturation schedules of commercially exploited lake whitefish ( Coregonus clupeaformis ) in the Laurentian Great Lakes (Lakes Michigan, Huron, and Superior) by estimating ages and lengths at 50% maturity, age-specific maturity ogives (age-specific probability of being mature), and probabilistic maturation reaction norms (PMRNs; a metric that accounts for effects of growth and mortality). Collectively, these estimates indicated variation in maturation schedules between sexes (i.e., males tend to mature at younger ages and shorter lengths than females) and among systems (midpoint estimates of PMRNs were smallest for Lake Michigan fish, intermediate for fish in the main basin of Lake Huron, and largest for fish in Lake Huron’s Georgian Bay and Lake Superior). Temporally, recent increases in age at 50% maturity in Lakes Huron and Michigan may primarily reflect plastic responses to decreased growth rates associated with ecosystem changes (e.g., declines of the native amphipod, Diporeia spp.). As plastic and adaptive changes in maturation schedules of fish stocks may occur simultaneously and require different management considerations, we recommend the concomitant analysis of multiple maturation indices.
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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 | 0.001 | 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) | 0.000 | 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".