Physiological correlates of seasonal growth patterns in lake trout <i>Salvelinus namaycush</i>
Bibliographic record
Abstract
Physiological correlates of seasonal growth patterns were measured in lake trout Salvelinus namaycush from two populations with contrasting diets (zooplankton-dominated diet in Louisa Lake; fish-dominated diet in Opeongo Lake). Fish in Opeongo Lake grew faster and were in better condition than fish in Louisa Lake. The most prominent biochemical difference between populations was higher citrate synthase (CS) and cytochrome c oxidase activity in the white muscle of fish from Opeongo Lake, indicating greater sustained swimming activity in this lake. In contrast, lactate dehydrogenase (LDH) activity in white muscle, an indicator of capacity for burst swimming, was similar between lakes. Nucleoside diphosphate kinase (NDPK) activity in white muscle, an indicator of protein synthesis, was higher in Opeongo Lake than in Louisa Lake but only in the autumn. In both lakes, protein concentration and therefore nutritional status increased as the growing season progressed from spring to summer to autumn. Biochemical indicators of growth and activity showed similar seasonal patterns in the two lakes with the spring characterized by high NDPK, high CS and high LDH activities (i.e. high levels of protein synthesis in association with high aerobic and anaerobic activities). These results suggest high foraging effort and allocation to growth early in the growing season in both lakes.
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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 | 0.000 |
| 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) | 0.001 | 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".