Dynamics of a polyphagous lake trout, Salvelinus namaycush (Walbaum), population in a Northwestern Ontario lake / by Helen Elizabeth Ball
Bibliographic record
Abstract
Tagging studies, index gill netting and an experimental \nwinter fishery were used to investigate the dynamics of a \npolyphagous lake trout (Salvelinus namaycush population in \nSqueers Lake of northwestern Ontario. The density and standing \ncrop of mature lake trout in Squeers Lake is higher than reported \nfor other lake trout populations. Lake trout in Squeers Lake \nexhibit a bimodal length distribution with a wide range in length \nat age, suggesting recruitment of juveniles into the adult \npopulation is regulated. Observed depth distribution of lake \ntrout indicates adults may limit young juveniles to deep water. \nExploitation in the 1970's may have produced several strong year \nclasses, but recruitment into the adult population did not occur. \nThe slow growth of lake trout in Squeers Lake appears to \nresult from the lack of available pelagic forage fishes. Thermal \nregimes limit foraging activity to Mysis relicta and Pontoporeia \nhovi during the summer months resulting in suboptimal growth. \nIn Squeers Lake, scales underestimate the age of lake trout; \nthe magnitude of the error increases with age. In slow growing \npolyphagous populations, age should be assessed using otoliths. \nThe wide range in size of fish at a given age suggests the \nchoice of aging tissue should be based on age rather than length. \nThe Ricker Yield model indicates Squeers Lake can withstand \nthe removal of four to eight times the allowable yield of lake trout recommended by the Morphoedaphic Index. . This model \naccurately reflected actual yield of the 1986 winter fishery. \nThe lottery system is Useful for experimental management \nprojects because it allows strict control and monitoring of \neffort and harvest. Anglers thought the lottery system was an \nunacceptable method for managing lake trout populations. \nRotational pulse fishing appears to be a simple and acceptable \nsystem for managing polyphagous lake trout populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".