Predicting the location of optimal habitat boundaries for lake trout (<i>Salvelinus namaycush</i>) in Canadian Shield lakes
Bibliographic record
Abstract
The optimal habitat boundary for lake trout (Salvelinus namaycush Walbaum) has been defined as that portion of the lake having both more than 6 mg·L1 oxygen and temperature less than 10 °C. Here we use an existing hypolimnetic oxygen model to define the lower boundary of the optimal habitat by identifying the depth at which 6 mg·L1 oxygen occurs at the end-of-summer stratification period. Then we develop a new model to predict the 10 °C depth or upper habitat boundary for the same date. These two boundaries can be used in conjunction with measured individual strata volumes to calculate the optimal habitat volume. Parameters needed for the models include lake morphometry, total phosphorus (TP) concentration, and Secchi depth or dissolved organic carbon (DOC) concentration. Thus, the optimal habitat model can be used to predict the effects on optimal habitat volumes of changes in trophic status (increases or decreases in TP), or changes in light transmission (caused by changes in DOC or its surrogate in these lakes, i.e., Secchi depth) that result from changes in climate, land use, acidbase status, or incident ultraviolet radiation levels.
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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) | 0.000 | 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".