Effectiveness of Using Summer Thermal Indices to Classify and Protect Brook Trout Streams in Northern Ontario
Bibliographic record
Abstract
We tested five thermal indices for their ability to differentiate streams containing brook trout Salvelinus fontinalis from streams not containing brook trout in forested watersheds of the Precambrian Shield, northern Ontario, with the goal of identifying and protecting riparian areas of thermally sensitive trout streams during timber harvesting. Logistic regression was used to predict brook trout presence and absence, with maximum summer temperature, mean summer temperature, mean sampling temperature, mean maximum summer temperature, and thermal stability as independent variables. Brook trout streams were cooler and thermally more stable than non-brook-trout streams, but temperatures overlapped considerably between the two types of stream. Correct classification of streams ranged from 60.3% for summer temperature stability to 67.1% for maximum summer and mean sampling temperatures. The models yielded correct predictions more often for brook trout absence (∼80%) than for brook trout presence (≤50%) because streams with temperatures above lethal limits clearly precluded brook trout presence, whereas cooler temperatures merely indicated thermal suitability. In cooler streams, other factors, such as suitable spawning and rearing habitat and migration barriers, likely contributed to variation in brook trout presence. The specific prediction probabilities of the models could be used to assign management protection levels or identify additional sampling requirements necessary for determining brook trout distributions in streams with suitable temperatures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".