Empirical modelling of maximum weekly average stream temperature in British Columbia, Canada, to support assessment of fish habitat suitability
Bibliographic record
Abstract
The objective of this study was to characterize the spatial variability of stream thermal regimes in British Columbia, Canada, with the specific goal of developing a predictive model to assist in provincial-scale assessment of fish habitat. It is part of a broader study to develop an approach to support the designation of “Temperature Sensitive Streams”, particularly in relation to the potential effects of forest harvesting and climate change. Stream temperature data were collected from researchers, consultants and government agencies. After checking for data quality, the annual maximum of a seven-day running average of mean daily water temperature (MWAT) was extracted for each station-year. A multiple regression model for the mean MWAT for each station was fitted for stations having basin areas between 1 and 104 km2. Predictor variables included the logarithm of catchment area, normal July–August air temperature for the location, the square root of the percentage of glacier cover in the catchment, the square root of the percentage of lake cover in the catchment, the mean catchment elevation, channel slope, a coefficient related to intensity of the mean annual flood, and the deviation of July–August air temperature during the monitoring year(s) from the average during a reference period. Model coefficients were consistent with the physical processes known to govern stream temperature. The standard deviation of prediction errors from a 10-fold cross-validation was 2.1°C. Lack of information on riparian shading is a likely source of a significant portion of the prediction error. The model can be used to provide an initial prediction of stream temperature regime for fish habitat assessment, as well as to provide first-order estimates of the sensitivity of MWAT to climatic warming and glacier retreat.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".