A Science-Based Approach for Identifying Temperature-Sensitive Streams for Rainbow Trout
Bibliographic record
Abstract
Abstract To regulate human-induced changes to fish habitat, resource managers commonly set standards based on maximum allowable changes. For example, new legislation in British Columbia (BC), Canada, calls for restrictions on harvesting of trees and related activities near temperature-sensitive streams. However, methods for designating such streams are still evolving. Our objective was to help develop such methods by (1) improving understanding of the temperature-dependent responses of fish and (2) devising improved methods for estimating the effects of forestry-related activities on stream temperature as well as the chance of exceeding upper temperature limits. Using previously published models, we found that for rainbow trout Oncorhynchus mykiss particular increases in stream temperature led to different effects on juvenile growth rate, egg survival rate, and resistance to mortality from diseases. In a separate analysis, to evaluate the chance that cumulative forestry activities will increase stream temperature by various amounts, we compiled summer temperature data for 104 streams in central BC that reflected different watershed features, contrasting summer climates, and various levels of land use. A classification and regression tree analysis of a summer maximum weekly average temperature (MWAT) index grouped streams into six categories as a function of watershed size, watershed elevation, and air temperature. We then analyzed the remaining unexplained variation among stream temperature indices using Bayesian regression. We found high probabilities that increases in road density and the density of road crossings of streams within watersheds are associated with increases in residual temperature. For instance, a Bayesian regression indicated a 6-in-10 chance that the MWAT in our study area will increase by 1.25°C for a road density of 2 km/km2 of watershed area and by 3.25°C for a road density of 4 km/km2. These analyses illustrate some possible ways to help designate temperature-sensitive streams.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".