Stream thermalscape scenarios for British Columbia, Canada
Bibliographic record
Abstract
Water temperature is a key feature of freshwater ecosystems but comprehensive datasets are severely lacking, a limiting factor in research and management of freshwater species and habitats. An existing statistical stream temperature model developed for British Columbia (BC), Canada, was refit to predict August mean stream temperatures, a common index of stream thermal regime also used in thermalscapes developed for the western United States (US). Thermalscapes of predicted August mean stream temperature were produced for 680,000 km of stream network at approximately 400 m intervals. Temperature predictions were averaged for 20-year periods from 1981-2100 to produce 86 scenarios: one for each historical period (i.e. 1981-2000, 2001-2020), and 21 for each future period (i.e. six global climate models and an ensemble average under three representative concentration pathways). The final model performance was consistent with other published regional-scale statistical models (R2 = 0.79, RMSE = 1.53 °C, MAE = 1.18 °C), performing well given the relative paucity of data, large geographic extent, and range of climatic and physiographic conditions. Model results suggested an average increase of August mean stream temperature of 2.9 ± 1.0 °C (RCP 4.5 ensemble mean ± SD) by end of century, with significant heterogeneity in predicted temperatures and warming rates across the province. Compared to stream temperature predictions from the western US, the predictions for BC showed good agreement at cross-border streams (Pearson’s r = 0.91), suggesting the possible integration of both products for a thermalscape covering much of western North America. These stream thermalscapes for BC address a major data deficiency in freshwater ecosystems and have potential applications to stream ecology, species distribution modelling, and evaluation of climate change impacts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".