Linking the thermal regimes of streams in the Great Lakes Basin, Ontario, to landscape and climate variables
Bibliographic record
Abstract
Abstract The lack of geographically broad‐scale temperature data has limited our ability to classify stream temperatures and assess the processes affecting them. Continuous data (1 July 2005–30 June 2006) from 90 sites throughout the Great Lakes Basin (GLB) were used to classify and model the thermal regimes of streams in Ontario. Existing and newly developed temperature metrics were used to characterize the data for each site. The 90 sites clustered into three thermal regimes based on maximum weekly maximum temperature (°C) and spring rate of change (°C · d−1). The centroids of regime 1, 2 and 3 had temperatures of 26.4, 28.4, 23.5°C and warming rates of 0.20, 0.12 and 0.10°C · d−1, respectively. There was a regional pattern in the thermal regimes; most sites in the north were regime 1 and most sites in the south were regime 2 but neither regime was limited to those areas. Regime 3 sites were found throughout the study area. Discriminant function analysis indicated that per cent riparian forest, mean annual air temperature, per cent surface water and groundwater discharge potential influenced the thermal regimes at the sites, and demonstrated how variables at three spatial scales influence stream temperatures. This study provides a framework for thermal assessments elsewhere and demonstrates how anthropogenic activities such as riparian deforestation, groundwater withdrawal, stream regulation and climate change will all affect the main drivers of thermal regimes in streams. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".