JAMES BUTTLE REVIEW: Quantifying the influence of forestry and forest disturbance on stream temperature: Methodologies and challenges
Bibliographic record
Abstract
Abstract Stream temperature governs many aquatic ecosystem processes and plays a key role in determining the distribution of cold‐water amphibians and cool‐ and cold‐water fish, including salmonids. Decades of research have focused on the effects of forestry and forest disturbance on stream temperature, and new projects are underway or being planned in jurisdictions including the Provinces of Alberta and British Columbia, Canada, and Washington State, USA. The objective of this paper is to provide a critical review of methodologies employed in previous studies. The review initially focuses on the range of metrics used to quantify stream thermal regimes and the factors that control stream temperature variability in time and space, then focuses on sampling and analytical methodologies used to quantify stream temperature response to forestry activity and forest disturbance. Empirical methods include sampling in time, space, or both, and may or may not include pre‐ and post‐harvest data. Process‐based mechanistic and hybrid empirical‐mechanistic models have also been applied. The advantages and disadvantages of these approaches are discussed, and recommendations provided to support the design and execution of future studies.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".