Influence of streamside buffers on stream temperature response following clear-cut harvesting in western Oregon
Bibliographic record
Abstract
Determining the effectiveness of different riparian buffers for mitigating forest-harvesting impacts on stream temperatures continues to be of interest throughout the world. Four small, low or medium elevation streams in managed western Oregon forests were studied to determine how the arrangement and amount of streamside retention strips (buffers) in clear-cut units influenced stream temperatures. Buffers included (i) no tree, (ii) predominantly sun-sided 12 m wide partial, and (iii) two-sided (Best Management Practice, (BMP)) 15–30 m wide buffers. Harvested units alternated with uncut units along 1800–2600 m study reaches. Impacts of harvesting on stream temperatures were determined by time series comparisons of postharvest and preharvest regressions. Trends for daily maximum and mean stream temperature significantly increased after harvest in no tree buffer units. Partial buffers led to slight (<2 °C) or no increased warming. BMP units led to significantly increased warming, slight, or no increased warming. Temperature responses in uncut units appeared to be linked to responses in upstream harvested units. In many instances, when harvested units exhibited significantly higher postharvest trends, lower trends were observed in the uncut units downstream. Stream temperature trends of 7 day moving maxima indicated warming through the no tree buffer units and some of the BMP units. Peaks in maxima were not maintained in downstream units. Stream temperature responses were related to buffer implementation and stream features, relating to cooling and warming.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".