Stream Temperature Response to 50% Strip-Thinning in a Temperate Forested Headwater Catchment
Bibliographic record
Abstract
Stream temperature is a critical parameter for understanding hydrological and biological processes in stream ecosystems. Although a large body of research has addressed the effects of forest harvesting on stream temperature, less is known about the responses of stream temperature to the practice of strip-thinning, which produces more coherent patches of shade and sunlight areas. In this study, we examined stream temperature response to 50% strip-thinning in a 17 ha headwater catchment. The thinning lines extended through the riparian zone. Paired-catchment analysis was applied to estimate changes in daily maximum, mean, and minimum stream temperatures for the first year following treatment. Significant effects on daily maximum stream temperature were found for April to August, ranging from 0.6 °C to 3.9 °C, similar to the magnitude of effect found in previous studies involving 50% random thinning. We conducted further analysis to identify the thermal response variability in relation to hydrometeorological drivers. Multiple regression analysis revealed that treatment effects for maximum daily stream temperature were positively related to solar radiation and negatively related to discharge. Frequent precipitation during the summer monsoon season produced moderate increases in discharge (from 1 to 5 mm day−1), mitigating stream temperature increases associated with solar radiation. Catchment hydrologic response to rain events can play an important role in controlling stream thermal response to forest management practices.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".