Stream temperature responses to partial-harvest logging in riparian buffers of boreal mixedwood forest watersheds
Bibliographic record
Abstract
As part of a larger study to examine the operational feasibility, ecological benefits, and environmental impacts of partial-harvest logging in riparian buffers along boreal mixedwood forest streams, we determined the effects on summer stream temperatures. Three logged study reaches were compared with three reference reaches over two prelogging and two postlogging summers. Partial-harvest logging resulted in an average removal of 10%, 20%, and 28% of the basal area from riparian buffers at the three logged sites. At the two more intensively logged sites, there were small (<10%) reductions in canopy cover (P = 0.024) and no significant changes in light at stream surfaces (P > 0.18). There were no measurable impacts on stream temperatures at two of the three logged sites. At the most intensively logged site, daily maximum temperatures were significantly higher (∼4 °C) for about 6 weeks in the first summer after logging than in prelogging years or at the reference sites (P < 0.001). Temperature increases were attributed to a logging-induced temporary disruption of cool water inputs from ground disturbance in a lateral-input seep area. Our results indicate that partial-harvest logging in riparian buffers of boreal mixedwood forest streams can sustain effective canopy cover and mitigate logging-induced water temperature increases.
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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) | 0.000 | 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".