Effects of Logging Second‐Growth Forests on Headwater Populations of Coastal Cutthroat Trout: A 6‐Year, Multistream, Before‐and‐After Field Experiment
Bibliographic record
Abstract
Abstract To understand how logging of second‐growth forests affects populations of coastal cutthroat trout Oncorhynchus clarkii clarkii, we examined trout relative abundance, body condition (mass relative to length), and physical and thermal habitat in the summer and winter in four headwater streams (two treatment streams and two nonlogged control streams) over a 6‐year period (2 years prelogging [1997–1998] and 4 years postlogging [1999–2002]). This is one of the first efforts to conduct a multiyear, replicated stream, before‐and‐after experiment on this scale to assess the effects of logging on fish and habitat. In the treatment streams, 21% of the watershed area was logged by clear‐cutting (no scarification or slash‐burning). Careful logging approaches were employed to remove most of the riparian overstory (i.e., no machines were used within 5 m of stream, logs were felled and yarded away from riparian zones, all shrubs were left behind, and large wood was left in streams). Because a cooler summer climate occurred coincidentally with our postlogging period (the mean daily average summer air temperature was 1–2°C cooler than the temperature during the prelogging period), the mean average and mean maximum daily stream temperatures declined after the logging period in the control streams and remained the same in the treatment streams. After accounting for the effects of climate, logging had warmed treatment streams by about 1°C. We could not detect any logging treatment effects on summer or winter relative abundance or condition, nor were any changes evident to instream physical habitat associated with the logging treatment. These results were probably attributable to the careful logging approaches employed and the cooler climate that occurred during the postlogging period.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".