Effects of stream-adjacent logging in fishless headwaters on downstream coastal cutthroat trout
Bibliographic record
Abstract
To investigate effects of headwater logging on downstream coastal cutthroat trout (Oncorhynchus clarkii clarkii) populations, we monitored stream habitat and biotic indicators including biomass, abundance, growth, movement, and survival over 8 years using a paired-watershed approach. Reference and logged catchments were located on private industrial forestland on ∼60-year harvest rotation. Five clearcuts (14% of the logged catchment area) were adjacent to fishless portions of the headwater streams, and contemporary regulations did not require riparian forest buffers in the treatment catchment. Logging did not have significant negative effects on downstream coastal cutthroat trout populations for the duration of the sample period. Indeed, the only statistically significant response of fish populations following logging in fishless headwaters was an increase in late-summer biomass (g·m−2) of age-1+ coastal cutthroat trout in tributaries. Ultimately, the ability to make broad generalizations concerning effects of timber harvest is difficult because response to disturbance (anthropogenically influenced or not) in aquatic systems is complex and context-dependent, but our findings provide one example of environmentally compatible commercial logging in a regenerated forest setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".