North American Journal of Fisheries Management 22:151–164, 2002 q Copyright by the American Fisheries Society 2002 Physical Aquatic Habitat II. Pools and Cover Affected by Large Woody Debris in Three Western Oregon Streams
Bibliographic record
Abstract
Abstract.—Large woody debris (LWD) is important in affecting stream channel morphology and aquatic habitat. Although the greatest effects on streams of the Pacific Northwest have been by LWD from large conifers, many riparian forests in the region are dominated by red alder Alnus rubra. The effects of the small size and short life of LWD from red alders on channel morphology may be different from that of conifers and are poorly understood. We added LWD (primarily red alder) to three third-order streams in the Oregon Coast Range and used digital terrain models to evaluate physical habitat for salmonids over 3 years. Total residual pool volume increased in two streams, but in the one with the lowest gradient it did not change in the treated portion and even decreased in the untreated portion. In all streams, both the relative proportion and absolute amount of residual pool volume from deep pools increased from their pretreatment values. Cover from LWD in pools increased after treatment and remained high, but the absolute amount of cover was poorly predicted by the volume of LWD. Overall, the changes in stream channel morphology and habitat were consistent with the effects of LWD, and these case studies indicate that small, red alder LWD can effectively modify physical aquatic habitat. Large woody debris (LWD) affects channel mor-phology and aquatic habitat in small streams of forested watersheds, a relationship that is of par-ticular importance in the Pacific Northwest of the USA and Canada. LWD affects morphology by reducing or redirecting stream power (Beschta and Platts 1987), detaining sediment (Bilby and Ward 1989), and creating pools (Montgomery et al. 1995). LWD improves physical aquatic habitat by increasing the hydraulic complexity of streams
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.010 | 0.001 |
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".