Riparian forest harvesting and its influence on benthic communities of small streams of sub-boreal British Columbia
Bibliographic record
Abstract
Forest harvesting in riparian areas can alter the leaf-litter inputs, shading, and stability of small streams, and many of the details of these impacts are known for coastal streams of the Pacific Northwest. However, little is known about how small streams in the drier, continental areas of western North America respond to logging. We conducted a study of paired stream reaches (comparing one recently harvested (≤3 years) reach and two upstream, forested reaches in each of five streams) in which periphyton, detritus, macroinvertebrate abundance and biomass, and physical features were measured in summer and autumn. In general, recently harvested stream sections tended to be wider and contained more riffle areas than the upstream forested sections. The amounts of leaf litter and algae varied among streams and were not consistently greater or lesser in the forested sections than in the harvested sections. Though the variation in amounts within streams was mainly seasonal, amounts differed more among streams than between pairs of harvested and forested reaches. The communities of benthic invertebrates differed significantly between forested and harvested reaches, but often in opposite directions between streams. The magnitude and direction of differences observed between treatments, streams, or seasons were associated with the specific stream and the method of riparian harvesting used.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".