Detecting effects of upper basin riparian harvesting at downstream reaches using stream indicators
Bibliographic record
Abstract
Stream evaluation field data from 44 basins in the Bowron River watershed were used in combination with results from GIS spatial analysis to investigate whether impacts from logging the riparian zone of upperbasin streams could be detected at downstream sites. The field data included responses to stream indicator questions taken from the BC Ministry of Forests and Range's Riparian Management Routine Effectiveness Evaluation (RMREE). The evaluation included questions associated with the following stream indicators: (1) channel bed condition, (2) channel bank condition, (3) in-stream large woody debris processes, (4) channel morphology, (5) aquatic connectivity, (6) fish cover, (7) moss, (8) fine sediment, and (9) aquatic invertebrates. This study examined the negative responses to these indicator questions in relation to the amount of upstream riparian harvesting that took place in each basin. Evaluated reaches that had been harvested to the stream bank were not significantly different from sample reaches with streamside buffers when both groups had harvested upstream riparian areas. Negative responses increased significantly at 30% upstream riparian harvest. Sites were grouped by this threshold (low/high) and compared to nonharvested sites to examine negative responses for each indicator. In discussing the results, we explore the potential role of recovery of harvested drainages, negative responses in the non-harvested group, elevation, soil erodibility, in-stream large woody debris processes, and aquatic invertebrate diversity (which may subsequently impact food and habitat supply for fish). The results support the best management practice of leaving a "no-harvest" riparian reserve on all small streams in order to mitigate downstream impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".