Effects of riparian forest thinning by two types of mechanical harvest on stream fish and habitat in northern Minnesota
Bibliographic record
Abstract
The response of fish communities and stream habitat to four riparian harvest treatments was studied in north-central Minnesota to determine if riparian harvest with two different harvest systems degrades stream systems. Treatments included control (no harvest), riparian control, cut-to-length riparian thin, and tree length riparian thin. Fish and habitat data were collected from 50 m reaches above, within, and downstream of each treatment 1 year preharvest (1997) and 3 years postharvest (1998–2000). Repeated measures analysis revealed few effects due to treatment; however, there was a 6%–10% significant reduction in canopy cover. Percent fine sediments increased significantly (15%) system-wide following forest harvest and persisted through 2000. This increase in fine sediments was correlated with a decrease in fish biotic integrity (r = –0.31). Habitat and fish variables were influenced more by year-to-year variation than by harvest treatment, suggesting that factors operating at a broader basin-wide scale may influence fish and habitat or mask any site-level harvest effects in this low-gradient stream system. Residual riparian basal areas ≥12.3m2/ha along reaches ≤200 m in length may be adequate to protect fish and habitat in these low-gradient streams, but basin-wide effects of harvest deserve more scrutiny.
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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 | 0.001 | 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".