The effectiveness of different buffer widths for protecting water quality and macroinvertebrate and periphyton assemblages of headwater streams in Maine, USA
Bibliographic record
Abstract
We evaluated the effect of timber harvesting on water quality and macroinvertebrate and periphyton assemblages in first-order streams in Maine, USA. Fifteen streams were assigned to one of five treatments: clearcutting without a stream buffer, clearcutting with 11 m buffers, clearcutting with 23 m buffers, partial harvesting with no designated buffer, and unharvested controls. Harvest blocks on both sides of the stream were 6 ha and partial harvesting within buffers was allowed. Specific conductivity, pH, dissolved oxygen, turbidity, and soluble reactive phosphorus did not change significantly for 3 years after harvesting in all treatments. Unbuffered streams had significantly elevated concentrations of chlorophyll a as well as increased abundance of algal feeding organisms (Diperta Cricotopus and Diptera Psectrocladius ). Streams with 11 m buffers had substantial (10-fold) but nonsignificant increases in chlorophyll a. No other significant changes were detected in other treatment groups. In all treatment groups, the dominant taxa (periphyton Achnanthes minutissimum and macroinvertebrate Chironomidae) are adapted to disturbed environments. We attribute the limited harvest-induced changes to lack of soil disturbance within 8 m of the stream, the small (≤40%) proportion of watersheds harvested, and the resilient nature of aquatic organisms. However, small-scale changes may not be detected due to the small sample size, an inherent limitation of field-based studies.
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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 | 0.001 | 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".