Aquatic arthropods and forestry: effects of large-scale land use on aquatic systems in Nearctic temperate regions
Bibliographic record
Abstract
Abstract Aquatic arthropods can be affected by forest management through increased amounts of light, discharge, and sediment runoff, alteration of the supply of basal resources, changes in the supply of large wood, temperature modifications, and food-web effects. This syndrome of alterations varies geographically in magnitude, and the specific details depend on initial biotic and abiotic conditions, local topography, climate, and the particular management practices used. Impacts on standing water appear to be subtle, and most attention has focussed on streams, where changes are often more obvious. The intensity of any changes in processes affecting aquatic arthropods depends, in part, on the proximity of logging to the shoreline and the proportion of watershed harvested, and also on the condition and frequency of forest access roads crossing or near water bodies. Some groups of species are particularly vulnerable, but others, particularly generalist species such as some Baetis Leach (Ephemeroptera: Baetidae) and some Chironomidae (Diptera), appear to benefit from harvesting. In general, outcomes of harvesting near streams are temporary increases in production and abundance but reductions in diversity. Impacts on all trophic levels, especially in streams, can occur from forest harvesting. The primary tool for mitigating these impacts is the use of riparian buffers, but there are still major uncertainties about the effectiveness of specific widths and configurations of buffers and their use for additional types of disturbance.
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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 | 0.000 | 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) | 0.002 | 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".