Effects of Selective Forest Harvesting on Organic Matter Inputs and Accumulation in Headwater Streams
Bibliographic record
Abstract
Abstract Organic matter inputs and accumulation were measured in streams of low-order forest watersheds across a gradient of selective harvesting with no protective riparian buffers assigned. Comparisons were made among sites in selection-cut (average 29% basal area removal), shelterwood-cut (average 42% basal area removal), diameter limit-cut (average 89% basal area removal), and undisturbed tolerant hardwood catchments. The diameter limit harvest was an intentionally high-disturbance treatment and is not a normal silvicultural prescription for tolerant hardwoods in Ontario. Time trend analyses were conducted to examine differences among sites over a pre- and postharvest experimental period. Selection-based harvesting at up to 42% basal area removal with no riparian buffers did not significantly alter average over-stream canopy cover, leaf litter and other organic matter inputs, benthic particulate organic matter accumulation, or woody debris abundance. Harvesting impacts on over-stream canopy cover and organic matter inputs appeared to be minimized by natural crown architecture (overlap in crowns of over-stream trees, residual mid-crown canopy) and by careful logging practices including retention of many immediate streamside trees (within a few meters of the stream channel) and avoidance of felling directly into the streams. Dissolved organic matter fluxes increased slightly for 1 year after harvest and were associated with increased water yield. At the diameter limit harvesting intensity (about 89% basal area removal), significant effects on organic matter inputs and accumulation in streams were detected. The results indicate that selective harvesting of hardwood forests at up to about 42% basal area removal can be conducted without causing significant reductions in organic matter inputs and accumulation in headwater streams, even without prescribed streamside buffer strips.
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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 | 0.000 |
| 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 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".