Effects of Selective Forest Harvesting on Organic Matter Inputs and Accumulation in Headwater Streams
Why this work is in the frame
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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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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 | 0.000 | 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 it