Effects of clear-cutting on decomposition rates of litter and forest floor in forests of British Columbia
Bibliographic record
Abstract
The rate of mass loss of three standard substrates (pine needle litter, aspen leaf litter, and forest floor material) was measured in forests and adjacent clearcuts at 21 sites throughout British Columbia, to test the hypotheses that (i) rates of mass loss are greater in clearcuts than in forests and (ii) clear-cutting would stimulate decomposition most in colder zones. Mass loss ranged from 53 to 75% after four years in pine needles, 49 to 70% after 3 years in aspen leaves, and 11 to 20% after 4 years in forest floor material. Mass loss from pine needles was significantly slower in clearcuts throughout the 4-year incubation. Aspen leaf litter and forest floor material lost mass at similar rates in forests and clearcuts. The effect of clear-cutting did not vary between relatively cold and warm sites. The effect of clear-cutting was not related to the size of the clearcuts, which ranged from 1 to 97 ha.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".