Effect of clear-cutting, prescribed burning and scarification on litter decomposition in an Eastern Ontario jack pine ( <i>Pinus banksiana</i> ) ecosystem
Bibliographic record
Abstract
Mass losses of litter bags containing leaf litter of Pinus banksiana Lamb., Quercus rubra L., Pteridium aquilium (L.) Kuhn, and Populus tremuloides Mchx. were compared in a jack pine ecosystem after clear-cutting, clear-cutting plus prescribed burning, and clear-cutting plus disk trenching scarification. Controls consisted of litterbags left in undisturbed plots. Mass losses were significantly affected by treatment types and litter types. The greatest differences among treatments were observed after 1 year of field incubation whereas there were considerably fewer differences among treatments after 3 years of incubation in the field. Initial decomposition of P. tremuloides and Q. rubra litter was greatest in the control plots. Scarified plots showed the slowest decomposition rates. Mass losses were not significant among treatments and litter types after 3 years incubation except for P. aquilinum litter on clear-cut and scarified plots.
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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".