Forest productivity after careful logging and fire in black spruce stands of the Canadian Clay Belt
Bibliographic record
Abstract
Some regenerating stands of the boreal forest exhibit low juvenile growth after major disturbances, which compromises sustainable forest management objectives. In black spruce – feather moss stands of eastern Canada subject to paludification, careful logging methods could decrease stand productivity with time by preventing a beneficial reduction in organic soil thickness. The aim of this project was to confirm decreases in juvenile growth between stands originating from careful logging and the former stands originating from old fires on the same sites. Stem analyses showed that stands originating from CPRS had significantly better juvenile height growth than the former stands but significantly lower growth than stands originating from recent fire in the study region. If organic matter thickness apparently played a role in the growth differences observed between fire and harvesting, it was not the only factor determining stand productivity. According to our results, cohort status, climatic regime, and quality of the residual organic matter are other factors that seem to drive productivity. Our results show that postharvest management approaches (e.g., site preparation) should be used to increase yields after harvest for the sites to express their full growth potential.
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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 | 0.001 | 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".