The impact of early precommercial thinning of dense jack pine (<i>Pinus banksiana</i> Lamb.) stands on the mortality of thinned stems
Bibliographic record
Abstract
Precommercial thinning of jack pine (Pinus banksiana) stands is a common silvicultural method to control stand density and growth in managed boreal forest stands. If employed too early, vigorous conifer re-growth can reduce the radial growth and potential yield of residual trees, thus requiring additional costly thinning treatments and extended rotation period. We examine thinned jack pine re-growth proportion as a function of remaining branch whorls on the stump of cut stems, and of thinning height following fire and salvage. Four salvaged and precommercially thinned stands in two forest fires that occurred in 1995 in the Abitibi-Temiscamingue region of Quebec were sampled. Significant relationships were identified between the number of branch whorls remaining on individual stems following precommercial thinning and the mortality proportion, and between the number of branch whorls remaining on individual stems following precommercial thinning and mean stump height. We suggest that precommercial thinning in dense jack pine stands be applied between 7 and 10 years following establishment at between 10 cm and 13 cm stump height. In addition, we identify various indicators that foresters can use on-site to better plan thinning operations.
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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 | 0.000 | 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) | 0.001 | 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".