Commercial thinning that maintained species diversity of a mixed black spruce–jack pine stand enhanced productivity
Bibliographic record
Abstract
Commercial thinning enables forest managers to meet timber production objectives. Thinning reduces tree density to alleviate competition for resources and favour growth of selected tree species. However, in doing so, thinning can homogenize the composition of mixed-species forests and raise biodiversity issues. There is increasing evidence that species richness can lead to higher productivity through a complementarity effect. Hence, thinning that would maintain species diversity of mixed-species forests could enhance stand productivity and help forest managers to reconcile timber production objectives and biodiversity issues. The objective of this study was to compare post-thinning stand production, experimentally over 10 years, in mixed and monospecific stands of black spruce (Picea mariana [Mill.] B.S.P.) and jack pine (Pinus banksiana Lamb.). The post-thinning stand production curve of the mixed stand converged toward that of the unthinned mixed stand while the production curves of the thinned and unthinned monospecific stands remained parallel. The convergent productivity of the mixed stand could be explained by a positive interaction between effects of thinning and niche complementarity. We propose that thinning that maintains species diversity of mixed stands could help forest managers who are implementing ecosystem management to reconcile timber production objectives with biodiversity issues.
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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) | 0.001 | 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".