Long-term precommercial thinning effects on <i>Larix occidentalis</i> (western larch) tree and stand characteristics
Bibliographic record
Abstract
Precommercial thinning (PCT) is used to increase tree size and shorten harvest rotation time. Short-term results from PCT studies often show a trade-off between individual-tree growth and net stand yield, while longer-term effects of PCT on tree growth and stand yield are less well documented. We used a 54-year-old PCT study to test long-term effects of forest density and thinning schedules on stand yield and tree-level characteristics in even-aged western larch (Larix occidentalis Nutt.) stands. The study has three target densities (494, 890, and 1680 trees·ha−1) crossed with three thinning schedules (target density achieved through one, two, or four entries). Analysis of variance (ANOVA) and linear contrasts were used to test the effects of density and number of entries on tree- and stand-level attributes. Thinning before stand age 10 years leads to long-term constant yield (219.0–269.5 m3·ha−1; P > 0.05) across the tested densities. We also found constant volume growth across stand densities during the most recent measurement interval (5.42–6.41 m3·ha−1·year−1; P > 0.05). Number of entries did not affect any tree- or stand-level attribute. The primary effect of early PCT is to control whether wood volume and growth are concentrated on few large, stable trees or spread over many small, unstable trees.
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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 | 0.001 |
| 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".