A comparison of thinning methods in red pine: consequences for stand-level growth and tree diameter
Bibliographic record
Abstract
Long-term replicated experiments that contrast thinning method (dominant thinning, thinning from below) while controlling stocking level are rare. Stand growth and tree size responses to thinning method can be useful for making decisions to achieve desired objectives, whether these are timber or wildlife habitat related. We examined data from two long-term (>50 year old) silvicultural experiments in red pine to understand how alternative thinning prescriptions influence stand-level basal area, volume, and biomass growth, as well as quadratic mean tree diameter. We found that gross growth in stands treated with dominant thinning was often, although not always, greater than growth in stands treated with thinning from below. However, the differences in growth between thinning methods are smallest at stocking levels and stand ages typical for red pine management. We found that biomass growth increases with dominant thinning were generally less than basal area or volume growth increase. Furthermore, greater gross growth associated with dominant thinning versus thinning from below must be weighed against the significantly smaller average tree sizes that result from repeated dominant thinning.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".