Effect of selective precommercial thinning on balsam fir stand yield and structure
Bibliographic record
Abstract
Silvicultural tools such as green retention harvesting and multiple variations of partial cut systems are being developed to implement ecosystem-based forest management. However, very little effort has been expended in developing silvicultural treatments for young stands. Results for a selective precommercial thinning (three thinning intensities and control) covering a 28-year period in a balsam fir-dominated stand are presented. Thinning did not significantly increase stand yield, nor change stand diameter diversity or distribution. Furthermore, diameter distributions and diversity of dead stems also did not differ significantly (P > 0.05) among thinning intensity. More important than intensity effects, statistical differences were found between initial stand densities. Low initial densities had greater yields and more diverse diameter distributions. Nevertheless, for low initial stand densities, light to moderate thinning seemed to increase yield, whereas moderate to heavy thinnings would be appropriate for high initial stand densities. Although selective precommercial thinning does not result in significant changes in stand structure, it could be used as a first step in increasing stand complexity within the context of ecosystem-based management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".