Growth responses to thinning from below in uneven-aged interior Douglas-fir dominated stands
Bibliographic record
Abstract
We assessed the impacts of three approaches to thinning from below with varying spatial patterns on several stand and individual tree variables for interior Douglas-fir ( Pseudotsuga menziesii var. glauca (Beissn.) Franco), interior spruce ( Picea glauca (Moench) Voss × Picea engelmannii Engelm.), and lodgepole pine ( Pinus contorta Dougl. Ex Loud. var. latifolia Englem.) in central British Columbia, Canada. The three thinning treatments were two experimental “clumped” treatments (3 m Clumped and 5 m Clumped) and the Standard (more uniform spacing) thinning treatment that was employed operationally at that time. We used long-term data from 24 plots measured five times over 21 years. Thinning increased stand basal area increment, with the plots that received the 5 m Clumped treatment having significantly higher periodic annual relative basal area increment than the unthinned Control plots. The responses for the two clumped treatments were not any lower than the Standard. The 3 m Clumped treatment was best if one is concerned about fast recovery of the growing space; however, the 5 m Clumped spacing treatment may be preferable if higher individual tree vigour is needed for resistance and resilience to fire, insects, and disease.
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) | 0.000 | 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".