Assessing small-stem density in northern hardwood selection system stands
Bibliographic record
Abstract
Data from three uneven-aged northern hardwood stands in New York State were analyzed to assess the effects of understory American beech (Fagus grandifolia Ehrh.) density on changes of small stems within the 2.54 to 5.08 cm diameter class during the first cutting cycle under single-tree selection system. Findings show that the amount of understory American beech on a regeneration plot, quantified using a species index value (SIV), affects the abundance of other species. Results reveal the future plot-level stocking of these small trees for (i) all species (including American beech) as related to time since cutting and residual basal area or (ii) non-beech species as influenced by beech interference (SIV), time since cutting, and residual basal area. Findings indicate that for plots with limited understory beech, small-stem stem density will increase from postcut levels to a peak at 8–12 years after selection system cutting and then decrease. The higher the residual basal area is, the sooner the numbers of small trees reach a peak level and the fewer are present of that threshold size. Findings confirm that no or only minimal numbers of small non-beech trees develop on plots with high levels of understory American beech (SIV ≥ 0.5).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".