Seedling size and woody competition most important predictors of growth following free-to-grow assessments in four boreal forest plantations
Bibliographic record
Abstract
Improvements to forest management decisions require accurate and quantifiable information. We examined the effects of various classes of competitors on crop tree growth in the context of free-to-grow standards using regression analysis. We found that seedling size accounted for most of the variation in height and volume growth of jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana [Mill.] BSP) plantations. Including herbaceous and woody competition as explanatory variables explained the additional variation on crop tree growth significantly. In the plantation initiation phase (years 2 to 6), the presence of herbaceous competitors generally reduced conifer growth but in the first part of the stem-exclusion phase (years 7 to 12) increased their growth. In all four boreal plantations in this study, woody competitors reduced conifer growth in both the initiation and stem-exclusion phases. These results have relevance to forest managers who develop and/or use free-to-grow surveys. Key words: vegetation management, silviculture, effectiveness monitoring, forest management, regeneration success, competition effect
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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 | 0.001 | 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".