Modeling trends in stem quality characteristics of loblolly pine trees in unthinned plantations
Bibliographic record
Abstract
Trends in stem quality characteristics of loblolly pine trees were investigated by using data from unthinned control plots established in plantations across 12 southern states in the United States and measured at 3 year intervals. At each measurement, the stem quality was classified into (i) single stem or forked, (ii) normal top or broken top, (iii) straight or sweep (bole sweep, butt sweep, short crook), and (iv) no disease or disease. Data through the first 15 years of observation showed that, on average, 4% of the trees were forked, 5% had broken tops, 41% had sweep, and 12% had disease or insect damage; 48% exhibited single stem, normal top, straight, and no disease or insect damage. Recovery rates out of forked, broken top, sweep, and disease classes were 37%, 83%, 30%, and 11%, respectively, over the 15 years. Multicategorical logit models were developed to predict stem quality characteristics from stand-and tree-level variables. Forked trees were related with tree diameter; broken tops were related with stand density, DBH, and relative height; sweep was related with stand age. Significant predictor variables for the incidence of disease or insect attack were not found. The occurrence of undamaged and disease-free trees can be predicted from DBH and relative height.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".