Projection of a diameter distribution through time
Bibliographic record
Abstract
Three approaches to characterizing the diameter distribution of a future stand are presented. The first approach is the "parameter-recovery" method, which links a whole-stand model to a diameter-distribution model. The next two approaches provide linkages between an individual-tree model and a diameter-distribution model. Tree-survival and diameter-growth equations were applied to the tree list (the "tree-projection" method) or to the diameter distribution (the "distribution-projection" method) at the beginning of the growth period. A numerical example of Weibull distributions that characterized diameter data from the Southwide Seed Source Study of loblolly pine (Pinus taeda L.) is presented. All three methods produced similar results in terms of Reynolds et al.'s (1988) error indices, whereas the distribution-projection method outperformed the other two methods in predicting total and merchantable volumes per hectare. This study demonstrated that the diameter-distribution model could be linked to either a whole-stand model or an individual-tree model with comparable success.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".