Evaluation of three methods for predicting diameter distributions of black spruce (<i>Picea mariana</i>) plantations in central Canada
Bibliographic record
Abstract
The direct parameter prediction method (PPM), moment-based parameter recovery method (PRM), and percentile-based parameter recovery method (PCT) for estimating the parameters of the three-parameter Weibull probability density function were evaluated for their applicability in predicting the diameter distribution of unthinned black spruce (Picea mariana (Mill.) B.S.P.) plantations. Employing diameter frequency data derived from 267 permanent sample plots situated throughout central Canada, fit (n = 214) and validation (n = 53) data sets were created. Using stepwise regression analyses in combination with seemingly unrelated regression techniques, the three methods were calibrated using commonly measured prediction variables (stand age, dominant height, site index, and stand density). Results indicated that, although all three methods were successful in predicting the diameter frequency distributions within the sample stands, the PCT was superior in terms of prediction error. Specifically, the PCT had the lowest mean error index (80.98), followed by the PRM (82.73) and the PPM (83.98). Consequently, among the three methods assessed, the PCT was considered the most suitable for describing unimodal diameter distributions via the three-parameter Weibull probability density function within unthinned black spruce plantations.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".