Characterizing diameter distribution of <i>Pinus nigra</i> stands in Türkiye with a Weibull distribution
Bibliographic record
Abstract
The objective of this study was to identify the most effective system for predicting parameters of the Weibull function that characterize diameter distributions of black pine ( Pinus nigra Arn.) stands in Türkiye. We examined three Parameter Recovery methods: the Moment Recovery method, based on diameter moments (diameter variance and quadratic mean diameter), the Percentile Recovery method, relying on diameter percentiles (the 31st and 63rd percentiles), and the Hybrid method, which combines elements of both approaches. Within each of the three methods, we derived regression coefficients from four estimation approaches: Seemingly Unrelated Regression (SUR), Cumulative Distribution Function Regression (CDFR), Maximum Likelihood Estimator Regression (MLER), and Stand Table Regression (STR). Our findings demonstrated that the Moment Recovery method exhibited superior performance compared to the Percentile Recovery and Hybrid methods. Additionally, the MLER approach surpassed the other three estimation techniques. Notably, the Moment Recovery method, coupled with regression coefficients estimated through MLER, emerged as the top-performing combination overall. These results hold significant implications for the development of a diameter distribution growth and yield model tailored to black pine stands in Türkiye.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".