Modeling stand- and tree-level growth of Chinese fir plantations
Bibliographic record
Abstract
Growth and yield systems are essential tools for enhancing forest management decision-making. This study systematically evaluated three stand-level models and two data types for predicting stand survival and basal area of Chinese fir ( Cunning lanceolate (Lamb.) Hook.) plantations in southern China. The first model links survival and diameter through the self-thinning concept. The second model incorporates stand diameter, the previous year’s diameter, and stand survival, while the third model treats stand survival and diameter as mutually independent functions of only stand age. Model 2 was the best performer for short-term prediction (2–4 years), whereas Model 3 excelled in longer projection periods (6–10 years). Despite the independent predictions of stand survival and diameter in Model 3, it closely tracked observed self-thinning trajectories in long-term predictions. Tree-level model growth derived from Models 2 and 3 performed optimally for short-term and long-term tree-level predictions, respectively. While limited to four experimental sites, this research contributes theoretical groundwork to growth and yield modeling for Chinese fir 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 | 0.001 | 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 | 0.001 |
| 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".