Evaluation and Calibration of the CroBas-PipeQual Model for Jack Pine (Pinus banksiana Lamb.) using Bayesian Melding Hybridization of a process-based forest growth model with empirical yield curves
Bibliographic record
Abstract
CroBas-PipeQual a été élaboré pour étudier les effets de croissance des arbres sur la qualité du bois. Ainsi, il s’agit d’un modèle d’intérêt pour maximiser la valeur des produits extraits des forêts. \nNous avons évalué qualitativement une version de CroBas-PipeQual calibrée pour le pin gris (Pinus banksiana Lamb.) de façon à vérifier l’intérêt de l’utiliser comme outil de planification forestière. Par la suite, nous avons fait une analyse de sensibilité et une calibration bayesienne à partir d’une table de production utilisée au Québec. \nLes principales conclusions sont: \n1. Les prédictions de hauteur sont les plus sensibles aux intrants et aux paramètres liés à la photosynthèse; \n2. La performance de CroBas est améliorée en tenant compte de la relation observée entre deux paramètres utilisés pour estimer la productivité nette et l'indice de qualité de station; et\n3. CroBas requiert d’autres améliorations avant de pouvoir être utilisé comme outil de planification.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".