StatSAW: modelling lumber product assortment using zero-inflated Poisson regression
Bibliographic record
Abstract
Realistic forestry value chain simulations require accurate representations of each component. For primary processing, this is complicated by the fact that a single raw material is converted into a wide range of lumber products. The aim of this study was to develop statistical models for predicting lumber product assortment from tree size information, while taking into account the high proportion of zeros in the data. Lumber recovery was simulated from a database of 1013 laser-scanned Picea mariana (Mill.) Britton, Sterns & Poggenb. and Abies balsamea (L.) Mill. stems using the sawing simulator Optitek. The number of boards per stem of specific products was modelled with zero-inflated Poisson regression using stem diameter and height as covariates. The number of boards per stem was strongly related to both diameter and height, but also changed according to input prices for lumber products. Zero-inflated models outperformed ordinary Poisson regression in all cases. The developed models will be integrated into simulation tools designed to optimize processes along the entire forest value chain from forest to end user.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".