Valuation of growing stock using multisource GIS data, a stem quality database, and bucking simulation
Bibliographic record
Abstract
Customer-oriented production as a sawmill strategy requires up-to-date information on the available raw material resources. Bucking is a process in which the tree stem is divided into products based on the roundwood user’s needs regarding products and their quality and dimensions. Optimization methods are employed in bucking to recover the highest value of the stem for a given product price matrix and requested length–diameter distribution. A method is presented here for assessing the value of harvestable timber stands based on their product yield. Airborne laser scanning, multispectral imagery, and field plots were used to produce timber statistics for a grid covering the target area. The statistics for the plots were generated from this grid. The value of the estimated tree list was assessed using a bucking-to-value simulator together with a stem quality database. Different product yield simulations in terms of volumes, timber assortment recoveries, wood paying capabilities (WPC) and value estimations based on the presented method, and extensive field measurements were compared. As a conclusion, this method can estimate WPC for pulpwood and sawlogs with root mean squared errors of 32.7% and 38.5%, respectively, relative to extensive field measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".