Use of Harvester Data to Estimate the Amount of Merchantable Non-Utilized Woody Material Remaining after Mechanized Cut-to-Length Forest Operations
Bibliographic record
Abstract
An agreement between the provincial government of Québec, Canada and the forest industry executing forest management activities on public lands has been established regarding non-utilized woody material (NUWM) left on the cutting area. Problems linked to this agreement are compounded by labor shortages, which have an impact on the precision of the mandatory inventories. The objectives of this study were to: (1) reconstruct and estimate the merchantable NUWM volume beyond the last processed log of balsam fir and white spruce with the use of harvester on-board computer (OBC) data, (2) design a software tool to estimate and spatialize merchantable NUWM, and (3) perform an explorative comparison between the OBC method and conventional field inventory. In total, five sites were harvested to develop the volume algorithms. Each site was harvested by a single-grip harvester operating a different OBC system (OPTI4G, Log Mate 500, and Log Mate 510). Results suggest that, with Varjo’s model and linear regression, estimation of NUWM volume using OBC data is possible. The spatialization tool positioned NUWM within the harvest area for StanForD and StanForD 2010. The explorative comparison highlighted a possible cost reduction of approx. 36.8 $/ha and an increase of precision for the OBC method.
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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) | 0.000 | 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) | 0.001 | 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".