A Procedure to Characterize Wood Pile Inventories at Roadside
Why this work is in the frame
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Bibliographic record
Abstract
Tracking roadside wood inventories is necessary for wood procurement. However, this operation is increasingly problematic due to the costs associated with reaching remote sites, labour shortage, and the methods providing limited information on the characteristics of the logs required for transport planning. To overcome these problems, an automated procedure has been developed in Arcmap to characterize individual wood pile inventories at roadside by using GPS points of forwarders, harvester production files, and the road network shape files. An inventory of the logs in the harvest area followed by a wood pile inventory at the roadside were made to evaluate how the procedure could trace logs from machine operating trail to predicted wood pile locations. The study was done at six harvest blocks in the Saguenay-Lac-Saint-Jean region in the province of Quebec, Canada. The procedure was not able to differentiate individual wood piles and aggregated several piles into predicted unloading areas. Results indicated a similarity index of 72% to manually inventoried wood piles. The similarity index could be explained by the low percentage of inventoried unpredicted wood pile lengths (3%) and a high percentage of overpredicted wood pile lengths (27%). The positive allocation rate could not be assessed at the level of the individual piles. On the other hand, the procedure properly allocated 96% of the logs to unloading areas. With the level of precision obtained, the developed procedure could be beneficial for managing the transportation of wood at the level of the road segment since it provides all the dendrometric data of the logs available for transport without requiring human intervention in the forest.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it