Track-Monitoring and Analyzing Machine Clearances during Wood Forwarding
Why this work is in the frame
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Bibliographic record
Abstract
This article reports on track-monitoring and analyzing machine clearances during wood forwarding across seasons and weather, using ultrasonic distance sensors in combination with time-stamped GPS xy locations, at 10 sec intervals. The resulting data, obtained from 54 harvesting blocks, were analyzed by machine type (two wood forwarders and one grapple skidder), stand type (softwood plantation versus natural hardwood stands), month, slope, cartographic depth-to-water (DTW) classes, number of passes along track, and machine speed. For the most part, clearances were highly variable, due to passing over stumps, rocks, harvest slash, brushmats, ruts, and snow cover when present. This variability was on average greater for the lighter-weight wood forwarders than for the heavier-weight skidder, with the former mostly moving along equally spaced lines on brushmats, while the paths of the latter spread away from central wood-landing sites. In terms of trends, machines moved 1) more slowly on wet ground, 2) faster during returning than forwarding, and 3) fastest along wood-landing roads, as to be expected. Low clearances were most notable during winter on snow-covered ground, and on non-frozen shallow DTW and wet multiple-pass ground. During dry weather conditions, clearances also increased from low-pass tracks to multi-pass tracks due to repeat soil compaction of broadened tracks. These results are presented block-by-block and by machine type. Each block-based clearance frequency pattern was quantified through regression analysis and using a gamma probability distribution function.
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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 | 0.001 |
| 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