A computer approach to finding an optimal log landing location and analyzing influencing factors for ground-based timber harvesting
Why this work is in the frame
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Bibliographic record
Abstract
Locating a log landing is an important task in forest operations planning. Several methods have been developed to find an optimal landing location and compute a mean skidding distance, but they simplify harvest unit attributes and do not simultaneously consider multiple design factors influencing optimal landing locations. In this study, we introduce a computerized model developed to determine the optimal landing location for ground-based timber harvesting. Using raster-based GIS data, the model finds skid trails from stump to each of candidate landings and selects the best landing location that minimizes total skidding and spur road costs. The model is applied to several hypothetical harvest units with different terrain and harvest volume attributes to analyze the effects of design factors influencing optimal landing locations. Unit boundary shapes, volume distribution, the presence of obstacles, terrain conditions, and spur road construction are considered as influencing design factors.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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