A modular terrain model for daily variations in machine-specific forest soil trafficability
Bibliographic record
Abstract
A modular approach is presented to assess terrain-specific soil trafficability in terms of soil resistance to penetration and machine-specific rut depths. These modules address: (1) soil resistance to cone penetration (cone index, or CI) as affected by soil moisture, texture and pore space (Module 1), (2) machine-induced rut depths (single-pass and multi-cycles) as affected by wheel loads, tire specifications and CI (accounting for depth of compactable soil, Module 2), (3) temporal variations in hydrothermal conditions, CI, and potential rut depths due to daily soil moisture and temperature variations (Module 3), and (4) spatial variations in CI and rut depth across terrain due to corresponding changes in soil moisture, depth of compactable soil, bulk density, texture, frost depth, organic matter and coarse fragments (Module 4). The approach is applied to off-road wood-forwarding operations. Modules 1 and 2 were calibrated to apply to a wide range of soil conditions. Modules 3 and 4 were initialized for a wood-forwarding case study at CFB Gagetown, New Brunswick, Canada. Model results should be most applicable for flat to near-flat terrain, with insignificant wheel obstructions, and no organic matter accumulations on top of the mineral soil. Key words: Forest harvesting, soil penetration resistance, cone index, soil rut depth, soil disturbance, soil trafficability, soil compaction, terrain modelling
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".