The role of brush mats in mitigating machine-induced soil disturbances: an assessment using absolute and relative soil bulk density and penetration resistance
Bibliographic record
Abstract
Forest soils often exhibit low bearing capacities and as a result are often incapable of withstanding high axle loads. In New Brunswick, Canada, five different brush amounts (0, 5, 10, 15, and 20 kg·m–2) were applied as brush mats on machine operating trails during a cut-to-length harvesting operation in a softwood stand to analyze soil disturbance as a result of off-road forest harvesting machine traffic. Soil absolute and relative bulk density and soil penetration resistance measurements were completed below the varying brush mats both before and after forwarding. The mean differences between pre- and post-impact absolute soil dry bulk density values recorded on track areas were 0.24 g·cm–3 for 5–20 kg·m–2 of brush and 0.33 g·cm–3 for 0 kg·m–2 of brush. On average, 40.5%, 17.9%, 14.3%, 15.5%, and 3.6% of all post-forwarding measurements exceeded the threshold for growth-impeding soil bulk density (80% standard Proctor density) for 0, 5, 10, 15 and 20 kg·m–2 of brush, respectively. Soil penetration values >3.0 MPa represented 23.7%, 15.0%, 9.4%, 4.6%, and 0.7% of all post-forwarding test plots with 0, 5, 10, 15, and 20 kg·m–2 of brush, respectively. The results suggest that softwood brush mats of 10 to 20 kg·m–2 placed on machine operating trails play a considerable role in reducing forwarder-induced soil compaction and penetration resistance.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".