Assessing the Ability of Hardwood and Softwood Brush Mats to Distribute Applied Loads
Bibliographic record
Abstract
In cut-to-length mechanized forest harvest operations, trees are cut, delimbed, and bucked to standard lengths directly in the harvest block. This in-stand processing, generates harvesting residue composed of tree limbs, tops, and foliage, which is frequently placed on machine operating trails to prolong trail trafficability and protect forest soils against heavy loadings. These so-called brush mats vary both in quantity and quality based on harvested wood and stand characteristics. The objectives of this study were to determine, quantify, and compare the load distributing capabilities of hardwood and softwood brush mats of different amounts (10, 20, 30, and 40 kg m-2) compared to no brush (0 kg m-2). This was done by laboratory tests analyzing the difference in strain recorded below brush mats at small scale when exposed to single and repetitive loadings. Brush mats (approx. 37 cm x 37 cm in area) were placed inside a test structure including a top open box with the bottom filled with a 15 cm thick layer of sand, below which strain gauges were installed. The entire test structure was positioned on a load frame programmed to lower a loading disk directly over the brush mat, thereby applying increasing loads up to 10 kN on the mat. Results suggest that for specific brush amounts and loadings, softwood brush showed a slightly better capacity to laterally distribute exerted loads than hardwood brush, especially at brush amounts of 10 and 20 kg m-2. At higher brush amounts, the differences of recorded loadings (strains) between the tested softwood and hardwood brush were reduced and at 40 kg m-2 hardwood brush contributed to a lower response of the strain gauges than softwood brush when subjected to 5 and 10 kN loadings.
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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) | 0.000 | 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) | 0.002 | 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".