Quantifying the use of brush mats in reducing forwarder peak loads and surface contact pressures.
Bibliographic record
Abstract
NacrtakForest biomass from timber harvesting residues is often used during mechanized forest opera tions to improve trafficability of strip roads (machine operating trails).In particular, during cut-to-length operations brush mats from harvesting residues are created on operating trails to reduce rutting.However, forest biomass is becoming increasingly important as a source of renewable energy.To maintain its full calorific value as a biofuel, brush (tree limbs, tops, and foliage) needs to be free of any mineral soil, which is considered a contaminant in this context.In cut-to-length operations, this eliminates any use of brush as a mat to improve trafficability on machine operating trails since it gets in direct contact with mineral soil.Using brush ex clusively for biofuel will leave operating trails uncovered and can result in severe damage to forest soils.To manage the two competing uses of brush, it would be helpful to determine minimum brush amounts needed for efficient soil protection as it would potentially allow utilizing remaining brush as biofuel.This study assessed brush mats for their ability to dis tribute applied loads.As load distributing capacity of a brush mat increases, so does the resulting soil protecting effect.A total of 15 test scenarios were performed with a forwarder to analyze differences in peak loads recorded underneath brush mats of 5, 10, 15, 20, 25, and 30 kg m -2 (green mass) each subjected to 12 traffic cycles of a forwarder including unloaded and loaded movements.Highest loads were recorded within the first few forwarding cycles located on the 5 kg m -2 brush mat and then decreased on average by 23.5% as brush amount increased up to 30 kg m -2 .When no brush was used (0 kg m -2 ) and the forwarder was in direct contact with the steel surface of the load test platform, we noticed that 97% of all peak surface contact pres sures recorded exceeded the 150 kPa pressure threshold, compared to only 41% when the forwarder was driven over the 30 kg m -2 brush mat.
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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 | 0.001 | 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".