The impact of disc settings and slash characteristics on the Bracke three-row disc trencher’s performance
Bibliographic record
Abstract
When reforesting, disc trenching is the most common site preparation method in Canada. The settings on today’s disc trenching units can be modified extensively, but the effect of these modifications on the work quality and machine performance is poorly understood. We studied a three-row Bracke T35.a disc trenching unit that used five different disc settings to prepare three sites with five different slash characteristics in New Brunswick. We measured the travel speed and the resulting microsite quality during 2–4 machine passes on all 25 treatment combinations, plus the fuel consumption using an engine control module during 1–3 machine passes on 15 treatment combinations. The results showed no difference in microsite quality between disc settings, but it was significantly higher with seasoned than fresh slash, parallel-aligned than perpendicular-aligned slash, and softwood than mixedwood slash. Fuel consumption was significantly lower with parallel-aligned slash and softwood slash, but it was also markedly lower with the least aggressive disc settings. Our results suggest that increased disc down pressure increases fuel consumption but does not increase microsite quality among slash, while parallel-aligned slash increases both the work- and fuel-efficiency of the disc trencher. We therefore recommend operators to use less aggressive settings when disc trenching among slash, but also to become more active in monitoring their work quality. Foresters can further increase disc trenching efficiency by prescribing the trenches parallel to slash alignment and/or in seasoned slash; however, such prescriptions must be balanced with potentially longer rotation periods and added costs to harvesting, machine relocation, and tree planting.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".