Development of Two Headers for a Versatile Woody Brush Harvester-Baler
Bibliographic record
Abstract
In 2006, a willow harvester was developed with a 1.97-m wide four-saw cutter and a 1.55-m wide rotary shredder placed in front of an agricultural round baler. The header worked well in level plantations and left a clean-cut stump. However, it was not designed to operate on uneven land where rocks and soil might cause saw blade malfunction. For this reason, a second more robust header was developed in 2007 to harvest natural brushes on fallow land. The new header was a 2.30-m wide flail shredder that both cut and conditioned the woody brush before ejecting it into the baler. It was evaluated in two field trials in eastern Canada. On a fallow and poorly drained land, the shredder header-baler harvested natural alder shrubs at an average rate of 4 t wet matter (WM)/h. In a level willow plantation, the shredder header-baler worked up to 12 t WM/h, compared to 8 t WM/h with the original four-saw header-baler. The bales typically weighed 400 kg, were 1.4 m in diameter and 1.2 m wide; they had a wet density of 220 kg/m at 50% moisture on a wet basis. The two headers can be adapted to the same round baler and offer a versatile alternative to harvest either wild brushes or planted woody crops. The technology may improve land management and provide a new source of otherwise neglected biomass.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.008 | 0.003 |
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".