Harvest of Natural Shrubs with a Biobaler in Various Environments in Québec, Ontario and Minnesota
Bibliographic record
Abstract
The biobaler is a novel approach to cut woody crops up to 150-mm basal diameter and compress the biomass into round bales. It can be used to harvest short-rotation woody crop plantations such as willow or hybrid poplar. It can also be used to clear wild brush, forest understory, and encroaching small trees to improve land management. A commercial version of the biobaler was evaluated for the latter purpose, i.e., to harvest natural shrubs in various environments in central Canada (at three sites in Qubec and two sites in Ontario) and in mid-western United States (at seven sites in Minnesota). More than 250 bales were harvested and monitored on natural stands in 2009 and 2010 to gain information on machinery management under wide-ranging conditions of crop species, density, and soil conditions. The harvest rate ranged from 2 to 26 bales/h (average of 14 bales/h). Bale mass averaged 477 kg at 46% moisture content [260 kg dry mass (DM)/bale at a density of 166 kg DM/m]. Diesel fuel consumption averaged 8.5 L/t DM. Harvesting cost with the biobaler was estimated at $33/t DM in high yield with rapid harvest (20 bale/h) and $64/t DM at low capacity (10 bales/h) which can be caused by lower yield or poor traction. The information will be helpful to evaluate the cost of environmental management of natural stands covered with brush and the potential biomass that may be recovered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".