Development of a Cutter-Shredder-Baler to Harvest Long-Stem Willow
Bibliographic record
Abstract
Willow is a fast-growing crop with a biomass potential between 10 and 20 t of dry matterper ha per year in a northern climate like Canada. Once established, willow reaches optimal yieldunder a 3-year cutting rotation. One method of harvesting is to cut and chip the whole plant forimmediate use or wet storage at 40 to 50% moisture content. Alternately, willow can be cut andeither bundled or baled for natural drying in storage. Near-commercial harvesters for willow areavailable to cut and chip with a forage-harvester platform. However, no commercial harvester oflong-stem willow in the form of bundle or bale is available. Design criteria have been developed tooptimize cutting, perform light shredding and bale willow stems. Cutting blades for fibrous stemshave been used at peripheral speeds between 10 and 118 m/s. A lower peripheral speed requiresless specific energy but may limit the harvesters capacity in a dense plantation. Light shredding witha hammer-type shredder improves the flexibility of the stems for subsequent compression in a baler.A large round baler is easier to align with the cutter-shredder mechanism than a large square balerbut the round baler requires good orientation of the stems to produce a uniform cylinder. Theselected design criteria are based on laboratory and field trials carried out with long stem willow. Theprototype was constructed in April-May 2006 and is being field tested over the next two years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".