INVESTIGATION INTO USE OF RESIDUE MANAGERS DURING DIRECT SEEDING WITH DOUBLE SHOOT ANGLE DISK OPENERS
Bibliographic record
Abstract
Direct seeding in fields with high amounts of residue has always been a problem for producers. Excess residue causes hair-pining of the straw which results in decreased penetration of disk openers. Hair-pinning also results in poor seed placement which decreases the crop establishment. In the past, residue managers have commonly been used in the United States to clear residue for precision planters in high value crops. The precision planter residue managers are costly and therefore are not commonly used to seed other types of crops. Manufacturers have recently designed universal cheaper types of residue managers. The new residue managers are designed for cereal, pulse and oilseed type crops to clear the residue away from the path of the opener to allow for good soil penetration, residue clearance and seed placement. The AgTech Centre was approached by the Alberta Reduced Tillage Linkages (ARTL) to test the performance of several different residue managers. The ARTL wanted to increase the exposure of residue managers in Alberta with hopes of more producers direct seeding in heavy residue conditions. The AgTech Centre performed an experiment to test various types of residue managers while seeding with disk openers under different conditions. Measurements were made and analyzed. Residue managers did increase the crop emergence of wheat and canola but results were not significant. Further testing and data is needed to conclude the study.
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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 | 0.002 | 0.007 |
| 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".