Field testing of a physical impact mill in the Canadian Prairies
Bibliographic record
Abstract
Herbicide resistance in western Canada has increased interest in alternative weed management strategies. Physical impact mills, a form of harvest weed seed control, have been identified as a strategy that may be well suited for Canadian use. The efficacy of the Harrington Seed Destructor, a physical impact mill, was evaluated in 20 producer fields in Alberta on a wide spectrum of weed species over 3 years. Significant differences in weed densities between the physical impact mill treatment and the regular harvest densities were few; however, some population density reductions were observed. Measurable reductions in weed densities may have been limited by the short timeframe of the experiment, the high initial densities of the weeds, or the targeted weed species having dormancy or longer term seedbanks. Additionally, identified knowledge gaps on how best to optimize physical impact mill efficacy may have reduced the efficacy of the physical impact mill in this study. This study showed no conclusive evidence for the efficacy of a physical impact mill on tested weed species under field conditions. However, it did provide a number of important considerations for future studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".