United States And Canadian Agricultural Herbicide Costs: Impacts On North Dakota Farmers
Bibliographic record
Abstract
Pesticides have become a major farm production cost over the past 25 years. There are price and label differences for agricultural herbicides between the United States and Canada. Trade names are different in some cases, label restrictions vary, and weights and measures are different. The reasons for the price differences are unclear. Whether they are due to increased costs in labeling requirements, different levels of competition and use, or market segmentation is not determined. The largest total impact of using lower priced Canadian herbicide is on HRSW, followed by durum and corn. The largest per acre impact is for canola, corn, and HRSW. Herbicides with the largest total impact are Puma, followed by Roundup and Fargo. Net farm income for large, medium, and small size representative farms would increase 3.8%, 4.6%, and 5.2%, respectively, if Canadian priced herbicides could be used in the United States. The statewide impact is $1.46 per acre, but regional or individual impacts could be much greater depending on crops grown or the specific weed problem faced by the individual producer.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".