A SURVEY OF CROP RESIDUE BURNING PRACTICES IN MANITOBA
Bibliographic record
Abstract
Crop residue burning has become a concern in the Prairies, Canada due to its adverse impact on human health,the environment, and soil quality. A telephone survey was conducted in 2001 to investigate crop residue burning situationson farms in four rural municipalities of Manitoba, Canada. The survey questionnaire included 45 questions developed toidentify the types and the percentage of producers who used burning as a crop residue management practice. Of the 84 eligiblerespondents, 47% practiced or possibly practiced crop residue burning. The motivating factors included the timeliness of fieldoperations, such as fall tillage, fall fertilizer application and spring seeding, lower cost for residue disposal, increased cropyield, and better control of weeds and crop diseases. The survey also selectively gathered information on producers and farmsbackground, crop, field equipment and farming practices, as these factors were expected to have impacts on the choice ofcrop residue management practices. Data show likelihood of reducing burning when producers practice longer crop rotations,use low disturbance disc-type seeding tools, and apply fertilizer in the spring. Education and awareness that leads producersaway from the traditional crop residue burning practices may be started for young producers and large farms.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".