Bibliographic record
Abstract
Spreading livestock manure in the winter has been a common practice in Ontario for many years. For the farmer, there are several advantages, including: a) ability to build smaller manure storages; b) ability to spread the manure at a time when there is less pressure to get to the crop in the ground; c) spreading manure on frozen ground may help to reduce soil compaction. Despite these practical reasons for spreading manure in the winter, the concern about impacts on water quality has lead to a general acceptance that spreading manure in the winter is no longer environmentally acceptable. Depending on the condition of the soil, runoff can potentially carry manure nutrients and bacteria to nearby surface waters. It is widely believed that frozen or snow-covered soils allow less infiltration than non-frozen bare soils. This is the main reason why policies within Canada recommend avoiding the winter spreading of manure. The objectives of this literature review are: 1. to provide a brief overview of some of the policies in Canada concerning the winter spreading of manure; and 2. to review North American research that examines the implications of spreading manure in the winter. Canadian Recommendations and Policies Most provinces in Canada recommend avoiding the application of manure on frozen or snow-covered ground. The policies for selected provinces are summarized in Table 1. Provinces not listed generally have recommendations similar to those outlined below. The guidelines tend to be fairly general and rely mainly on the common sense of the manure applicator.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.716 | 0.464 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".