Risk Assessment of Unsuitable Winter Conditions for Manure and Nutrient Application across Ontario
Bibliographic record
Abstract
Practical guidelines addressing the timing of manure and nutrient application must consider the concerns of the farm operators while ensuring the protection of the environment. An approach was developed and analyzed through case studies to determine the first recommended day in the spring, and the last in the fall, for manure and nutrient application based on probability analysis. Since most manure and nutrient application guidelines recommend avoiding adverse conditions, the three criteria established to perform a risk assessment were: (i) a frost depth greater than 0.05 m; (ii) a snow accumulation of greater than 0.05 m; and (iii) a soil volumetric water content greater than or equal to that of the plastic limit for the soil. Climatic data and typical soil information for seven locations in Ontario were used to model volumetric soil water contents, frost depths, and snow accumulation from the simultaneous heat and water (SHAW) model for a 48-yr period (1954-2001). Applying the three criteria to the modeled output, the average range between the least limiting probability (0.1, or one in ten year occurrence) and the greatest limiting probability (0.001, or one in one thousand year occurrence) analyzed among the locations was 16 d in the spring as compared to 29 d in the fall. Although geographical location affected the predicted spring start and fall end recommended manure and nutrient application dates, local climate and soil hydraulic properties also played an important part in the determination of these days. Overall the prediction method developed performed reasonably well and provided insight into the environmental factors influencing manure and nutrient application timing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".