<scp>A</scp>ssessing the implementation of Ontario's Nutrient Management decision support system
Bibliographic record
Abstract
Abstract While land use planners increasingly rely on information technology to assist decision making, there are few empirical studies that assess the actual implementation of decision support systems. This article assesses the implementation of Ontario's Nutrient Management (NMAN) computer program using a case study of the livestock building permitting procedures in East Perth Township. In the 1970s, some townships in Ontario required livestock operators to obtain a Certificate of Compliance prior to receiving a building permit. Applicants were required to satisfy minimum distance separation criteria, ratio of animals to land area, and manure storage requirements. In the 1990s, the process changed when many townships enacted Nutrient Management By‐laws, requiring applicants to prepare a nutrient management plan, in addition to the minimum distance separation criteria and manure storage requirements. The NMAN software fulfills a dual role. Livestock operators use the NMAN to prepare a nutrient management plan, whereas review agency officials use the NMAN software to ensure the nutrient management plans meet provincial requirements. We conduct a review of 122 livestock building permit applications in East Perth Township to compare performance measures pre‐ and post‐use of the NMAN software. We frame the assessment around contextual, process, and outcome criteria from decision support system (DSS) and policy‐oriented implementation studies. The findings indicate that with the NMAN the review agency officials receive more information concerning the physical properties of fields that receive manure, soil nutrient levels, crop rotation patterns, manure application method and timing, among other farming practices. The review agencies have more knowledge about the operators' capacity to safely dispose of manure generated at the proposed and expanding facility. While the process continues to be fair, the overall length of time to process a building permit application was longer. This was often a result of the time needed to gather the information to input into the NMAN software. However, the length of time required by the review agencies was significantly shorter. Opportunities for future research include applying the field‐based nutrient application rates on a watershed scale, and assessing farmers' use and user satisfaction with the NMAN software.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".