Report on a scoping study for an agro-ecosystem indicator of the risk of water contamination by pathogens from agricultural operations
Bibliographic record
Abstract
Sustainable use of Canada's natural resources by the agri-food sector requires both the benchmarking of the magnitude of those resources and continued monitoring of their quality. A further step required is the development of management practices and their implementation at locations of greatest vulnerability. Freshwater resources in agricultural regions are important for continued primary production, but also because the water is commonly used for recreational purposes, provides the basis for fisheries, is harvested from these regions for potable supplies used by municipalities, and provides a varied habitat for plants and animals. Establishing vulnerable locations and providing a minimum level of water quality monitoring is expensive, and identifying likely beneficial management practices across the country requires considerable effort. Remote sensing techniques coupled with simulation modelling offers the most effective means of achieving these objectives, but our knowledge of many processes that result in the contamination of surface and ground water resources at the watershed and regional level is still rudimentary in many cases (Goss, 1994). Another approach is to identify and develop indicators that combine elements of the processes that contribute to contamination with some simple measures of the potential for contamination (Girardin et al.,1999). This report considers the information requirements to develop an agroecosystem indicator that assesses the risk that pathogens from one or more agricultural operations may contaminate water resources. It consists of three major sections: the first is a review of the literature that identifies the potential sources of pathogens on agricultural operations, and the relative importance of those sources in leading to degradation of water resources, the second part deals with the activities underway in organizations other than agricultural departments in Canada, and in jurisdictions outside of Canada, the third section considers elements and critical control points from the literature review, and comments on existing or planned indicator frameworks-for other AEIs that could contribute to a pathogen AEI.
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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 | 0.053 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".