Indicator of risk of water contamination by P for Soil Landscape of Canada polygons
Bibliographic record
Abstract
The indicator of risk of water contamination (IROWC) is a component of the Agriculture and Agri-Food Canada Agri-Environmental Indicator project. The IROWC measures progress in reducing the risk of water contamination from agricultural activities, focusing on N and P. The objective of this study was to propose a methodology for an IROWC-P applicable at the Soil Landscape of Canada (SLC) polygon level (1:1 000 000 map scale) using an indexing approach. The sources of data included Census of Agriculture, SLC and soil survey databases and provincial soil test data. The IROWC-P considers the following site characteristics: soil erosion and potential for overland flow, annual P balance (crop residues, manure and inorganic fertilizer), soil test P (STP) and degree of soil P saturation (DSPS). IROWC-P classifies polygons for their potential risk of P transfer to surface waters according to five vulnerability classes (i.e., very low, low, medium, high and very high). The methodology was tested on a pilot basis for selected SLC polygons in the province of Quebec using 1981 and 1991 census data. Preliminary results indicated that the proposed methodology showed some sensitivity to changes in agricultural practices between 1981 and 1991 and reflected differences in risk of P contamination from areas of intensive compared to areas of extensive agriculture. The difference between the selected areas was mainly attributed to the STP, DSPS, manure and inorganic fertilizer P polygon characteristics. The temporal variations in the IROWC-P ratings were attributed mainly to the manure and inorganic fertilizer P polygon characteristics. Key words: Degree of soil P saturation, soil P index, environmental risk, soil test P
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.002 | 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".