Risk Indicator for Agricultural Inputs of Trace Elements to Canadian Soils
Bibliographic record
Abstract
Trace elements (TEs) are universally present in environmental media, including soil, but agriculture uses some materials that have increased TE concentrations. Some TEs (e.g., Cu, Se, and Zn) are added to animal feeds to ensure animal health. Similarly, TEs are present in micronutrient fertilizers. In the case of phosphate fertilizers, some TEs (e.g., Cd) may be inadvertently elevated because of the source rock used in the manufacturing. The key question for agriculture is "After decades of use, could these TE additions result in the deterioration of soil quality?" An early warning would allow the development of best management practices to slow or reverse this trend. This paper discusses a model that estimates future TE concentrations for the 2780 land area polygons composing essentially all of the agricultural land in Canada. The development of the model is discussed, as are various metrics to express the risk related to TE accumulation. The elements As, Cd, Cu, Pb, Se, and Zn are considered, with inputs from the atmosphere, fertilizers, manures, and municipal biosolids. In many cases, steady-state concentrations could be toxic, but steady state is far in the future. In 100 yr, the soil concentrations (Century soil concentrations) are estimated to be up to threefold higher than present background, an impact even if not a problematic impact. The geographic distribution reflects agricultural intensity. Contributions from micronutrient fertilizers are perhaps the most uncertain due to the limited data available on their use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".