Solid/liquid partition coefficients to model trace element critical loads for agricultural soils in Canada
Bibliographic record
Abstract
Canadian consumers are demanding a sustainable agricultural industry as well as products delivered under Best Management Practices (BMPs). Trace element accumulation in soils may influence crop productivity, food quality and ecosystem and human health. Canada’s feed and foodstuff export industry has already faced cases of penalties for high trace element content [cadmium (Cd) in durum wheat]. Thus, it is imperative to be able to estimate the accumulation and potential short- and long-term impacts of trace elements in soil. A national-level Trace Element Indicator (TEI) based on present loadings of trace elements to agricultural land is in progress. An Expert Panel including Canadian, American and Australian experts guided the assembly of a proposed methodology for this TEI. The proposed TEI, described briefly here, is a critical load approach with a single expression of the risk of impact from single or multiple trace elements from multiple sources (manures, biosolids, effluents and fertilizers and natural processes), invoked in a stochastic manner. Two key data requirements are the current background levels of trace elements in soil, and the leachability of these trace elements. A survey of total and soluble concentrations of 54 elements in up to 112 soils was completed. Although preliminary in scope, these represent key soil series in Canada. From this, a database of the solid/liquid partition coefficient, Kd, was computed. These Kd values will be used to characterize the leachability of the trace elements. Key words: Cadmium, copper, zinc, lead, nickel, uranium, metals, Kd, distribution coefficient
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".