Using soil geochemical data to estimate the range of background element concentrations for ecological and human-health risk assessments
Bibliographic record
Abstract
A workshop on the role of geochemical data in ecological and human-health risk assessments was sponsored by Health Canada and Environment Canada in 2010. Participants from Geological Survey of Canada developed recommendations for acquiring and analyzing soil geochemical data to support risk assessment and outlined a procedure for estimating geochemical background, released as GSC Open File 6645. The following practices are proposed: 1) the collection of soil samples from pedologic horizons (the C, in particular) rather than depth-based intervals; 2) use of a spatially random sample design; 3) analysis of the less than 2 mm fraction (without ball or ring pulverizing) as a standard. Additionally, analysis of the silt-sized and finer fraction (<0.063 mm) provides more information on the mineral phases and residence sites of elements in soils and the patterns of regional variation; 4) dissolution using the USEPA 3050B aqua regia variant. Additionally, a method for estimating the amount of loosely held 'bioaccessible' amounts of the total-element concentration should be considered (e.g. water leach); 5) archiving of sample splits; and 6) evaluation of chemical data through the insertion, analysis, and monitoring of QA/QC samples. The procedure for estimating geochemical background is based on plotting maps and graphs using the 'rgr' library and functions in R. R is an open source software environment and is available through CRAN mirror sites linked to http://www.r-project.org/. Metadata for 700 geochemical surveys carried out by the GSC and provincial agencies can be accessed through the Geochemical Data Repository at Natural Resources Canada.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".