Geostatistical analysis and integration of soil chemistry data with remote sensing information in the Sudbury area, Ontario.
Bibliographic record
Abstract
The presence of anomalous concentrations of metals within the soil profile can strongly affect its biological availability to plants, causing potential toxicity when exceeding threshold concentrations, and favoring numerous chemical exchanges. These interactions further facilitate metal dispersion in the hydrogeological and ecological systems, in response to weathering and erosion. Studies of the geospatial distribution of trace metal contaminants in Sudbury soils is thus important to unravel the dominant processes controlling dispersion patterns, contributing to sustainability of mining practice. A kriging geostatistical approach was applied to geochemical data obtained from the Sudbury Soil Survey to map multiscale geographic, enrichment trends in metal concentrations. Ordinary kriging prediction maps were developed to re-evaluate the multiscale spatial distribution of the chemicals of concern. Results show an anomalous distribution of metals centered on historical smelters, forming dominant northeast and southwest enrichment trends. The existence of these trends was validated by implementing a geostatistical Gaussian conditional simulation method, which reproduced the same spatial variability observed in the ordinary kriging maps and efficiently replicated the observed trends. The correlation analysis of the trends with remote sensing data, suggests that prevailing wind directions are likely one of the dominant driving forces controlling the trends. Integrating these results with satellite data showed improved vegetation regrowth patterns consistent with the geochemical northeast-southwest trend providing further, independent validation of the kriging results. Re-evaluation of the regional, geospatial distribution of the measured trace element concentrations will assist the monitoring and improved understanding of soil contamination trends and their impact on vegetation and other aspects of the biosphere in the Greater Sudbury area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".