Magnetic susceptibility mapping of the Sudbury area, Ontario, Canada: evaluating pollution distributions decades later
Bibliographic record
Abstract
This study evaluates the use of magnetic susceptibility and magnetic parameter measurements in assessing spatial and temporal variations of pollutants that emanated from mining industries in and around Sudbury, Ontario, Canada. For this purpose, in situ magnetic susceptibility (κin situ) was measured at 106 sites on a grid of 10 km × 10 km and 5 km × 5 km. The κin situ values ranged from 2 × 10−5 to 149 × 10−5 SI, and the highest κin situ values were observed near the active (Copper Cliff) and inactive (Coniston) mining sites. The lowest κin situ values were measured at increased distances from possible pollution sources; therefore, mapping of in situ magnetic susceptibility values is a proxy to polluted areas in and around Sudbury. To evaluate potential anthropogenic and (or) lithogenic input to κin situ, low-frequency mass specific magnetic susceptibility (χlf) variations with depth were classified into four different types of profiles. For further investigation of magnetic minerals in the samples, laboratory measurements of magnetic susceptibility, frequency dependence of magnetic susceptibility, hysteresis properties, thermosusceptibility curves, anhysteretic and isothermal magnetizations, and scanning electron microscopy – energy-dispersive X-ray spectroscopy (SEM–EDS) were also conducted on the soil samples. Laboratory measurements indicated that ferrimagnetic minerals (e.g., magnetite) of variable grain size were the dominant magnetic minerals, with the exception of one site that contained an iron sulfide (greigite) phase near a mine waste site. Magnetic spherules observed in SEM micrographs are of variable sizes (6–60 μm), suggesting that suspended particulate matter (PM10) is present, and may be a health concern. At some sites, EDS analysis showed that heavy metals (Co, Al, and Ni), which threaten human health, are also present in the study 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".