Assessing the use of magnetic methods to monitor vertical migration of metal pollutants in soil
Bibliographic record
Abstract
In order to assess the use of magnetic methods to study vertical migration behavior of metal pollutants in natural soils, a controlled experiment was performed near Belle River, Ontario, Canada. The soil at the site consists primarily of clay-rich glacial till overlain by localized alluvium. Twenty PVC tubes (16″ × 8″) were inserted vertically into the ground as test capsules. Magnetite powder (<5 μm) was distributed on the surface of the soil inside ten tubes (10 grams/tube) to simulate anthropogenic contamination, while the other ten were used as controls. While the surficial magnetic susceptibility (MS) remained fairly stable in controls, decreases of 15–60% were observed in contaminated soil tubes. Post-test MS profiles from soil cores in contaminated tubes show that the magnetic signal is strongest at depths between 4 and 6 cm. Magnetic measurements and chemical analysis (using SEM-EDS) on soil layers with enhanced magnetic signal indicate the presence of iron containing particles, likely magnetite. Overall, the results suggest that magnetite powder migrated vertically downwards at a rate of ∼14 cm/year over the four month period, probably as a result of rainwater infiltration. Such magnetic methods and chemical analytical techniques are useful in the investigation of migration of metal pollutants and the potential depth of soil contamination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".