Natural abundance of Sb and Sc in pristine groundwaters, Springwater Township, Ontario, Canada, and implications for tracing contamination from landfill leachates
Bibliographic record
Abstract
Using ICP-SMS and the clean lab methods and procedures developed for determining trace element concentrations in polar snow and ice, a lower limit of detection (LOD) of 30 pg l(-1) for Sb and 5 pg l(-1) for Sc was achieved, allowing the natural abundances of Sb and Sc to be measured in pristine groundwaters. Water samples were collected from natural flows and wells between Elmvale and Wyevale in Springwater Township, Ontario, Canada. The water in this region is derived from chemical reactions between meteoric fluids and the Quaternary sediments which cover the bedrock (dolomitic limestone) to depths of more than 100 m. The chemical composition of these waters (pH 8) is primarily a reflection of reactions between the percolating fluids with calcite and dolomite. The maximum concentration of Sb was 5.0 ng l(-1), and the average of all samples collected was 2.2 +/- 1.2 ng l(-1) (n = 34). The average concentration of Sc was 8.6 +/- 4.7 ng l(-1) (n = 28). The paucity of published Sb concentration data available for comparison is probably because most of the analytical methods commonly used to date, including GFAAS, HG-AAS, HG-AFS, INAA, and ICP-QMS, have lower limits of detection which are inadequate for reliably determining the natural abundance of Sb in many uncontaminated groundwaters. Also, the measurement of extremely low concentrations of Sb requires extra care to avoid possible contamination. Given the extensive use of Sb in plastics, we show that some of the containers used to collect and store samples, and for handling and preparing samples for chemical analyses, may be important sources of contamination in the laboratory. The Sb and Sc concentrations reported here should serve as reference values for this region, against which contamination by various human impacts in future could be compared.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".