Barium Leaching from Alluvial Soils and Certified Reference Materials
Bibliographic record
Abstract
Despite the fact that Ba is the 14th most abundant element on Earth, and concentrations in soil are often elevated, relatively few studies deal with the occurrence of Ba in soil. Information on how changing environmental conditions, assessed by laboratory or field investigations, can potentially affect Ba mobility is only scarcely available. Nevertheless, the increased use of barite, for example as a weighting agent for drilling mud in the oil and gas sector, attracted the attention to this element in recent years. Other applications of Ba are its use in superconductors and contrasting agents. In British Columbia, matrix numerical soil standards for Ba were supplemented with an extraction method for the determination of soluble barium (Alberta Environment, 2009). Leaching tests and extractions that have been developed to evaluate heavy metal mobility in soils, ask for a careful interpretation when Ba is considered, and are probably not always suited for Ba because of unintended side effects such as precipitation reactions. In the present study, the release of Ba from soil samples characterized by a varying clay and organic matter content and a wide range of total Ba concentrations, was investigated using commonly applied single and sequential extractions, pHstat leaching tests and column tests. Additionally, certified reference materials were analyzed, in order to provide data for Ba, which are not yet available at the moment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".