Analytical methods used to characterize the solid-phase speciation of metal(loid)s
Bibliographic record
Abstract
The ecosystem and human health risks associated with metal(loid)s in soils, sediments and mine wastes are strongly influenced by their solid-phase speciation. This presentation will review a range of methods commonly used by geoscientists to measure the various chemical (e.g. oxidation state) and physical (e.g. morphology, particle size) forms of an element which together make up the total concentration of that element in a sample. Traditional macroscopic techniques for determining solid-phase speciation include methods such as sequential chemical extractions, which can be used for indirectly assessing the partitioning of metals in solid materials, and X-ray diffraction (XRD), which can be used to identify crystalline phases. Microscopic methods range from optical techniques (e.g. transmitted and reflected light microscopy) to microbeam methods that are used to determine near-surface compositions (e.g. electron microprobe, laser-ablation ICP-MS, proton-induced X-ray emission (PIXE)). Over the last two decades, many environmental investigations have employed synchrotronbased microscopic methods that can be used to determine the in situ speciation of metal(loid)s in solid materials. With careful sample collection and preparation, techniques such as X-ray absorption fine structure spectroscopy (XAFS) can provide information on metal(loid) oxidation states and coordination environments that are essential for assessing the environmental risks associated with these elements. Recent studies demonstrate that determination of the total concentrations of metal(loid)s in soils, sediments and mine wastes does not give sufficient information on the environmental availability of these elements, or their potential risks to human health. In the future, ecological and human health risk assessments should incorporate information on the solid-phase speciation of metal(loid)s to ensure that realistic management guidelines are established.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".