Field cryofocussing hydride generation applied to the simultaneous multi-elemental determination of alkyl-metal(loid) species in natural waters using ICP-MS detection
Bibliographic record
Abstract
Two hydride generation manifold systems, utilizing flow injection and cryotrapping techniques for alkyl-metal(loid) speciation analysis in natural waters, are described in this paper. They provide shipboard capacity for simultaneous derivatization of analytes with NaBH4 and cryotrapping of the generated products in a field packed column at -196 degrees C. The first system is a large-volume hydride generator, using a reagent-injection flow technique as a flow batch type, that has been fully optimized and applied to the simultaneous detection of alkylated species in estuarine waters. The technique permits the analysis of a large volume sample (0.5-11) at a sampling rate of 3 h-1. The second is an online continuous flow hydride generator. A sampling rate of 3-12 h-1 can be achieved with samples of 0.1-0.51. In addition, shipboard operation eliminates major problems related to sample pretreatment, transport and storage. Ultra-trace multi-element determination is finally performed in the laboratory by cryogenic GC hyphenated with ICP-MS. Routine detection limits of 0.5-10 pg (as metal) for 0.51 water samples were achieved for the selected alkyl-metal(loid) species of arsenic, germanium, mercury and tin. Concentrations of various species, obtained from water samples taken from the Rhine estuary, are also presented. These species include alkylated arsenic compounds, other than methyl derivatives, that have been tentatively identified and are reported here for the first time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".