Application of high‐temperature fusion for analysis of major and trace elements in marine sediment trap samples
Bibliographic record
Abstract
Multielemental information is important for a broad range of ocean biogeochemical studies, yet the quantity of sample material available for analysis is often extremely limited. Here we describe a simple, rapid, and accurate method for multielemental analyses of oceanic sediment trap material. This method involves high‐temperature fusion using a lithium metaborate flux for sample digestion and elemental quantification using high‐resolution inductively coupled plasma mass spectrometry (HR‐ICP‐MS). Each analysis consumes only 1 to 2 mg of sample material yet enables simultaneous measurements of 18 elements (Mg, Al, Si, P, Ca, Sc, Ti, V, Mn, Fe, Co, Ni, Cu, Zn, Sr, Cd, Ba, and Pb) with accuracy of > 90% for most elements. The fusion method introduces minimal contamination when appropriate sample handling and procedural precautions are employed. Elemental quantification of samples prepared for ICP‐MS analysis using fusion agrees well with those prepared using acid digestion. Additionally, the fusion method has several advantages over acid digestion. No highly toxic reagents like hydrofluoric acid are used. Refractory mineral dissolution is more complete. Si, Ca, and other trace elements can be analyzed without potential losses due to coprecipitation with CaF2. The simplicity and reproducibility of the procedure makes it especially suitable for routine, ongoing analyses of oceanic particulate materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".