Determination of manganese and nickel in slurry sampling by graphite furnace atomic absorption spectrometry
Bibliographic record
Abstract
Methods for the determination of manganese and nickel in lake and marine sediment slurries by graphite furnace atomic absorption spectrometry using permanent modifiers are proposed. The slurries were maintained homogeneous with air bubbling with an aquarium pump. For manganese, the best modifier was ruthenium permanent with mo of 0.9 pg and of 1.0, 1.2, 1.5, and 1.8 pg, for Rh, without modifier, Ir, and Zr, respectively. For nickel, the best modifier was rhodium permanent with mo of 33 pg, followed by 85, 120, 132, and 240 pg, without modifier, Zr, Ir, and Ru, respectively. After determining manganese and nickel in two certified marine sediment samples (n = 10) from NRCC, PACS-2, and MESS-2, and in the San Joaquin 2079 soil, the results agreed at the confidence level of 95% with the certified value for all analytes studied using aqueous calibration. Calibration curves of all analytes had correlation coefficients R2 higher than 0.99. Recovery studies made in four levels for each analyte in sediments from Lake Pampulha showed acceptable values. The limits of detection (LODs) were 4.0 and 0.9 µg L-1 for manganese and nickel, respectively.Key words: manganse, nickel, sediments, GF AAS.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".