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 m o 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 m o 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 R 2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".