A revised digestion method to characterize manganese content in solids
Bibliographic record
Abstract
Quantifying manganese (Mn) content in solids is critical for understanding its roles in aquatic ecosystems, soils, water treatment plants and distribution systems. No studies have yet used standard Mn oxides to compare the performance of the numerous digestion methods found in the literature. Nine digestion methods (including USEPA 3050B) were compared using four Mn oxides with varying oxidation states. The HCl concentrate (12.4 M) heated to at least at 40 °C provided quantitative digestion of all Mn oxides tested with ≈ 100 % recovery. HCl concentration is important only for MnO 2 digestion, while temperature influences both MnO and MnO 2 recovery. Complete recovery of various Al, Cu and Fe standard oxides using a 12.4 M HCl digestion at 95 °C. Digestion of environmental samples for Al, Ca, Fe, Mg and Mn content yielded higher metal content using the HCl method (except for Al). HCl 12.4 M digestion provided better performance than other digestion methods found in the scientific literature because of its high reducing capacity. • Most digestion methods found in the literature do not digest all Mn oxidation states. • Hydrochloric acid is shown to be essential to dissolve all oxidation state of Mn oxides.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".