Molecular size distribution characteristics of the metal–DOM complexes in stream waters by high-performance size-exclusion chromatography (HPSEC) and high-resolution inductively coupled plasma mass spectrometry (ICP-MS)
Bibliographic record
Abstract
In this work, the complexation of the metals Fe, V, Ce, Th, U, Mo, Cu, Ni, Co, Cr, Zn, Pb and Cd with different molecular size (MS) fractions of dissolved organic matter (DOM) in natural waters was investigated. In order to assess the MS distribution of the metal–DOM complexes, DOM samples from stream waters were concentrated by reverse osmosis, and were then analyzed by high-performance size-exclusion chromatography (HPSEC) coupled with on-line UV-Vis absorbance and high-resolution inductively coupled plasma mass spectrometry (ICP-MS). The MS distribution of overall DOM and its metal-bound complexes was evaluated. The results indicate the following order of decreasing number-averaged molecular weight: Cu > Ni > (Co, Zn, Cr) > Pb > Cd for the DOM-bound complexes of transitional metals, which is consistent with Irving-Williams series, and (Fe, V, Ce) > Th > U > Mo for the DOM-bound complexes of the other metals. The results suggest that the metal distribution among the different MS fractions was closely related to metal binding strength; metals with high binding strength were distributed more in the larger MS fractions, and metals with low strength were distributed more in the smaller MS fractions. Possible mechanisms for these observations were discussed. This study is significant to the understanding of metal complexation with DOM in natural waters.
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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) | 0.000 | 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) | 0.000 | 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 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".