An enhanced capillary electrophoresis method for characterizing natural organic matter
Bibliographic record
Abstract
Natural organic matter (NOM) is ubiquitous and is one of the most complex naturally occurring mixtures. NOM plays an essential role in the global carbon cycle; atmospheric and natural water photochemistry; and the long-range transport of trace compounds and contaminants. There is a dearth of separation techniques capable of resolving this highly complex mixture. To our knowledge, this is the first reported use of ultrahigh resolution counterbalance capillary electrophoresis to resolve natural organic matter. The new separation strategy uses a low pH, high concentration phosphate buffer to reduce the capillary electroosmotic flow (EOF). Changing the polarity of the electrodes reverses the EOF to counterbalance the electrophoretic mobility. Sample stacking further improves the counterbalance separation. The combination of these conditions results in an electropherogram comprised up to three hundred peaks superimposed on the characteristic "humic hump" of NOM. Fraction collection, followed by three-dimensional emission excitation spectroscopy (EEMs) and UV spectroscopy generated a distinct profile of fluorescent and UV absorbing components. This enhanced counterbalance capillary electrophoresis method is a potentially powerful technique for the characterization and separation of NOM and complex environmental mixtures in general.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".