Combined use of resin fractionation and high performance size exclusion chromatography for characterization of natural organic matter
Bibliographic record
Abstract
The number and complexity of natural organic matter (NOM) species limits identification of individual NOM compounds. The objective of this study was to employ several characterization techniques (resin fractionation, high performance size exclusion chromatography (HPSEC), and strategic UV254 absorbance) to samples from seven surface water sites in North America, and overcome the shortfalls of each tool. Resin fractionation indicated the samples were all high in hydrophobic acids (HOA), hydrophilic neutrals (HIN) and hydrophilic acids (HIA). Site B was the only site where HIAs were the highest NOM contributors. In the HPSEC analysis, each fraction exhibited a particular molecular weight (MW) range: 100-300 Da (HIN), 1-2 kDa (HOA), and the HIA fractions exhibited MWs between these two ranges. Strategic UV254 measurements were taken at two sites to supplement the HPSEC results, and determine the difference in UV absorbance per unit dissolved organic carbon (SUVA value). Most fractions showed SUVA values of approximately 5 L/mg-m; however, the hydrophilic bases and hydrophobic neutral fractions could not be accurately evaluated due to the very low DOC concentrations for these two fractions (< 0.2 mg/L). These methods are complimentary NOM characterization techniques, and the combined methodology addresses the analytical limits of each tool.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".