Wastewater Colloidal Organic Carbon: Characterization of Filtration Fractions Using <sup>1</sup>H NMR
Bibliographic record
Abstract
The current study separates colloidal organic carbon (COC) of municipal wastewater using membrane and ultrafiltration filters followed by characterization using 1H nuclear magnetic resonance (NMR) and UV absorbance with the goal of determination of size-specific characteristics, which may be used to correlate contaminant partitioning to natural COC. Passing fractions included 49.7, 44.8, 39.3, and 33.1 mg/L COC for filter sizes 1.5 μm, 0.45 μm, 100 kDa, and 1 kDa, respectively. The methodology used for processing COC prior to 1H NMR characterization was novel and successful in concentrating COC without modification of structures, which is the general drawback of other separation techniques such as resin extractions. This concentration technique is quite simple (i.e., not dependent on specialized instrumentation) and allows much shorter NMR experimental durations saving time and cost of analysis. Further work using NMR techniques will allow for greater understanding of COC molecular characteristics and be valuable for use in predictive modeling improvements.
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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) | 0.002 | 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".