Characterizing Dissolved Organic Carbon Using Asymmetrical Flow Field-Flow Fractionation with On-Line UV and DOC Detection
Bibliographic record
Abstract
A method of characterizing dissolved organic carbon (DOC) by asymmetrical flow field-flow fractionation with on-line UV and DOC detection is described and applied to standards and natural water samples. Poly(styrenesulfonate) polymer standards, Suwannee River humic standards, and naturally occurring surface water and groundwater DOC were analyzed using this coupled detection technique. Molecular weight determinations in the samples and standards were 6-30% lower with DOC analysis than UV analysis. This difference was attributed to the insensitivity of the latter technique to nonaromatic carbon and suggests the molecular weight determined with the DOC detector is a more accurate representation of the actual molecular weight of the DOC. A normalized intensity comparison (NIC) method was proposed to distinguish differences in the relative amounts of aromatic and aliphatic carbon in DOC by comparing the two detector responses. The NIC method was applied to yield an average aromatic content of the bulk DOC and to detail the aromatic content over a range of molecular weights in a single DOC fraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".