Identification of humic acid-like and fulvic acid-like natural organic matter in river water using fluorescence spectroscopy
Bibliographic record
Abstract
Identifying the extent of humic acid (HA)-like and fulvic acid (FA)-like natural organic matter (NOM) present in natural water is important to assess disinfection by-product formation and fouling potential during drinking water treatment applications. However, the unique fluorescence properties related to HA-like NOM is masked by the fluorescence signals of the more abundant FA-like NOM. For this reason, it is not possible to accurately characterize HA-like and FA-like NOM components in a single water sample using direct fluorescence EEM analysis. A relatively simple approach is described here that demonstrates the feasibility of using a fluorescence excitation-emission matrix (EEM) approach for identifying HA-like and FA-like NOM fractions in water when used in combination with a series of pH adjustments and filtration steps. It is demonstrated that the fluorescence EEMS of HA-like and FA-like NOM fractions from the river water sample possessed different spectral properties. Fractionation of HA-like and FA-like NOM prior to fluorescence analysis is therefore proposed as a more reasonable approach.
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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 | 0.000 | 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) | 0.000 | 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".