Identification of humic acid-like and fulvic acid-like natural organic matter in river water using fluorescence spectroscopy
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it