Contributions of spatial, temporal, and treatment impacts on natural organic matter character using fluorescence-based measures
Bibliographic record
Abstract
The potential application of fluorescence spectroscopy for monitoring of organic matter concentration and character at four water treatment facilities was investigated. Results are presented showing impacts on natural organic matter (NOM) due to intake location on the same water body and from individual unit processes including ozonation, granular-activated carbon filtration, and coagulation/flocculation. For validation and comparison of fluorescence methods, organic matter was quantified and characterized using liquid chromatography-organic carbon detection (LC-OCD). Principal component analysis (PCA) and parallel factors analysis were used for dimensionality reduction and to represent individual organic components observed through fluorescence excitation-emission matrices. Fluorescence results generally agreed with LC-OCD characterization, indicating that complete treatment reduced organic concentrations and preferential removal of humic-like material was associated with coagulation/flocculation. PCA results indicated higher concentrations of humic-like material at the Island water treatment plant intake that was not well reduced by inline polyaluminum chloride coagulation and direct filtration. Through fluorescence spectroscopy, ozonation increased Rayleigh scattering, which is correlated to small colloidal/particulate concentrations. Full-scale results from four water treatment plants presented demonstrate that fluorescence methods can characterize NOM, providing similar identification of trends to LC-OCD, with possible online application and use in real-time water treatment process control.
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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 | 0.001 | 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 | 0.002 |
| 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 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".