Quantification of natural DOM from UV absorption at two wavelengths
Bibliographic record
Abstract
Environmental context. Dissolved organic matter (DOM) is part of the global carbon cycle, ecologically and geochemically active, and costly to remove in water treatment. Spectroscopic monitoring at a single wavelength provides some indication of DOM concentration, but variations in optical properties mean that accurate determinations currently rely on slow and costly laboratory methods. We show that for water samples containing non-anthropogenic DOM, ultraviolet absorbance at two wavelengths can quantify DOM rapidly, cheaply and accurately, and also indicate its quality. Abstract. The precise simulation of ultraviolet absorption by 23 contrasting surface-water DOM samples was achieved with a model based on two components, one absorbing light strongly (A) and the other weakly (B). The parameterised model can be used to predict [DOC] in water samples simply from absorbance values at two wavelengths, while information on DOM quality is provided by the calculated fractionation into A and B. The model was tested by predicting [DOC] for a separate dataset obtained by combining results for 12 samples each from surface waters in the UK, Canada and the USA, and from UK groundwaters. A close correlation (R2 = 0.997) was obtained, with only slight underestimation of the true [DOC]. The proportions of components A and B varied considerably among the sites, which explains why precise prediction of [DOC] from absorbance data at a single wavelength was not possible. When the model was applied to samples collected from river locations in a heterogeneous UK catchment with areas of industry and high human population, [DOC] was underestimated in many cases, which may indicate the presence of non-absorbing pollutant DOM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".