The Contribution of Endmembers to Mixtures of Leaf Leachates and Riverine DOM can be Determined by Measuring Their Size and Fluorescence Properties
Bibliographic record
Abstract
The molecular mass distribution (MMD) and fluorescence properties of dissolved organic matter (DOM) are important characteristics for tracing and predicting its pathways, processes, and fate in aquatic systems. For the first time, asymmetrical flow field-flow fractionation (AF4) with coupled absorbance and fluorescence detectors was used to determine the contribution of endmembers to three mixtures of leaf leachate and riverine DOM in various proportions. Parallel factor analysis (PARAFAC) and fractogram deconvolution were used to decompose and distinguish the size distributions and fluorescence excitation-emission matrices (EEMs) of mixture constituents. It was determined that: 1) Both size and optical properties were conservative tracers in mixtures; 2) Fractogram deconvolution was extremely helpful for discriminating endmember size properties; 3) The contributions of endmembers to overall DOC concentration were accurately estimated using both the proportion of a humic-like PARAFAC component (0.93 < R 2 < 1.00), and the ratios of deconvoluted peaks (0.88 < R 2 < 0.98). The fluorescence at the peak maximum of the MMD was lacking in protein-/polyphenol-like and microbial humic-like fluorescence compared to the whole sample (−11 ± 9 and −10 ± 7%, respectively); however, the contribution of endmembers to the MMD (A 254 ) were also effectively predicted using both the proportion of a microbial humic-like PARAFAC component (0.91 < R 2 < 0.98) and the ratio of deconvoluted peaks (0.94 < R 2 < 0.98).
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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 | 0.000 | 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 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".