A critical assessment of physicochemical indices used to characterise natural dissolved organic carbon (DOC), their inter-relationships, and the effects of pH
Bibliographic record
Abstract
Environmental context Dissolved organic carbon (DOC) is ubiquitous in freshwater and concentrations are rising universally while pH is decreasing with climate change. This study demonstrates the interrelationships among DOC characterisation techniques and the pH-sensitive aspects of these techniques that were previously not well understood. As DOC regulates important processes within ecosystems, understanding DOC behaviour at altered pH and identifying techniques to effectively evaluate DOC composition are critical requirements for monitoring aquatic ecosystem health. Rationale Dissolved organic carbon (DOC) is both ubiquitous and heterogeneous in freshwater. Freshwaters are browning universally and pH values are decreasing with climate change. DOCs influence water pH, whereas changes in water pH potentially alter the conformation and function of DOCs. The physicochemical properties of DOCs can be characterised by optical and chemical indices, but the inter-relationships among them, and the effects of low pH, are not well understood. Methodology We characterised five naturally sourced DOCs, spanning large differences in origin and composition, at pH 7 and 4, using multiple indices: specific absorbance coefficient at 340 nm, molecular weight index, fluorescence index, octanol–water partition coefficient, molecular charge, proton binding index, size-fractionation, and percentage humic-acid-like, percentage fulvic-acid-like and percentage protein-like components by fluorescence-based parallel factor analysis. Results Many of the indices changed between pH 7 and 4 as reflected in the corresponding absorbance and fluorescence profiles. Generally, apparent aromaticity, apparent molecular weight and molecular charge all decreased with low pH, while lipophilicity increased. Key positive correlations occurred between aromaticity and apparent molecular weight, chemical reactivity and apparent molecular weight, and aromaticity and chemical reactivity, and a negative correlation between lipophilicity and molecular charge. These relationships were pH dependent. Discussion Our results highlight that physicochemical indices used to characterise DOCs from distinct sources should consider pH and be interpreted carefully. The pH-dependent changes in many of the indices are likely alterations in the conformation, reflected in the optical signatures, rather than changes in the composition of DOCs. In contrast, increased lipophilicity and reduced charge at lower pH are due to actual changes in DOC molecules, resulting from proton binding. The ecological functions of DOCs are dependent on source and will likely change with natural acidification events such as increasing atmospheric CO2.
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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".