Characterization of freshwater natural dissolved organic matter (DOM): Mechanistic explanations for protective effects against metal toxicity and direct effects on organisms
Bibliographic record
Abstract
Dissolved organic matter (DOM) exerts direct and indirect influences on aquatic organisms. In order to better understand how DOM causes these effects, potentiometric titration was carried out for a wide range of autochthonous and terrigenous freshwater DOM isolates. The isolates were previously characterized by absorbance and fluorescence spectroscopy. Proton binding constants (pKa) were grouped into three classes: acidic (pKa≤5), intermediate (5<pKa≤8.5) and basic (pKa>8.5). Generally, the proton site densities (LT) showed maximum peaks at the acidic and basic ends around pKa values of 3.5 and 10, respectively. More variably positioned peaks occurred in the intermediate pKa range. The acid-base titrations revealed the dominance of carboxylic and phenolic ligands with a trend for more autochthonous sources to have higher total LT. A summary parameter, referred to as the Proton Binding Index (PBI), was introduced to summarize chemical reactivity of DOMs based on the data of pKa and LT. Then, the already published spectroscopic data were explored and the specific absorbance coefficient at 340nm (i.e. SAC340), an index of DOM aromaticity, was found to exhibit a strong correlation with PBI. Thus, the tendencies observed in the literature that darker organic matter is more protective against metal toxicity and more effective in altering physiological processes in aquatic organisms can now be rationalized on a basis of chemical reactivity to protons.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".