Emergent functional behaviour of humic substances perceived as complex labile aggregates of small organic molecules and oligomers
Bibliographic record
Abstract
Environmental context The correlation of physicochemical characteristics of humic substances with their function is crucial to our understanding of how environmental pollutants interact with humic substances. We have developed an approach that models emergent functions of fulvic and humic acids depending on sample characteristics. The results will be useful for predicting the sequestration of organic contaminants in soil under various conditions. Abstract The structural organisation of humic substances (HS) has been a central question of earth sciences for several decades. The latest experimental results have led to the recognition of HS as complex mixtures of small molecules and oligomers. We investigate the correlation between the chemical composition of HS, perceived as labile aggregates, and the emergent functions. Computational modelling was used to help to understand the processes and mechanisms on the molecular scale that occur in different fractions of the HS, fulvic acids (FA) and humic acids (HA), as they interact with metal ions and organic pollutants. The importance of non-covalent interactions in the emergent functions of HS is highlighted. H-bonding, hydrophilic/hydrophobic surface areas and p-stacking interactions play a significant role in aggregation processes as well as in the sorption of environmental pollutants. In a highly hydrophilic system with small molecules (the SRFA-22 model), H-bonding is the main force that drives the aggregation process. However, in a highly aromatic and hydrophobic model with larger molecular fragments (SRHA-6), hydrophobic and p-stacking interactions dominate in the aggregation process. The chemical properties of contaminants significantly affect their mechanisms of sorption by HS. The interaction of a polar pollutant, phenol, with HS occurs through H-bonding, whereas non-polar benzene interacts through hydrophobic and p-stacking interactions. The non-polar pollutant results in a much stronger sorption by HS and causes an additional structural rearrangement of the aggregates, which make it more stable in the environment.
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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) | 0.031 | 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".