Aquatic Colloids and Nanoparticles: Current Knowledge and Future Trends
Bibliographic record
Abstract
Environmental Context. The fate and behaviour of trace pollutants are very strongly modified, and usually dominated, by their physical and chemical interactions with naturally occurring aquatic colloids (defined as solid phase material with one dimension between 1 nm and 1 µm). This review summarises the area and key advances in the field of natural aquatic colloids, including technique development and quantification of colloidal structure and interactions with pollutants. The review also discusses areas in which significant advances are likely to be made or are needed and, as such, provides a framework for further work in the next few years. Abstract. Natural aquatic colloids are materials with one dimension between 1 nm and 1 µm. More informally defined, nanoparticles are materials with at least one dimension less than 100 nm. Both colloids and nanoparticles have significant effects on pollutant, nutrient, and pathogen chemistry, transport and bioavailability, and may themselves be bioavailable. Techniques for their fractionation, characterization and analysis have improved greatly in recent years. Although knowledge of their structure and environmental impact has also increased, it has not done so to the same degree and thus the field awaits the substantial application of new methodologies. This paper reviews the current state of the art in this area and also discusses likely future developments.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".