Refined Tunable Methodology for Characterization of Contaminant–Particle Relationships in Surface Water
Bibliographic record
Abstract
To understand contaminant transport in aquatic systems, it is essential to define the physical characteristics of the primary particulate carriers. The distribution of organic pollutants with particle-size class and particle morphology in a freshwater embayment (Hamilton Harbor, western Lake Ontario) was studied using a sequence of novel sample preparation and characterization techniques. Water samples (24 L) were fractionated according to particle-size distribution using differential cascade sedimentation and centrifugation methods. These size fractions were subsequently subjected to a physicochemical characterization using scanning transmission electron microscopy and energy-dispersive spectroscopy to identify flocs and individual colloidal particles in the size range of 1 nm to 1 mm in diameter. Analytical chemical analyses were performed to identify organic contaminants in extracts prepared from particle-size classes, including polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs). The contaminant distribution trends were very similar for all compound classes studied; contaminants were primarily associated with fractions containing particles less than 2 mum in diameter. Morphological characterization of these fractions showed the majority of the particulates to be humic fractals. The results of this study show that contaminants in aquatic systems can be preferentially associated with specific types of particle carriers, the characteristics of which can be clearly defined in terms of size and morphology.
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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 | 0.003 | 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) | 0.001 | 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".