Analyses of particles in raw waters containing iron and organic carbon
Bibliographic record
Abstract
Many impurities in water supplies exist in the form of particles, which are removed in the water treatment process. In this study, the size of particles in raw water containing iron and organic carbon was measured. Methods for the separation of these particles from suspension were suggested based on the measured particle sizes. Samples from a groundwater and a surface water supply were analysed. The sizes of the particulates were measured with microscopes and particle counters. The groundwater studied contained about 7 mg L(-1) of iron and particles with a mean size of 7.8 microm. The particles in the surface water, with an iron content of 0.45 mg L(-1), were smaller with a mean size of 1.5 microm. The size of particles in surface water increased about eight times during quiescent settling; this increase of the particle size was not observed in the groundwater. The large size of the particles identified in the groundwater suggested direct filtration as the optimal method for their removal. Batch tests were completed with three types of filter media, preceded by oxidation with ozone. The effective size of the filter media used varied from 0.3 to 0.8 mm. In the filtration, 99-95% of the iron was removed, confirming that the particles formed in the groundwater after oxidation were large enough to be removed by direct filtration.
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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".