Simple methods for evaluating the population of naidids in drinking water treatment plants
Bibliographic record
Abstract
The use of biofiltration ozonation followed by biofiltration in drinking water to improve the physicochemical and biological qualities of drinking water can promote the development of macro-invertebrates in filters colonized by bacteria. The passage of invertebrates in filtered water should be avoided both from an aesthetic point of view and from the potential of bacteria growth support on their surface. This paper focuses on the development of two simple methods to estimate the population of members of the naidid family from the class of oligochaetes in biofilters and in water. The first method involves a naidid column trap used to concentrate naidids present in the effluent of biofilters. During the study, the concentrations of naidids recorded in the biofilter effluent studied varied from 0 naidid per m3 in cold water (1°C) to 20 naidids per m3 in warm water (22°C). The second method enumerates naidids in a sample of biofilter media taken using a core sampler. Typical naidid densities encountered ranged from the limit of detection (1 naidid per 4 mL) in cold water (1°C) to 25 naidids per mL in warm water (25°C) at the surface of the biofilter. Statistical analyses done on the second method have shown that samples taken at the surface of the filter media at depths between 0 and 200 mm were statistically different from those taken at deeper levels and represented the maximum naidid densities measured in the biofilter studied.Key words: drinking water, biofiltration, population of naidids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".