Study on effect of defluoridation by Serpentine-a new drinking water defluoridation
Bibliographic record
Abstract
Objective Study on effect of defluoridation and determine the optimum condition of defluoridation with Serpentine, provide a new measure to prevent Endemic Fluorsis for the extensive disease areas.Methods Determine the effect of each factor to defluoridation capacity of Serpentine by the methods of single factor analysis and orthogonal experiment.Results The factors influenced the effect of drinking water defluoridation significantly in order were: contact time, size, concentration of the solution of regeneration material. The optimum method of drinking water defluoridation included: size, 60~80 Mu; concentration of 0.0739 mol/L of alum solution; 30 min of contact time. The water temperature had little effect on defluoridation; The difference of defluoridation capacities of serpentine was not significant when pH value of water was within 7.16~8.18(P0.05).Conclusions This kind of method of defluoridation by serpentine had an obvious effect and it was easy to operate and apply.It will be of actual value to apply in the rural areas where concentrative water supply systime was not installed.
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 | 0.000 | 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) | 0.001 | 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".