Designing 3D printed Unit cell models for Arsenic Filtration
Bibliographic record
Abstract
Arsenic contamination of water is a widespread problem that affects many different parts of the world. Natural weathering and anthropogenic sources, such as mining and use of coal-fired power plants, leads to the exacerbation of this issue. Ranging from the developed nations, including the United States and Canada, to developing countries such as Bangladesh, India and China, arsenic contamination has affected numerous different populations across the world and proven to be extremely injurious to human health. Arsenic exposure has many health hazards including gastrointestinal, cardiac and neurological disorders. Our aim is to develop the next-generation low-cost filter cartridges for removing arsenic from water in our lab at our lab at CEAS. Polyurethane (PU) foam is a very common and cheap material that is used in several items of day to day use such as soles of sneakers. We are attempting to make a PU foam based arsenic filter using iron-oxide nanoparticles which is affordable to poor people in developing countries. We would like to not only make PU foam based filter cartridge but also test in our state-of-the-art experimental setup. This will help us to evolve the design of our water filter and bring it closer to the real-life application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".