Cadmium Water Pollution Associated with Motor Vehicle Brake Parts
Bibliographic record
Abstract
Abstract With increasing industrial growth, there is a greater need to understand factory production processes, the resulting products, and the pollution caused by the fabrication processes leading to these products. Cadmium (Cd) is used in the electro-less Nickel-Cadmium bath phase of the brake manufacturing process, which provides the brake coating that produces corrosion-resistant brake parts. During the operation, the friction created during braking corrodes the Cd layer and releases Cd particles into the environment. Cd particles can enter water bodies and drinking water supplies through stormwater runoff. This research will first examine Cd pollution associated with motor vehicle brake discs from cradle to grave. Following this comprehensive look into the role of Cd in the brake manufacturing process as well as Cd speciation in natural waters, three interventions are proposed to prevent Cd pollution associated with brake parts: (i) Carbon-reinforced silicon carbide as an alternative for metal based brake parts; (ii) bacteria “coating” instead of Cd coating; (iii) permeable roads that can effectively remove Cd from runoff with nearly 98% reduction. A discussion into the advantages and disadvantages of each proposition are provided with this presentation.
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 | 0.001 | 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) | 0.001 | 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".