Decarbonization of Transport and Oral Health
Bibliographic record
Abstract
The decarbonization of transport is a global initiative aimed at reducing greenhouse gas emissions and addressing the risks of global warming. This article explores the potential connections between the decarbonization of transport and oral health, highlighting the need for further research in this area. Emissions from vehicle exhausts, such as carbon dioxide, methane, and nitrous oxide, may have a modest impact on the risk of early childhood caries and other oral health diseases like periodontal diseases, oral cancer, and dental caries. Active transportation, which promotes regular exercise, has beneficial effects on overall health, including stimulating salivary protein production and reducing the risk of diabetes and cardiovascular diseases, both of which are linked to poor oral health. Transitioning to electric vehicles can also reduce noise pollution, positively impacting mental well-being, which is associated with improved oral hygiene practices. Furthermore, the development of sustainable infrastructure, including efficient public transportation systems, can enhance access to dental services. Further research is needed to establish stronger evidence for these connections and to explore how the global decarbonization of transport agenda can incorporate oral health considerations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".