The Missing Health Link: How a transition to electrified vehicles may benefit more than just the environment
Bibliographic record
Abstract
Worldwide, more than 1 billion vehicles are used regularly, with the majority being gasoline powered. These transport methods are known emitters of carbon dioxide, particulate matter (PM), and other pollutants (i.e., nitric oxides). Such compounds pose a great environmental risk, but recent research has also suggested health consequences. PM, a microscopic carcinogenic substance, affects many biological systems and has been associated with medical concerns in clinical and laboratory settings. In clinical settings, research on the effects of PM of 25 pm or lower in diameter (PM25) have focused on interactions with the cardiovascular, respiratory, and nervous systems. Vulnerable populations (i.e., the elderly and hospitalized patients) disproportionately experience an increase in cardiovascular and respiratory deaths along with hospital admissions for heart disease and asthma. Also, studies have found an increase in tumorigenesis.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".