Trip motive in time and space: the impact on black carbon exposure
Bibliographic record
Abstract
Background: Exposure in transport contributes to almost a quarter of accumulated exposure over a day, however in Flanders individuals travel for only 6% of the time. Evidence is emerging that high concentration peaks, for example in transport, are responsible for important health effects. Objectives: We evaluate trips with different motives, and try to discover spatial and temporal characteristics typical for trips with a specific motive. The effect on in-traffic exposure to Black Carbon (BC) is assessed. Methods: In 2010, 62 people volunteered to participate in a Flemish study examining personal exposure to the air pollutant BC using portable µ-aethalometers. The participants were also equipped with an electronical diary to register activities and trips. A Global Positioning System was built in the handheld computer and tracked the trips of volunteers. Results: Over 1500 trips were registered, and assigned by the participants to 6 modes of transport. Trip motive was defined as one of 10 activities performed at the destination side of a trip (only if this was a home-based activity, the diary entry at the origin was defined as trip motive). 61% of all trips with motive work were on weekday peak hours. Half of social and leisure trips are in the weekend and another third is on off-peak hours. Transport modes are distributed quite evenly over trips with different motives. More than 50% of all non-rail commute trips are on highways or on other major roads, daily shopping trips are mainly driven on local roads. As a result, average exposure during commute trips is highest (5.7 µg/m³). Exposure during daily shopping trips is much lower (4.0 µg/m³). In addition, commute trips have almost twice the duration of daily shopping trips. Conclusions: Average concentrations encountered during trips with different motives, are mainly driven by the timing of those trips and road choice. Exposure to BC is highest during car commute trips.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".