Supporting Transportation Decision-Makers with Tool Design and Data Uncertainty Visualizations
Bibliographic record
Abstract
RESUME : Les decideurs en matiere de transport des agences gouvernementales jouent un role important dans la gestion des conditions du reseau de circulation, qui a leur tour ont un impact majeur sur le bien-etre des citoyens. Les pratiques, les defis et les besoins de ce groupe de praticiens sont moins representes dans la litterature HCI. D’autre part, il existe un manque d’outils permettant de repondre aux besoins des decideurs et de les aider a suivre et analyser les conditions de circulation sur le reseau routier. De plus, presque toutes les donnees de transport comportent une certaine incertitude, et une bonne communication de l’incertitude des donnees par le biais de visualisations peut avoir un effet important sur la qualite des decisions prises par les experts en transport. Cependant, il y a peu de connaissances sur la facon dont les decideurs en matiere de transport gerent les donnees incertaines et percoivent les visualisations de l’incertitude des donnees.----------ABSTRACT : Transportation decision-makers from government agencies play an important role in addressing the traffic network conditions, which in turn, have a major impact on the well-being of citizens. The practices, challenges, and needs of this group of practitioners are less represented in the HCI literature. On the other hand, there is a gap for tools that can fulfill decision-makers’ needs and help them monitor and analyze traffic conditions on the road network. Additionally, almost all transportation data have some uncertainty, and good communication of data uncertainty through visualizations can have a great effect on the quality of the decisions that transportation experts make. However, there is little knowledge about how transportation decision-makers deal with uncertain data and perceive data uncertainty visualizations.
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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 | 0.021 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".