Route profiling - putting context to work.
Bibliographic record
Abstract
Intelligent Transportation Systems are characterised by a requirement\nfor detailed information on extensive transport networks. This\ninformation is typically gathered from sensors deployed throughout\nthe network and is used for management and maintenance operations.\nIn this paper we present the design and prototype implementation\nof a context-aware route profiling application intended for use\nby road management authorities in the Republic of Ireland. Our\ndesign allows data from a variety of sources to be combined to generate\ndetailed information on traffic flow and journey times along\nthe national road network. This information can be tagged with\nrelevant context data reflecting the conditions under which sensor\ndata was collected. The set of relevant contextual information includes\ndetails on temporal, spatial, weather and road usage pattern\ncontexts.\nThe prototype implementation relies on GPS data from a fleet of\nprobe vehicles. An evaluation of this prototype is presented along\nwith a discussion on the benefits of using context-aware computing\ntechniques in a real world scenario.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".