Evaluating VANET information retrieval context aware systems using the average distance measure ADM
Bibliographic record
Abstract
Information Retrieval (IR) techniques are utilized in developing context aware systems for VANET safety and convenience services. For the safety services a context aware system for the Automatic Crash Notification called IR-CAS ACN is developed while the context aware Congested Road Notification system IR-CAS CRN is developed for the convenience services. Different IR models like the vector space, fuzzy logic and binary models are proposed for each of these systems. The performance of the proposed models for IR-CAS ACN is compared using test collections that are based on nineteen years of real life crash records associated with their severity levels while the performance of the IR-CAS CRN is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The highway capacity manual (HCM) speed-flow curves along with the Greenshield model are utilized in generating these freeway flow cases and their levels of service. The average distance measure (ADM) is used to evaluate the tested IR models. Results show that using the vector space model for severity estimation by calculating the Manhattan distance between the crash/congestion current context vectors and the severest crash/congestion context vectors outperforms the fuzzy and binary severity estimation models.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".