Bibliographic record
Abstract
Malaysia has been developed from one stage to another stage. By looking at the transportation in Malaysia, there are about too many types of transport that are commonly used either private or public_ s- uCh as Car and bus. One Of the franjoft which now days got attention from the public is transit such as KTM Commuter, Express Rail Link (ERL), I<L Monorail, Putra LRT and STAR -LRT. Mos± people like to use this type of transport because to avoid from traffic congestion on the road or to choose the easiest method to move from one destination to another. Nevertheless, these transit also facing with a lot of problems including the customer satisfaction and delay of train. Anyhow, the customer always faces difficulty to krnd the shortest route. Failed to do so the cost will be increased according to the route chosen and the time is not accurate. This is because there are too many option of route to go to each destination. This problem comes when users need to change from one train to another train to reach the destination. Until now Malaysia-has five type of transit that is often used. Malaysian Network Transit Route Advisor (MANTRA) is a web based system that especially developed to guide the people when using the Malaysian Transit. MANTRA has two main functions. The first is to estimate the lowest cost base on time travelling from one station to another and the second function is estimate the lowest cost base on the ticket price. MANTRA using Dijkstra's Algorithm to realize these functions. frIANTRA 98% successfully to advice the tourist goes to the destination using Malaysia Transit.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.784 | 0.641 |
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".