Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Transportation in Malaysia started since the British colonial period and as of 2016, it can be said that the country's transport network is highly diversified having undergone years of development. At present, Malaysia's road network covers an extensive estimate of 230,000 km in 2015 with a national road development growth index of 2.29 from a mere 1.42 in 2010. Since the 10th Malaysia plan, Malaysia's network growth remained at an annual 10.9% growth bringing this to an astounding 68% overall growth as of 2015 and this focus is set to continue in the 11th Malaysia Plan from 2016-2020 leveraging new investments in road, rail and air services. According to statistics obtained from JPJ, the number of new vehicles registrations spiked from 25418 in 2012 to 40753 in the following year and as of 2016, there is an observable average of 8000 to 11000 new registrations per month. By the first quarter of 2016, the number of new registered motor vehicles in Wilayah Persekutuan, Kuala Lumpur obtained from Road Transport Department is reported at 31476 and the total number of vehicles in the state is 6,149,414. Due to the rapid surge in urban transport and the wide availability of mobile vehicles, it becomes necessary to develop a real time monitoring system that could effectively measure the traffic movement of vehicles. This is because vehicles tend to emit unnecessary greenhouse gases which may pollute the environment and affect the population health. In this project, we aim to conduct a first class survey which aims at studying traffic flow in KL areas and also identify the cause of traffic congestion through state of the art data analytic methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it