Data Drowsiness Video at Night-time
Bibliographic record
Abstract
Title of dataset: Dataset: Drowsiness driver at night-time, Bandung, Indonesia Creator: • 1st creator: Ratnadewi • 2nd creator: Novie Theresia Br. Pasaribu • 3rd creator: Agus Prijono • 4th creator: Roy Pramono Adhie Publisher: Universitas Kristen Maranatha Date of publication: 30 October 2018 Resource type: dataset Contributor: Direktorat Riset dan Pengabdian Masyarakat, Direktorat Jenderal Penguatan Riset dan Pengembangan Kementerian Riset, Teknologi, dan Pendidikan Tinggi according to the 2018 fiscal year research contract. Number: 106/SP2H/LT/DPRM/2018, date 26th March 2018 Location: Bandung Related journal article: Ratnadewi, Agus Prijono, Novie Theresia Br. Pasaribu, Roy Pramono Adhie License/rights: CC-BY International 4.0 Funding: • Direktorat Riset dan Pengabdian Masyarakat, Direktorat Jenderal Penguatan Riset dan Pengembangan Kementerian Riset, Teknologi, dan Pendidikan Tinggi • Universitas Kristen Maranatha Technical metadata: can be seen in each data Data access and intellectual property The dataset can be freely accessed from Osf.io Repository (Ratnadewi, et. Al 2018) and the intellectual property holder is the creators of the dataset: Ratnadewi, Agus Prijono, Novie Theresia Br. Pasaribu, Roy Pramono Adhie. Data sharing and re-use The dataset is shared openly from Osf.io Repository (Ratnadewi, et. Al 2018) and can be formally cited, distributed, and re-use under CC-BY-4.0 licence. Data preservation and archiving The data is preserved and archived in Osf.io Repository by abiding to the repository’s terms and conditions. Acknowledgements We would like to appreciate the permission from the Universitas Kristen Maranatha to publish the data. Funding program Direktorat Riset dan Pengabdian Masyarakat, Direktorat Jenderal Penguatan Riset dan Pengembangan Kementerian Riset, Teknologi, dan Pendidikan Tinggi Grant title Penelitian Dasar Unggulan Perguruan Tinggi Research Grant, Direktorat Riset dan Pengabdian Masyarakat, Direktorat Jenderal Penguatan Riset dan Pengembangan Kementerian Riset, Teknologi, dan Pendidikan Tinggi according to the 2018 fiscal year research contract. Number: 106/SP2H/LT/DPRM/2018, date 26th March 2018 Author contributions All authors have the same amount of contribution to this data set. Conflict of interest Four authors declare no conflicts of interest upon the publication of this data paper.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.034 |
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".