SoberJack [impaired driving prevention system]
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
AlcoShield’s Sober Jack, an impaired driving prevention system, is a novel design which is intended to deter any form of drunk vehicle operation. In fact, impaired driving is still a major concern in today's society and contributes to almost 50% of the total number of motor vehicle accidents each year in Canada. Excessive and unnecessary resources are then dedicated to resolve such incidents which could frankly have been easily avoided. The financial implications of these activities resulted in an expenditure of over $20.62 billion in Canada in the year 2010. Realizing the urgent need to mitigate such accidents, the team at AlcoShield has taken up the responsibility of making the streets safer and sober with their SoberJack product.\nAlthough similar products do currently exist in the market, the major flaw they have is the lack of user authentication. They are easily able to be deceived by passengers breathing into them instead of the driver. Thus Phase 2 includes the development of the facial detection system to identify the driver. For this phase, the camera captures a certain number of the driver’s pictures as references for later identity checks.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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