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
The polarization state of light provides valuable information about scenes that cannot be obtained directly from intensity or spectral images. Polarized light reflected from scenes has been found to be useful and can reveal contrasts that do not appear in classical intensity images and find many applications in remote sensing, biomedical imaging, or industrial control. Cost, size, and technological complexity of polarimetric imagers depend on the number of polarimetric parameters they measure. In this context, a key issue is to evaluate the added value of each measured polarimetric parameter in order to optimize the compromise between complexity and efficiency of these systems. In target detection applications, the relevant criterion for quantifying the performance of an imaging configuration is contrast (or discrimination ability). Analysis of the contrast and its optimization in polarimetric images have been investigated in the radar and optics communities. We investigate in the paper how the polarisation imaging can be applied in automotive vision based sensor. This study present various type of polarisation sensitive optical system. Detection of small and low-contrast objects has been found to be improved with the help of this kind of optical system.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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