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
A software application, SIST, has been developed for the simulation of the video at the output of a thermal imager. The approach offers a more suitable representation than current identification (ID) range predictors do: the end user can evaluate the adequacy of a virtual camera as if he was using it in real operating conditions. In particular, the ambiguity in the interpretation of ID range is cancelled. The application also allows for a cost-efficient determination of the optimal design of an imager and of its subsystems without over- or under-specification: the performances are known early in the development cycle, for targets, scene and environmental conditions of interest. The simulated image is also a powerful method for testing processing algorithms. Finally, the display, which can be a severe system limitation, is also fully considered in the system by the use of real hardware components. The application consists in Matlab<sup>tm</sup> routines that simulate the effect of the subsystems atmosphere, optical lens, detector, and image processing algorithms. Calls to MODTRAN® for the atmosphere modeling and to Zemax for the optical modeling have been implemented. The realism of the simulation depends on the adequacy of the input scene for the application and on the accuracy of the subsystem parameters. For high accuracy results, measured imager characteristics such as noise can be used with SIST instead of less accurate models. The ID ranges of potential imagers were assessed for various targets, backgrounds and atmospheric conditions. The optimal specifications for an optical design were determined by varying the Seidel aberration coefficients to find the worst MTF that still respects the desired ID range.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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