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 effectiveness of infrared imagery in poor visibility situations is well established and the range of applications is expanding as we enter a new era of inexpensive thermal imagers for mobile phones. However there is a problem in that the counterintuitive reflectance characteristics of various common scene elements can cause slowed reaction times and impaired situational awareness−consequences that can be especially detrimental in emergency situations. While multiband infrared sensors can be used, they are inherently more costly. Here we propose a technique for adding a daylight color appearance to single band infrared images, using the normally overlooked property of local image texture. The simple method described here is illustrated with colorized images from the visible red and long wave infrared bands. Our colorizing process not only imparts a natural daylight appearance to infrared images but also enhances the contrast and visibility of otherwise obscure detail. We anticipate that this colorizing method will lead to a better user experience, faster reaction times and improved situational awareness for a growing community of infrared camera users. A natural extension of our process could expand upon its texture discerning feature by adding specialized filters for discriminating specific targets.
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 | 0.001 |
| 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