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
This is an uncooled LWIR dataset named LGC. It was captured utilizing the IRay Tech LGC6122 uncooled infrared core, a device also employed inpractical applications. A significant advantage of our dataset lies in the 72 mm focal length of the LGC6122, which facilitates the detection of human subjects at distances of up to 1.3 kilometers and vehicles at distances of 1.7 kilometers. In contrast, the FLIR dataset features a focal length of 13 mm, while the KAIST dataset has a focal length of 7.5 mm. The LGC6122 operates at a standard resolution of 640 × 512 pixels and functions withinthe 8–12 μm wavelength range, characteristic of typical LWIR detectors. The infrared images included in our dataset were captured at Kunming Pool Qixi Park in Xi’an, Shaanxi, China. Data collection occurred during nighttime and afternoon hours in both spring and winter seasons. The scenes represented in our dataset encompass buildings, lakes, and trees observed from considerable distances, thereby offering a diverse array of challenging scenarios for infrared image enhancement. Given the labor-intensive process of identifying appropriate locations for long-distance target detection, our dataset comprises a total of 668 images.
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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