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 work investigates target detection using simulated hyperspectral imagery captured from highly oblique angles. This paper seeks to determine which domain, radiance or reflectance, is more appropriate for the off-nadir case. An oblique atmospheric compensation technique based on the empirical line method (ELM) is presented and used to compensate the simulated data used in this study. The resulting reflectance cubes are subjected to a variety of standard target detection processes. A forward modeling technique that is appropriate for use on oblique hyperspectral data is also presented. This forward modeling process allows for standard target detection techniques to be applied in the radiance domain. Results obtained from the radiance and reflectance domains are comparable. Under ideal circumstances, however, the radiance domain results are slightly better than the results observed in the reflectance domain. These somewhat favorable results for the radiance domain, considered with the practicality and potential operational applicability of the forward modeling technique presented, suggest that the radiance domain is an attractive option for oblique hyperspectral target detection.
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) | 0.000 | 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