Hyperspectral and multispectral sensors for remote sensing
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
Remote Hyperspectral and Multispectral sensors have been developed using modern CCD and CMOS fabrication techniques combined with advanced dichroic filters. The resulting sensors are more cost effective while maintaining the high performance needed in remote sensing applications. A single device can contain multiple imaging areas tailored to different multispectral bandwidths in a highly cost effective and reliable package. This paper discusses a five band visible to near IR scanning sensor. By bonding advanced dichroic filters onto the cover glass and directly in the imaging path a highly efficient multispectral sensor is achieved. Up to 12,000 linear pixel arrays are possible<sup>1</sup> with this advanced filter technology approach. Individual imaging areas on the device are designed to have unique pixel sizes and clocking to enable tailored imaging performance for the individual spectral bands. Individual elements are also based on high resolution Time Delay and Integration technology<sup>2,3</sup> (TDI) to maximize sensitivity and throughput. Additionally for hyperspectral imagers, a split frame CCD design is discussed using high sensitivity back side illuminated (BSI) processes that can achieve high quantum efficiency. As these sensors are used in remote sensing applications, device robustness and radiation tolerance was required.
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 | 0.000 |
| 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 | 0.001 |
| 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