AIRIS the Canadian Hyperspectral Imager
Bibliographic record
Abstract
The Defence Research and Development Canada (DRDC) Agency has successfully completed a Technical Demonstration Program (TDP) to assess the "Military Utility of Airborne Hyperspectral Imagery ". This required developing a sensor, the airborne infrared imaging spectrometer (AIRIS), and collecting in-flight imagery data. The AIRIS instrument was designed with flexibility and modularity in mind, allowing the study of a wide range of applications. AIRIS simultaneously operates two 8times8 element detector arrays to cover the 2.0 to 12 micron region of the electromagnetic spectrum. It also simultaneously collects broadband video imagery from the visible to the long wave IR. AIRIS was mounted in National Research Council's (NRC) Convair 580 aircraft. A series of three data collection flight tests were conducted in the summer of 2005. The first test collected phenomenological data over rural, suburban and urban areas. The second test used several targets of different types. The last flight collected phenomenological data over the Atlantic ocean. Data analysis showed that sub-pixel targets can be detected and identified from their spectral features. Over the next three years, a real-time processing capability will be added to AIRIS, making its data directly exploitable for Canadian Forces applications
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".