Bibliographic record
Abstract
The company Telops has performed studies that indicate their FIRST system is capable of providing long-wave infrared (LWIR) airborne hyperspectral images. Defence R&D Canada Valcartier (DRDC Valcartier), being interested in evaluating a possible Canadian high performance airborne hyperspectral imager, has started a collaborative effort with Telops with the goal to perform two field trials with the FIRST hyperspectral imaging system in airborne mode in order to determine its current capability as an airborne hyperspectral imager. The plan for the first trial is presented in this technical note. The trial will take place at Valcartier between December 3rd and 14th, 2007. During this trial, Telops FIRST hyperspectral long-wave infrared imager will be operated on an aircraft in a push-broom configuration to obtain hyperspectral imagery of the DRDC Valcartier Lemay park and a variety of installed targets. The ground experiments and ground truth are organized by DRDC Valcartier. Telops has the responsibility for the airborne instrumentation and the platform. During this trial, DRDC Valcartier will setup four small experiments: a gas plume detection experiment, a plastic plume experiment, a powdered chemical sensing experiment and an unexploded ordnance detection experiment. The main objective of this trial is to investigate the possibilities of detection and identification by the FIRST hyperspectral imager.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.027 |
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".