Integrated Multispectral Camouflage for Mobile Weapon Systems (An Effectiveness Evaluation)
Bibliographic record
Abstract
The surveillance capabilities of armed forces throughout the world have increased tremendously during the past few years. The threat to military assets is multispectral and Camouflage, Concealment and Deception (CCD) measures must be provided in all the proper spectral regions in order to counter this threat. At the end of 1997, Director Soldier Systems Program Management (DSSPM) tasked Defense Research Establishment Valcartier (DREV) to determine the overall effectiveness of new mobile camouflage equipment against modern imaging systems from the ultraviolet (UV) to the thermal infrared (IR) spectral regions. An Integrated Multispectral CAmouflage for Vehicle Systems (IMCAVS) was designed by Barracuda Technologies of Sweden for the newly introduced Canadian Forces (CF) reconnaissance vehicle: the Coyote. To verify the enhanced characteristics of this new generation of camouflage equipment, a trial, under the umbrella of NATO, was conducted at CFB Valcartier in August 1998. This paper presents results and comments on the specially designed concealment suite for the Coyote vehicle. This experimental concealment suite is designed to reduce the signature of the vehicle in the UV, visible and in both infrared spectral bands. It describes the design and characteristics of the Coyote concealment suite, a description of the experimental conditions and the instrumentation deployed during this trial. An indication of the results of a human perception experiment on the performance of the concealment suite in the visible band and electro-optical measurements taken in the UV and the two infrared (IR) bands are presented. Additionally, FLIR over flights conducted by CF188 aircraft are referenced. Finally, some conclusions and recommendations are tabled.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".