CAMEVAL-2002 Land Forces Signature Reduction Trial: Ground Truthing, Calibration and Multi-Sensor Data Acquisition for DRDC Experiments
Bibliographic record
Abstract
The CAMEVAL-2002 Land Forces Signature Reduction (LFSR) trial was conducted at the Canadian Forces Base (CFB) Petawawa in June 2002. For the LFSR trial, 27 military vehicles (primarily Leopard C2 tanks and Coyote reconnaissance vehicles) were deployed under forest canopy, along tree lines and in open field. A subset of the Leopards and Coyotes was deployed with a trial camouflage screen, another subset was covered with the current in-service camouflage, and a final subset was left uncovered. During the LFSR trial, Defence Research and Development Canada (DRDC) Ottawa and DRDC Valcartier acquired airborne and spaceborne Synthetic Aperture Radar (SAR) imagery and airborne Hyperspectral Imagery (HSI) data over the trial sites, and conducted extensive calibration and ground truthing activities in support of these acquisitions. This report documents the SAR and HSI data acquisition, ground truthing and calibration activities completed during the trial, thereby providing the foundation for any future analyses that use these multi-sensor data. This report also describes the five DRDC Ottawa and DRDC Valcartier analyses that were planned at the time of the trial.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".