Detection of motion-defined form using night vision goggles
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
Perception of motion-defined form is important in operational tasks such as search and rescue and camouflage breaking. Previously, we used synthetic Aviator Night Vision Imaging System (ANVIS-9) imagery to demonstrate that the capacity to detect motion-defined form was degraded at low levels of illumination (see Macuda et al., 2004; Thomas et al., 2004). To validate our simulated NVG results, the current study evaluated observer’s ability to detect motion-defined form through a real ANVIS-9 system. The image sequences consisted of a target (square) that moved at a different speed than the background, or only depicted the moving background. For each trial, subjects were shown a pair of image sequences and required to indicate which sequence contained the target stimulus. Mean illumination and hence image noise level was varied by means of Neutral Density (ND) filters placed in front of the NVG objectives. At each noise level, we tested subjects at a series of target speeds. With both real and simulated NVG imagery, subjects had increased difficulty detecting the target with increased noise levels, at both slower and higher target speeds. These degradations in performance should be considered in operational planning. Further research is necessary to expand our understanding of the impact of NVG-produced noise on visual mechanisms.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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