An Investigation of the Use of Real-time Image Mosaicing for Facilitating Global Spatial Awareness in Visual Search
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Bibliographic record
Abstract
Three experiments have been completed to investigate whether and how a software technique called real-time image mosaicing applied to a restricted field of view (FOV) might influence target detection and path integration performance in simulated aerial search scenarios, representing local and global spatial awareness tasks respectively. The mosaiced FOV (mFOV) was compared to single FOV (sFOV) and one with double the single size (dFOV). In addition to advancing our understanding of visual information in mosaicing, the present study examines the advantages and limitations of a number of metrics used to evaluate performance in path integration tasks, with particular attention paid to measuring performance in identifying complex routes.\nThe highlights of the results are summarized as follows, according to Experiments 1 through 3 respectively.\n1.\tA novel response method for evaluating route identification performance was developed. The surmised benefits of the mFOV relative to sFOV and dFOV revealed no significant differences in performance for the relatively simple route shapes tested. Compared to the mFOV and dFOV conditions, target detection performance in the local task was found to be superior in the sFOV condition. \n2.\tIn order to appropriately quantify the observed differences in complex route selections made by the participants, a novel analysis method was developed using the Thurstonian Paired Comparisons Method.\n3.\tTo investigate the effect of display size and elevation angle (EA) in a complex route environment, a 2x3 experiment was conducted for the two spatial tasks, at a height selected from Experiment 2. Although no significant differences were found in the target detection task, contrasts in the Paired Comparisons Method results revealed that route identification performance were as hypothesised: mFOV > dFOV > sFOV for EA = 90°. Results were similar for EA = 45°, but with mFOV being no different than dFOV. As hypothesised, EA was found to have an effect on route selection performance, with a top down view performing better than an angled view for the mFOV and sFOV conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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