Automated Change Detection in an Undersea Environment using a Statistical Background Model
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
Marine scientists are turning increasingly to underwater video cameras in their research. These provide enormous quantities of visual data that often overwhelm the manual processing abilities of the scientists. To cope with such large data sets, an automated change detection system is proposed that helps isolate the time periods in which significant activity is found in the video sequence. Unlike change detection algorithms in use in terrestrial environments, the system must account for the photometric complexity of underwater video, including interference from small floating particles ("sea snow"), the scatter of light as it propagates through water, and the non-uniform frequency decay of light intensity with distance. In addition, certain activity, such as the motion of swimming fish that are attracted by the use of artificial lighting, is considered a distracter, and should, ideally, be ignored. These factors are addressed by our system, in large part through the use of Mixture-of-Gaussians background models.
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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