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
This paper describes an automated surface surveillance system, developed on behalf of the Government of Canada to detect and track illegal vessels. The scenario involves a moving target having speed significantly less than the searcher speed, slowly approaching Canada's coastline. The crux of the surveillance problem is to determine the sequence of sub-regions to search in order to maximize the probability of target detection. The complexity of our surveillance problem lies in the absence of knowledge on the target location, speeds and course. Additionally, the searcher is frequently confronted with insufficient time to area search the sub-regions. The presence of false targets and the occurrence of irregular search area further compound the problem. Our decision support system is a combination of established theories on probability maps, barrier patrol and a novel construction of heuristics for area searching irregular regions. Our approach also involves extensive use of visualization tools to aid code debugging and validation. More importantly, our automated surveillance system provides a user-friendly environment for decision planners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 |
| 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