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 addresses the problem of searching multiple non-adversarial targets using a mobile searcher in an obstacle-free environment. In practice, we are particularly interested in marine applications where the targets drift on the ocean surface. These targets can be surface sensors used for marine environmental monitoring, drifting debris, or lost divers in open water. Searching for a floating target requires prior knowledge about the search region and an estimate of the target's motion. This task becomes challenging when searching for multiple targets where persistent searching for one of the targets can result in the loss of other targets. Hence, the searcher needs to trade-off between guaranteed and fast searches. We propose three classes of search strategies for addressing the multi-target search problem. These include, data-independent, probabilistic and hybrid search. The data-independent search strategy follow a pre-defined search pattern and schedule. The probabilistic search strategy is guided by the estimated probability distribution of the search target. The hybrid strategy combines data-independent search patterns with a probabilistic search schedule. We evaluate these search strategies in simulation and compare their performance characteristics in the context of searching multiple drifting targets using an Autonomous Surface Vehicle (ASV).
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it