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 a computationally viable localization technique for an autonomous underwater vehicle (AUV) that is used to carry visual inspection and nondestructive testing equipment inside large water conduits and tunnels without decommissioning the service. The localization technique is required to estimate the instantaneous location of the robot with sufficient accuracy for the control system of the robot in real time. The proposed technique features a sensor fusion framework that incorporates a monocular camera and an inertial navigation system (INS). Localization is carried out using a standard Lucas-Kanade algorithm which searches for a subset of matching pixels between two sequential images to estimate a motion vector for the time interval between the two images. The novelty of the proposed technique is in regards with the use of an INS to predict a rotation and translation vector between the two sequential images. This prediction is used to minimize the search region of the Lucas-Kanade algorithm and hence significantly reduce the computational load of the overall localization process. Experimental results on a special testbed verify that the proposed system not only reduces the computational load but also improves the accuracy since finding a false match in the minimized search region is unlikely.
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 | 0.000 |
| 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