Bibliographic record
Abstract
This paper describes a localization approach which compensates for vehicle motion during the interrogation-reception time interval between the vehicle and transponders in an acoustic long baseline (LBL) system lake trial. In the lake trial, a maneuvering vehicle was localized by both GPS and the LBL system. When localizing the vehicle with the static-vehicle localization model, where the vehicle is assumed to be static during the interrogation-reception time interval, the averaged localization error was found as 19±14 cm by comparing acoustic localization results with GPS data. This error was significant larger than the expected 4 cm posterior uncertainties and the vehicle motion was thought as the main cause of this error. To address this problem, a motion-compensated inversion approach is developed base on Bayesian inversion algorithm which includes travel-time corrections as additional unknown parameters with prior determined by interpolating the vehicle location at interrogation time instants using static-vehicle localization model results. After processing the trial data with the motion-compensated inversion approach, the averaged localization error was decreased to 3.4±1.4 cm, much accurate than the error for the static inversion.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".