Comparison of MBARI Autonomous Underwater Mapping Results for ORION Monterey Accelerated Research System (MARS) and Neptune Canada
Bibliographic record
Abstract
We present results from MBARI's two contrasting missions of MBARI's autonomous underwater mapping vehicle; the first in Monterey Canyon and the second in Barkley Canyon offshore Vancouver Island. The data requirements for both expeditions were similar, to confirm cable routing plans as part of the U.S. Ocean Observatory Initiative (OOI) and Neptune Canada project. The paper compares the data sets obtained by the MBARI mapping AUV. We also discuss how the various sonar and navigation subsystem technologies individually contributed. The paper then outlines how the AUV successfully accomplished the mission objectives by giving detailed technical and operational information with regarding to each mission's specific needs. Topics include aiding of AUV terrain following navigation using previously obtained ship based multibeam maps, operational and technical constraints of simultaneous data collection from the multibeam, subbottom, side scan, and Doppler velocity log (DVL) acoustic devices as well as surface aided navigation. Details of the resulting maps will be contrasted between the two regions contrasting the AUV high resolution processed data plots image with visually documented bottom and subbottom conditions. The paper concludes with analysis of the Monterey Canyon multibeam mapping data and the altered cable route and further contrasts those results with pending Barkley Canyon decisions for a southern bound cable extension.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".