Preparatory Tests with an Explorer Class Autonomous Underwater Vehicle for Missions under Sea Ice
Bibliographic record
Abstract
The autonomous underwater vehicle (AUV) operations described here are preparatory missions to enable operation of an AUV under sea ice in polar regions. The proposed polar projects include operations under land fast ice in the Arctic through an ice moon pool and a planned project to assess sea ice mass balance and habitat assessment in the Southern Ocean in East Antarctica. This paper focuses on the preparatory missions, done in open water, and the planned Southern Ocean project. The plan is to use an autonomous underwater vehicle (AUV) for the under ice component of measurements. The ultimate goals are to quantify the size and shape of ridge keel structures and their contribution to the sea ice mass balance over a study region; to understand the processes that link sea ice with the distribution of ice algae and krill; to provide the necessary field measurements, over sufficiently large areas, for the calibration/validation of satellite and aircraft-based measurements of the sea ice and snow cover thickness; and to provide baseline measurements of sea ice thickness for future climate monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".