Positioning and telemetry from ROV survey ARTofMELT2023/1_22-7 on 2023-06-04, survey 2
Bibliographic record
Abstract
The horizontal position of the remotely operated vehicle (ROV) during the ARTofMELT2023 expedition in May and June 2023 was measured using an acoustic Long Base Line (LBL) positioning system (LinkQuest Pinpoint) with an operating frequency of 26.77-44.62kHz. It consists of a transceiver onboard the ROV and 3 transponders which were deployed on 5 m long chains through the drifting ice cover at different horizontal distances from the ROV ice access hole. Due to the high latitude and glitches in the Pinpoint software's coordinate conversions, the surveys were virtually moved and 'fake' geographic positions centered around 1°N/1°E were used. The survey track was smoothed using a Kalman filter from initial acoustic fixes and cleaned for most obvious outliers. This calibration result in a floe-fixed, relative coordinate system (distance, relative, X and distance, relative, Y) with the origin (X=0 m, Y=0 m) at the ROV hole. The final position was recorded in the SPOT.ON survey systems software (OceanModulesTM). A quality flag for the position is introduced based on the time to the closest fix with "1" indicating good positon (fix reached <= 3s), "2" medium position (fix reached > 3s & <= 5s), and "3" bad position (fix reached > 5s). Depending on the scientific aim, a position with quality flag "3" can still be useful. The ROV depth was measured by an integrated pressure sensor (Keller A-21Y, Keller AG) included in the main electronics housing of the ROV and calibrated to 0 during pre-survey procedures, when the top side of the ROV was at the same level as the water surface. The accuracy of the sensor is 0.10 m. ROV attitude (roll, pitch, heading) was measured with an onboard inertial measuring unit (Microstrain) with three axis accelerometer, magnetometer, and gyroscope.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".