Processed bathymetry data (ESRI ASCII grids) from the continental slope of the Canadian Beaufort Sea acquired near-bottom with the MBARI mapping AUV in 2022 and with earlier with surface ships.
Bibliographic record
Abstract
This gridded bathymetry data set was generated from severeal sources. High-resolution near-bottom swath bathymetry data was acquired in 2022 with a Reson SeaBat 7125 Multibeam sonar system on the MBARI Mapping AUV missions 20220830m1 and 20220906m2 that were conducted during IBRV Araon cruise ARA13C in the Beaufort Sea. Surface ship bathymetry was collected with a Kongsberg EM302 multibeam sonar system during the 2010 CCGS Amundsen ArcticNet UNB cruise, and with a Kongsberg EM2045 multibeam sonar system on CCGS Sir Wilfrid Laurier IOS cruise 2019-090.The files are in ESRI ASCII grid format, are unprojected in geographic coordinates, and were processed using the open-source multibeam sonar processing software MB-System. The grids have 2-meter horizontal resolution. Files with "TopoDiff" in the file name are difference grids in which bathymetry values from earlier surface ship surveys are substracted from the values collected during the 2022 near-bottom AUV surveys. The MB-SYSTEM and GMT commands that were used to generate these grids are given in the associated Figure.cmd file that can be downloaded with these grids. The AUV survey missions data was acquired as part of a collaborative research project between MBARI, the Geological Survey of Canada, and the Korea Polar Research Institute. Funding for the AUV mapping was provided from the David and Lucile Packard Foundation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.017 |
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".