ROV-mounted high-resolution laser scan data acquired at vertical deep-sea habitats, Galapagos Islands, 2023 (FKt230918)
Bibliographic record
Abstract
ROV SuBastian-mounted laser scanner data for high-resolution mapping of steep, vertical and overhanging cold water coral reefs in the Galápagos, Ecuador, was collected during Falkor (too) cruise FKt230918 in 2023. The Voyis Insight Micro laser scanner system was mounted on the vehicle porch using an adjustable bracket to set the pitch during three ROV SuBastian dives (S0578, S0584, S0597). Mapping was done by vertically moving the vehicle from the base of a cliff to the top, then shifting slightly over for another line. Navigation data from the SprintNav system were combined in the Voyis ViewLS software to provide georeferenced 3D point clouds data. All mounting offsets are outlined in the associated cruise report (https://doi.org/10.48336/7sjp-t131), but this represents the data prior to the inclusion of patch test offsets. The data files are in ASCII XYZ comma-separated format with the following six columns: Column 1: Scalar Field 1, ZDA timestamp. Column 2: X Position Value (relative). Column 3: Y Position Value (relative). Column 4: Z Position Value (depth). Column 5: Scalar Field 2, Intensity. Column 6: Scalar Field 3, Quality. Funding for the project was provided through Schmidt Ocean Institute; processing of this data was supported through a Canada Research Chair in Ocean Mapping award.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".