Towards mapping Nereocystis luetkeana kelp beds: using the holonomic iROV SeaBiscuit and sonar fusion
Bibliographic record
Abstract
Mapping the density and distribution of kelp beds and assessing change over yearly cycles are important objectives for coastal oceanography. The intelligent, position-aware, holonomic ROV (iROV) SeaBiscuit has been designed specifically for this nearshore 3D mapping application. With the aim of providing high-usability maps on a budget, SeaBiscuit fuses the data from two complementary sonars to explore and map the nearshore. Holonomic motion in the horizontal plane and a streamlined profile designed to aid station-keeping provide the high degree of manoeuvrability required to operate in this complex environment. The orthogonal arrangement of a forward-facing multibeam sonar and a 360 degree scanning sonar provide increased coverage and allow 3D maps to be generated in-transit using the holonomic capabilities of the vehicle. Successful field trials saw the mapping of a piling dock before the surveys moved to the kelp beds of British Columbia, Canada. SeaBiscuit was initially calibrated and tested on an 'artificial' kelp bed of kelp stipes transplanted to a sheltered but open-water real world environment. The first successful open ocean kelp bed maps were gathered in 2011. It was possible to identify clusters of stipes and to convert this into a measure of biomass across the kelp bed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".