Multibeam sonar water column data processing tools to support coastal ecosystem science
Bibliographic record
Abstract
Multibeam sonar water column data are routinely collected by hydrographic survey vessels to observe and validate minimum depth measurements over anthropogenic submerged objects, which may pose a hazard to navigation. A large volume of additional data is collected during this process but mostly goes unused. This project investigates the development of processing tools and algorithms to automate the extraction of oceanographic and ecological features from the water column data files to enhance the usefulness of these datasets. Sample data from a Gulf of Mexico Research Initiative (GoMRI) project is explored where multibeam data is collected simultaneously with a towed profiling high resolution in situ ichthyoplankton imaging system (with CTD, dissolved oxygen, PAR, and chlorophyll-a fluorescence sensors). The objective is to identify and map spatial and temporal variations in biomass throughout the water column by correlating the acoustic data with imagery and sensor output from the towed profiling system. Processing the dataset to isolate and identify objects and signal patterns of interest, which might normally be considered noise, is investigated. The developed tools will aid in biological and physical feature extraction to further enhance the application of multibeam acoustic water column data.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.001 |
| 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".