Reproducibility of single-beam acoustic seabed classification
Bibliographic record
Abstract
Single-beam acoustic seabed classification continues to be a popular method for mapping seabeds and their sediments. Modern methods can generate maps of acoustic classes that are useful and reasonably accurate. Research toward improved methods continues. A continuing impediment to this research is ranking the accuracy of maps produced by new methods. Non-acoustic data, or ground truth, is usually sparse compared to the detail of the acoustic survey, which can mean that ranking maps for accuracy can be inconclusive. Here we present a new tool for ranking classification maps, namely the reproducibility of acoustic classes from repeated surveys of the same area on different days. Methods that have high reproducibility achieve that by capturing echo characteristics that are strongly influenced by seabed type while suppressing details that are driven by sea state or the water column. Six surveys, done with a 50 kHz sounder over a pair of transects near Miami, FL, USA, between 1 May and 13 August 2007, were used to evaluate two questions. First, how reproducible were classifications of this dataset using QTC IMPACT¿ (Quester Tangent Corporation)? Second, can classification be improved with adjustments to the standard IMPACT processing? Reproducibility was quantified with the overall accuracy and Kappa statistics, which are both derived from the confusion matrix whose rows and columns are numbers of sites with particular class assignments under distinct circumstances.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".