Harmonizing multi-source backscatter data using bulk shift approaches to generate regional seabed maps: Bay of Fundy, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Advances in sonar technology have revolutionized our ability to map the seafloor, however, differences between legacy and modern data pose challenges when analysing multi-source datasets. Acoustic backscatter recorded via multibeam echosounder is commonly used to characterize the seafloor, but a lack of standardized calibration often yields relative rather than absolute backscatter measurements, hindering comparison between surveys. ‘Bulk shift’ methods have been developed for harmonizing legacy backscatter datasets using overlapping survey areas for relative statistical calibration. This becomes increasingly difficult, though, given many datasets collected over extensive time periods. Backscatter data were collected in the Bay of Fundy, Canada, using multiple sonar systems and vessels over an 18-year period. Here, we propose a reproduceable strategy for harmonizing this large volume of disparate backscatter data using the bulk shift method. A final, harmonized map is presented for the entire Bay of Fundy and is validated using in situ observations from seafloor imagery.
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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 | 0.001 | 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 it