Exploring the Effects of Acoustic Frequency on Terrain Attributes and Classifications Derived from Digital Bathymetric Models at Multiple Scales
Bibliographic record
Abstract
In recent years, new multibeam echosounders that can simultaneously collect data at multiple frequencies have become available. However, the effects of acoustic frequency on bathymetric data have yet to be characterized, as early research on these new systems has instead focused on backscatter data. Here we explore such effects by deriving terrain attributes and classifications from bathymetric data from Head Harbour, Nova Scotia, Canada, that were collected at five different operating frequencies. The geomorphometric analyses were conducted on bathymetric surfaces generated from data collected at each operating frequency using four scales of analysis. Results show that bathymetry, its derived terrain attributes, and terrain classifications produced with them are all dependent on the acoustic frequency used to collect bathymetric data. While the observed effects on the regional bathymetry were relatively minor, local bathymetry, terrain attributes and terrain classifications were highly impacted by the frequency used when collecting data. The impacts were less important when the terrain attributes and classifications were generated using broader scales of analysis. These results raise questions about how bathymetry is measured and defined and how we should interpret the outcomes of marine geomorphometric analyses. This is particularly relevant as such analyses have become a key component of marine habitat mapping and submarine geomorphology mapping.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 0.014 |
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; both teacher heads agree on what is shown here.
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".