High-frequency acoustics of marine vegetation – Role of frequencies and imaging angles
Bibliographic record
Abstract
Marine vegetation is extremely varied and an essential component of shallow-water habitats. This paper will present two independent strands of work, using a variety of high-frequency acoustic sources to image macrophytes for different purposes. The first series of studies focuses on gas-filled kelp Nereocystis luetkeana, a seaweed ranging along the west coast of North America from California to Alaska, and is an important component of its diverse coastal ecosystems. As part of an investigation into grey whale habitats, we have measured propagation and attenuation through kelp beds of different densities, using broadband (1-20 kHz) white noise (Wladichuk, 2010, and other works). These field measurements in British Columbia (Canada) were compared with Monte Carlo simulations of sound propagation through kelp beds of increasing densities. The second series of measurements (started in Kruss et al., 2007 and continued through to Kruss et al., 2017) looked at gas-free macrophytes in shallow polar habitats; namely Saccharina latissima (L.) and Laminaria digitata (Huds.) in Svalbard fjords. These were mapped with traditional single-beam echo-sounders (such as the Biosonics DTX, 420 kHz) and with multibeam echosounders (such as the Imagenex 837 Delta-T, 260 kHz), investigating the ranges of angles at which macrophytes could be reliably mapped and what signal processing approaches were the most adapted. These two types of work are combined to show how they can be interpreted in the light of the seminal work done by Jean-Pierre Hermand on seagrass acoustics, for example Hermand et al. (2000) and Hermand (2004), and how his scientific legacy influences future efforts in acoustic mapping of marine vegetation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".