Acoustical oceanography meets bioacoustics: Teaching ocean acoustics to biologists and physicists at the University of Victoria
Bibliographic record
Abstract
The graduate-level Ocean Acoustics course at the University of Victoria is targeted at students in different departments with diverse backgrounds, ranging from physics to biology. We lead the students through a series of lectures, discussions, and assignments to provide fundamental knowledge of acoustical oceanography and marine bioacoustics, with subjects ranging from acoustic propagation modelling and geophysical inverse theory to soundscape ecology and the impacts of underwater noise on marine life. Given the varied background of the students, we must balance the material so that the course covers physical concepts without being too “math heavy” for the biologists, and so the physicists can understand and appreciate the biology. We frame assignments around skills that the students can use in their graduate research, and also give the students opportunities to review scientific papers and give presentations to the class. We have taught this course for two years now, and students have come out with a deeper understanding of the physics of sound and how it is affects marine life, providing a good foundation for their graduate research in various aspects of underwater acoustics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".