Bibliographic record
Abstract
This paper describes the academic program in Ocean Acoustics and Acoustical Oceanography at the University of Victoria in Canada. The program was established when a Research Chair in Ocean Acoustics consisting of two faculty members was funded in 1995 by the Canadian Natural Sciences and Engineering Research Council (NSERC). The Research Chair graduate program offered two courses in Ocean Acoustics, and courses in Time Series Analysis and Inverse Methods. Funding for students was obtained entirely through partnership research programs with Canadian marine industry, the Department of National Defence in Canada and the Office of Naval Research. The program was successful in graduating around 30 M.Sc. and Ph.D. students to date, about half of whom were Canadians. Notably, all the students obtained positions in marine industry, government, or academia after their degrees. The undergraduate program consisted of one course in Acoustical Oceanography at the senior level (3rd year) that was designed to appeal to students in physics, biology, and geology. The course attracted about 30 students each time, primarily from biology. The paper concludes with perspectives on difficulties in operating an academic program with a low critical mass of faculty and in isolation from colleagues in the research field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".