Bibliographic record
Abstract
The last time I discussed underwater transduction technologies with Joe Blue was at the 125th Meeting of the Acoustical Society of America in May 1993. As we enjoyed lunch on the patio of a downtown Ottawa bistro under a sunny spring sky, the discussion touched on broadband transducers for naval applications. His ideas were insightful, motivating me to redesign and improve my original 1989 broadband barrel-stave flextensional transducer the following year. Over the last decade 30 experimental units were built at DRDC Atlantic, most of them used in marine mammal and coastal surveillance applications [D.F. Jones, J. Acoust. Soc. Am. 117, 2447 (2005); 118, 2038–2039 (2005)]. This paper will present electroacoustic measurements made at both the DRDC Atlantic Acoustic Calibration Barge on Bedford Basin near Halifax, Nova Scotia and the NAVSEA Seneca Lake Sonar Test Facility near Dresden in upstate New York. The performance parameters of interest include resonance frequencies, mechanical quality factors, transmitting voltage response versus water depth, and directivity patterns. [Work supported in part by the Office of Naval Research.]
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".