Bibliographic record
Abstract
I was awarded the Hunt Fellowship in 2000-01 as a postdoctoral researcher at the University of Rochester working under the supervision of Robert Waag. My research during the Fellowship experimentally analyzed methods of compensating for focus aberration in medical ultrasound imaging using multirow and matrix transducer arrays. I was subsequently recruited to a faculty position at Western University in London, Ontario, at the time Western established its Biomedical Engineering program, which I currently direct while continuing to conduct research in biomedical ultrasound. One of the enduring benefits I acquired during my Fellowship was to develop my skill at and appreciation of precise scientific communication, which are characteristics Professor Waag instilled in all of his trainees. Another valuable benefit the Fellowship granted me was an opportunity to begin forming my own scientific network through the ASA and other organizations. Holding the Hunt Fellowship provided me both encouragement and financial resources to become more active in the biomedical ultrasonics research community, which was a key step toward launching my academic career.
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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".