Bibliographic record
Abstract
President's Message Message du Président Rejuvenation in sight!Rajeunissement en vue !n my previous post at this time last year, I told you about the many difficulties with our various computer servers (journal, conference, website, etc.).The situation is now back under control and our journal is professionally hosted by Public Knowledge Project (PKP), the very creators of the Open Journal System platform we use.I am very grateful to all CAA members for their patience, especially when we lost our Google Scholar! indexing, and in particular to Cécile Le Cocq, our system administrator and journal manager, for her help in fixing the many SQL scripts and routines.For the first time since the COVID-19 pandemic, our annual Acoustics Week in Canada 2022 conference was held entirely in person in the memorable city of Saint John's (NL).On page 32, you will find a summary of the conference written by the AWC2022 organizing team, led by Professors Len Zedel and Benjamin Zendel of Memorial University.I would like to take this opportunity to warmly thank them and their colleagues for their excellent work and for a conference that I believe was particularly appreciated by all participants.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.152 | 0.095 |
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".