Bibliographic record
Abstract
Special issues with regional topics and articlesAcoustics is a broad subject matter, as you know, that currently employs hundreds of us across the country in fields as different as teaching, research, consulting and others.To reflect such diversity and to -maybe-help each of us discover a new professional in the neighborhood, the Canadian Acoustics journal is currently inviting submissions for a series of special "regional" journal issues from individuals, groups and companies located within the greater-areas of major cities in Canada.Special issues of the Canadian Acoustics journal have been tentatively programmed for June 2015 (Montreal), June 2016 (Toronto), and June 2017 (Halifax) while other dates will later be added for the other cities. How to be part of it?To contribute to these special "regional" journal issues, authors are invited to submit their manuscript (2 pages maximum), in English or in French, under "Special Issue" section through the online system at http://jcaa.caa-aca.cabefore April 30 th of the publication year.The first author must be located in the greater area of the targeted city.Two versions of the same article can be published in the two official languages.Each manuscript will be reviewed by the Canadian Acoustics Editorial Board that will enforce the journal publication policies (original content, non-commercialism, etc., refer to Journal Policies section online for further details) while welcoming promotion of authors' expertise, companies services, and consultants' success stories and the like.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.751 | 0.665 |
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".