Post-conference report on AWC 2017
Bibliographic record
Abstract
More than 185 people converged to learn, share and socialize at Acoustics Week in Canada 2017.The event was hosted October 11-13, 2017 at the Delta Hotel and Conference Centre in Guelph, Ontario.The conference featured 3 keynote speakers, technical presentations and a large exhibition of acoustical products and services.The conference brought together thinkers and doers in Canadian Acoustics to discuss current developments in the field.Wednesday started with a keynote address by Elliot Berger titled Bang! Damage from Impulse Noise and the Effectiveness of Hearing Protection, which was followed by a day packed with technical presentations.The first feature of Thursday was John Bradley's keynote titled A Rationale for a National Classroom Acoustics Standard.The technical presentations which followed also featured an extensive workshop on Tools and Guidelines for the Calculation of ASTC by Christoph Hoeller and Jeffrey Mahn of the National Research Council.The final morning of technical presentations was started with a keynote by Samir Ziada titled Flow-Excited Acoustic Resonances in Shallow Cavities.All in all, the papers and presentations illustrated the diverse subjects covered within the association.However no subject area was better represented than architectural acoustics, with one third of the papers in this subject area.The presentations sparked many discussions -during breaks, meals and social events.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.543 | 0.343 |
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".