Bibliographic record
Abstract
The Toronto Section meeting held on 12 May 2020 presented many challenges for organizers Craig Jasman and Jordan Sweigman, with provincial lockdown orders in place and loss of the Ryerson University venue. It was decided to take our annual “Gadget Night” meeting virtual via Go-To-Meeting. Many thanks to home office for the use of this platform. A few “dress rehearsals” were held prior to the meeting to work out any issues. After Tony Meerakker kicked off the live streaming, Anthony P. Kuzub from C BC began the meeting with his home-brewed measurement software. This took up his garage and involved building a mezzanine level to support all of his gear. Luke Sackrider from Corus remotely showcased Nagios broadcast system monitoring and how it was deployed. François Legrand from CBC presented his third attempt at building a SMPTE ST 2110 compatible Christmas tree. Brian Young from Vistek capped off the evening with a demonstration of audiovisual products that could be used in these times. In addition to the PowerPoint presentations, presenters also used multiple cameras and additional graphics controlled by Open Broadcaster Software (OBS) to stream content. The presentations were followed by a Q&A Session. With more than 82 attendees, the level of interest was high as the meeting ran overtime. The feedback received, with respect to the virtual meeting, was very positive. Recordings of the event are available athttps://www.youtube.com/watch?v=OHM5eiXCkcg&t=25s.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.787 | 0.611 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".