Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It's time for the Super Awesome Science Show SASS Class on COVID-19 and heart health.I want to thank everyone who reached out to me. We received quite a few Emails and DMs and we got to so many during this discussion.We again are joined by Ian Paterson. He is a Professor in the Department of Medicine in the Division of Cardiology of the Faculty of Medicine and Dentistry at the University of Alberta. He's a cardiac researcher who has been working to better understand the effects of COVID-19 at the cardiac level. His latest study is called the Multi-organ Imaging With Serial Testing In Covid-19 Infected Patients, better known as MOIST.If you didn't hear your question, make sure to contact me on Twitter, by Email and now, via voice message at Speakpipe.com/SASS. Just follow the link below and send me your thoughts. Twitter: @JATetroEmail: thegermguy@gmail.comGuest: Ian Patersonhttps://www.ualberta.ca/medicine/about/people/details.html?n=Ian-PatersonMOIST Study:https://www.ualberta.ca/research/our-research/covid19-research.html?search=paterson&details=multi-organ-imaging-with-serial-testing-in-covid-19-patientsBeTheCure to enroll in the study: https://bethecure.ca/find-a-study/#studies/5c26010a08393b05921bc3c765803e2b731bf9ec Learn more about your ad choices. Visit megaphone.fm/adchoices
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.585 | 0.001 |
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 it