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
On this episode of the podcast we're off to Montreal Toronto! If you've listened to a lot of episodes of the podcast, you would know that we very rarely travel our home country of Canada, so when we had the opportunity to go to Montreal Toronto, we were very excited about it! We spent our first 3 days of the trip in Montreal, and we take you with us as we explore downtown Montreal, the Old Town, and up Mont Royal. This episode also includes a conversation from our local friend, Karl-Philip Valle, who shares what he loves most about living in Montreal. While in Montreal, we had the opportunity to eat poutine, and explore so many awesome areas of the city. After 3 days in Montreal, we headed on to Toronto where we spent 5 days exploring the city. We explored downtown, Kensington Market, the CN Tower and the Harbourfront, and the Distillery District, among other areas. We had so much fun checking out awesome cafes and eating great food and immersing ourselves in Canada's largest and most multi-cultural city. We had the opportunity to meet up with friends and family. Finally, Amanda shares about her solo adventure to Niagara Falls and how the met her expectations and was a great day of exploring! Enjoy! You can also check out our new travel community on Facebook where you can ask questions, get travel recommendations, and find community members in your area! This episode of the podcast is brought to you by YOU, the listeners of the show! Thank you to all of our supporters on Patreon - you are the reason we can continue producing TWW! If you want to support the show AND get great rewards, join us at www.patreon.com/theworldwanderers. Music Credits: www.bensound.com
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.586 | 0.014 |
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