Bibliographic record
Abstract
I did a trip out to Alberta and it was super easy with my Kona Electric. I did find out some interesting things so tune in to find out more. True North EV will be hosting a meeting on Google Meet chatting about all things electric.The meeting will start at 7 pm CST on Saturday August 22.To join the meeting on Google Meet, click this link: https://meet.google.com/jkr-kvzo-msn Or open Meet and enter this code: jkr-kvzo-msnHere is a link to Steve's YouTube channel:https://www.youtube.com/channel/UCTIKxPx0ZwFSR7w1g3Gi68g Here is the link to kilowatt podcast:https://pca.st/podcast/09216500-6e77-0134-787d-4ffec63d9550Here is the link to the two idiots podcast:https://anchor.fm/twoidiotspodcastHere is the link to EV Resources youtube:https://www.youtube.com/channel/UC6_8Fx3_XrBo3nvRRDXheJALeave a voice message on anchor:https://anchor.fm/truenorthev/message--- Send in a voice message: https://anchor.fm/truenorthev/message
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 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.656 | 0.008 |
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; both teacher heads 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".