349 - Canada Leads World in Harvesting Organs of MAID Donors
Bibliographic record
Abstract
Dr. Kevin Stillwagon is our guest today. Dr. Stillwagon is a doctor and a former commercial airline pilot and will give us his opinion on some of the strange decisions that the FAA is making concerning pilots.We're Number 1! But not in a good way as we lead the world in organ donations from deceased MAID patients. Clearly, morals are on the decline and we will look at a number of stories highlighting that.Dr. Stillwagon's Course: https://odem.cloud/program-details/1798Show Resources: https://bit.ly/3XIiHJF ☆ We no longer can trust our mainstream media, which is why independent journalists such as myself are the new way to receive accurate information about our world. Thank you for supporting us - your generosity and kindness to help us keep information like this coming! ☆~ L I N K S ~ ➞ DONATE AT: https://www.lauralynn.tv/ or lauralynnlive@protonmail.com➞ SHOP: https://teespring.com/stores/laura-lynns-store-2➞ TWITTER: @LauraLynnTT➞ FACEBOOK: Laura-Lynn Tyler Thompson➞ RUMBLE: https://rumble.com/c/LauraLynnTylerThompson➞ BITCHUTE: https://www.bitchute.com/channel/BodlXs2IF22h/➞ YOUTUBE: https://www.youtube.com/LauraLynnTyler➞ TWITCH: https://www.twitch.tv/lauralynnthompson➞ DLIVE: https://dlive.tv/Laura-Lynn➞ ODYSEE: https://odysee.com/@LauraLynnTT:9➞ GETTR: https://www.gettr.com/user/lauralynn➞ LIBRTI: https://librti.com/laura-lynn-tyler-thompson
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 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".