Who killed Sara and Taliyah? - Part 2
Bibliographic record
Abstract
In July 2016, a Calgary mother, Sara Baillie, was found dead in her home, and her five-year-old daughter, Taliyah Marsman, was missing.Three days later, an Amber Alert came to a heartbreaking end when the little girl was found dead.On this episode of Global News podcast Crime Beat, crime reporter Nancy Hixt takes a look at who killed Sara and Taliyah.Hours after Taliyah's body was recovered, police announced a man was charged in the case.Edward Downey was accused of two counts of first-degree murder.Sara and Taliyah's family was left with so many questions.The man accused of this incomprehensible crime wasn't even on the family's radar.Why would Downey kill Sara, let alone her child?For more details on the case check this out https://wp.me/p2Y4rw-nJ4MIf you enjoy Crime Beat, please take a minute to rate it on Apple Podcasts or Google Podcasts, tell us what you think and share the show with your friends.Contact:Twitter: @nancyhixtFacebook: https://www.facebook.com/NancyHixtCrimeBeat/Email: nancy.hixt@globalnews.caLearn more about your ad choices. Visit megaphone.fm/adchoices
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.252 | 0.079 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".