Science AMA Series: I’m Dr. Adrian Owen, a neuroscientist whose research focuses on brain imaging, cognitive function and consciousness. We’re finding new ways to decode the complex workings of the brain. AMA.
Bibliographic record
Abstract
I’m Dr. Adrian Owen, a professor of neuroscience, here to answer your questions about our breakthroughs in brain science. I’ve been fascinated with the human brain for more than 25 years: how it works, why it works, what happens when it doesn’t work so well. At the Owen Lab at Western University in Canada, my team studies human cognition using brain imaging, sleep labs, EEGs and functional MRIs. We’ve learned that one in five people in a vegetative state are actually conscious and aware (I recently wrote a book on it – www.intothegrayzone.com, if you’re interested). We’ve also examined whether brain-training games actually make you smarter (pro tip: they don’t). Now my team is working on a cool new project to understand what happens to specific parts of people’s brains when they get too little sleep. We’re testing tens of thousands of people around the world to learn why we need sleep, how much we need, and the long- and short-term effects sleep loss has on our brains. A lot of scientists and influencers, such as Arianna Huffington and her company Thrive Global, have already raised awareness about the dangers of sleep loss and the need for research like this. Since we can’t bring everyone to our labs, we’re bringing the lab to people’s homes through online tests we’ve designed at www.worldslargestsleepstudy.com or www.cambridgebrainsciences.com. We hope to be able to share our findings in science journals in about six months. So … if you want to know about sleep-testing, brain-game training or how we communicate with people in the gray zone between life and death … AMA! I will be here at 1:00pm EDT (10:00am PDT / 5:00pm UTC), with researchers from my lab, Western University and the folks who host the www.worldslargestsleepstudy.com platform—ask me anything! Update: We’re here now! Ask us anything! Proof that I am real: http://imgur.com/a/NvPMK Update 2: I appreciate all the questions! I tried my best to answer as many as I could. This was really fun. See you next time. Now, time for some pineapple pizza! http://imgur.com/a/Yy88r
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.152 | 0.089 |
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".