A Day in the Life of Dr. Bean and How the NIH Is Wasting $20 Billion per Year
Bibliographic record
Abstract
Interviewer: Good morning, Dr. Bean. Thank you for accepting this opportunity to be interviewed. Your comments will be very useful for the new generation of young and upcoming scientists. Given your very successful career, you likely have much to say and lots of advice to give. My objective this morning is to describe one of your typical days. I am sure it will be fun. Should we start? Dr. Bean: Yes, my pleasure. Please go ahead. Interviewer: My interview will be broken into blocks of 2 hours. So, let us start with the first 2 hours of your day. Dr. Bean: Sure, I get up at 6 AM. I first make coffee and eat my breakfast, which brings me to 6:30 AM; then I do my exercise on a treadmill I have at home, finishing at 7:30 AM. You see, at 62, I must do this, otherwise, who knows what might happen. Then, I walk to work and open my office at exactly 8 AM. Interviewer: Sounds great. I guess at this time you are well-rested, relaxed, and ready to go. Dr. Bean: Absolutely! This is the premium time of my day from 8 to 10 AM. And I have strict instructions to my secretary—never book any appointments in this time block. I want this time for myself, for the most challenging part of my job. Interviewer: And what is that? Dr. Bean: Writing grants. You see, now I am …
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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 | 0.005 | 0.102 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".