Bibliographic record
Abstract
ACROSS 1. Mount ___ 5. Binges 9. Red face causes 14. God with a hammer 15. "Smart" one 16. Money, in slang 17. Unique web address 19. Down in the dumps 20. Red face cause, with 53 across 22. Canadian competitor to ESPN 23. Totally 24. Red face cause 30. Carve in stone 34. Set ___: establish a goal 35. "Major" animal 37. Gynecology or oncology lead-in 38. Red face cause 42. Knee brace, for ex. 43. "I, Claudius" role 44. All ready 45. Central Pennsylvania town 47. Red face causes 50. ___titer; lab slip order to test for Rheumatic fever 52. Boy 53. See 20 across 60. Parisian pet sound 61. Materialistic 63. Angry 64. Italian father of modern experimental biology 65. "To Sir with Love" singer 66. Demands 67. First floor apartment, often 68. Org. DOWN 1. "Yadda, yadda, yadda" 2. As a result 3. Like a busybody 4. Song and dance, e.g. 5. Mary Stuart's father 6. Grads 7. A Spice Girls 8. Organization established by MLK and others in 1957 9. Vial 10. Pair 11. British term for "not of the upperclass" 12. Lodge group 13. "Come to think of it ..." 18. Place to put feet up 21. TV show "Ted ___" 24. Author Chayefsky 25. Prefix with meter 26. Harder to find 27. Animation 28. Buzz 29. Negotiation author and guru 31. Rwandan people 32. Caused a red face 33. Party givers 36. "D ___" - spelling aide for kindergarteners 39. Mayan ruins locale in Mexico 40. __ pro nobis 41. Equal: Prefix 46. Boxed successfully 48. Composer Debussy 49. Ending for the study of 51. Wood planks in house building 53. Cork's country 54. Fall need for some 55. Rain in Spain collector 56. Big name in faucets 57. Ward of "Once and Again" 58. Ten or tort ending 59. "___ vous plait" 60. 60 sec. 62. Red face cause
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 | 0.007 | 0.017 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".