Bibliographic record
Abstract
ACROSS 1. Namesakes for a Bond actor 6. Honey Boo Boo's real name 11. African antelope 14. Not prepared 15. Spoke incoherently 16. ___ Cruces, N.M. 17. Autoimmune disorder associated with antibodies embedded throughout this puzzle 20. Baron von ___ (Simpsons' character) [containing 17 across antibodies] 21. Madame ___ 22. Absolute worst 23. Animation 24. Fractional part of of decimal 28. Beads used for money of old 32. Beelike 33. Rent 5. Community shared scooter 36. New York county 37. Ship journey 38. Tulip, for one 39. Anger, e.g. 40. Foundation 41. Poet Elinor 42. Trap 44. Measurement of DNA, for one 45. His "4" was retired 46. Catch ___(ride) 48. On-line payment 51. Powerful lines of wind storms 56. Symptoms of 17 across 59. Conk out 60. Beat up 61. Spec. offering to produce commercials 62. President of Syria 63. Small harps 64. Holes in the head? DOWN 1. Elon in the news 2. "Sounds of Silence" instrumental 3. Saucers, commonly, for short 4. Archeologists' activities 5. Secure 6. Precedes "are too" on the playground 7. Catalogs 8. Lab test 9. "Children of the Albatross" author 10. Buy more minutes, as for phone service 11. Insect's tongue-like structure 12. First killer whale in captivity 13. Played 18. Name meaning "oath of God" 19. Beef cut 23. Arrangement holder 24. Parsonage 25. What ___ in the neck!" 26. Certain car models 27. Alzheimer's disordered protein 28. Tail motions 29. Throat dangler 30. Actor McCarthy 31. Family name of popular supermarket chain in Canada 33. Remove hair, in a way 34. Test in Toulouse 37. Actor's goal 38. What to do when a restaurant has no liquor lic.? 40. Name for the "midnight zone" of the deep ocean 41. Moses, for one 43. Snout 44. Hide well 46. Befuddle 47. Names of movie chimney sweep and Sesame Street boy 48. Icelandic epic 49. 2015 Harlem Globtrotters player 50. Voted in the affirmative 52. 2021 Oscar winning film 53. Hungarian calvalryman 54. Worker benefit with flex schedule 55. The Beatles' "___ Leaving Home" 57. Writing assignment 58. Big ___, Calif.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".