Bibliographic record
Abstract
Across1. Particular, for short 5. ___-relief 8. Earth-observing satellite mission 15. Designer Wang 19. Campus building 20. "Bleah!" 21. Communicator of a type 22. Assayers' stuff 23. Chill 24. "___ any drop to drink": Coleridge 25. Spanish sherry 26. Mrs. Addams, to Gomez 27. Advise on rituximab administration after giving 65 down? 31. Designate anew 32. Shops for 34. Center of a ball? 37. "Comprende?" 38. Bag holder 42. Before Febrero 43. Decorated, as a cake 45. Advice about dosing TNF inhibitors after giving 61 across? 48. Outbuildings 50. Bog 51. Charge 52. Singer Winehouse 53. Advice on whether to adjust prednisone dosage when giving 56 across? 56. See 107 and 53 across 57. Idle 61. See 78 and 88 across 62. Exchange blows 63. Annual insurance option, abbr. 64. Hold off 65. Ower 69. Rock cavity 70. "The Matrix" hero 71. Moon feature 72. Stare at oneself in the mirror, say 73. Nap noises 74. Cloak-and-dagger org. 75. "So ___ me!" 76. Be inclined 77. Statehouse V.I.P. 78. Advice on JAK inhibitor delay before giving 61 across? 84. Juliet, to Romeo 85. Former French coin 86. ___ de deux 87. Pooh's creator 88. Interval to wait to give 61 across following IVIG dosed at 1 mg/kg? 94. "Had enough?" 95. Arguments 96. "Too soon ___ late smart" German folk saying 97. Charlotte co. hub 100. Dict. listing 101. Stain 104. Zulu-named knives 107. Advise on methotrexate interval following administration of 56 across? 114. Bit 115. French kingdom name 116. 1990's Indian P.M. 117. Lad, in other words 118. Bummed out 119. Red fluorescent dyes 120. Canadian hockey great 121. Ancient 122. Adjusts, as a clock 123. One of the founders of the ‘Societe Nationale des Beaux Arts’ 124. Founder Balanchine org. 125. Org. founded by Balanchine Down 1. Certain herring 2. Beep 3. Hebrew month 4. Cloudless 5. Put together 6. Ancient meeting places 7. Azalea, for one 8. Circular 9. BBs, e.g. 10. Israeli ambassador Gilon 11. Gossip 12. ___ a stop 13. "Belling the Cat" author 14. ___ Price 15. Against at the polls 16. World renown Danish soccer player who survived cardiac arrest in 2021 and returned to play in 2022 17. Printemps, in other words 18. Barbecue pit remains 28. "Absolutely!" 29. Ride to another town, maybe 30. Foe 33. Japanese drama 34. 45, e.g. 35. Bounce back, in a way 36. Has follower 39. Append 40. "Annabel Lee" and "The Raven" 41. Bauxite, e.g. 44. Banned poison 45. Born, in bios 46. "The Lion King" lion 47. Australian runner 49. Producer Cowell 50. Coat 54. Basis for advanced imaging tech. 55. Saturate, in dialect 56. To partner 57. Grassland 58. Astern 59. #26 of 26 60. Goof up 63. Zoom meeting need, at times 64. Night activity? 65. See 27 across 66. "... ___ he drove out of sight" 67. First name in Ghandi film 68. "First Blood" director Kotcheff 69. See 45 across 70. Bubkes 71. Blackguard 73. Area 74. Matter 75. "Help!" 77. Destroy the interior of 78. Tough 79. Scale notes 80. Top secret? 81. "___ Coming" (1969 hit) 82. "Men always hate most what they ___ most": Mencken 83. Astute 84. Data center manager, for short 85. Shirt tag, abbr. 88. Kallman syn, for one 89. Letter from an Apostle 90. End of 9th inning? 91. Furniture and decorating chain stores 92. "Mike" author 93. "Folk singer from outer space" singer 97. Turkey town 98. Gather on the surface, chemically 99. ___ Fail (Irish coronation stone) 102. Cowboy, for one 103. Wow, amazing! 105. Plane and naut leaders 106. Site of Alcatraz Is. 108. Cooperstown nickname 109. Decline 110. Dilly 111. Like some orders 112. Appraiser 113. Harassed 114. Stomach muscles, briefly
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".