Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsRichard K. GuyRichard Guy (rkg@cpsc.ucalgary.ca) is the luckiest person in the world. He's been paid to enjoy mathematics from kindergarten to post-graduate level in Europe, Asia and America. He's been privileged to work with Erdos, Conway, and Berlekamp. More than fifty other co-authors include Lehmer, Selfridge, Knuth, Matijasevich, and Martin Gardner. At 85 he's working on becoming the oldest inhabitant of the University of Calgary, where he continues to put in a full day's play. He and Louise still hike and ski in the mountains.Marc M. PaulhusMarc Paulhus (paulhusm@math.ucalgary.ca) is a mathematical consultant in Calgary, Alberta. He specializes in algorithm design and optimization and has worked on a variety of projects from scheduling to finance. He has also acted as the University-Industry facilitator for the Pacific Institute for the Mathematical Sciences (PIMS) at the University of Calgary for the past five years. Marc enjoys the mountains and is passionate about traveling to exotic locations.
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.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it