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 AcknowledgementsI must acknowledge several people who most kindly shared their expertise with me. Listed alphabetically: Warrant Officer (Mine Warfare) Robert 'Dixie' Dean RN, Mine Warfare Publications, HMS Collingwood; George Delorme, President, Haggie North America, Montreal; Simon Dixon, Fishing Sector Manager, Bridon International Ltd (formerly British Wire Ropes Ltd.); Dr Will Edwards, James Cook University, Australia; Dr Stephen J. Eichhorn, Professor of Materials Science, University of Exeter; Dennis Fetter, Technical Director, WireCo World Group, Kansas City; Linda Fitzpatrick, Scottish Fisheries Museum, Anstruther; Linda Gjerstad, RAPP Hydema A/S, Bodø, Norway; Professor Ingo Heidbrink, Old Dominium University, Norfolk va; Lt Cdr Rob Hoole RN, Mine Warfare and Clearance Diving Officers' Association; Cdr Paul Jones RN, Commanding Officer, HMS Excellent; Dr Trevor Kenchington, Gadus Associates, Halifax NS; Prof. Angela Moles, University of New South Wales; Mike Montgomerie, Gear technologist, Seafish Ltd, Grimsby; Des Pawson, Museum of Knots, Ipswich; August Rich, Managing Director, Drahtseilwerk Dietz GmbH & Co. KG, Neustadt bei Coburg, Germany; Capt. Frank Scott; and A. Steven Toby, naval architect, Alion Science & Technology, Virginia.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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