Bibliographic record
Abstract
mm diameter polariscopes 69-70 a acoustic emission (AE) testing grain-boundary embrittlement 241-245 principles 114-116 AE see acoustic emission airborne synthetic aperture radar 372-376 allotropy 33 alloys material properties 48-55 see also individual alloys… α-alloys, titanium-based 54 α-β-alloys, titanium-based 54 β-alloys, titanium-based 54 α-Fe see ferrite phases amorphous solids characteristics 43-44 continuous random-network model 33 cracking modalities 5 glass transitions 40-41 random close-packing model 34 random-coil model 33-34 structure 33-34 thermal tempering 42-43 see also glass anelastic creep see delayed elastic creep; time-dependent elastic creep annealing, glass 136-137 antiphase boundary defects 39 apparent activation energy, cracking 257-258 Arctic and Arctic Research Institute (ARRI) borehole indenter 121-123 ARRI see Arctic and Arctic Research Institute Ashby-Verrall (A-V) creep 15-16, 133-134 asphalt concrete, thermal cycling 113-114, 115-116 ASTM C848-88 test 97 ASTM E-139 105 austenite phases (γ-Fe) 49-50 Australia, First Nations 2 A-V creep see Ashby-Verrall creep average rupture rate 223 average viscous strain rates 21 c C60 see buckminsterfullerene Canada Dow's Lake studies 347-356 postglacial uplift 346 cantilever beam bending tests 102-103 carbide precipitates 39, 51 carbon allotropes 33 401
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.821 | 0.709 |
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 source (direct Gemma or distilled Codex), 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".