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
Ej. 1 Identidad en Bool Desarrollar un estrategia ganadora para (x Bool) [Id(Bool, x, yes) Id(Bool, x, no)] Ej. 2 Identidad en Desarrollar un estrategia ganadora para (x ) [Id(, x, 0) (y ) Id(, x, s(y)] Ej. 3 Identidad y Funcin Desarrollar un estrategia ganadora para Id(A, a, a') Id(B, f(a), f(a')), dado los conjuntos A y B y la funcin f(x) B (x: A).Ej. 4 Identidad y Producto Cartesiano Desarrollar un estrategia ganadora para Id(AB, <a, b>, <a', b'>) dado los conjuntos A y B, y las premisas refl(A, a, a') Id(A, a, a'), refl(B, b, b') Id(B, b, b') y la afirmacin hipottica refl((A, B)), <x, y>) Id(AB, <x, y>, <x, y>) (x A, y B).I.4 Anticipar en Lugar de Evaluar I.4.1 Anticipaciones, Instrucciones y Expectativas I.4.2Expectativas y Afirmaciones de Identidad, Bool y Nmeros Naturales I.4.3Ejemplos Ejemplo 1 Silogismo en Barbara Ejemplo 2 Todo elemento de Bool es o bien idntico a s o bien idntico a no Ejemplo 3 Todo nmero natural n es o bien idntico a 0 o bien idntico al sucesor de un nmero natural Ejemplo 4 La identidad entre los argumentos a y a' de la funcin f implica la identidad entre f(a) y f(a').Ejemplo 5 Identidad de pares respecto al producto Cartesiano Conclusiones de la Primera Parte: El Autntico Dialgico
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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