Bibliographic record
Abstract
Los datos coleccionados del mundo fisico, biologico o producto de la actividad humana usualmente estan altamente correlacionados entre ellos, estableciendose el cuestionamiento de si menos variables pueden contener casi la misma informacion. Una solucion cruda es mirar simplemente a la matriz de correlacion de Pearson y omitir uno de un par de variables altamente correlacionadas. En contraste con esto, nosotros desarrollamos un metodo sistematico de condicionar una o mas variables, y observar la resultante matriz de covarianzas. Si las variables tienen una pequena varianza despues de condicionar, entonces las variables condicionantes contienen la mayor parte de la informacion de todas variables originales. Paralelamente a los usuales tests aplicados en juzgar cuantos componentes principales son suficientes para representar toda la data, usamos la cantidad de varianza explicada por la(s) variable(s) condicionante(s), como una medida de la informacion contenida. El trabajo explica la computacion e incluye ejemplos usando conjuntos de datos publicados. El enfoque esta basado en la alta ganancia respecto al uso de componentes principales, y posee la obvia ventaja respecto a ellos de omitir simplemente algunas de las variables originales a partir de otras consideraciones. El metodo ha sido codificado en Visual-Basic anadido a una hoja de calculo Excel
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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 | 0.014 | 0.089 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".