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
En diversos ámbitos de la ciencia con gran frecuencia los resultados suelen reflejarse por medio de curvas (datos funcionales). Con este trabajo se pretende dar una solución al problema de la predicción espacial de datos funcionales cuando no se evidencia estacionariedad. El predictor propuesto tiene la misma forma matemática de un predictor kriging clásico, pero teniendo en cuenta curvas en lugar de datos univariados. Luego, a través de un procedimiento similar al del kriging universal de la geoestadística en una dimensión se deducen los sistemas matriciales que permiten determinar los pesos de cada una de las variables funcionales medidas en los sitios visitados. La metodología propuesta se valida mediante el análisis conjunto de datos reales de temperaturas tomadas en estaciones meteorológicas de Canadá. / Abstract. In various fields of science very often the results of certain measurements are usually reflected by curves (functional data).In this paper we give a solution to the problem of spatial prediction of functional data stationarity when there is no evidence. The predictor proposed has the same mathematical expresion of a classic kriging predictor, but considering curves instead of univariate data. Using a procedure similar to the universal kriging in geostatistical onedimensional a matrix system is derived for determining the weights of each of the functional variables measured in the sites visited. The proposed methodology is validated by analyzing a real data set corresponding to temperature curves obtained in several weather stations of Canada.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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