Kriging Universal para Datos Funcionales
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.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".