Uso del método de descomposición empírica de modos para eliminar fluorescencia en espectros Raman de tejido biológico
Bibliographic record
Abstract
La espectroscopia Raman ha sido utilizada para diversas aplicaciones biomedicas con exito. Sin embargo, un problema con los espectros Raman de tejido biologico es la presencia de ruido asociado a diversos factores entre ellos el ruido debido a la fluorescencia, generado por la excitacion biomolecular. Este ruido algunas veces es mayor a la propia senal Raman por lo que la reduccion o eliminacion del mismo es fundamental para un correcto analisis de los espectros. En este trabajo se propone el uso del metodo de descomposicion empirica de modos (EMD, por sus siglas en ingles) para eliminar la fluorescencia y el ruido en espectros Raman. EMD es un metodo de separacion de senales adaptativo y libre de parametros usado para senales no estacionarias. EMD fue probado en tejido biologico mostrando resultados similares a los metodos utilizados actualmente como el algoritmo de Vancouver (VRA, por sus siglas en ingles). Ademas, se calculo el coeficiente de correlacion entre EMD y VRA siendo de 0.92.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".