Professional Burnout of Faculty Members of Higher Educational Institutions
Bibliographic record
Abstract
La identificación rápida y confiable de microorganismos de importancia médica continúa siendo un desafío en el diagnóstico clínico. En este estudio se utilizó espectroscopía Raman, combinada con la técnica SERS (Surface Enhanced Raman Spectroscopy), como herramienta complementaria para la caracterización de bacterias y proteínas. Se analizaron tres especies bacterianas (Klebsiella pneumoniae, Salmonella enterica y Bacillus pumilus), cuya identidad fue confirmada mediante secuenciación del gen ADNr-16S, obteniéndose coberturas del 100 % y porcentajes de identidad entre 99 % y 100 %. En este estudio se optimizaron protocolos de síntesis de nanopartículas de plata y oro sintetizadas ex situ e in situ, evaluando el efecto del agente reductor, el pH y el tiempo de incubación. Las nanopartículas sintetizadas por el método ex situ y a pH 9 generaron espectros con mayor definición y menor interferencia de fluorescencia. Los espectros Raman obtenidos mostraron picos característicos asociadas a proteínas, ácidos nucleicos y componentes de la membrana celular bacteriana, en concordancia con la literatura. Los resultados demuestran que la metodología implementada permite obtener espectros reproducibles y diferenciables entre especies bacterianas, así como identificar picos estructurales en proteínas recombinantes. La espectroscopía Raman con SERS se confirma como una técnica rápida y complementaria para el análisis de material biológico de interés biomédico.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".