Estimación del daño en paneles de vidrio
Bibliographic record
Abstract
La prediccion de la carga de falla de placas de vidrio con diferentes tipos de apoyo sometidas a carga uniforme, ha sido uno de los objetivos principales en los codigos de diseno de los Estados Unidos, Canada y la Union Europea. Las metodologias y codigos de diseno se basan en conceptos y criterios aplicables a la prediccion de la carga, asociada a una probabilidad especifica, que lleva a la falla a elementos estructurales de vidrio. El objetivo de este trabajo es estimar curvas de fragilidad de cortinas de vidrio sujetas a diferentes escenarios de presion uniforme representativos de las cargas de viento esperadas en este tipo de estructuras, tomando como base el modelo de prediccion del tiempo de vida. La capacidad estructural de los elementos de vidrio se determino experimentalmente para especimenes de vidrio de silice nuevo, tal y como se obtuvieron de fabrica, es decir sin ningun tratamiento previo. A pesar de que este tipo de material es fragil y su resistencia presenta mucha variabilidad, se utiliza cada dia con mayor frecuencia como elemento estructural. La capacidad y demanda se asocian mediante el modelo de prediccion de vida util. Los resultados nos permiten comprender los mecanismos de falla de los paneles de vidrio para diferentes espesores, asi como estimar su probabilidad de falla usando como herramienta a las curvas de fragilidad.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".