Tootfinder

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos, Christiane Kuhl, Daniel Truhn, Sven Nebelung
https://arxiv.org/abs/2510.05664 …

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To …

Die Mšgoi apὸ anatolôn, die Magier aus dem Osten sind schon ein gutes Stück gekommen, auf ihrem Weg.
#Johannes der Täufer ruft uns aber zur Umkehr auf, zur Buße, denn die Ankunft des Herrn ist nah.
Ein Impuls dazu ebenfalls unter

Bayesian Gaussian Methods for Robust Background Modeling in CALorimetric Electron Telescope (CALET) Gravitational-Wave Searches
Bisweswar Sen
https://arxiv.org/abs/2509.25893 ht…

Bayesian Gaussian Methods for Robust Background Modeling in CALorimetric Electron Telescope (CALET) Gravitational-Wave Searches
The search for gamma-ray counterparts to gravitational-wave events with the CALET Gamma-ray Burst Monitor (CGBM) requires accurate and robust background modeling. Previous CALET observing runs (O3 and O4) relied on averaged pre/post-event baselines or low-order polynomial fits, approaches that neglect correlated noise, temporal non-stationarity, and the propagation of background uncertainty into derived flux upper limits. These simplifications can lead to reduced sensitivity to faint or atypica…