Wafer-scale uniformity improvement of Dolan-bridge Josephson junction by shadow evaporation bias correction. [PDF]
Moskaleva DA +9 more
europepmc +1 more source
ABCnet: Adversarial bias correction network for infant brain MR images. [PDF]
Chen L +9 more
europepmc +1 more source
What Do Large Language Models Know About Materials?
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source
Integrating double techniques of statistical downscaling and bias correction to reduce bias in projections trends of future climate datasets. [PDF]
Alasqah A +5 more
europepmc +1 more source
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
No conclusive link between indoor fuel use and atopic dermatitis: a systematic review and meta-analysis with rigorous publication bias correction. [PDF]
Lang J +7 more
europepmc +1 more source
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
Power and sample size considerations for test-negative design with bias correction: a case study on the world first malaria vaccine. [PDF]
Ko YK, Alfvén T, Yoneoka D.
europepmc +1 more source
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models. [PDF]
Austin GI +7 more
europepmc +1 more source

