Public articles linked to the same research event.
bioRxiv This work builds BoneGraph, a bone-science-specific system delivered as a five-tab web application over a shared substrate of 7,449 documents, 248,629 SPECTER2 passage vectors, and a bone knowledge graph of 1,597 concepts and 1,699 causal relations, reporting MRR 0.928 on a 30-question seven-domain retrieval benchmark, 92.6% accuracy for the bone-region classifier on held-out MURA, and an increase in answer accuracy from 42% to 78% when the correct passage is supplied, with all inference performed locally on a single NVIDIA Jetson AGX Orin and no third-party API calls.
This work builds BoneGraph, a bone-science-specific system delivered as a five-tab web application over a shared substrate of 7,449 documents, 248,629 SPECTER2 passage vectors, and a bone knowledge graph of 1,597 concepts and 1,699 causal relations, reporting MRR 0.928 on a 30-question seven-domain retrieval benchmark, 92.6% accuracy for the bone-region classifier on held-out MURA, and an increase in answer accuracy from 42% to 78% when the correct passage is supplied, with all inference performed locally on a single NVIDIA Jetson AGX Orin and no third-party API calls.
This work builds BoneGraph, a bone-science-specific system delivered as a five-tab web application over a shared substrate of 7,449 documents, 248,629 SPECTER2 passage vectors, and a bone knowledge graph of 1,597 concepts and 1,699 causal relations, reporting MRR 0.928 on a 30-question seven-domain retrieval benchmark, 92.6% accuracy for the bone-region classifier on held-out MURA, and an increase in answer accuracy from 42% to 78% when the correct passage is supplied, with all inference performed locally on a single NVIDIA Jetson AGX Orin and no third-party API calls.
This work builds BoneGraph, a bone-science-specific system delivered as a five-tab web application over a shared substrate of 7,449 documents, 248,629 SPECTER2 passage vectors, and a bone knowledge graph of 1,597 concepts and 1,699 causal relations, reporting MRR 0.928 on a 30-question seven-domain retrieval benchmark, 92.6% accuracy for the bone-region classifier on held-out MURA, and an increase in answer accuracy from 42% to 78% when the correct passage is supplied, with all inference performed locally on a single NVIDIA Jetson AGX Orin and no third-party API calls.