{"id":"W2108317086","doi":"10.2147/ijn.s4375","title":"Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping","year":2009,"lang":"en","type":"article","venue":"International Journal of Nanomedicine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Government of Canada; Genome Alberta; Government of Alberta; Genome Canada","keywords":"Computer science; Ontology; Inference; Semantic reasoner; Data integration; Context (archaeology); Semantic mapping; Semantic integration; Visualization; Virtual reality; Data science; Information retrieval; Artificial intelligence; Data mining; Semantic computing; Semantic Web; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002756563,0.000574393,0.0007636131,0.003189609,0.0007249094,0.004511277,0.001389817,0.0007224036,0.001972494],"category_scores_gemma":[0.007638482,0.0005756107,0.002471736,0.002338493,0.001780957,0.003480564,0.004165449,0.001116261,0.0003337331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000940848,"about_ca_system_score_gemma":0.001523108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00636396,"about_ca_topic_score_gemma":0.006460991,"domain_scores_codex":[0.9981596,0.0006872976,0.0001923973,0.0003048382,0.0005714784,0.0000843652],"domain_scores_gemma":[0.9978144,0.001115976,0.0002240024,0.0004653147,0.0002745192,0.0001058148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002325517,0.000177658,0.004886286,0.0006251676,0.0003625821,0.001096135,0.00362615,0.4454126,0.02052757,0.301959,0.003308456,0.2177859],"study_design_scores_gemma":[0.00004430114,0.0001128537,0.001914058,0.0001530406,0.0001296838,0.0005744473,0.001040984,0.7860865,0.01234303,0.1685421,0.02892821,0.0001308244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006500046,0.00007427509,0.9912952,0.0002310279,0.00001971052,0.0000547958,0.000238112,0.0005184643,0.001068255],"genre_scores_gemma":[0.1698434,0.0002929376,0.8283746,0.00007973953,0.00001511318,0.0001830416,0.0006100089,0.000113094,0.0004881716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00636396,"threshold_uncertainty_score":0.01457828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04994161222320141,"score_gpt":0.3538430991709638,"score_spread":0.3039014869477624,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}