{"id":"W4393740670","doi":"10.5281/zenodo.1490878","title":"Ruthwell Cross 3D Model High Resolution (112M poly count)","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Resolution (logic); Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008723417,0.0037506,0.001858741,0.002512591,0.000927472,0.00303292,0.006293831,0.002958226,0.08021919],"category_scores_gemma":[0.002415676,0.001179302,0.00265739,0.003076638,0.0006208863,0.001623983,0.003192753,0.002802354,0.179298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282168,"about_ca_system_score_gemma":0.001828985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02345675,"about_ca_topic_score_gemma":0.04898197,"domain_scores_codex":[0.9990222,0.000131512,0.00006178508,0.0002990145,0.0003083664,0.0001770887],"domain_scores_gemma":[0.9991641,0.0001038436,0.00005361662,0.0003506293,0.0002366441,0.00009113869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009494093,0.00004219146,0.0005041727,0.000365794,0.00004253369,0.00004119461,0.00001694801,0.0007775862,0.0004100306,0.0004617957,0.9900922,0.007150403],"study_design_scores_gemma":[0.0002372785,0.00005323212,0.00326508,0.0002408485,0.00006236507,0.0004322569,0.00008083226,0.00408089,0.002413828,0.002297491,0.9867632,0.00007264064],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001104462,0.0005625016,0.002553957,0.0002121741,0.0001457495,0.0000807922,0.9797325,0.01103448,0.004573407],"genre_scores_gemma":[0.001598474,0.0001458271,0.002366745,0.00007917177,0.00001578099,0.00009978039,0.9928378,0.0008081588,0.002048292],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08021919,"threshold_uncertainty_score":0.26836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02489273771922793,"score_gpt":0.2920421882229112,"score_spread":0.2671494505036833,"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."}}