{"id":"W7128617454","doi":"10.7910/dvn/yr3qjm","title":"Sub-evaluation 5 Imfusion Dataset","year":2025,"lang":"","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Segmentation; Medical imaging; Image segmentation; Pattern recognition (psychology); Feature (linguistics)","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":[0.002278722,0.003771561,0.001832073,0.00434601,0.001706224,0.002492002,0.004778051,0.00327527,0.0296842],"category_scores_gemma":[0.008435071,0.0005584667,0.002321395,0.003775211,0.00101124,0.002021791,0.003420031,0.002286055,0.04192527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002344517,"about_ca_system_score_gemma":0.002479371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02150489,"about_ca_topic_score_gemma":0.04852537,"domain_scores_codex":[0.9971118,0.0006805623,0.0003035402,0.0007527197,0.0007904079,0.0003608543],"domain_scores_gemma":[0.9972991,0.0006575698,0.0001678525,0.0007899957,0.0007906461,0.0002948048],"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":[0.0002957969,0.0001507658,0.001489418,0.0008343314,0.0001099233,0.00006227113,0.00003677201,0.0007561301,0.0004539764,0.0005911186,0.982998,0.01222155],"study_design_scores_gemma":[0.001543644,0.0003881135,0.01378103,0.0008837526,0.0002564442,0.001014219,0.0003712467,0.01024007,0.003386074,0.00488949,0.9631005,0.0001454743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005769128,0.001527931,0.001166336,0.0007061788,0.0004001493,0.0002839341,0.9811826,0.004255862,0.004707882],"genre_scores_gemma":[0.002458218,0.0001363783,0.001594757,0.0001648101,0.0000375933,0.0001947492,0.993966,0.0001461194,0.001301316],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0296842,"threshold_uncertainty_score":0.0993036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09934813363863283,"score_gpt":0.4224706381209627,"score_spread":0.3231225044823299,"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."}}