{"id":"W4393702513","doi":"10.5281/zenodo.3580962","title":"Neuromod Natural Image Bank","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Natural (archaeology); Image (mathematics); Computer science; Business; Computer vision; Geography; Archaeology","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":[0.001094174,0.005859783,0.002098601,0.003996103,0.00114554,0.002075,0.004820034,0.003683374,0.05260501],"category_scores_gemma":[0.003897064,0.0009702449,0.002563505,0.003278447,0.001003838,0.00110997,0.002459084,0.002140871,0.07084448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366877,"about_ca_system_score_gemma":0.002101679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01875941,"about_ca_topic_score_gemma":0.03874372,"domain_scores_codex":[0.9988625,0.0001829489,0.0001131758,0.0003482338,0.0003203696,0.0001727884],"domain_scores_gemma":[0.9985555,0.0003449232,0.0001005543,0.0004653761,0.0003810526,0.000152547],"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":[0.0003386202,0.000204744,0.0004373508,0.001352085,0.0001069402,0.0001134108,0.00002802807,0.00132454,0.001242558,0.0003365655,0.9765759,0.01793916],"study_design_scores_gemma":[0.002098027,0.0004682635,0.009015835,0.0006443379,0.0003499638,0.0014478,0.0002275102,0.01778245,0.01164001,0.003856843,0.9521921,0.0002768666],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002932258,0.0006376228,0.001781309,0.0002427454,0.0002496297,0.0004587204,0.9831213,0.0079383,0.002638299],"genre_scores_gemma":[0.002704295,0.0001989564,0.003352995,0.0001213046,0.00002745846,0.0006107535,0.9902497,0.0003769607,0.002357637],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05260501,"threshold_uncertainty_score":0.1759813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03414170576146574,"score_gpt":0.3078908350683859,"score_spread":0.2737491293069202,"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."}}