{"id":"W4394048112","doi":"10.5281/zenodo.3580961","title":"Neuromod Natural Image Bank","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","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); Business; Computer science; Geography; Computer vision; 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":[0.001088744,0.006838348,0.002411662,0.004523974,0.001153563,0.002251144,0.005488554,0.003974983,0.0421484],"category_scores_gemma":[0.003311787,0.001125557,0.002559424,0.004200444,0.001070209,0.001328883,0.002554574,0.00241439,0.06310931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001639363,"about_ca_system_score_gemma":0.002341691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02436577,"about_ca_topic_score_gemma":0.04988983,"domain_scores_codex":[0.9987564,0.0001844715,0.0001245186,0.0003665744,0.0003689984,0.000199058],"domain_scores_gemma":[0.9987481,0.0002639101,0.0001039516,0.0003989366,0.0003357911,0.0001493294],"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":[0.000294128,0.0001909548,0.0003497116,0.001314754,0.0001036958,0.0001287731,0.00002544346,0.001387945,0.001336562,0.00038652,0.9783996,0.0160821],"study_design_scores_gemma":[0.001593669,0.0003322961,0.00679874,0.0005182152,0.0002802497,0.001417007,0.0001821776,0.01487191,0.009912108,0.003403681,0.9604416,0.0002482487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002545947,0.0007308508,0.00165792,0.0002061276,0.0002097802,0.0003979102,0.9850261,0.006655272,0.002570129],"genre_scores_gemma":[0.001896653,0.0001989587,0.002814789,0.0001014374,0.00002024364,0.0004743103,0.9921603,0.000299641,0.002033742],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0421484,"threshold_uncertainty_score":0.1410005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03045889428530564,"score_gpt":0.2783754192639122,"score_spread":0.2479165249786066,"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."}}