{"id":"W4393568293","doi":"10.5281/zenodo.3608382","title":"CC20 Artifact - Automatic Fusion","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Artifact (error); Fusion; Computer science; Artificial intelligence; Computer vision; Linguistics; Philosophy","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":[0.01228732,0.003786759,0.002198436,0.004910396,0.00157301,0.00825065,0.005980792,0.002383169,0.08263756],"category_scores_gemma":[0.04198185,0.001756757,0.003270484,0.004889589,0.00139377,0.007412432,0.01063559,0.00336201,0.1164591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002133735,"about_ca_system_score_gemma":0.00450256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00638833,"about_ca_topic_score_gemma":0.003620789,"domain_scores_codex":[0.9793027,0.002986331,0.001602588,0.003685118,0.01099697,0.001426217],"domain_scores_gemma":[0.9600313,0.002629332,0.0008021854,0.02385968,0.01135206,0.00132543],"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":[0.001657073,0.0002586933,0.002251499,0.0007210339,0.0002178413,0.0002945012,0.0003313039,0.003716644,0.0178501,0.0138626,0.7246853,0.2341535],"study_design_scores_gemma":[0.0004168637,0.0004221657,0.005150468,0.0002476499,0.0001135474,0.0009232884,0.0001855399,0.05398447,0.08973411,0.02522499,0.8232164,0.0003804942],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"dataset","genre_scores_codex":[0.005766159,0.0006713783,0.2384484,0.0009069716,0.001759109,0.0007001825,0.01784973,0.6989728,0.0349252],"genre_scores_gemma":[0.1099705,0.0007176306,0.4292963,0.001968171,0.0007076437,0.001556309,0.1780811,0.2167573,0.06094509],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.08263756,"threshold_uncertainty_score":0.2764502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04989373221940601,"score_gpt":0.3036789553385009,"score_spread":0.2537852231190949,"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."}}