{"id":"W6885973822","doi":"10.14278/rodare.915","title":"Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.","year":2021,"lang":"en","type":"dataset","venue":"RODARE","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"European Commission","keywords":"Medical imaging; Microscopy; Brain tissue; Image registration; Magnetic resonance imaging; Convolutional neural network; Image fusion; Human brain","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001250907,0.0004846557,0.001131887,0.0006203381,0.00006940144,0.00005432062,0.001559481,0.0005609557,0.001438706],"category_scores_gemma":[0.002552111,0.0005265726,0.00005548869,0.0007724228,0.0001976572,0.0003240837,0.001974208,0.0008134104,0.0001281834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001395225,"about_ca_system_score_gemma":0.000907412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005985437,"about_ca_topic_score_gemma":0.0085256,"domain_scores_codex":[0.9954718,0.0004637683,0.001154128,0.001180271,0.001276334,0.000453683],"domain_scores_gemma":[0.9957889,0.0003376036,0.0007768988,0.002625225,0.0002259978,0.0002453559],"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":[0.00007371175,0.0002571406,0.0009616773,0.0008392599,0.00005192698,0.0001804734,0.0001682951,0.000001435882,0.02669344,6.731513e-7,0.9666492,0.0041228],"study_design_scores_gemma":[0.00174227,0.00003388733,0.006810734,0.001959176,0.00009927194,0.00004071707,0.0001577041,0.001181168,0.001947271,0.000002720627,0.9855511,0.00047399],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001853923,0.007334838,0.000008227557,0.0011699,0.0003766738,0.0004768828,0.9887388,0.0000278979,0.00001281103],"genre_scores_gemma":[0.0009751865,0.0005775883,0.001479626,0.0001587208,0.000278479,0.00001400052,0.9963894,0.0001044064,0.00002258048],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02474617,"threshold_uncertainty_score":0.9997186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01113291654717922,"score_gpt":0.3352030612199418,"score_spread":0.3240701446727625,"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."}}