{"id":"W6930143501","doi":"10.5281/zenodo.10635831","title":"SIGMA Rat Brain Templates and Atlases Version 2.0","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Pipeline (software); Template; Segmentation; Scanner; Set (abstract data type); Sigma; Data set","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":[0.002241864,0.002066713,0.001751134,0.004904186,0.0011417,0.003147719,0.003254506,0.001769427,0.09195678],"category_scores_gemma":[0.004519681,0.002095094,0.001838048,0.003664253,0.0008031413,0.002528854,0.002832025,0.003419732,0.05663653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112537,"about_ca_system_score_gemma":0.003811145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004320099,"about_ca_topic_score_gemma":0.007746045,"domain_scores_codex":[0.9989159,0.0001828289,0.0001720696,0.0002119862,0.0004121669,0.0001051341],"domain_scores_gemma":[0.9984457,0.000327128,0.0001742396,0.0005134552,0.0004146198,0.0001249554],"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":[0.00071175,0.0001216564,0.001831594,0.002712795,0.0002517513,0.0007910841,0.0006868065,0.005346721,0.02400047,0.03298455,0.7175237,0.2130372],"study_design_scores_gemma":[0.0001019197,0.0002009982,0.003787698,0.0005627645,0.0001917605,0.002323929,0.00017746,0.004638997,0.01223965,0.02610506,0.9494892,0.0001805522],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.008898402,0.003266345,0.6739858,0.0007182005,0.0009967691,0.001578111,0.2036375,0.07203572,0.03488328],"genre_scores_gemma":[0.02566589,0.004753479,0.589351,0.001052114,0.0002792144,0.00990091,0.2583033,0.05555363,0.05514034],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.09195678,"threshold_uncertainty_score":0.3076261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06781685779061739,"score_gpt":0.2901420574432881,"score_spread":0.2223251996526707,"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."}}