{"id":"W6902543811","doi":"10.7479/hyek-4pt0","title":"Clinical CT dataset Tyrannosaurus rex MB.R.91216","year":2018,"lang":"en","type":"dataset","venue":"Museum für Naturkunde Berlin - Leibniz-Institut für Evolutions- und Biodiversitätsforschung","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Minnow Environmental (Canada)","funders":"","keywords":"Skull; Computed tomography; Scanner; Image quality; Tomography; Medical imaging","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":[0.0006827755,0.002445046,0.001342277,0.003656803,0.0006713214,0.00146426,0.002730816,0.001967299,0.02966196],"category_scores_gemma":[0.003349142,0.0005052634,0.000998792,0.003705828,0.0004933355,0.0007412484,0.001713405,0.001070419,0.04180827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163627,"about_ca_system_score_gemma":0.001747998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01956331,"about_ca_topic_score_gemma":0.03873342,"domain_scores_codex":[0.9993554,0.00006760182,0.0001068741,0.000221211,0.0001700234,0.00007896117],"domain_scores_gemma":[0.9990534,0.0002144294,0.0001310958,0.0002202528,0.0002791651,0.000101632],"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":[0.0003059437,0.00008615018,0.004493673,0.001795518,0.0001042601,0.0004985474,0.00007268145,0.0008538316,0.001020193,0.0004840966,0.9714842,0.01880091],"study_design_scores_gemma":[0.0003618584,0.00006680773,0.0177331,0.0006557378,0.0001244963,0.001604197,0.0002039192,0.00160152,0.001601431,0.00100582,0.974978,0.00006323631],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001853086,0.0004666913,0.0003210186,0.0001069748,0.00003297456,0.00005607334,0.9951677,0.0008004548,0.001195014],"genre_scores_gemma":[0.001650812,0.0001436537,0.000643444,0.00003204289,0.000008554754,0.0001491237,0.9967678,0.00005248105,0.0005522343],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02966196,"threshold_uncertainty_score":0.0992291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0418444422091106,"score_gpt":0.3692589451994077,"score_spread":0.3274145029902971,"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."}}