{"id":"W6908067114","doi":"10.25545/4upj00/slpmma","title":"ContinuousStateDynamics.m","year":2023,"lang":"en","type":"dataset","venue":"UNB Dataverse","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001759374,0.004090637,0.002331912,0.003556579,0.001061634,0.00442761,0.005163874,0.003925459,0.2393541],"category_scores_gemma":[0.01061776,0.001500125,0.002267887,0.006462688,0.0007772601,0.002867129,0.003802642,0.002579415,0.311066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154177,"about_ca_system_score_gemma":0.00247875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01393116,"about_ca_topic_score_gemma":0.01833477,"domain_scores_codex":[0.9986076,0.0003034877,0.00011417,0.0005177701,0.0002702408,0.0001868518],"domain_scores_gemma":[0.9970095,0.001089895,0.0002088456,0.000990878,0.0003509726,0.0003499258],"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":[0.00007470761,0.00001829375,0.0003212768,0.0005866891,0.00003594369,0.00001184651,0.00001569916,0.0003673305,0.00005068491,0.0005718943,0.9959587,0.001986886],"study_design_scores_gemma":[0.000532167,0.00003505474,0.001147517,0.0003903135,0.00003545842,0.00005138575,0.00003451775,0.001603615,0.000369946,0.004224022,0.9915375,0.00003864341],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006376337,0.00009761309,0.0001357531,0.000121815,0.00003805905,0.00000872413,0.9956532,0.002871139,0.001009987],"genre_scores_gemma":[0.0005252489,0.0001165527,0.0004953415,0.0001461626,0.00001626378,0.00006045875,0.9972795,0.0006585364,0.0007018971],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7606459,"threshold_uncertainty_score":0.8007193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860764664438054,"score_gpt":0.2831893263088993,"score_spread":0.2645816796645187,"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."}}