{"id":"W4398629369","doi":"10.7910/dvn/pkjufn/m47oou","title":"FCC2001.246.ran","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Range (aeronautics); Earth's magnetic field; Ran; Meteorology; Atmospheric sciences; Environmental science; Climatology; Geology; Geography; Physics; Computer science; Materials science; Magnetic field","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":[0.00125345,0.002443811,0.001634852,0.003456567,0.0007477906,0.003030977,0.003620273,0.002623717,0.1465474],"category_scores_gemma":[0.007364278,0.0008948066,0.001453992,0.006296963,0.0004907825,0.001453033,0.001862522,0.001638912,0.1739466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001768824,"about_ca_system_score_gemma":0.002051939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03360469,"about_ca_topic_score_gemma":0.04475453,"domain_scores_codex":[0.9990952,0.0002159163,0.0001025789,0.0002639575,0.0001628663,0.0001595797],"domain_scores_gemma":[0.9978683,0.0006176041,0.0002070466,0.0005517701,0.000468099,0.0002870728],"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":[0.00004402232,0.000009522983,0.0002822949,0.000290953,0.00001994315,0.000007285978,0.000006769229,0.0001943529,0.00002749746,0.0002873152,0.9979028,0.0009272973],"study_design_scores_gemma":[0.0005418517,0.00002646123,0.00240081,0.0003646735,0.00003893895,0.00004915556,0.00004291134,0.0009151587,0.0002435089,0.00191107,0.9934349,0.00003056586],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004974015,0.00005539429,0.00003365612,0.00007311266,0.00002238392,0.000004907987,0.9987086,0.000349254,0.0007029391],"genre_scores_gemma":[0.0003475173,0.00005777879,0.0001344504,0.00008912101,0.00001242057,0.00003441519,0.9985928,0.0001025452,0.0006289957],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8534526,"threshold_uncertainty_score":0.4902499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976153797269264,"score_gpt":0.2759116896298448,"score_spread":0.2561501516571522,"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."}}