{"id":"W4398684243","doi":"10.7910/dvn/pkjufn/dldi6f","title":"FCC2003.169.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":"Earth's magnetic field; Range (aeronautics); Meteorology; Ran; Environmental science; Atmospheric sciences; Remote sensing; Geography; Geology; Physics; Computer science; Engineering; Magnetic field; Aerospace engineering","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":[0.001318044,0.002477803,0.001624678,0.003519689,0.0007578488,0.003167362,0.003692369,0.002705062,0.1529239],"category_scores_gemma":[0.007595213,0.0009358382,0.001478979,0.006613519,0.0005043869,0.001471698,0.001928517,0.001639389,0.1811613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001796708,"about_ca_system_score_gemma":0.002159662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03449687,"about_ca_topic_score_gemma":0.04470305,"domain_scores_codex":[0.9990609,0.0002178355,0.0001094925,0.0002745498,0.0001685911,0.0001686377],"domain_scores_gemma":[0.997771,0.0006098201,0.0002166399,0.0005978842,0.0004825818,0.0003221016],"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":[0.00004319855,0.000009173718,0.0002789808,0.0002743779,0.0000194611,0.000007261549,0.000007012227,0.0001823583,0.0000278873,0.0002755104,0.9980021,0.0008726059],"study_design_scores_gemma":[0.0005382875,0.00002715184,0.002350018,0.000343213,0.00003764314,0.00004954633,0.00004397674,0.0008829493,0.0002411399,0.001814529,0.9936413,0.00003025156],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005171329,0.000051723,0.00003343857,0.00007154144,0.00002288476,0.000005067533,0.9987125,0.0003654866,0.0006855353],"genre_scores_gemma":[0.0003451809,0.00005401615,0.0001317092,0.00008954231,0.00001240483,0.00003348461,0.998597,0.0001095677,0.0006270909],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8470761,"threshold_uncertainty_score":0.5115815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197430599522316,"score_gpt":0.2749926882248495,"score_spread":0.2552496282726179,"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."}}