{"id":"W4398283525","doi":"10.7910/dvn/pkjufn/slbqpj","title":"FCC2002.084.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; Environmental science; Atmospheric sciences; Remote sensing; Geology; Geography; Physics; Computer science; Magnetic field; Aerospace engineering; 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":[0.001408944,0.002364526,0.001684211,0.003631924,0.0007379305,0.002884959,0.003734439,0.002639401,0.1414178],"category_scores_gemma":[0.008941055,0.0008758102,0.001538819,0.006434007,0.0004916183,0.001408809,0.001902838,0.001602645,0.1564821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001741476,"about_ca_system_score_gemma":0.002233839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03488497,"about_ca_topic_score_gemma":0.04503652,"domain_scores_codex":[0.9990094,0.0002467071,0.0001043163,0.000295316,0.0001756703,0.0001685883],"domain_scores_gemma":[0.99749,0.0007446836,0.0002567233,0.0006492007,0.0005216392,0.0003376853],"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":[0.00004802977,0.000009109969,0.000387706,0.0003013702,0.00002508119,0.000007631515,0.000006870967,0.0002010045,0.00002515804,0.0002679443,0.9977129,0.001007193],"study_design_scores_gemma":[0.0006053436,0.00002874518,0.003067399,0.0004307021,0.00005298102,0.00005591969,0.00004763036,0.0009998364,0.0002404992,0.002121606,0.992314,0.00003524289],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005559947,0.00006267249,0.00003446032,0.00008339181,0.00002238746,0.000005091675,0.9988588,0.0003216855,0.0005559817],"genre_scores_gemma":[0.0004134658,0.00006922268,0.0001521624,0.0001092669,0.00001705395,0.00004413952,0.9984404,0.0001027656,0.0006515735],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8585823,"threshold_uncertainty_score":0.4730896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198179097441933,"score_gpt":0.2741131898192298,"score_spread":0.2542952800750365,"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."}}