{"id":"W4398689576","doi":"10.7910/dvn/pkjufn/3c5rwr","title":"FCC2002.110.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; Atmospheric sciences; Environmental science; Meteorology; Remote sensing; Physics; Geography; 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":[0.001348299,0.002547978,0.001715995,0.003459915,0.0007952466,0.003233114,0.003855889,0.002751767,0.1370365],"category_scores_gemma":[0.007643822,0.0008970795,0.001502075,0.006850548,0.0004959988,0.001561644,0.001874104,0.001639789,0.1745479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001804339,"about_ca_system_score_gemma":0.002120324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03364701,"about_ca_topic_score_gemma":0.04659203,"domain_scores_codex":[0.9989988,0.000243972,0.0001102066,0.0002936685,0.0001807139,0.000172766],"domain_scores_gemma":[0.9978441,0.0005837107,0.0002028112,0.0005955941,0.000481086,0.0002926975],"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":[0.00004463008,0.000009785666,0.0002927704,0.0002841988,0.00002065713,0.000007748307,0.000006991379,0.000203645,0.00002896423,0.0002930978,0.9978811,0.0009263486],"study_design_scores_gemma":[0.0004883811,0.00002647858,0.002322524,0.0003489497,0.00003795573,0.00004998397,0.00004211112,0.0009342476,0.0002364813,0.001815535,0.9936673,0.000029995],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005637605,0.00006204608,0.00003725352,0.00007403407,0.00002402909,0.000005340848,0.9985556,0.000404996,0.0007803811],"genre_scores_gemma":[0.0003426972,0.00005520531,0.0001352535,0.00008187102,0.00001155497,0.00003338417,0.9986749,0.000103217,0.000561889],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8629636,"threshold_uncertainty_score":0.4584326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968353745563291,"score_gpt":0.2733919459470038,"score_spread":0.2537084084913709,"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."}}