{"id":"W4398396423","doi":"10.7910/dvn/pkjufn/u5zcyv","title":"FCC2003.135.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; Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Climatology; Geography; Geology; Physics; 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":[0.001291091,0.00249872,0.001695053,0.003457693,0.0007621272,0.003252927,0.003708116,0.0026073,0.1518166],"category_scores_gemma":[0.006991685,0.0009146281,0.001515236,0.00666114,0.0004884194,0.001489185,0.001918031,0.001617286,0.1864793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001752881,"about_ca_system_score_gemma":0.002102996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03341085,"about_ca_topic_score_gemma":0.04541315,"domain_scores_codex":[0.9990457,0.0002198761,0.0001110575,0.0002819013,0.0001700184,0.0001714037],"domain_scores_gemma":[0.9979612,0.0005409671,0.00020135,0.0005582548,0.0004610583,0.0002772077],"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":[0.00004635915,0.00001018824,0.0002865595,0.0003003232,0.00002217495,0.000008050682,0.00000727859,0.0001953665,0.00003352721,0.0002982812,0.9978478,0.0009441554],"study_design_scores_gemma":[0.0005503409,0.00002749072,0.00228063,0.0003440085,0.00003937732,0.00005213734,0.00004227851,0.0008504955,0.0002560547,0.001749915,0.993777,0.00003028918],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000509465,0.00005265191,0.00003421939,0.00006165748,0.00002139219,0.00000484286,0.9986473,0.0003984906,0.0007286278],"genre_scores_gemma":[0.000307908,0.00005177921,0.0001301967,0.00007658268,0.00001103064,0.00003055813,0.9986953,0.0001135313,0.0005831107],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8481835,"threshold_uncertainty_score":0.507877,"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."}}