{"id":"W4398399748","doi":"10.7910/dvn/pkjufn/gtmsle","title":"FCC2001.219.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); Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Physics; Geology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002186447,0.0003243182,0.0006985884,0.0001570804,0.00008464808,0.00004049416,0.0002307803,0.0003082506,0.02223651],"category_scores_gemma":[0.0005022415,0.0003204059,0.0006619772,0.0002037353,0.00005425033,0.0001121041,0.0002343459,0.0005559714,0.3513671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170275,"about_ca_system_score_gemma":0.0005784705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002974458,"about_ca_topic_score_gemma":0.00003167669,"domain_scores_codex":[0.9978728,0.0001011299,0.000391166,0.0006026723,0.0006468323,0.000385438],"domain_scores_gemma":[0.9972734,0.00003167977,0.0001363258,0.001590513,0.0000783928,0.0008897087],"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":[0.0005244744,0.0001246774,0.000005149422,0.001643167,0.0003180702,0.001036872,0.000005709686,5.411978e-7,0.000009545195,0.000003959737,0.9954099,0.0009179197],"study_design_scores_gemma":[0.002346919,0.00006158005,0.0003002791,0.0002497357,0.001832155,0.0001450483,0.00002374096,0.00001028837,0.000004970851,0.00000923633,0.9947491,0.0002669732],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001661326,0.00001981689,0.0000615274,0.0000532766,0.0006525011,0.0007435018,0.997945,0.0001016247,0.0004061382],"genre_scores_gemma":[0.00001533306,0.001429839,0.0001855678,0.003976495,0.001187721,0.00004545779,0.9928966,0.00003287973,0.0002301359],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3291306,"threshold_uncertainty_score":0.9999248,"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."}}