{"id":"W4398280815","doi":"10.7910/dvn/pkjufn/hthufo","title":"FCC2003.065.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); Ran; Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Physics; Geology; Materials science; Computer 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":[0.001437193,0.002399684,0.001714738,0.003594972,0.0007471456,0.002918106,0.003794124,0.002665553,0.1437521],"category_scores_gemma":[0.009010882,0.0008895806,0.001580768,0.006485967,0.0004993142,0.001428741,0.001920349,0.001621876,0.1596309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00176284,"about_ca_system_score_gemma":0.002225678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03472493,"about_ca_topic_score_gemma":0.04520276,"domain_scores_codex":[0.9989883,0.0002526711,0.0001097697,0.0003011368,0.0001764517,0.0001715629],"domain_scores_gemma":[0.9974721,0.0007471756,0.0002557554,0.0006653533,0.0005234453,0.0003361271],"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":[0.00004758002,0.000008972573,0.0003682443,0.0003023074,0.00002508561,0.000007614067,0.00000688287,0.0002016479,0.00002450165,0.0002682311,0.9977412,0.0009977606],"study_design_scores_gemma":[0.0006129285,0.00002856983,0.002928485,0.0004267626,0.00005323974,0.00005661258,0.00004686201,0.001038546,0.0002442954,0.00222486,0.9923036,0.00003525667],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005460028,0.00006260192,0.00003638968,0.00008405424,0.00002306431,0.000005230384,0.9988318,0.000341798,0.0005603958],"genre_scores_gemma":[0.0004116444,0.00006859674,0.0001555391,0.0001109856,0.00001702367,0.00004366358,0.9984396,0.0001066189,0.0006464001],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8562478,"threshold_uncertainty_score":0.4808988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971484281753135,"score_gpt":0.2746593021223578,"score_spread":0.2549444593048265,"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."}}