{"id":"W6899380029","doi":"10.5880/intermagnet.2013","title":"Global magnetic observatory data 2013","year":2019,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Observatory; Data quality; Data archive; Digital data; Data set; Baseline (sea); Data security","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":[],"consensus_categories":[],"category_scores_codex":[0.001065853,0.001310358,0.001089719,0.003132612,0.0008227928,0.002110803,0.001924882,0.001005825,0.05896712],"category_scores_gemma":[0.006436712,0.0004460657,0.0007395596,0.008318673,0.0003711824,0.001639797,0.002136453,0.001629636,0.09800141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001520637,"about_ca_system_score_gemma":0.00268926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04786454,"about_ca_topic_score_gemma":0.06715893,"domain_scores_codex":[0.9990126,0.0001457925,0.0001426258,0.0002462169,0.0002788799,0.0001737522],"domain_scores_gemma":[0.9980239,0.0002753236,0.0002266397,0.0004145901,0.0008800245,0.000179578],"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":[0.00002518132,0.000007369208,0.0008256125,0.0001685009,0.00001172759,0.00001186721,0.00001924413,0.0001222401,0.00005180109,0.0004614101,0.9965563,0.001738721],"study_design_scores_gemma":[0.00007947968,0.000007159396,0.007047207,0.0001433189,0.00001181784,0.00002560521,0.0001058326,0.000178822,0.0001672552,0.000895027,0.9913167,0.00002178491],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001266867,0.00002751313,0.00005581261,0.0000707167,0.00003145189,0.000005987844,0.9985389,0.000160475,0.0009824977],"genre_scores_gemma":[0.0004411408,0.00003260559,0.0001625794,0.00003351952,0.00001011007,0.00003555891,0.9983537,0.00005478796,0.0008759484],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05896712,"threshold_uncertainty_score":0.1972647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.165102991039269,"score_gpt":0.3749921182029191,"score_spread":0.2098891271636501,"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."}}