{"id":"W2997216514","doi":"10.15978/j.cnki.1673-5668.201901003","title":"A Bibliometric Analysis of Ocean Networks Canada","year":2019,"lang":"en","type":"article","venue":"Science Focus","topic":"Arctic and Russian Policy Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Bibliometrics; Environmental science; Regional science; Oceanography; Geography; Computer science; Geology; Library science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001635288,0.0003291138,0.0006919859,0.05799642,0.00342522,0.004706554,0.001018616,0.0004235695,0.005821814],"category_scores_gemma":[0.0172419,0.0001785341,0.0006276688,0.1494714,0.0007117823,0.0014657,0.001435216,0.0003725795,0.000897557],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02748908,"about_ca_system_score_gemma":0.04316819,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9636825,"about_ca_topic_score_gemma":0.9631377,"domain_scores_codex":[0.9970424,0.0002865344,0.0002391027,0.0002998116,0.001684557,0.000447608],"domain_scores_gemma":[0.9784666,0.004015222,0.002174595,0.0005499065,0.01364677,0.001147016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002374945,0.00007981863,0.8744671,0.0008398073,0.0006264239,0.0003027157,0.004278398,0.004012546,0.0006956831,0.01262038,0.03569358,0.06614611],"study_design_scores_gemma":[0.00001200105,0.00002276376,0.9326496,0.0002347486,0.0001954942,0.0001198975,0.006901561,0.004184733,0.0004069355,0.0006553122,0.05458161,0.0000353512],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8308127,0.007291223,0.001069228,0.001838714,0.000071657,0.0001286929,0.0865927,0.0002192834,0.07197572],"genre_scores_gemma":[0.9561628,0.003733344,0.001168235,0.00007851067,0.00004923969,0.00006378047,0.03133493,0.00004979783,0.007359296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9725109,"threshold_uncertainty_score":0.1994482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462042547182059,"score_gpt":0.3014164525178794,"score_spread":0.2867960270460588,"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."}}