{"id":"W6959347145","doi":"10.7685/jnau.201809016","title":"基于Scopus的植物表型组学研究进展分析","year":2018,"lang":"en","type":"article","venue":"UEA Digital Repository (University of East Anglia)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chinese academy of sciences; Bibliometrics; China; Scientometrics; Scopus; Citation; Statistical analysis; Basic research","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00002707859,0.00008387709,0.00009881207,0.00001922176,0.0002065856,0.00003576291,0.0002370325,0.00005137298,0.01188633],"category_scores_gemma":[0.00001089192,0.0001002521,0.00008537011,0.000189373,0.0006186321,0.0004829018,0.0002193844,0.00004333447,0.002846106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001714517,"about_ca_system_score_gemma":0.00000768427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001960524,"about_ca_topic_score_gemma":0.0001229023,"domain_scores_codex":[0.9993109,0.000009722125,0.00007368096,0.0002026736,0.0002308676,0.0001721533],"domain_scores_gemma":[0.999562,0.000008084344,0.00007713829,0.0002077344,0.00002949912,0.0001155194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004763742,0.0009836912,0.6346131,0.00004515788,0.0001151322,0.0003555901,0.004625109,0.000009276174,0.05423164,0.003071362,0.2736976,0.02777605],"study_design_scores_gemma":[0.0009966355,0.0005250479,0.5491793,0.00003603528,0.00004272387,0.00008630678,0.02614827,0.0001491299,0.005216295,0.0001938855,0.416871,0.0005552985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5879428,0.00001144844,0.00007337105,0.00006343167,0.0001140958,0.00003818708,0.00003128339,0.00003813091,0.4116873],"genre_scores_gemma":[0.9847742,0.000002643352,0.00004233991,0.00002878157,0.00003837991,8.411857e-8,0.00001504222,0.00000532846,0.01509324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3968314,"threshold_uncertainty_score":0.9979303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0135200639470876,"score_gpt":0.1806907751249115,"score_spread":0.1671707111778239,"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."}}