{"id":"W2891798643","doi":"10.3390/su10093135","title":"Evolution of the Scientific Literature on Input–Output Analysis: A Bibliometric Analysis of 1990–2017","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Mainstream; Social Sciences Citation Index; Citation analysis; Bibliographic coupling; Field (mathematics); Beijing; Social network analysis; Citation index; Science Citation Index; Frontier; Regional science; China; Bibliometrics; Data science; Chinese academy of sciences; Network analysis; Institution; Index (typography); Citation; Computer science; Social science; Political science; Sociology; Library science; 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":["bibliometrics","sts"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.002391914,0.0002364223,0.0005345835,0.01507658,0.0003928083,0.00007510283,0.0007676657,0.0001565649,0.0005832349],"category_scores_gemma":[0.002011481,0.0001622767,0.0007978686,0.2611299,0.003314703,0.0003565243,0.0005770184,0.0002169603,0.00001166432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002798298,"about_ca_system_score_gemma":0.0001234489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001450908,"about_ca_topic_score_gemma":0.0005663284,"domain_scores_codex":[0.9966412,0.0004370144,0.0006047497,0.0007385149,0.001088364,0.0004901963],"domain_scores_gemma":[0.9969248,0.0001423372,0.0004007136,0.002064921,0.000321484,0.0001456983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008274099,0.0005445905,0.9906765,0.0000605701,0.0004071296,0.00000112887,0.0014873,0.004389818,0.0003265538,0.000252803,0.000198392,0.00157247],"study_design_scores_gemma":[0.0001527402,0.0001938039,0.9895766,0.000006395453,0.001373089,3.266414e-7,0.0007292571,0.002736578,0.001260016,0.003425647,0.0003940107,0.0001515184],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972482,0.0001055211,0.0003805801,0.0001809986,0.000111921,0.0005497133,0.00007344659,0.00001953974,0.001330111],"genre_scores_gemma":[0.9988362,0.000002960734,0.00006852076,0.0000291104,0.00001861477,0.00001403637,0.00001714937,0.000007703254,0.001005755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2460533,"threshold_uncertainty_score":0.9993977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008454790924146686,"score_gpt":0.2672923266494303,"score_spread":0.2588375357252836,"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."}}