{"id":"W4301055875","doi":"10.1145/3487553.3524659","title":"Semi-automated Literature Review for Scientific Assessment of Socioeconomic Climate Change Scenarios","year":2022,"lang":"en","type":"article","venue":"Companion Proceedings of the Web Conference 2022","topic":"Climate Change Communication and Perception","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Climate change; Scientific literature; Artificial intelligence; Citation; Data science; Multinomial logistic regression; Scientometrics; Machine learning; Systematic review; Socioeconomic status; Bibliometrics; Scopus; Support vector machine; Vocabulary; Data mining; Political science; Sociology; MEDLINE; Ecology; Library science; Linguistics","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00239204,0.0001152324,0.0002975144,0.0001150421,0.001386299,0.0001249038,0.001107353,0.00004962774,0.001455286],"category_scores_gemma":[0.00008372452,0.0001037803,0.000173211,0.0005450465,0.0003041551,0.0002574854,0.0005840646,0.0002367411,0.000003504108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002185142,"about_ca_system_score_gemma":0.0001883316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002639816,"about_ca_topic_score_gemma":0.00005432659,"domain_scores_codex":[0.9985992,0.0001075309,0.000403798,0.0002482381,0.0003998394,0.0002414156],"domain_scores_gemma":[0.9983366,0.00006925687,0.0006573289,0.0002308782,0.0006537896,0.0000521564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001120094,0.00115397,0.04330717,0.01225627,0.000122259,2.596341e-7,0.113141,0.00002778401,0.1266776,0.5489081,0.1382128,0.01608079],"study_design_scores_gemma":[0.002583973,0.0004872801,0.03895332,0.01052454,0.0003913443,0.000015179,0.08250824,0.1201703,0.0006624458,0.005659365,0.7366908,0.001353267],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9221947,0.009407205,0.000005902566,0.03605782,0.002440062,0.005579624,0.001373221,0.0004037583,0.02253768],"genre_scores_gemma":[0.9758491,0.02272322,0.00008464608,0.0004195415,0.0000525843,0.0004612702,0.00009721681,0.00001175021,0.0003006915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5984779,"threshold_uncertainty_score":0.9999138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.250506218709045,"score_gpt":0.4215341113535383,"score_spread":0.1710278926444933,"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."}}