{"id":"W2917452130","doi":"10.1002/wcc.576","title":"Frontiers in data analytics for adaptation research: Topic modeling","year":2019,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Climate Change","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Adaptation (eye); Corporate governance; Leverage (statistics); Data science; Topic model; Convention; Climate change adaptation; Vulnerability (computing); Political science; Climate change; Computer science; Sociology; Social science; Business; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007799397,0.0001245166,0.0004368225,0.0003470336,0.0003614901,0.00008078039,0.0007041884,0.00007870225,0.0001236355],"category_scores_gemma":[0.0002750148,0.0001113074,0.0001353916,0.0007991454,0.00007117434,0.0005934939,0.000767879,0.0001882442,0.00009090336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001753483,"about_ca_system_score_gemma":0.00005104111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00010907,"about_ca_topic_score_gemma":0.002283693,"domain_scores_codex":[0.9974548,0.000737867,0.0005117635,0.000483969,0.0003511666,0.0004604482],"domain_scores_gemma":[0.9988197,0.000302727,0.0001302269,0.0004996904,0.0001608097,0.00008681877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001715696,0.0002661156,0.009321008,0.001037838,0.0000731067,0.000007560795,0.1457882,0.002588287,0.00001624718,0.01107536,0.009751912,0.8199028],"study_design_scores_gemma":[0.0002321321,0.00005224028,0.0001473595,0.0007117902,0.00003555408,3.339221e-7,0.02553132,0.8843768,2.536175e-7,0.02012347,0.06861006,0.0001787501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2719417,0.1503023,0.4292999,0.05443639,0.01207297,0.02522913,0.000826634,0.000403707,0.05548728],"genre_scores_gemma":[0.728947,0.1242736,0.1357518,0.0006992465,0.003813161,0.001322397,0.002106222,0.0001032278,0.002983329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8817884,"threshold_uncertainty_score":0.4538983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.661302599603033,"score_gpt":0.5346772663937048,"score_spread":0.1266253332093281,"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."}}