{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03890215,0.001682846,0.002961196,0.01158172,0.001667293,0.01373036,0.003628171,0.003242004,0.003924277],"category_scores_gemma":[0.1113164,0.001063831,0.002869233,0.01848955,0.004564946,0.01586477,0.005586138,0.007499276,0.00203466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003351198,"about_ca_system_score_gemma":0.004529995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004205671,"about_ca_topic_score_gemma":0.003364885,"domain_scores_codex":[0.9718868,0.02039286,0.001637499,0.003207623,0.002626847,0.0002483329],"domain_scores_gemma":[0.7860616,0.1942783,0.004333099,0.01010074,0.004078287,0.001147945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001726882,0.000307222,0.02471119,0.004218298,0.0008667624,0.0003117254,0.005268088,0.0288584,0.00127292,0.4433542,0.0378102,0.4528484],"study_design_scores_gemma":[0.00003122153,0.00004580056,0.003545511,0.0009542344,0.00007062948,0.0001512456,0.001950243,0.1688802,0.0004236837,0.7834033,0.04045489,0.00008896903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008556634,0.02781768,0.9090545,0.04092802,0.0006705133,0.0005237316,0.004900708,0.001095615,0.006452637],"genre_scores_gemma":[0.2178002,0.02889227,0.7327695,0.004133839,0.004397281,0.002240553,0.007078578,0.0004827612,0.002204964],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03890215,"threshold_uncertainty_score":0.2057367,"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."}}