{"id":"W4393983535","doi":"10.1002/geo2.138","title":"Governing AI, governing climate change?","year":2024,"lang":"en","type":"article","venue":"Geo Geography and Environment","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Climate change; Political science; Environmental resource management; Environmental science; Geology; Oceanography","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":[],"consensus_categories":[],"category_scores_codex":[0.00007080466,0.000147175,0.0001053375,0.00007941495,0.00008883212,0.0000709568,0.00006389933,0.00007763796,0.0001408749],"category_scores_gemma":[0.000001201556,0.0001386693,0.00006776348,0.00008038108,0.00006511453,0.0001369951,0.00009397901,0.0001770679,0.0001349686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002340837,"about_ca_system_score_gemma":6.794671e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002226735,"about_ca_topic_score_gemma":0.000002246235,"domain_scores_codex":[0.9992806,0.000004226524,0.0001042663,0.0001873699,0.0001157112,0.0003078622],"domain_scores_gemma":[0.9997917,0.00002004729,0.000008353162,0.0001382217,8.44202e-7,0.00004087778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001181777,0.00003324029,0.1165008,0.0009540649,0.0003859517,0.0001484999,0.001370047,0.006348449,0.001005552,0.006950818,0.004408453,0.8618823],"study_design_scores_gemma":[0.000146069,0.00006685522,0.1217571,0.0001498122,0.00005047239,0.00001835744,0.0004779349,0.01460319,0.0002610854,0.0007454425,0.861326,0.0003977208],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9059489,0.07860085,0.002062038,0.0008629857,0.0009820664,0.0002643861,0.0001985567,0.002248577,0.00883163],"genre_scores_gemma":[0.9678675,0.03152284,0.0002710372,0.0000861969,0.0001193988,0.00006410878,0.00001577031,0.0000273628,0.00002581933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8614846,"threshold_uncertainty_score":0.565477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006510053501450864,"score_gpt":0.1660360896217048,"score_spread":0.1595260361202539,"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."}}