{"id":"W4362597443","doi":"10.37256/aie.4120232503","title":"Towards Artificial Intelligence in Sustainable Environmental Development","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Evolution","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Canada West","funders":"","keywords":"Sustainability; Environmentally friendly; Globe; Sustainable development; Popularity; Resilience (materials science); Extreme weather; Environmental resource management; Climate change; Environmental planning; Biodiversity; Agriculture; Environmental impact assessment; Business; Natural resource economics; Engineering; Environmental science; Ecology; Political science; Economics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001305385,0.000401139,0.0003254233,0.001214123,0.0005155279,0.0004359508,0.0007408793,0.000214791,0.001172287],"category_scores_gemma":[0.0004026256,0.0004192625,0.0001047106,0.003272251,0.0002721921,0.002187569,0.0006090762,0.0003501784,0.01266003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004729009,"about_ca_system_score_gemma":0.0001479985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001526435,"about_ca_topic_score_gemma":0.0008925114,"domain_scores_codex":[0.9962131,0.00002943623,0.001097114,0.000814436,0.0006919305,0.001153972],"domain_scores_gemma":[0.9990436,0.00006638488,0.0002444674,0.0004618273,0.000140342,0.00004333447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001243938,0.0003523954,0.002657101,0.0001196658,0.0000144966,0.0000853353,0.000378199,0.00393298,0.001638209,0.6444505,0.000533505,0.3457132],"study_design_scores_gemma":[0.00004574803,0.00004844425,0.0224673,0.0001837345,0.00004432825,0.00001019445,0.01948423,0.1909238,0.02857032,0.6746352,0.06196469,0.00162203],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6165063,0.0002810225,0.3660824,0.002446771,0.002839748,0.001573995,0.00001745841,0.001119872,0.009132357],"genre_scores_gemma":[0.9975392,0.00003035801,0.0004576011,0.000262477,0.0009193487,0.0001177799,0.0001811808,0.00004683826,0.0004451992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3810329,"threshold_uncertainty_score":0.9998259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07786998916749589,"score_gpt":0.2930122406025756,"score_spread":0.2151422514350798,"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."}}