{"id":"W4396494884","doi":"10.1038/s43247-024-01392-w","title":"Generative AI tools can enhance climate literacy but must be checked for biases and inaccuracies","year":2024,"lang":"en","type":"article","venue":"Communications Earth & Environment","topic":"Climate Change Communication and Perception","field":"Social Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"National Science Foundation","keywords":"Generative grammar; Consistency (knowledge bases); Context (archaeology); Literacy; Hazard; Computer science; Dissemination; Scale (ratio); Climate change; Generative model; Machine learning; Data science; Artificial intelligence; Psychology; Geography; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03644146,0.0007114955,0.0006087926,0.002575982,0.0018866,0.006903332,0.001488348,0.001392333,0.01083213],"category_scores_gemma":[0.2411069,0.0005273967,0.0006217592,0.001800161,0.004096092,0.009166569,0.006131361,0.002690184,0.002424625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334254,"about_ca_system_score_gemma":0.001941971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001611721,"about_ca_topic_score_gemma":0.002938572,"domain_scores_codex":[0.9626213,0.02907708,0.001511197,0.001222011,0.004781405,0.0007870074],"domain_scores_gemma":[0.5971924,0.349942,0.007941091,0.02766464,0.01527406,0.00198581],"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":[0.0005036428,0.0007943861,0.09939974,0.003576518,0.0001843716,0.0007720568,0.3054914,0.003908282,0.01347486,0.02945,0.01714619,0.5252987],"study_design_scores_gemma":[0.0002545248,0.001682851,0.1635926,0.008099413,0.0004499592,0.00166703,0.2035964,0.0397355,0.02260628,0.248578,0.3089189,0.0008185348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.6706388,0.0009694069,0.1919997,0.01430482,0.0005793583,0.00203148,0.001011416,0.0076721,0.1107929],"genre_scores_gemma":[0.891388,0.0004651852,0.09799948,0.002414222,0.0001279799,0.001468508,0.0004136167,0.0007718399,0.004951158],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.03644146,"threshold_uncertainty_score":0.1927232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4487225377091554,"score_gpt":0.4707821507009489,"score_spread":0.02205961299179343,"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."}}