{"id":"W4294549672","doi":"10.1146/annurev-environ-120920-100056","title":"Digitalization and the Anthropocene","year":2022,"lang":"en","type":"article","venue":"Annual Review of Environment and Resources","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Anthropocene; Planetary boundaries; Sustainability; Politics; Natural resource economics; Unintended consequences; Equity (law); Environmental resource management; Corporate governance; Dematerialization (economics); Business; Economic system; Geography; Political science; Economics; Ecology","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":[0.002195582,0.00005782177,0.0001781401,0.00004810035,0.0003178179,0.000028047,0.0001526366,0.0000092778,0.0008949439],"category_scores_gemma":[0.0004379755,0.000029534,0.00003685987,0.0002050095,0.000382941,0.00007759663,0.0003381387,0.0000543108,0.000004461103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004308605,"about_ca_system_score_gemma":0.000002777677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003265846,"about_ca_topic_score_gemma":8.124403e-8,"domain_scores_codex":[0.9985861,0.0001806944,0.0003856145,0.0001538456,0.0006280302,0.00006578288],"domain_scores_gemma":[0.999264,0.000291578,0.0002510787,0.0001497263,0.00002185982,0.00002175047],"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":[0.00009878256,0.0001152526,0.01873318,0.000335018,0.00002981694,0.000003012314,0.005670775,0.00006761493,0.00004227098,0.0230737,0.02028994,0.9315406],"study_design_scores_gemma":[0.0003558802,0.00005867349,0.007190371,0.0001056401,0.00001273584,0.00001111828,0.002647847,0.0002809857,0.000008577816,0.002234282,0.9870307,0.00006323316],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6792527,0.2629253,0.0004242054,0.01783386,0.0001777844,0.0008196178,0.00008900413,0.00001773865,0.0384598],"genre_scores_gemma":[0.9528561,0.04314359,0.00005231439,0.002266421,0.00002512463,0.00001561626,0.000007824775,0.000004938373,0.001628117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9667407,"threshold_uncertainty_score":0.9799008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02841441088118458,"score_gpt":0.3047905890116118,"score_spread":0.2763761781304272,"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."}}