{"id":"W4392898235","doi":"10.61838/kman.aitech.1.4.3","title":"The Environmental Impacts of AI and Digital Technologies","year":2023,"lang":"en","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Sustainability; Software deployment; Thematic analysis; Emerging technologies; Sustainable development; Public engagement; Public policy; Business; Knowledge management; Environmental resource management; Environmental planning; Engineering; Qualitative research; Computer science; Public relations; Political science; Sociology; 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":[],"consensus_categories":[],"category_scores_codex":[0.00003118835,0.00003076755,0.00003244501,0.00001500015,0.00002187542,0.00001546316,0.00003939499,0.00002001508,0.000002881688],"category_scores_gemma":[0.00002049157,0.00001879199,0.00001043254,0.00004678109,0.0000739497,0.00005532255,0.0000421134,0.00003564492,0.000007035827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001020446,"about_ca_system_score_gemma":0.000001513174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001858675,"about_ca_topic_score_gemma":0.000003172624,"domain_scores_codex":[0.999805,9.154821e-7,0.00004468429,0.00003119185,0.00003379244,0.00008443606],"domain_scores_gemma":[0.9998784,0.00002758637,0.000002550301,0.00008186822,0.000001386246,0.000008203609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006271427,0.00001283366,0.7093033,0.00008512934,0.00004224729,0.000006683288,0.0004240945,0.0002628502,0.004201487,0.002586383,0.00456063,0.278508],"study_design_scores_gemma":[0.0003962614,0.0001193817,0.8599968,0.000008193043,0.000008762158,0.000008274859,0.01979015,0.02824581,0.03623778,0.03891868,0.01595809,0.0003117978],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982265,0.0001164385,0.00003991575,0.0002826263,0.00001520708,0.00003929765,0.000003882995,0.00031271,0.0009634292],"genre_scores_gemma":[0.9997678,0.00006294504,0.000004386859,0.000001041844,0.000001656621,0.000001825499,9.134994e-7,0.000003137561,0.0001562305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2781962,"threshold_uncertainty_score":0.07663149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002567224309467485,"score_gpt":0.1755987539740334,"score_spread":0.1730315296645659,"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."}}