{"id":"W7017299201","doi":"","title":"AI’s Sustainability Dilemma: How Personalized Recommendations Influence Carbon Footprints","year":2025,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Green IT and Sustainability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Personalization; Sustainability; Mobile apps; Sustainable development; Ecosystem","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":[0.00201826,0.0001044518,0.0002343555,0.000192728,0.0001612547,0.0002574702,0.00026209,0.0001197856,9.008256e-7],"category_scores_gemma":[0.003191791,0.00008105907,0.0001962107,0.0003982304,0.00001751704,0.001068575,0.0000309944,0.0002338893,9.81763e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002902719,"about_ca_system_score_gemma":0.0002378423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003089052,"about_ca_topic_score_gemma":0.00000567945,"domain_scores_codex":[0.9985936,0.0001123764,0.0007306534,0.00004531845,0.0003334707,0.0001845917],"domain_scores_gemma":[0.9951774,0.0002374358,0.0006657885,0.0001890093,0.003691005,0.00003939979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001972647,0.0001110619,0.6130535,0.003915609,0.001192698,4.118213e-7,0.01051316,0.1661628,0.0001181755,0.07567428,0.1214839,0.00757715],"study_design_scores_gemma":[0.00211712,0.00004440855,0.07381395,0.0001722895,0.0001335926,0.000006082133,0.008952451,0.03021354,0.0002082059,0.003122407,0.880979,0.0002369613],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9104715,0.0002083466,0.0178162,0.04742005,0.009116963,0.003505538,0.0001143456,0.0002273399,0.01111975],"genre_scores_gemma":[0.9984241,0.00000565372,0.00003222779,0.0001282957,0.00005757482,0.00004607001,0.000005864715,0.000004926209,0.001295344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7594951,"threshold_uncertainty_score":0.759051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004845364475918715,"score_gpt":0.2350283614136797,"score_spread":0.230182996937761,"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."}}