{"id":"W3195124498","doi":"10.1109/cvprw53098.2021.00262","title":"Reconsidering CO2 emissions from Computer Vision","year":2021,"lang":"en","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Pillar; Architecture; Enforcement; Climate change; Work (physics); Affect (linguistics); Choice architecture; Computer science; Greenhouse gas; Law enforcement; Ethical issues; Computer security; Environmental economics; Business; Political science; Engineering; Sociology; Law; Psychology; Engineering ethics; Economics; Social psychology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000035154,0.00006530568,0.00009130855,0.00001433123,0.00003715807,0.00003108764,0.00003918608,0.00004571456,0.001438537],"category_scores_gemma":[0.00001709194,0.00006110228,0.00003857695,0.00006584261,0.000008335734,0.00006436791,0.00004529478,0.00008400261,0.0000498372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003141304,"about_ca_system_score_gemma":0.00001624073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001655933,"about_ca_topic_score_gemma":0.00006933353,"domain_scores_codex":[0.999586,0.00001271654,0.000108552,0.0001187588,0.00005312431,0.0001208657],"domain_scores_gemma":[0.9996251,0.00006824255,0.000004045818,0.0001961405,0.00003919018,0.00006730085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000207113,0.0002459886,0.06324989,0.0003633001,0.0002868687,0.001107985,0.004178836,0.1074587,0.07269785,0.001481427,0.2480678,0.5008407],"study_design_scores_gemma":[0.0006419178,0.00003381408,0.05763943,0.00008849975,0.00002188853,0.0000291405,0.001183239,0.7781401,0.05765184,0.00863134,0.09533398,0.0006048647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485731,0.0001406646,0.0405702,0.0002504776,0.0003678479,0.00003583744,0.000003044627,0.0003260553,0.009732734],"genre_scores_gemma":[0.986602,0.000009149116,0.01263279,0.00006579325,0.00008169366,0.000001593517,0.00001068319,0.00001069862,0.0005855768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6706814,"threshold_uncertainty_score":0.9994743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01913647150492459,"score_gpt":0.2198415523321046,"score_spread":0.2007050808271801,"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."}}