{"id":"W1998298178","doi":"10.1089/tmj.2010.0057","title":"The Impact of Telemedicine on Greenhouse Gas Emissions at an Academic Health Science Center in Canada","year":2010,"lang":"en","type":"article","venue":"Telemedicine Journal and e-Health","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Greenhouse gas; Tonne; Environmental science; Calculator; Carbon dioxide; Energy consumption; Metric (unit); Greenhouse effect; Environmental engineering; Global warming; Engineering; Waste management; Climate change; Operations management; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005792568,0.0003361389,0.0009308864,0.0005871481,0.001154497,0.00001469372,0.0003413733,0.00006883998,0.0003678017],"category_scores_gemma":[0.0003055336,0.0001833743,0.0000720412,0.0008933956,0.0004724847,0.0001512241,0.00008321929,0.002298502,0.000001713174],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001950898,"about_ca_system_score_gemma":0.01739882,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.607328,"about_ca_topic_score_gemma":0.7123597,"domain_scores_codex":[0.9946036,0.000127144,0.001984387,0.0004193737,0.001452032,0.001413471],"domain_scores_gemma":[0.9952251,0.0001762119,0.001217966,0.0004833393,0.0002271539,0.002670286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003816992,0.0001827125,0.6382397,0.0001567393,0.00002904214,0.00003591602,0.001555074,0.000002962599,0.001415062,0.00006792777,0.08722141,0.2707117],"study_design_scores_gemma":[0.00631401,0.009558766,0.9656801,0.0007763512,0.00001396278,0.00237748,0.003493323,0.0003201029,0.0001099462,0.00008666954,0.01112093,0.0001483361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8723262,0.00119638,0.00001046461,0.1248337,0.0005765887,0.0008524713,0.00002605372,0.00002066018,0.00015754],"genre_scores_gemma":[0.9791776,0.008573384,0.0001916318,0.01094168,0.0009373142,0.00001039921,0.00004394405,0.00003268944,0.00009136543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3274404,"threshold_uncertainty_score":0.9985977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04668555425076167,"score_gpt":0.4142202102190762,"score_spread":0.3675346559683145,"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."}}