{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004022162,0.0003687427,0.0002234183,0.0006138424,0.001449354,0.0008355061,0.0008597309,0.000467873,0.002732663],"category_scores_gemma":[0.002080971,0.000148547,0.000426656,0.001280136,0.0004016589,0.0003092059,0.0006028156,0.0004536159,0.0001445108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04814741,"about_ca_system_score_gemma":0.03229628,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9893647,"about_ca_topic_score_gemma":0.9922076,"domain_scores_codex":[0.9988124,0.0001643149,0.00002388432,0.0001050887,0.00045119,0.0004431025],"domain_scores_gemma":[0.9988809,0.0002256377,0.0001305891,0.00001526347,0.0005312764,0.0002161926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008434092,0.0005769933,0.7975041,0.0004859726,0.0002926594,0.001401483,0.001750501,0.07113372,0.003650675,0.00237287,0.008809798,0.1111776],"study_design_scores_gemma":[0.0001196423,0.0003416748,0.9064472,0.0001083337,0.0001801872,0.0003406522,0.004147738,0.07802973,0.003051858,0.000481398,0.006695461,0.00005604123],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889214,0.0005122802,0.0008268596,0.00128682,0.00001166831,0.00006064967,0.001120247,0.0000453258,0.007214824],"genre_scores_gemma":[0.9967781,0.0002674656,0.0006501628,0.00008993183,0.000004723689,0.0000139448,0.0003085629,0.000005557577,0.001881649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04814741,"threshold_uncertainty_score":0.3493356,"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."}}