{"id":"W1566162777","doi":"","title":"First Nations led telemedicine: From access to effective use","year":2010,"lang":"en","type":"article","venue":"The Atrium (University of Guelph)","topic":"Digital Imaging in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Telemedicine; Internet privacy; Business; Computer science; Telecommunications; Economic growth; Economics; Health care","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":[0.0002313559,0.0001334888,0.0003027008,0.0002604105,0.0002375399,0.00002724649,0.0005240559,0.00006716239,0.0004390579],"category_scores_gemma":[0.001324393,0.0001098946,0.0001002847,0.0006483953,0.0003593696,0.0004437905,0.0002800357,0.0003524076,0.0001417244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004913344,"about_ca_system_score_gemma":0.00006591869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003724664,"about_ca_topic_score_gemma":0.003134183,"domain_scores_codex":[0.9990518,0.00003000479,0.0001194575,0.0002387593,0.0003624139,0.0001975257],"domain_scores_gemma":[0.9979918,0.0008296832,0.00009891916,0.000590533,0.0002850327,0.0002040244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002263464,0.0009052081,0.2503019,0.0003441367,0.001078853,0.0004975148,0.01246528,0.0001162201,0.5662584,0.00188874,0.1414166,0.02246373],"study_design_scores_gemma":[0.001916285,0.0002010128,0.9562346,0.000150093,0.0003344696,0.00001968063,0.0009615868,0.0002450882,0.0007393385,0.0001400121,0.03894231,0.0001154777],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639103,0.00002290087,0.001594642,0.02557957,0.0004310825,0.0008192224,0.00004182174,0.00009118112,0.007509294],"genre_scores_gemma":[0.9952302,0.00001677409,0.001794336,0.0007472198,0.0002278388,0.000001065264,0.00003871838,0.00001613616,0.001927685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7059328,"threshold_uncertainty_score":0.5630602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568040590077387,"score_gpt":0.2595508426100635,"score_spread":0.2438704367092896,"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."}}