{"id":"W2884938217","doi":"10.1109/access.2018.2858751","title":"From E-911 to NG-911: Overview and Challenges in Ecuador","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Universidad de las Fuerzas Armadas ESPE","keywords":"Service (business); Attendance; Population; Computer science; Computer security; Business; Economic growth; Marketing; Sociology","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":[0.0001362151,0.0001503634,0.0002224769,0.00009754443,0.00003034495,0.00006191572,0.0002637451,0.00009211413,0.00005954643],"category_scores_gemma":[0.0000094539,0.0001465754,0.00002542561,0.0001657573,0.00002112863,0.0002511822,0.00004717221,0.00009474323,0.0002009988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003821152,"about_ca_system_score_gemma":0.000008532658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006544535,"about_ca_topic_score_gemma":0.001848952,"domain_scores_codex":[0.9991777,0.0000263557,0.0002021104,0.0002301795,0.0001107944,0.0002528946],"domain_scores_gemma":[0.999534,0.00005400927,0.00001889317,0.0002680926,0.0000275843,0.00009745838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00015174,0.0001294959,0.01809724,0.0018004,0.0002079827,0.0001631616,0.009833206,0.002025591,0.01117505,0.0001821658,0.01776281,0.9384711],"study_design_scores_gemma":[0.003895195,0.0004329119,0.6152146,0.002603343,0.00007520927,0.00002009014,0.000924703,0.02123769,0.1233485,0.0025483,0.227107,0.002592565],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849204,0.009459891,0.0001097223,0.0005570329,0.001343781,0.0002185559,0.00001587106,0.0001535766,0.003221144],"genre_scores_gemma":[0.996916,0.00169426,0.00005356318,0.0001579639,0.001078459,0.00002684426,0.000002598009,0.00003214959,0.00003819516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9358786,"threshold_uncertainty_score":0.5977171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09725560515705992,"score_gpt":0.3081313626655543,"score_spread":0.2108757575084944,"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."}}