{"id":"W2967611518","doi":"10.1145/3340496.3342760","title":"A recommendation system for emergency mobile applications using context attributes: REMAC","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Context (archaeology); Recommender system; Mobile computing; World Wide Web; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009863507,0.001156383,0.001146594,0.004112665,0.0007810738,0.001007885,0.001237109,0.0009036539,0.002702939],"category_scores_gemma":[0.003172737,0.0003926606,0.001009428,0.002452847,0.0001320466,0.001285864,0.0005359238,0.000913193,0.002625522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005607519,"about_ca_system_score_gemma":0.0007383339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01952914,"about_ca_topic_score_gemma":0.04546141,"domain_scores_codex":[0.9991512,0.0001164327,0.0001090943,0.0002914404,0.0002628218,0.00006891982],"domain_scores_gemma":[0.9983311,0.0004071646,0.0001179831,0.0003048711,0.0007249701,0.0001138476],"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":[0.001066546,0.001141639,0.03985738,0.0007480074,0.0005541062,0.0009398059,0.0004341546,0.01360991,0.01972361,0.001593165,0.06797595,0.8523557],"study_design_scores_gemma":[0.0002287538,0.000860734,0.05275169,0.0002405947,0.000639811,0.001743735,0.0009278216,0.8420873,0.02715999,0.002933199,0.07008488,0.0003415355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3474799,0.006516936,0.5042795,0.002035989,0.0007232733,0.003514601,0.02611274,0.09237215,0.01696502],"genre_scores_gemma":[0.4914361,0.001199403,0.4804877,0.0005777169,0.0001723791,0.0006303105,0.01781667,0.0003313635,0.007348378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01952914,"threshold_uncertainty_score":0.03883094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06202352660829485,"score_gpt":0.3058432957261746,"score_spread":0.2438197691178797,"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."}}