{"id":"W3003750732","doi":"10.1109/iscc47284.2019.8969704","title":"Trustworthiness and Comfort-Aware Participant Recruitment for Mobile Crowd-Sensing in Smart Environments","year":2019,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reputation; Task (project management); Computer science; Trustworthiness; Human–computer interaction; Set (abstract data type); Selection (genetic algorithm); Crowdsourcing; Mobile device; Artificial intelligence; Internet privacy; Engineering; World Wide Web","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":[0.0004834257,0.0001853136,0.0002861386,0.00008915151,0.0001076664,0.0001484965,0.0002030599,0.00007558461,0.00001685777],"category_scores_gemma":[0.00001179908,0.0001646171,0.00005439977,0.0001451342,0.00004135361,0.0002490799,0.000193997,0.0001071722,0.0000214554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005475961,"about_ca_system_score_gemma":0.00002755684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008276092,"about_ca_topic_score_gemma":0.00006234949,"domain_scores_codex":[0.9984395,0.00005051088,0.0003310429,0.0005410984,0.0001782393,0.0004596141],"domain_scores_gemma":[0.9991115,0.0001561978,0.00008033319,0.0005271631,0.0000135667,0.0001111765],"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":[0.0001764937,0.0007194697,0.168264,0.0004471368,0.0001791452,0.00012015,0.01235933,0.02559852,0.02452822,0.01231292,0.001270008,0.7540246],"study_design_scores_gemma":[0.00297901,0.0004777249,0.01656722,0.0002503991,0.00003159215,0.00006339698,0.0009329091,0.9200086,0.01286725,0.001293841,0.04365019,0.0008778768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8510816,0.0001021517,0.1466278,0.0001577716,0.0003256426,0.001263767,0.000001522776,0.00007794518,0.0003618825],"genre_scores_gemma":[0.9861737,0.0000145491,0.01194227,0.0002897665,0.00002375113,0.00008796957,0.000003888643,0.00001667311,0.001447436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8944101,"threshold_uncertainty_score":0.6712891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05322203796506527,"score_gpt":0.2810886583752472,"score_spread":0.2278666204101819,"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."}}