{"id":"W3041522139","doi":"10.1109/mobilecloud48802.2020.00015","title":"Participant Comfort Adaptation in Dependable Mobile Crowdsensing Services","year":2020,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Crowdsensing; Adaptation (eye); Computer science; Human–computer interaction; Computer security; Psychology","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":[0.002107808,0.0009545416,0.0007584835,0.0003906342,0.0009512542,0.001232669,0.002418742,0.001003916,0.002878376],"category_scores_gemma":[0.006372628,0.0003080883,0.0005213957,0.0003274628,0.0007406218,0.001492033,0.002896715,0.0008436088,0.0005998794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006737097,"about_ca_system_score_gemma":0.0007478024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488406,"about_ca_topic_score_gemma":0.001745745,"domain_scores_codex":[0.9982781,0.000626051,0.00007009923,0.0003992168,0.0003358772,0.0002907318],"domain_scores_gemma":[0.9976286,0.0009434153,0.0002538088,0.0004395629,0.0003512801,0.0003832683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.004356313,0.002110108,0.03187059,0.0008189449,0.000398157,0.003931149,0.004392357,0.4365206,0.1346615,0.0428279,0.007776946,0.3303354],"study_design_scores_gemma":[0.0001318734,0.0007379202,0.006102627,0.00002631543,0.0000773208,0.0005522636,0.0009274891,0.9578097,0.008385481,0.01998656,0.005175404,0.00008701798],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4942488,0.0004795853,0.4909153,0.0006391764,0.0001946216,0.0005168173,0.000185072,0.001667634,0.01115304],"genre_scores_gemma":[0.9924015,0.00002622794,0.00651453,0.00004005853,0.00001406645,0.0000518479,0.00002779299,0.00002522523,0.0008987124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002878376,"threshold_uncertainty_score":0.01114732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04634032800768971,"score_gpt":0.2469797998175974,"score_spread":0.2006394718099077,"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."}}