{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002559254,0.0001418361,0.000207043,0.0000648355,0.0001103738,0.0002630769,0.0003444948,0.00005492583,0.00002071872],"category_scores_gemma":[0.00001610607,0.0001335654,0.00004687446,0.0004994645,0.00002081619,0.0005360717,0.0001658367,0.0001427923,0.00008129004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002442387,"about_ca_system_score_gemma":0.00005311908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004957213,"about_ca_topic_score_gemma":0.0003013244,"domain_scores_codex":[0.9985713,0.00006271287,0.0003269322,0.0004269321,0.000229097,0.0003830704],"domain_scores_gemma":[0.9993349,0.00006784242,0.00007284664,0.0003158974,0.000045332,0.000163203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008277575,0.0003318638,0.01229052,0.0004409739,0.00007363137,0.000730215,0.1129716,0.5922508,0.06579824,0.02196722,0.0008967551,0.1921654],"study_design_scores_gemma":[0.0003015569,0.00007533058,0.0005577483,0.00004305841,0.000005559679,0.00001542854,0.002189074,0.9832705,0.01105656,0.0002129368,0.00207126,0.0002009571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.752844,0.0001226562,0.2434018,0.0007016457,0.0001300556,0.0002086228,5.112859e-7,0.0003706776,0.002220023],"genre_scores_gemma":[0.9739864,0.000004265738,0.02451826,0.001356544,0.00004921653,0.00001112924,0.00000227429,0.00001134722,0.00006049996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3910197,"threshold_uncertainty_score":0.5446638,"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."}}