{"id":"W4255476311","doi":"10.36227/techrxiv.12101277.v1","title":"MobileCrowdSensing (MCS)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Participatory sensing; Android (operating system); Computer science; Wearable computer; Mobile device; Measure (data warehouse); Wearable technology; Human–computer interaction; Android application; Embedded system; Real-time computing; Data science; Data mining; World Wide Web; Operating system","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002927927,0.0004539873,0.0005270845,0.0001451316,0.0001770669,0.001092669,0.001610066,0.000349042,0.00003116854],"category_scores_gemma":[0.0001041921,0.0004439309,0.0002922785,0.0003229773,0.00006262467,0.0001738327,0.003978478,0.001078825,0.0003320219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008588203,"about_ca_system_score_gemma":0.000287616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009606606,"about_ca_topic_score_gemma":0.000006888398,"domain_scores_codex":[0.9971249,0.0001163422,0.0004633333,0.001333769,0.0004550534,0.0005065731],"domain_scores_gemma":[0.9974046,0.0001075479,0.0002117288,0.001841284,0.0001383783,0.0002964312],"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":[0.00002665974,0.0002894711,0.0003429651,0.001201627,0.0005298892,0.00169359,0.008459855,0.02761219,0.03011623,0.2721122,0.1282245,0.5293908],"study_design_scores_gemma":[0.0004146759,0.00008943318,0.0002894363,0.0004757353,0.0000547679,0.0001567922,0.000108513,0.8774267,0.02455256,0.06407467,0.03043508,0.00192165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007630588,0.0002838376,0.9421364,0.004834084,0.002095783,0.0003414762,0.000002867482,0.002107195,0.04056779],"genre_scores_gemma":[0.8407201,0.00002123962,0.1560862,0.001967135,0.0004580051,0.00001426812,0.0000105504,0.0000442631,0.0006782677],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8498145,"threshold_uncertainty_score":0.9999443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03159488426572834,"score_gpt":0.2499058764141223,"score_spread":0.2183109921483939,"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."}}