{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004100316,0.001010997,0.0009621026,0.001046656,0.000665906,0.0009037856,0.001700221,0.000970847,0.006112144],"category_scores_gemma":[0.001568387,0.0003563618,0.0005099071,0.001107535,0.000544632,0.0008488872,0.002582777,0.0005237447,0.001938218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007108237,"about_ca_system_score_gemma":0.000709874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004573751,"about_ca_topic_score_gemma":0.00439795,"domain_scores_codex":[0.9988039,0.0001003536,0.00003760848,0.0003573854,0.0005795561,0.0001212234],"domain_scores_gemma":[0.9989386,0.0002120278,0.00008588349,0.0003268201,0.0002958823,0.0001407179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001333018,0.0004454717,0.01383818,0.001962919,0.0003276053,0.0009540799,0.0007188739,0.06240655,0.1873835,0.02219568,0.06937315,0.639061],"study_design_scores_gemma":[0.0002482321,0.0009111047,0.01504775,0.0001648476,0.0001742713,0.001330048,0.0003275796,0.5671634,0.1864945,0.01420992,0.2136817,0.0002466106],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.148754,0.004455192,0.7544596,0.001394376,0.001865679,0.001378974,0.01094453,0.03561923,0.04112853],"genre_scores_gemma":[0.8194783,0.001180936,0.1601582,0.0006354321,0.0004657043,0.000478618,0.003204913,0.0006568144,0.01374111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006112144,"threshold_uncertainty_score":0.02044719,"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."}}