{"id":"W4253955821","doi":"10.36227/techrxiv.12101277","title":"MobileCrowdSensing (MCS)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Participatory sensing; Android (operating system); Computer science; Wearable computer; Mobile device; Human–computer interaction; Embedded system; World Wide Web; Data science; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006171518,0.0001310894,0.0002492945,0.00006980139,0.0004528304,0.0002226961,0.0003326729,0.0002540894,0.003095457],"category_scores_gemma":[0.0004004122,0.0001367495,0.000225863,0.0002326883,0.0002388816,0.00004177076,0.0001877225,0.0004199843,0.000393241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001525243,"about_ca_system_score_gemma":0.0007396218,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03357697,"about_ca_topic_score_gemma":0.03427774,"domain_scores_codex":[0.9984446,0.0002824155,0.0002383425,0.0004355284,0.0003829058,0.0002162402],"domain_scores_gemma":[0.9991101,0.0001404921,0.00009383559,0.0003317106,0.0001465893,0.0001772467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004812249,0.0006421825,0.006641674,0.000831163,0.001155476,0.00008289362,0.2298252,0.0190521,0.0008211035,0.3694652,0.07702556,0.2944093],"study_design_scores_gemma":[0.000386722,0.00005335863,0.002129294,0.0002870378,0.000687635,3.74511e-7,0.04122205,0.03700451,0.0009044299,0.2584183,0.6567102,0.002196066],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05743284,0.0003608868,0.06021232,0.05045214,0.001271396,0.001184194,0.00002653242,0.001277876,0.8277818],"genre_scores_gemma":[0.9936008,0.00006448337,0.0006856701,0.000997507,0.0007024974,0.00001571091,0.00004115271,0.00001064968,0.003881563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.936168,"threshold_uncertainty_score":0.9978158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07128802614824367,"score_gpt":0.3605677078097633,"score_spread":0.2892796816615196,"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."}}