{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000358442,0.0009551666,0.0009376191,0.001232927,0.0005240373,0.0008815175,0.00150472,0.0009202636,0.005105212],"category_scores_gemma":[0.001287847,0.0003128284,0.0004470028,0.001365598,0.0003764643,0.0006792283,0.001898202,0.0004314811,0.001793513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006359966,"about_ca_system_score_gemma":0.0004963209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004114673,"about_ca_topic_score_gemma":0.004488247,"domain_scores_codex":[0.9988562,0.00008891434,0.00003375735,0.0003813186,0.000547311,0.00009251485],"domain_scores_gemma":[0.999195,0.0001615162,0.00009006439,0.0001987936,0.0002518836,0.0001026884],"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":[0.001229689,0.000491914,0.02807124,0.001774211,0.0003436587,0.0008956622,0.0006366665,0.04452996,0.2273244,0.01205105,0.04836085,0.6342908],"study_design_scores_gemma":[0.0002223428,0.00102509,0.03049984,0.0001703728,0.000187837,0.001385755,0.0003751278,0.6230881,0.2053184,0.009281103,0.1281952,0.0002509108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2562291,0.005290843,0.6433663,0.001432789,0.002199498,0.001370502,0.01848771,0.02878333,0.04283994],"genre_scores_gemma":[0.8520551,0.001077452,0.1315187,0.0005059448,0.0005027276,0.0003676694,0.002900955,0.0004184034,0.01065311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005105212,"threshold_uncertainty_score":0.01707858,"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."}}