{"id":"W7099797904","doi":"","title":"MOBILITY/SMART Panel Mobility, Smart Cities and Urbanicity: Handling Mobile Citizen Data","year":2015,"lang":"en","type":"article","venue":"","topic":"Ichthyology and Marine Biology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Standardization; Mobile telephony; Metis; Mobile broadband; Term (time); Wireless; Key (lock); Wireless network; Mobile technology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000964788,0.0002960841,0.0001696979,0.0008314989,0.001036845,0.006267737,0.0004406685,0.001050979,0.01174603],"category_scores_gemma":[0.002517822,0.000170938,0.0002586542,0.002162657,0.001810152,0.006432791,0.002277307,0.001259673,0.002580081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002039817,"about_ca_system_score_gemma":0.002200625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01412335,"about_ca_topic_score_gemma":0.018053,"domain_scores_codex":[0.9992132,0.0002266619,0.00003109299,0.00008480203,0.0002445427,0.0001996549],"domain_scores_gemma":[0.9987285,0.0003826277,0.0002057119,0.00006720842,0.0004044738,0.0002114727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001079743,0.0001016496,0.04920859,0.001199692,0.00004982801,0.0005915153,0.008247985,0.003359633,0.002099883,0.3473567,0.1442331,0.4434434],"study_design_scores_gemma":[0.000009907088,0.00007275852,0.03468086,0.001193267,0.00003546961,0.00053089,0.02089242,0.003070118,0.001492898,0.06330551,0.8746392,0.00007673071],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1661114,0.03975103,0.04156589,0.2367872,0.003136774,0.0003076788,0.002719973,0.0006217302,0.5089983],"genre_scores_gemma":[0.8671725,0.04107049,0.01301702,0.01027015,0.001900789,0.00009800556,0.001136897,0.0002049531,0.06512923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01412335,"threshold_uncertainty_score":0.03929436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06313716338688237,"score_gpt":0.2673076111148243,"score_spread":0.2041704477279419,"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."}}