{"id":"W3097669541","doi":"10.18280/jesa.530412","title":"Data Collection for Mobile Crowd Sensing Based on Tensor Completion","year":2020,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Data collection; Context (archaeology); Mobile device; Big data; Set (abstract data type); Tensor (intrinsic definition); Data set; Adaptive sampling; Sampling (signal processing); Data mining; Real-time computing; Artificial intelligence; Computer vision; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001450386,0.001257503,0.001004869,0.001443037,0.001069864,0.001093921,0.001114368,0.0006944272,0.001103665],"category_scores_gemma":[0.003922399,0.0004840405,0.001322145,0.001727315,0.001201305,0.002005772,0.002313009,0.001382813,0.0004996703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002532,"about_ca_system_score_gemma":0.001866055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01059699,"about_ca_topic_score_gemma":0.008053827,"domain_scores_codex":[0.9985801,0.0004090267,0.0000928179,0.0003642567,0.000397558,0.0001561838],"domain_scores_gemma":[0.9983595,0.0004262647,0.0001869597,0.0002706835,0.0005981273,0.0001583611],"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":[0.0007500901,0.0003025553,0.007229773,0.0005593548,0.0002576009,0.0005273382,0.001590259,0.4071001,0.08511446,0.03820685,0.008588473,0.4497731],"study_design_scores_gemma":[0.000009568674,0.00006928478,0.000685512,0.00001004934,0.000015684,0.00007344443,0.000122711,0.9825966,0.00648381,0.008165852,0.001740143,0.00002733997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01245027,0.00009403341,0.9862207,0.0001528985,0.00003663539,0.00007569378,0.0001065427,0.0003528024,0.0005103937],"genre_scores_gemma":[0.3640569,0.0003993061,0.6320172,0.0001432476,0.0001409964,0.0002868857,0.0008912364,0.0001334047,0.00193083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01059699,"threshold_uncertainty_score":0.0210706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05728904568907689,"score_gpt":0.2808527932297992,"score_spread":0.2235637475407223,"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."}}