{"id":"W2860417883","doi":"10.1109/iccw.2018.8403744","title":"Robust Quality Metric for Scarce Mobile Crowd-Sensing Scenarios","year":2018,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Metric (unit); Outlier; Computer science; Quality (philosophy); Range (aeronautics); Sample (material); Centrality; Sample size determination; Data mining; Statistics; Mathematics; Artificial intelligence; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001305479,0.0002215445,0.0002985323,0.0002502188,0.0005829682,0.0004840093,0.0006524222,0.0001114096,0.00003051457],"category_scores_gemma":[0.0002952088,0.0001990119,0.0001676446,0.001025091,0.0001564391,0.00037716,0.0002712321,0.0001282293,0.0001089197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008134825,"about_ca_system_score_gemma":0.0001001247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002541293,"about_ca_topic_score_gemma":0.00008073755,"domain_scores_codex":[0.9977809,0.0001120377,0.0004231265,0.0007335927,0.000340903,0.0006094418],"domain_scores_gemma":[0.997735,0.0004318859,0.0001419357,0.001051522,0.0004611157,0.0001785424],"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":[0.00007910201,0.0003173901,0.001165504,0.0002076576,0.0001408036,0.00002065512,0.004344084,0.006726695,0.04184196,0.0413575,0.02592387,0.8778748],"study_design_scores_gemma":[0.00134382,0.0006392382,0.001551323,0.00009338621,0.00003310065,0.00009640877,0.0005026687,0.8397813,0.1154766,0.002557756,0.03682247,0.001101925],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08683205,0.00008627465,0.9055036,0.0002488327,0.000706883,0.0004003173,0.000001792438,0.0005124887,0.005707809],"genre_scores_gemma":[0.7727172,0.000001834106,0.2247948,0.0005487904,0.0003569258,0.00001069083,0.000001714242,0.00001915887,0.001548795],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8767729,"threshold_uncertainty_score":0.8115468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05405144786018457,"score_gpt":0.3000625953822249,"score_spread":0.2460111475220403,"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."}}