{"id":"W2082247991","doi":"10.1109/percomw.2013.6529488","title":"Sensor Mobile Enablement (SME): A light-weight standard for opportunistic sensing services","year":2013,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; Ontario Council on Graduate Studies, Council of Ontario Universities","keywords":"Interoperability; Computer science; Mobile device; Android (operating system); Coding (social sciences); Mobile computing; Mobile telephony; Computer network; World Wide Web; Mobile radio; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004153857,0.0002788418,0.0003244646,0.0001258203,0.0004082729,0.0008117984,0.0004678843,0.00008811389,0.0001371761],"category_scores_gemma":[0.0000167289,0.0002292721,0.0001288192,0.000231229,0.00003167654,0.0005017692,0.0002320625,0.0001080814,0.000140968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008722872,"about_ca_system_score_gemma":0.0001017042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001555209,"about_ca_topic_score_gemma":0.00004160791,"domain_scores_codex":[0.9978163,0.00005809792,0.0004342397,0.0006460429,0.0003726711,0.0006726711],"domain_scores_gemma":[0.9981878,0.0001679881,0.0001483508,0.0008448376,0.0003799953,0.0002710507],"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":[0.00007995937,0.0003586087,0.0005740215,0.001414164,0.0003852649,0.0001894515,0.01134131,0.001023655,0.1718113,0.05731969,0.06236786,0.6931347],"study_design_scores_gemma":[0.001748127,0.0007948708,0.00007664366,0.0003101229,0.00006875841,0.0001613508,0.002550658,0.5433326,0.06734122,0.007329476,0.3749429,0.001343223],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04815117,0.00008589508,0.9377169,0.001715816,0.0006993781,0.00145719,0.000009953668,0.000712878,0.009450797],"genre_scores_gemma":[0.755347,0.00001429924,0.2374007,0.001565529,0.0002818117,0.0001173948,0.00001389714,0.00004402935,0.005215376],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7071958,"threshold_uncertainty_score":0.9349443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009740589626307085,"score_gpt":0.2267921661279509,"score_spread":0.2170515765016438,"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."}}