{"id":"W1746043309","doi":"10.1109/ccgrid.2015.155","title":"Towards Context-Aware Mobile Crowdsensing in Vehicular Social Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Crowdsensing; Context (archaeology); Service (business); Wireless sensor network; Context awareness; Computer security; Computer network; Business; Geography","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":[0.001129761,0.0005594976,0.0006551869,0.0006801922,0.0008822107,0.001568001,0.001141922,0.001242934,0.0003458162],"category_scores_gemma":[0.002933988,0.00038506,0.0007407452,0.0005869939,0.0008874337,0.001675056,0.002333893,0.0009716173,0.0001738201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008962345,"about_ca_system_score_gemma":0.001236886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007660269,"about_ca_topic_score_gemma":0.007538053,"domain_scores_codex":[0.999029,0.0003675268,0.0000481884,0.0001872072,0.0002616107,0.0001063786],"domain_scores_gemma":[0.9991294,0.0003709534,0.0001015372,0.00009803071,0.0002116607,0.00008853415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002263724,0.0002359132,0.006996437,0.0003674893,0.0001289762,0.001008494,0.0015862,0.7142293,0.03613689,0.11204,0.003843449,0.1232004],"study_design_scores_gemma":[0.000007966371,0.00003186794,0.000311744,0.00001663755,0.00001427941,0.00006546247,0.0002606589,0.9760833,0.00197534,0.01756976,0.003645155,0.00001775424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04383957,0.0008050173,0.9505903,0.0008034404,0.0000977618,0.0001438271,0.00005753849,0.0003320639,0.003330368],"genre_scores_gemma":[0.8496587,0.0007315408,0.1476137,0.0002017395,0.0001007767,0.0001188516,0.00006420854,0.00002646789,0.001484007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007660269,"threshold_uncertainty_score":0.01523137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557711469805104,"score_gpt":0.2555800667137301,"score_spread":0.230002952015679,"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."}}