{"id":"W2140551145","doi":"10.1109/lcn.2009.5355060","title":"Using passive RFID tags for vehicle-assisted data dissemination in intelligent transportation systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Exploit; Intelligent transportation system; Vehicular ad hoc network; Computer science; Scheme (mathematics); Dissemination; Vehicular communication systems; Computer network; Communications system; Government (linguistics); Telecommunications; Embedded system; Computer security; Wireless ad hoc network; Wireless; Transport engineering; 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":[0.0002786523,0.0001156332,0.0001696059,0.00009513593,0.00006977737,0.0001321519,0.0005542705,0.00007826716,0.000004615926],"category_scores_gemma":[0.000005856614,0.0001050574,0.00002966346,0.0002976718,0.00001296204,0.000599907,0.00002008926,0.00007122576,0.000001961426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004783769,"about_ca_system_score_gemma":0.00006005936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006925606,"about_ca_topic_score_gemma":0.00004730837,"domain_scores_codex":[0.998762,0.00003040504,0.0003966468,0.0003948129,0.0001962626,0.000219838],"domain_scores_gemma":[0.9990811,0.0001252712,0.0001260528,0.0005003269,0.0000946645,0.0000726],"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":[0.0000394124,0.0002156965,0.0005955521,0.00004447157,0.00001531848,0.00004440144,0.0007304492,0.00202502,0.001056973,0.05010129,0.002967009,0.9421644],"study_design_scores_gemma":[0.0002006825,0.00005654214,0.002716022,0.00009150706,0.00001116069,0.000005085653,0.0001584973,0.9954679,0.0001092179,0.0006206626,0.000420225,0.0001424663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009975354,0.00008831017,0.9883479,0.0003542533,0.0003443335,0.0004049462,0.00002644489,0.00007161351,0.0003868607],"genre_scores_gemma":[0.975538,0.00001487381,0.02386148,0.0001150406,0.00006030001,0.000009514947,0.0002588459,0.000005468991,0.0001364533],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9934429,"threshold_uncertainty_score":0.4284118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070092372173552,"score_gpt":0.3443556933207333,"score_spread":0.2373464561033781,"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."}}