{"id":"W4409129151","doi":"10.1109/jiot.2025.3557451","title":"A Surrogate Metric-Based Framework for Placing Infrastructure Sensing Units to Enhance Cooperative Vehicle-Infrastructure Perception","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Anhui Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Metric (unit); Perception; Critical infrastructure; Computer network; Computer security; Distributed computing; Telecommunications; Engineering; Operations management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005176048,0.0002401235,0.0003949342,0.0006085346,0.0001371595,0.0001978684,0.0001889495,0.00033236,0.00003836921],"category_scores_gemma":[0.0007036172,0.000215897,0.000133017,0.0008919769,0.00002273208,0.000258957,0.00002145063,0.0009184344,0.000004382675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00035779,"about_ca_system_score_gemma":0.00009480113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003702263,"about_ca_topic_score_gemma":0.000004776668,"domain_scores_codex":[0.9985708,0.00008331802,0.000600078,0.0002013489,0.0002614566,0.0002830228],"domain_scores_gemma":[0.9986183,0.0003017535,0.0001969712,0.000150466,0.0006240014,0.0001085176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006590679,0.00001206061,0.000220055,0.0002798386,0.0003087294,0.00001291435,0.006230405,0.5731831,0.2794093,0.000149864,0.01617785,0.1233568],"study_design_scores_gemma":[0.001171825,0.0004987394,0.0006071441,0.003751661,0.00009674618,0.00009889053,0.001404981,0.3348058,0.6502272,0.001423244,0.005433403,0.0004803449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4715986,0.00003437861,0.5253462,0.00004515857,0.002615404,0.0001925683,0.000006241176,0.00005671818,0.0001047529],"genre_scores_gemma":[0.9820833,0.000004294058,0.01711769,0.0002330268,0.0004112804,0.000004173522,0.000002188362,0.00003222998,0.0001118684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5104847,"threshold_uncertainty_score":0.8804026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117077969873291,"score_gpt":0.2812209448925032,"score_spread":0.2700501651937702,"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."}}