{"id":"W4410049259","doi":"10.1145/3722570.3726896","title":"Quantitative Assessment of mmWave Point Cloud for Target Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Cloud computing; Remote sensing; Point cloud; Point (geometry); Artificial intelligence; Geology","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":[0.002680395,0.0005769487,0.0004244009,0.003668458,0.000273599,0.001504338,0.000715925,0.0007303545,0.001378878],"category_scores_gemma":[0.006559795,0.0001685961,0.0002892524,0.00182884,0.0004393177,0.0017028,0.001077903,0.0004530791,0.0005025147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000511428,"about_ca_system_score_gemma":0.0003646667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001726005,"about_ca_topic_score_gemma":0.002133755,"domain_scores_codex":[0.9976481,0.0003122972,0.0001098871,0.0001760601,0.001614199,0.0001394747],"domain_scores_gemma":[0.995603,0.00143434,0.0006104771,0.0005086387,0.001716772,0.0001267907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009040415,0.0002762391,0.07503725,0.001098427,0.000262232,0.0003199117,0.0004752971,0.1659193,0.2342796,0.007727414,0.003186504,0.5105138],"study_design_scores_gemma":[0.00003128552,0.0005464381,0.1005005,0.0001661258,0.00007238165,0.0005303209,0.0004714644,0.7696594,0.1165695,0.005191125,0.006136332,0.000125149],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2947616,0.001138669,0.6957835,0.0002013715,0.0001133163,0.0002159779,0.001723529,0.001253966,0.004808191],"genre_scores_gemma":[0.8570781,0.0004296135,0.1402034,0.00007107615,0.00004815168,0.00008175019,0.001377085,0.0001237702,0.0005870825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003668458,"threshold_uncertainty_score":0.01417542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261373904868553,"score_gpt":0.3079683315486329,"score_spread":0.2953545924999474,"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."}}