{"id":"W4409793594","doi":"10.61091/jcmcc127a-184","title":"Research on point cloud data optimization and feature extraction during 3D reconstruction of high-speed rail wheelsets","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Optical Systems and Laser Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Point cloud; Extraction (chemistry); Feature (linguistics); Computer science; Cloud computing; Point (geometry); Feature extraction; Data extraction; Artificial intelligence; Data mining; Pattern recognition (psychology); Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007414667,0.0008316767,0.0009698013,0.001295855,0.0004672772,0.001160936,0.001174545,0.0007377529,0.0008990981],"category_scores_gemma":[0.001825142,0.0006104146,0.001352095,0.001967754,0.0004387768,0.001824227,0.0008368935,0.0007719182,0.0004078199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004438211,"about_ca_system_score_gemma":0.001154029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007129296,"about_ca_topic_score_gemma":0.003509433,"domain_scores_codex":[0.9990447,0.00007934569,0.00005649971,0.0002337486,0.0004762935,0.000109417],"domain_scores_gemma":[0.9994436,0.0001123203,0.00008293891,0.00008416638,0.0002414992,0.00003553559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002727434,0.0001613183,0.01030639,0.0004705183,0.0001388202,0.0004842275,0.000615478,0.3504396,0.1143092,0.005058848,0.002511058,0.5152317],"study_design_scores_gemma":[0.000007175802,0.00004431176,0.003581537,0.000009808637,0.00001854367,0.0001145168,0.000128985,0.9772471,0.0170114,0.0008616163,0.0009540566,0.00002090227],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05524856,0.0002520315,0.9431267,0.0001106199,0.00003202125,0.00004520486,0.00008731116,0.0004956402,0.0006019307],"genre_scores_gemma":[0.6691308,0.0008639386,0.3271903,0.00007204965,0.00004524802,0.0001199622,0.0006573318,0.0001770805,0.001743336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007129296,"threshold_uncertainty_score":0.01417559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803735622393428,"score_gpt":0.2821785178382757,"score_spread":0.2641411616143415,"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."}}