{"id":"W4400486064","doi":"10.61091/jcmcc120-01","title":"Fusion of Multiple Basic Element Features for Airborne LiDAR in-house Surveys","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Fusion; Remote sensing; Element (criminal law); Sensor fusion; Computer science; Environmental science; Geology; Artificial intelligence; Political science","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":[0.0003041229,0.0005107929,0.0005639429,0.002666607,0.0002405832,0.0006086241,0.0004885575,0.0003355494,0.001004539],"category_scores_gemma":[0.001000772,0.0003314799,0.0006966136,0.002468527,0.000133478,0.001041639,0.0008528174,0.0002983254,0.0006359478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002386698,"about_ca_system_score_gemma":0.0002853612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00258304,"about_ca_topic_score_gemma":0.00420676,"domain_scores_codex":[0.999468,0.00006397448,0.00002152442,0.00008030734,0.0002969558,0.00006917752],"domain_scores_gemma":[0.999631,0.00004936135,0.00004155156,0.00007223255,0.0001829663,0.00002286763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003115154,0.0001800029,0.02829954,0.0002199514,0.0001495759,0.0003956357,0.0002795274,0.05453178,0.1235425,0.001500772,0.001910553,0.7886788],"study_design_scores_gemma":[0.00003397232,0.0003714073,0.07928719,0.00003832194,0.000136553,0.0005940123,0.0005520727,0.8504919,0.06060636,0.002063367,0.005740823,0.00008390327],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3424132,0.0003117755,0.6520708,0.00007924745,0.00005979687,0.00011684,0.0006333,0.001381828,0.002933209],"genre_scores_gemma":[0.8362445,0.0001529782,0.1620157,0.00002053286,0.00001698149,0.00004370212,0.0008449325,0.00004931212,0.0006113968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002666607,"threshold_uncertainty_score":0.005136013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798959818981755,"score_gpt":0.2448519271720154,"score_spread":0.2268623289821979,"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."}}