{"id":"W7093080805","doi":"10.5281/zenodo.17400552","title":"A 3D INDOOR-OUTDOOR BENCHMARK DATASET FOR LoD3 BUILDING POINT CLOUD SEMANTIC SEGMENTATION","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Point cloud; Segmentation; Benchmark (surveying); Semantics (computer science); Point (geometry); Quality (philosophy); Key (lock)","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":[0.0006319293,0.003948571,0.001744122,0.003063874,0.0009142277,0.001724637,0.004129854,0.002498214,0.009014936],"category_scores_gemma":[0.00141486,0.0006357091,0.002563932,0.004342945,0.000729168,0.0009152623,0.002134228,0.001737495,0.01311228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001661221,"about_ca_system_score_gemma":0.001373836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03838079,"about_ca_topic_score_gemma":0.1103023,"domain_scores_codex":[0.9986326,0.0001751866,0.0001030806,0.0003944294,0.0004869097,0.0002077693],"domain_scores_gemma":[0.9994372,0.00009083944,0.00003993048,0.0001953772,0.0001714339,0.00006528154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005867222,0.0006233902,0.006996653,0.002376407,0.0003754812,0.0006845351,0.0001797159,0.03379085,0.005049548,0.002538526,0.8717888,0.07500935],"study_design_scores_gemma":[0.0006142987,0.0003151665,0.03383854,0.0006919391,0.000214607,0.001635093,0.000852323,0.1343939,0.01695015,0.006187839,0.8040461,0.0002600742],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02033346,0.001144447,0.01269379,0.0003318455,0.0002472142,0.0003521222,0.9411218,0.01668338,0.007091964],"genre_scores_gemma":[0.01075441,0.0001719433,0.01130752,0.00007303221,0.00001311356,0.000176069,0.9760608,0.0003114533,0.001131676],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03838079,"threshold_uncertainty_score":0.07631481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06436399035214176,"score_gpt":0.2546175599059443,"score_spread":0.1902535695538026,"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."}}