{"id":"W4410907418","doi":"10.21428/d82e957c.3dadc687","title":"SemanticOBB: Semantic Front Estimation for Indoor 3D Objects","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Front (military); Estimation; Computer science; Artificial intelligence; Computer vision; Environmental science; Geology; Engineering; Systems engineering; Oceanography","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":[0.0009424943,0.003301871,0.002506681,0.003170976,0.0009465629,0.002910764,0.003500929,0.002345643,0.008540768],"category_scores_gemma":[0.002961963,0.001156838,0.002219449,0.00220829,0.001032671,0.003023388,0.0048173,0.002301201,0.008621076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008242484,"about_ca_system_score_gemma":0.001197105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009569378,"about_ca_topic_score_gemma":0.0181486,"domain_scores_codex":[0.9987584,0.0001158198,0.00003835734,0.0003531355,0.0005777733,0.0001564457],"domain_scores_gemma":[0.999405,0.0001315971,0.00005918331,0.0002047113,0.0001485989,0.00005084221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009659425,0.0004722699,0.005092782,0.001217869,0.000388436,0.0003563794,0.0004017905,0.1066815,0.04025074,0.0124803,0.05067516,0.7810169],"study_design_scores_gemma":[0.0000743686,0.0001428329,0.002386665,0.0001927667,0.00005664932,0.0004350456,0.0002833331,0.9216042,0.02527729,0.02406451,0.02540235,0.00008005003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009920426,0.0007416526,0.9539658,0.0001695134,0.0001361278,0.0001605819,0.002898685,0.02940633,0.002600839],"genre_scores_gemma":[0.139941,0.0005895245,0.8389608,0.0002717411,0.00008289066,0.0002117698,0.01468841,0.002345619,0.002908191],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009569378,"threshold_uncertainty_score":0.02857172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005960954610245333,"score_gpt":0.2226246702902938,"score_spread":0.2166637156800485,"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."}}