{"id":"W2129091674","doi":"10.1177/0278364910369190","title":"Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation","year":2010,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":157,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Multidisciplinary University Research Initiative; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Point cloud; Computer science; Domain (mathematical analysis); Object (grammar); Artificial intelligence; Adaptation (eye); Cognitive neuroscience of visual object recognition; Domain adaptation; Robot; Field (mathematics); Robotics; Point (geometry); Computer vision; Data set; Object detection; Set (abstract data type); Machine learning; Data mining; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002569685,0.00007345519,0.0001034633,0.0003668881,0.00005813712,0.0001506917,0.0005574819,0.00006385976,0.00002275267],"category_scores_gemma":[0.000373565,0.0000583141,0.00002102948,0.0002060383,0.000080288,0.0002670636,0.0001240551,0.0007311075,0.000005124034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001030792,"about_ca_system_score_gemma":0.0001243789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004587135,"about_ca_topic_score_gemma":0.0003156286,"domain_scores_codex":[0.9985289,0.0001140328,0.0003579104,0.00009475154,0.0007415702,0.0001628543],"domain_scores_gemma":[0.9987991,0.0003093514,0.00007275501,0.0001912906,0.0005716664,0.00005581836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006531948,0.00003732682,0.000793264,0.0000120285,0.000055081,0.00004661402,0.0004937951,0.947285,0.04345724,0.0005927963,0.0001786165,0.006982905],"study_design_scores_gemma":[0.0004416777,0.00003237821,0.0007444893,0.00007316613,0.000007210634,0.0001612512,0.0003856448,0.9929315,0.0005483212,0.004399104,0.0002096101,0.00006559758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910311,0.0001286664,0.1060623,0.001230034,0.001056539,0.0001199618,0.00001978136,0.0000109818,0.0003405843],"genre_scores_gemma":[0.9690511,0.0003389633,0.03014506,0.00002468163,0.0003892223,5.409703e-7,0.00002423997,0.00001876391,0.000007385186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07802,"threshold_uncertainty_score":0.3176339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1304818124340743,"score_gpt":0.3624452842621493,"score_spread":0.2319634718280749,"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."}}