{"id":"W4248235930","doi":"10.32920/ryerson.14656941","title":"Principal Component Analysis for ICP Pose Estimation of Space Structures","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pose; Point cloud; Principal component analysis; Laser scanning; Computer vision; Rendezvous; Artificial intelligence; Computer science; 3D pose estimation; Iterative closest point; Norm (philosophy); Pattern recognition (psychology); Algorithm; Engineering; Laser; Spacecraft; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001219486,0.0002256553,0.000645472,0.0002136012,0.00001795607,0.00006368521,0.0001396201,0.0002079369,0.0001090483],"category_scores_gemma":[0.00001907529,0.0002077745,0.0004257002,0.0001642579,0.00001159732,0.00003052764,0.00008110422,0.0001492223,0.000001102308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006620341,"about_ca_system_score_gemma":0.0000308421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003777753,"about_ca_topic_score_gemma":0.0002702384,"domain_scores_codex":[0.9990015,0.0000220573,0.0003792869,0.0002465823,0.0001851847,0.000165405],"domain_scores_gemma":[0.9992077,0.00005277954,0.0001125786,0.0004570951,0.0001156548,0.0000541903],"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":[0.000007385454,0.000009135983,0.0004076556,0.0006868993,0.001811612,0.000001091175,0.0002687013,0.9913347,0.002046055,0.001311864,0.00004485629,0.002070023],"study_design_scores_gemma":[0.0002434435,0.00001064881,0.01260713,0.00005201555,0.0007343602,8.42808e-7,0.000185981,0.9797972,0.005525842,0.0002760283,0.0003253397,0.000241101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3943457,0.001373289,0.6017337,0.00004909064,0.0006734403,0.0005895868,0.00007329159,0.0001511038,0.001010738],"genre_scores_gemma":[0.9776381,0.0000368564,0.02165713,0.000005260877,0.00009728066,0.00006166844,0.0002850461,0.00002766764,0.0001909644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5832924,"threshold_uncertainty_score":0.8472798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01229969339641531,"score_gpt":0.2419011953052978,"score_spread":0.2296015019088825,"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."}}