{"id":"W2132831870","doi":"10.1109/robot.2006.1642246","title":"Underwater 3D SLAM through entropy minimization","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University; York University; University of Michigan","keywords":"Underwater; Exploit; Artificial intelligence; Computer science; Simultaneous localization and mapping; Computer vision; Robotics; Minification; Entropy (arrow of time); Remotely operated underwater vehicle; Robot; Geology; Mobile robot; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004821893,0.0006304478,0.0006737277,0.0007724838,0.0003618868,0.0005146347,0.0005521457,0.0003605685,0.001305639],"category_scores_gemma":[0.001010792,0.0004477956,0.0005193363,0.000792529,0.0005579572,0.0009568116,0.001528654,0.0005466197,0.0006451366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003945461,"about_ca_system_score_gemma":0.0006432572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00273413,"about_ca_topic_score_gemma":0.002573503,"domain_scores_codex":[0.9995605,0.00008278724,0.00001800047,0.00006999997,0.0002316679,0.00003703445],"domain_scores_gemma":[0.999689,0.0001066266,0.00005436422,0.00007231552,0.00006274875,0.00001492153],"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":[0.00009685131,0.00003581705,0.000672814,0.00007647102,0.00006023783,0.00006059009,0.0001009776,0.638485,0.01925308,0.01355821,0.002042399,0.3255575],"study_design_scores_gemma":[0.00001204244,0.00003077971,0.0003911839,0.000006241141,0.000005590864,0.00003781898,0.00001498206,0.9835888,0.005339678,0.00897508,0.001586757,0.00001095135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005658313,0.0000367127,0.9932094,0.00003185396,0.000008015804,0.00001278196,0.00002869727,0.0004003622,0.0006138749],"genre_scores_gemma":[0.3951667,0.00020692,0.5998293,0.00007866692,0.00005883879,0.0001458487,0.0004375767,0.0003545406,0.003721545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00273413,"threshold_uncertainty_score":0.00543642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007451691811504245,"score_gpt":0.1882559464205212,"score_spread":0.1808042546090169,"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."}}