{"id":"W4319998008","doi":"10.1109/tro.2022.3229842","title":"Improving Self-Consistency in Underwater Mapping Through Laser-Based Loop Closure","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subsea; Bathymetry; Underwater; Simultaneous localization and mapping; Computer science; Real-time computing; Consistency (knowledge bases); Navigation system; Global Positioning System; Computer vision; Marine engineering; Artificial intelligence; Engineering; Mobile robot; Robot; Geography; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001612776,0.0006532401,0.0007156739,0.0008239414,0.0005170438,0.0008893933,0.001278161,0.0005919331,0.0008514196],"category_scores_gemma":[0.008443642,0.0004967861,0.0004633152,0.0006504309,0.0008193771,0.001689799,0.001892517,0.0009062625,0.0003752953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004946836,"about_ca_system_score_gemma":0.0009698606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004670021,"about_ca_topic_score_gemma":0.004420997,"domain_scores_codex":[0.998992,0.0001712768,0.00006424879,0.0002492039,0.0004273418,0.00009583958],"domain_scores_gemma":[0.996735,0.001303502,0.0003998061,0.0006961447,0.000778861,0.00008653723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003174549,0.0001838694,0.004438372,0.00009000903,0.00007131194,0.0001291737,0.0006956971,0.6230507,0.05162561,0.003484085,0.00114271,0.314771],"study_design_scores_gemma":[0.00001491266,0.00004842501,0.0007097822,0.000005507833,0.000006858866,0.00003518492,0.00004031448,0.9869326,0.01012228,0.001362899,0.0007045966,0.00001655579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05922407,0.00007111131,0.9383849,0.00005254642,0.00003205024,0.00002414135,0.00002938202,0.001349209,0.0008326137],"genre_scores_gemma":[0.6812067,0.0000562767,0.3169889,0.00005636081,0.00002352436,0.00006458115,0.0002072624,0.0003577558,0.001038615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004670021,"threshold_uncertainty_score":0.009285688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04157715412287492,"score_gpt":0.255746105938395,"score_spread":0.21416895181552,"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."}}