{"id":"W4391936084","doi":"10.1109/lra.2024.3367270","title":"Laser-to-Vehicle Extrinsic Calibration in Low-Observability Scenarios for Subsea Mapping","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subsea; Observability; Calibration; Laser; Environmental science; Computer science; Remote sensing; Marine engineering; Geology; Engineering; Physics; Optics; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003314458,0.000116462,0.0001268788,0.0001649258,0.0001060586,0.0004746907,0.0002012263,0.0000591132,0.000002293425],"category_scores_gemma":[0.00002689878,0.0001147534,0.00004058449,0.0004643688,0.0000205313,0.0005723115,0.00005065703,0.0001133709,0.00001048931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004820561,"about_ca_system_score_gemma":0.0000258089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001892146,"about_ca_topic_score_gemma":0.0000154748,"domain_scores_codex":[0.9988779,0.00004934974,0.000289758,0.0003969462,0.000157743,0.0002282969],"domain_scores_gemma":[0.9993815,0.0002154278,0.00003711562,0.0002600692,0.00002924931,0.00007668365],"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":[0.000004371286,0.00004377322,0.001205279,0.0002188501,0.000009934201,0.0000175921,0.001043493,0.9281554,0.009217848,0.004516562,0.004999152,0.05056777],"study_design_scores_gemma":[0.0001673811,0.0000157646,0.005208387,0.0001641857,0.000002820614,0.000002804027,0.00001124972,0.992657,0.0005718728,0.0003430022,0.0007111825,0.0001443219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3000014,0.00003292432,0.6893249,0.009473085,0.0007041029,0.0002286189,0.000006660227,0.0002249123,0.000003429504],"genre_scores_gemma":[0.9133589,0.00000855536,0.08441795,0.002001329,0.0001390069,0.00002582943,0.00001873959,0.00001183364,0.00001790644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6133574,"threshold_uncertainty_score":0.4679509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02175942113393331,"score_gpt":0.2446531001852521,"score_spread":0.2228936790513188,"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."}}