{"id":"W3129130691","doi":"10.48550/arxiv.2102.05212","title":"Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Specular reflection; Artificial intelligence; Polarimetry; Computer vision; Azimuth; Computer science; Monocular; Polarization (electrochemistry); Remote sensing; Optics; Geology; Physics; Scattering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003221323,0.0008895259,0.0006262655,0.0006653313,0.000238561,0.0008110621,0.0007643827,0.0006488483,0.001812433],"category_scores_gemma":[0.001514064,0.0005315903,0.0004947961,0.0008460477,0.0005720243,0.001548403,0.001833607,0.001281008,0.0008708519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002928388,"about_ca_system_score_gemma":0.0008692113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002403049,"about_ca_topic_score_gemma":0.003224009,"domain_scores_codex":[0.9996984,0.00006277275,0.000009755902,0.0000671836,0.000122763,0.00003920382],"domain_scores_gemma":[0.9995767,0.000113403,0.00005577251,0.0001280164,0.00009702551,0.00002904742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000343243,0.0001304795,0.001071485,0.0002607505,0.00007128312,0.0001615411,0.0002550123,0.3184969,0.07883779,0.01925124,0.005213909,0.5759063],"study_design_scores_gemma":[0.00002065064,0.00005962807,0.0004346725,0.00002056812,0.00001270291,0.0001974278,0.00004607221,0.962371,0.02178528,0.01160301,0.003423845,0.00002509802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007432844,0.00008404771,0.9909998,0.0000746876,0.00001599586,0.00001558054,0.00008082735,0.0003768712,0.0009194014],"genre_scores_gemma":[0.2613346,0.000445904,0.7340975,0.0001915145,0.00006037864,0.0000901888,0.0006911782,0.0001578273,0.002930885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002403049,"threshold_uncertainty_score":0.006063163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07291582563181237,"score_gpt":0.1779849374642914,"score_spread":0.1050691118324791,"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."}}