{"id":"W4391232500","doi":"10.2139/ssrn.4695849","title":"Learning Monocular Depth Estimation for Defect Measurement from Civil RGB-D Dataset","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Monocular; Artificial intelligence; RGB color model; Estimation; Computer science; Computer vision; Geography; Pattern recognition (psychology); Economics","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":[0.0003582817,0.002326531,0.001415044,0.002823767,0.0003587202,0.0007044647,0.001773669,0.00179313,0.003203286],"category_scores_gemma":[0.001462499,0.0005568232,0.001465056,0.002655964,0.0004890363,0.001152182,0.001449326,0.001047486,0.004714978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000595633,"about_ca_system_score_gemma":0.001177709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01478847,"about_ca_topic_score_gemma":0.03316948,"domain_scores_codex":[0.9992035,0.0000413899,0.00002722832,0.0003159771,0.0002521913,0.0001596503],"domain_scores_gemma":[0.9993638,0.00007730324,0.00006746843,0.0002346072,0.0002086887,0.00004813178],"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":[0.0008812789,0.001150345,0.02125468,0.000971101,0.0003781318,0.0005411101,0.0001177946,0.07691646,0.06615745,0.0007797303,0.1184098,0.7124422],"study_design_scores_gemma":[0.0001101937,0.000506836,0.05110606,0.0001675506,0.0001852179,0.000926666,0.00031021,0.877986,0.04287975,0.003391883,0.02231359,0.0001159322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4901589,0.006736305,0.3377739,0.0008279252,0.0007891435,0.0005683706,0.1151191,0.03881544,0.009211011],"genre_scores_gemma":[0.6248733,0.001345307,0.1789742,0.0003636547,0.0001475071,0.000384542,0.186325,0.000506227,0.007080284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01478847,"threshold_uncertainty_score":0.02940482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212223791621982,"score_gpt":0.2392827588499221,"score_spread":0.2271605209337023,"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."}}