{"id":"W3207940304","doi":"10.1007/s44150-021-00008-7","title":"Infrared thermography for a quick construction progress monitoring approach in concrete structures","year":2021,"lang":"en","type":"article","venue":"Architecture Structures and Construction","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Schedule; Computer science; Thermography; Quality assurance; Image processing; Quality (philosophy); Image quality; Field (mathematics); Construction engineering; Remote sensing; Image (mathematics); Computer vision; Real-time computing; Artificial intelligence; Engineering; Infrared; Operations management; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001518935,0.0003087926,0.0003477991,0.0001598568,0.0003589665,0.0001929535,0.0001319401,0.0002107744,0.0001133109],"category_scores_gemma":[0.00004222379,0.0002452255,0.0001234488,0.0004371158,0.0004674266,0.000175132,0.00001958512,0.0003605599,5.097987e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007813116,"about_ca_system_score_gemma":0.00006520284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001342392,"about_ca_topic_score_gemma":0.0001485501,"domain_scores_codex":[0.9982096,0.0001832017,0.0003428491,0.0005905514,0.0002360704,0.0004377206],"domain_scores_gemma":[0.999295,0.0001091449,0.0001388587,0.0002186958,0.00009998995,0.0001383439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001862987,0.000002284889,0.5663082,0.0001256041,0.00006329671,0.000009541159,0.0008176538,0.0004447236,0.0004346682,0.003344026,0.00001126322,0.4282524],"study_design_scores_gemma":[0.001359176,0.0001255934,0.8827509,0.00005707781,0.00004697869,0.001109166,0.003916434,0.0006260977,0.001434165,0.1071562,0.0008906119,0.0005275697],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927925,0.003210433,0.0008315034,0.00009298787,0.0008736146,0.0004198582,0.0001576086,0.00009375358,0.001527684],"genre_scores_gemma":[0.9269893,0.00009901396,0.07228822,0.00004631664,0.0003334231,0.00001294807,0.0001955579,0.00001063393,0.00002463362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4277249,"threshold_uncertainty_score":1,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01179941501330297,"score_gpt":0.2194513547554588,"score_spread":0.2076519397421558,"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."}}