{"id":"W2189438588","doi":"10.21611/qirt.2010.150","title":"High resolution and automatic survey of buildings by IR thermography","year":2010,"lang":"en","type":"article","venue":"Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Thermography; Remote sensing; Computer science; Resolution (logic); Infrared; Geology; Artificial intelligence; Optics; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0009060612,0.0002669827,0.0003370041,0.0005152581,0.00008622938,0.00006417737,0.0007230922,0.0001816087,0.000142182],"category_scores_gemma":[0.0007389792,0.0002091546,0.0001446621,0.000655622,0.0006039753,0.0003314257,0.0000960295,0.0005385328,0.000002204185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000150511,"about_ca_system_score_gemma":0.00001761317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001240869,"about_ca_topic_score_gemma":0.00001009231,"domain_scores_codex":[0.9985269,0.00005130559,0.0005033572,0.0002642692,0.0004400209,0.0002140894],"domain_scores_gemma":[0.9979681,0.0003908127,0.0004235335,0.0001692039,0.000995516,0.00005278587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001244038,0.00004872103,0.01436134,0.00007835304,0.0002734323,7.994449e-8,0.0005256975,0.00003128927,0.8473037,0.1343475,0.001338797,0.001566576],"study_design_scores_gemma":[0.0006761798,0.0002776818,0.4749402,0.0002133273,0.00004106752,0.000004798531,0.0005126267,0.01008465,0.4502234,0.06244159,0.0001629917,0.000421476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991799,0.00009329226,0.0004411718,0.000124432,0.001191135,0.0002914417,0.0003491212,0.0001520416,0.00555837],"genre_scores_gemma":[0.9742067,0.0001192095,0.02549329,0.00002760076,0.00001943919,0.00003725182,0.00001220105,0.00003384985,0.00005041008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4605789,"threshold_uncertainty_score":0.8529077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03885926781472328,"score_gpt":0.2780779651701177,"score_spread":0.2392186973553944,"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."}}