{"id":"W3215780924","doi":"10.3390/engproc2021008029","title":"Defect Segmentation in Concrete Structures Combining Registered Infrared and Visible Images: A Comparative Experimental Study","year":2021,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Convolutional neural network; Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Transfer of learning; Computer vision; Image segmentation; Modality (human–computer interaction); Artificial neural network; Infrared; 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":[0.00006243649,0.0001501286,0.0002172056,0.00007063584,0.00005066605,0.00008543026,0.00004698473,0.00003130401,0.00004660445],"category_scores_gemma":[0.000009658836,0.0001450173,0.00002616347,0.0001377776,0.00002571651,0.0001969879,0.00004586775,0.0001298971,0.000001533701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000747021,"about_ca_system_score_gemma":0.00001262928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003638592,"about_ca_topic_score_gemma":0.00002549336,"domain_scores_codex":[0.9992616,0.00004387513,0.0002087926,0.0001965645,0.0001054905,0.0001836516],"domain_scores_gemma":[0.9997321,0.00003653624,0.00002570418,0.0001370321,0.00002978423,0.00003889555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009565203,0.00004286532,0.06210008,0.00008488021,0.0002608767,0.0002504359,0.05891753,0.005649616,0.8688053,0.0005396837,0.002460342,0.0007926691],"study_design_scores_gemma":[0.003893883,0.0002775116,0.05500573,0.00007616357,0.00002146265,0.00003530556,0.09577105,0.003857361,0.8402007,0.0002499564,0.0001861145,0.0004248165],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925882,0.0006462523,0.0004936682,0.000005191857,0.0002646055,0.0002535172,0.00000279941,0.00009639356,0.005649389],"genre_scores_gemma":[0.9969434,0.00001030056,0.002842431,0.00005350663,0.0000266628,0.00003039244,0.00001078298,0.00001510523,0.00006744374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03685352,"threshold_uncertainty_score":0.5913633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02346391638765048,"score_gpt":0.2911517031890447,"score_spread":0.2676877868013942,"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."}}