{"id":"W7117721168","doi":"10.1108/f-06-2025-0101","title":"A multiclassifier convolutional neural network to identify defect type and severity in roofing elements","year":2025,"lang":"en","type":"article","venue":"Facilities","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ponding; Convolutional neural network; Roof; Artificial neural network; Asset management; Multispectral image; Vegetation (pathology)","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":[0.00008369712,0.000086428,0.0001010536,0.00005933216,0.00004736469,0.00002983583,0.00004676879,0.00003796357,0.00001831059],"category_scores_gemma":[0.00005112822,0.00008926938,0.00001772402,0.0001564584,0.0000183328,0.00007724349,0.00004500058,0.0001062407,0.000006601473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007665898,"about_ca_system_score_gemma":0.00001291031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006072793,"about_ca_topic_score_gemma":0.0001425979,"domain_scores_codex":[0.9994557,0.00001243939,0.000133369,0.000103926,0.00006038155,0.0002342027],"domain_scores_gemma":[0.999835,0.00003271293,0.000005861378,0.00007078377,0.00002938178,0.00002626421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006367306,0.000007685632,0.814439,0.0002871189,0.00009297507,0.00001356959,0.002079207,0.153459,0.002839896,0.001660712,0.01129129,0.01376583],"study_design_scores_gemma":[0.0004285495,0.00001884324,0.9315084,0.0001431223,0.000009610027,0.000002620884,0.0007658437,0.03431463,0.00047798,0.001336938,0.03076568,0.0002277791],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963694,0.0003781117,0.0006954493,0.00002765555,0.001147837,0.0001012875,0.00001054935,0.0000652704,0.001204503],"genre_scores_gemma":[0.9985449,0.000008608095,0.000575828,0.00006779742,0.0000736468,0.00001345206,0.000004237827,0.000004686387,0.0007068526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1191444,"threshold_uncertainty_score":0.3640299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01113195770870708,"score_gpt":0.2595906076665078,"score_spread":0.2484586499578008,"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."}}