{"id":"W4410577751","doi":"10.1016/j.dib.2025.111643","title":"Dataset for training neural networks in concrete crack detection: laboratory-classified beam and column images","year":2025,"lang":"en","type":"article","venue":"Data in Brief","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Column (typography); Artificial neural network; Training (meteorology); Computer science; Artificial intelligence; Beam (structure); Pattern recognition (psychology); Structural engineering; Engineering; Geography; Telecommunications","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":[0.0008318611,0.00195173,0.0009015691,0.002356445,0.0008504385,0.0009411165,0.002852309,0.002050828,0.005872244],"category_scores_gemma":[0.002185009,0.0004837886,0.001229575,0.002308209,0.0006087979,0.0008136216,0.001203636,0.001752894,0.008346893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385998,"about_ca_system_score_gemma":0.001250508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0194991,"about_ca_topic_score_gemma":0.04205099,"domain_scores_codex":[0.9990528,0.00008942238,0.00009241877,0.0002695431,0.0003501966,0.0001456937],"domain_scores_gemma":[0.9985738,0.0002471121,0.0001130135,0.000393433,0.0005604773,0.0001122339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007882893,0.002066139,0.01210081,0.002465537,0.0002435069,0.0006665956,0.0002021535,0.01946345,0.02474014,0.001375071,0.7875963,0.1482919],"study_design_scores_gemma":[0.0009469886,0.001178909,0.1362586,0.0007918712,0.0002744718,0.002440645,0.00111677,0.1648764,0.08939569,0.004684142,0.597654,0.0003815005],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08898889,0.001610572,0.01960125,0.0006770889,0.0005887233,0.001292258,0.8633235,0.01298993,0.01092788],"genre_scores_gemma":[0.03602448,0.0003216113,0.02533664,0.0001369682,0.00004433745,0.0007275586,0.9346018,0.0002699525,0.002536743],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0194991,"threshold_uncertainty_score":0.03877121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01742514736631927,"score_gpt":0.2597453537830169,"score_spread":0.2423202064166977,"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."}}