{"id":"W4408785837","doi":"10.1016/j.dib.2025.111516","title":"Manually classified dataset of leaning and standing personnel images for construction site monitoring and neural network training","year":2025,"lang":"en","type":"article","venue":"Data in Brief","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Universidad de Lima","keywords":"Training (meteorology); Computer science; Artificial neural network; Artificial intelligence; Geography; Meteorology","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":[0.000518931,0.001231034,0.0008195536,0.002313453,0.0006674185,0.0007250728,0.001612631,0.001406879,0.005455085],"category_scores_gemma":[0.001491843,0.0003153635,0.0008871784,0.002334633,0.0005467914,0.0005525987,0.000904155,0.001001734,0.006003309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001061515,"about_ca_system_score_gemma":0.001408257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01623738,"about_ca_topic_score_gemma":0.04236989,"domain_scores_codex":[0.9991149,0.00006730538,0.00006557336,0.0002714128,0.0003192238,0.0001614743],"domain_scores_gemma":[0.9988555,0.0001333184,0.0001021883,0.0002746167,0.0005344512,0.0000999917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001474657,0.002168141,0.0316771,0.00285511,0.0002559525,0.001424438,0.0004781215,0.01581172,0.04233365,0.001400983,0.5996802,0.3004399],"study_design_scores_gemma":[0.0003472973,0.00111722,0.3367568,0.001021807,0.0002900425,0.00300122,0.002813846,0.08572165,0.0781242,0.002617443,0.4878705,0.0003180351],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2347927,0.001862879,0.02741711,0.0007414278,0.0007695628,0.001143105,0.7080961,0.00832072,0.01685635],"genre_scores_gemma":[0.09143747,0.0004040973,0.02591488,0.0001479096,0.0000743964,0.0006100409,0.8763482,0.0002358152,0.004827252],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01623738,"threshold_uncertainty_score":0.03228575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02960529523307171,"score_gpt":0.2732369971244471,"score_spread":0.2436317018913754,"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."}}