{"id":"W4409111910","doi":"10.1016/j.dib.2025.111537","title":"Image dataset for foreign object detection in iron ore conveyor belt systems","year":2025,"lang":"en","type":"article","venue":"Data in Brief","topic":"Belt Conveyor Systems Engineering","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Tecnológico Vale","keywords":"Conveyor belt; Iron ore; Computer science; Object (grammar); Image (mathematics); Computer vision; Artificial intelligence; Mining engineering; Engineering; Metallurgy; Materials science; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005509746,0.00219286,0.001218254,0.00316512,0.0007562059,0.0009707137,0.002128347,0.00203744,0.004802292],"category_scores_gemma":[0.001364623,0.0003430291,0.00112787,0.002665229,0.0003939279,0.0008556948,0.001216186,0.001128211,0.007291758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068682,"about_ca_system_score_gemma":0.001053269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01767716,"about_ca_topic_score_gemma":0.0373163,"domain_scores_codex":[0.9991221,0.00005841208,0.00009363512,0.0002542336,0.0002896447,0.0001820315],"domain_scores_gemma":[0.9994739,0.0000638096,0.00006176152,0.000138524,0.0001908127,0.00007137714],"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":[0.001061249,0.001062131,0.01347048,0.003526188,0.000331306,0.001370286,0.000298416,0.008470974,0.02971894,0.001196138,0.7837791,0.1557149],"study_design_scores_gemma":[0.0006172051,0.001086232,0.1631583,0.0008615275,0.000351334,0.00425096,0.001720442,0.08641501,0.04380053,0.00300223,0.6944439,0.0002922419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08023195,0.003421566,0.01117832,0.0005612767,0.0005349845,0.0008558938,0.8851232,0.01060074,0.007492045],"genre_scores_gemma":[0.03137581,0.0003923369,0.009849527,0.00008470498,0.00005501822,0.0003094641,0.9561625,0.0001348253,0.001635885],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01767716,"threshold_uncertainty_score":0.03514856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685399481250731,"score_gpt":0.251290693280559,"score_spread":0.2344366984680517,"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."}}