{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000632941,0.0002173979,0.0003328088,0.0003497122,0.00003017061,0.00008977985,0.0005973863,0.0001473411,0.000005568003],"category_scores_gemma":[0.0001826049,0.0002542755,0.00002188189,0.0004708635,0.00002090091,0.0005882464,0.0001650188,0.0002320088,0.0000132716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002402206,"about_ca_system_score_gemma":0.00002971037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001190336,"about_ca_topic_score_gemma":0.001724961,"domain_scores_codex":[0.9985584,0.00003535344,0.000495908,0.0004083412,0.0001160978,0.0003858999],"domain_scores_gemma":[0.9985521,0.0001619832,0.00003529329,0.001186269,0.00002073981,0.00004357951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003718427,0.0003405954,0.01047869,0.03784459,0.0006098034,0.0003967191,0.0009200062,0.1315876,0.1929534,0.008024435,0.5556157,0.06085657],"study_design_scores_gemma":[0.001502627,0.0000227367,0.002429673,0.0004758873,0.0000240031,0.00001679239,0.000176095,0.8537966,0.002829642,0.00003244601,0.1383048,0.0003886879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1145551,0.006259307,0.7559475,0.0001128638,0.01162741,0.007807873,0.09786574,0.001612151,0.004212042],"genre_scores_gemma":[0.9802263,0.00003445223,0.001361598,0.00002692186,0.000130714,0.0003625924,0.01773899,0.00006579903,0.00005265844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8656712,"threshold_uncertainty_score":0.9999909,"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."}}