{"id":"W4399178918","doi":"10.18280/mmep.110514","title":"An Intelligent Detection Method for Conveyor Belt Deviation State Based on Machine Vision","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Belt Conveyor Systems Engineering","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conveyor belt; Machine vision; State (computer science); Belt conveyor; Computer vision; Artificial intelligence; Computer science; Engineering; Mechanical engineering; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004152639,0.0007642596,0.0008071554,0.002076143,0.0003303889,0.000754908,0.001070892,0.0008199837,0.0008550741],"category_scores_gemma":[0.001003944,0.0003482689,0.0004890626,0.001000416,0.0003964977,0.0009970673,0.0004990931,0.0006746726,0.0004760013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000473759,"about_ca_system_score_gemma":0.0007570573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001668033,"about_ca_topic_score_gemma":0.001878398,"domain_scores_codex":[0.9994254,0.00004085424,0.00003400094,0.000143572,0.0003094229,0.00004680938],"domain_scores_gemma":[0.9994268,0.00009720599,0.000104435,0.00004762646,0.0002968515,0.00002702218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001779836,0.0001267221,0.002138105,0.0003101212,0.00004655953,0.0002161026,0.0001435595,0.01284322,0.2323141,0.002324201,0.002158701,0.7472007],"study_design_scores_gemma":[0.00004831299,0.0005015712,0.008616876,0.00004421884,0.0001006856,0.00112665,0.00009446676,0.8023441,0.1787367,0.00146042,0.006796312,0.0001296568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01618559,0.000364965,0.9809002,0.00006606304,0.00007195325,0.00007090321,0.00003167998,0.001502779,0.0008059258],"genre_scores_gemma":[0.3831606,0.0007400006,0.6131946,0.0001154897,0.00009376245,0.0001414641,0.0001393987,0.00006917909,0.002345436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002076143,"threshold_uncertainty_score":0.00343734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179848175141764,"score_gpt":0.251931005702572,"score_spread":0.2339461881883956,"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."}}