{"id":"W4414681828","doi":"10.1007/978-3-032-00986-9_7","title":"Robot Vision System for Retail Shelf Monitoring","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Robot; Mobile robot; Object (grammar); Machine vision; Off the shelf; Object detection","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":[0.0002172232,0.0004477126,0.0006073178,0.0006696354,0.0003230115,0.0005571693,0.001203239,0.0008080286,0.01591888],"category_scores_gemma":[0.0002134573,0.0002567637,0.0002948784,0.0004932137,0.0001180792,0.0004844961,0.0003736818,0.0004474713,0.007145674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003696973,"about_ca_system_score_gemma":0.0005341236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002249334,"about_ca_topic_score_gemma":0.002898701,"domain_scores_codex":[0.9998293,0.00001514222,0.000005602895,0.00005200438,0.00007700591,0.000020919],"domain_scores_gemma":[0.9998595,0.00001482512,0.000008166838,0.0000184254,0.0000878284,0.0000111601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004724589,0.0002143118,0.001261243,0.0002882624,0.00003838762,0.0002251435,0.00008323329,0.004175728,0.1660144,0.001928657,0.06208606,0.7632122],"study_design_scores_gemma":[0.0002481789,0.001722228,0.01929266,0.0001836534,0.0003047815,0.002365084,0.0002083259,0.5168103,0.242936,0.002905953,0.2127915,0.0002313811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06287006,0.002701158,0.8361737,0.0005968746,0.0008247334,0.0005225223,0.001842998,0.03817042,0.0562976],"genre_scores_gemma":[0.3741604,0.001331776,0.5078372,0.001301566,0.0002616422,0.0004793651,0.003886451,0.0006283793,0.1101132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01591888,"threshold_uncertainty_score":0.05325395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04296925997596372,"score_gpt":0.2862569333898263,"score_spread":0.2432876734138626,"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."}}