{"id":"W3132698244","doi":"10.3390/s22103617","title":"SCD: A Stacked Carton Dataset for Detection and Segmentation","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Carton; Computer science; Classifier (UML); Artificial intelligence; Segmentation; Computer vision; Engineering","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":[0.0007271649,0.005490373,0.00179536,0.004822586,0.001332297,0.002382848,0.004485599,0.002992002,0.01055758],"category_scores_gemma":[0.0019501,0.001060668,0.002357104,0.004137369,0.0006886494,0.001904932,0.00211539,0.002147134,0.01231854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002049314,"about_ca_system_score_gemma":0.00224195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05347751,"about_ca_topic_score_gemma":0.121169,"domain_scores_codex":[0.9984533,0.0001181369,0.00009050827,0.0005779369,0.0004529865,0.0003071905],"domain_scores_gemma":[0.9990478,0.0001136177,0.00007936985,0.0003201202,0.0003325766,0.0001064284],"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":[0.0009142411,0.0006264132,0.005696862,0.001389363,0.0003685571,0.0008188902,0.0001337782,0.01567065,0.01757177,0.001880438,0.8027446,0.1521845],"study_design_scores_gemma":[0.0005740017,0.0005529847,0.03235344,0.0005622276,0.0003713369,0.00297946,0.0007010279,0.313134,0.06037677,0.007883869,0.580013,0.0004978282],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07560414,0.00525608,0.0502433,0.001034973,0.001388508,0.001223905,0.7426597,0.1024367,0.02015254],"genre_scores_gemma":[0.0407306,0.0005893434,0.04630344,0.000371708,0.00008770305,0.0003468012,0.9057047,0.001760115,0.004105629],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05347751,"threshold_uncertainty_score":0.1063325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.018622672787664,"score_gpt":0.2711889744878899,"score_spread":0.2525663017002259,"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."}}