{"id":"W4298009668","doi":"10.18280/ts.390420","title":"Image Information Recognition and Classification of Warehoused Goods in Intelligent Logistics Based on Machine Vision Technology","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Linyi University","keywords":"sort; Computer science; Machine vision; Process (computing); Artificial intelligence; Image (mathematics); Architecture; Image processing; Industrial engineering; Engineering; Database","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":[0.0003719586,0.0002239496,0.0003157498,0.0007842141,0.0001960865,0.0009439618,0.0004807534,0.0006030506,0.0006906083],"category_scores_gemma":[0.000611542,0.0001611636,0.0006066716,0.0006927882,0.0004814092,0.001195787,0.0003210479,0.0004149104,0.00055653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006775087,"about_ca_system_score_gemma":0.0004929676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004981528,"about_ca_topic_score_gemma":0.002828873,"domain_scores_codex":[0.9997783,0.00003618178,0.0000120705,0.00004774182,0.00009096575,0.00003474636],"domain_scores_gemma":[0.9998559,0.00004329539,0.00002333961,0.00002115729,0.00004805552,0.000008230155],"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":[0.0004969554,0.0001916676,0.005869254,0.0001891822,0.00007035835,0.0002941681,0.000258124,0.3372133,0.1207579,0.02135776,0.002420556,0.5108809],"study_design_scores_gemma":[0.000005219345,0.00008739963,0.002759908,0.000010652,0.00001430751,0.00008859067,0.00004049467,0.9704633,0.02259604,0.002278982,0.001638418,0.00001666952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07602231,0.0003485747,0.9207579,0.0001705071,0.00003885416,0.00003708388,0.00003301424,0.0005560314,0.002035673],"genre_scores_gemma":[0.7880251,0.0006539935,0.2064543,0.0001004834,0.00002566852,0.000039898,0.0002035216,0.00004444882,0.004452757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004981528,"threshold_uncertainty_score":0.00990504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03490903876256178,"score_gpt":0.2505125495636232,"score_spread":0.2156035108010614,"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."}}