{"id":"W4399530294","doi":"10.1109/iccbd-ai62252.2023.00021","title":"A real-time method for detecting Canada Goldenrod based on an improved YOLOv5 network","year":2023,"lang":"en","type":"article","venue":"","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Changzhou University","keywords":"Computer science; Real-time computing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003547196,0.0007738523,0.0005122201,0.0005854445,0.0002871827,0.0005390715,0.001107552,0.000581249,0.002312658],"category_scores_gemma":[0.0006524269,0.0002819303,0.0003838406,0.000370424,0.0002731992,0.0007369358,0.0006844648,0.0004730129,0.0007062178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006680246,"about_ca_system_score_gemma":0.0007272956,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01030391,"about_ca_topic_score_gemma":0.01365041,"domain_scores_codex":[0.999786,0.00001821501,0.000008269277,0.00007354024,0.00006551149,0.00004850382],"domain_scores_gemma":[0.9997922,0.00003638217,0.00002420697,0.00001995344,0.0001100543,0.00001730997],"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":[0.0007268298,0.0001396985,0.00585585,0.0002706918,0.0001286295,0.0003243314,0.0001182038,0.1313345,0.1489438,0.003646641,0.006100149,0.7024106],"study_design_scores_gemma":[0.00001680181,0.0001213863,0.002246222,0.0000140955,0.00004496937,0.0001195584,0.0000218591,0.9656739,0.02894388,0.0006909291,0.00208622,0.00002016524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1107843,0.0008969191,0.8774763,0.0002019503,0.0002011599,0.0001098565,0.0003087047,0.003655542,0.00636537],"genre_scores_gemma":[0.7712467,0.0004690726,0.2183181,0.0002293147,0.00005899659,0.00008732512,0.0008040649,0.0001201433,0.0086662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9896961,"threshold_uncertainty_score":0.0204879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864228479329888,"score_gpt":0.299243502663217,"score_spread":0.2806012178699181,"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."}}