{"id":"W4406272481","doi":"10.1080/07373937.2025.2450700","title":"Online detection of potato drying stages based on improved YOLOv7-tiny model","year":2025,"lang":"en","type":"article","venue":"Drying Technology","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"Tianjin Municipal Education Commission","keywords":"Process (computing); Computer science; Feature (linguistics); Identification (biology); Artificial intelligence; Product (mathematics); Process engineering; Pattern recognition (psychology); Agricultural engineering; Machine learning; Mathematics; 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":[0.0001955425,0.0004024756,0.0003845977,0.0003629942,0.0001197891,0.0003374908,0.0007180578,0.0003225699,0.001116597],"category_scores_gemma":[0.0003948552,0.0002019894,0.0004137223,0.000194091,0.0001663645,0.0005973086,0.0003918348,0.0003235247,0.0002906434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000335551,"about_ca_system_score_gemma":0.0003502188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004303844,"about_ca_topic_score_gemma":0.005548562,"domain_scores_codex":[0.9999161,0.000005712384,0.00000362193,0.00003670344,0.00002267113,0.00001525729],"domain_scores_gemma":[0.9998932,0.0000239504,0.00001738087,0.00001358511,0.00004208882,0.000009776517],"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":[0.0003686087,0.0001690398,0.0120276,0.000264486,0.0001204908,0.0002663764,0.0001265923,0.3367756,0.1755924,0.003238476,0.002930604,0.4681198],"study_design_scores_gemma":[0.000003379844,0.00004035501,0.001648334,0.000004111307,0.00001490174,0.0000412347,0.000006735778,0.9898986,0.007622838,0.0002514687,0.000460774,0.000007261613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2427456,0.0006384968,0.7494944,0.0001457742,0.0001268109,0.00006943784,0.0002296506,0.002424908,0.004124928],"genre_scores_gemma":[0.9359119,0.0002585747,0.05942907,0.00008715906,0.00002017583,0.00004963158,0.0003172978,0.00005655884,0.003869599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004303844,"threshold_uncertainty_score":0.008557558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01244850459846058,"score_gpt":0.2373619736724489,"score_spread":0.2249134690739883,"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."}}