{"id":"W7118126080","doi":"10.23977/jeis.2025.100218","title":"Design and Implementation of Apple Ripeness Grading System Based on Lightweight ResNet18","year":2025,"lang":"","type":"article","venue":"Journal of Electronics and Information Science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ripeness; Grading (engineering); Inference; Grading scale; Statistical model; Image processing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002316392,0.0001362214,0.0002558997,0.0002154313,0.0005009174,0.0003433081,0.0002796979,0.00006751295,0.00001246222],"category_scores_gemma":[0.00004897893,0.00005720302,0.00005511068,0.001358313,0.0001587036,0.002631917,0.0000449723,0.0001656317,7.227853e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001224289,"about_ca_system_score_gemma":0.0003170597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002090334,"about_ca_topic_score_gemma":0.000007352371,"domain_scores_codex":[0.9981931,0.00006746886,0.0007946591,0.0001256918,0.0005369768,0.0002821185],"domain_scores_gemma":[0.9981875,0.0001920858,0.0008750248,0.00004833555,0.0005936,0.0001034229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009129327,0.0002548644,0.01568295,0.0008362561,0.0001121821,0.000003637354,0.003402354,0.003768224,0.3147749,0.2756216,0.004777445,0.3798527],"study_design_scores_gemma":[0.004954339,0.01386889,0.2883311,0.002672376,0.0004035078,0.0001703456,0.02183686,0.1434299,0.4000761,0.001698711,0.121409,0.001148879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879424,0.00144216,0.006077668,0.002630307,0.000449192,0.0004688199,0.000009779673,0.000007196938,0.0009724845],"genre_scores_gemma":[0.9984109,0.000930116,0.0003206026,0.0002690958,0.0000529459,0.000002440927,0.000003011306,3.750352e-7,0.00001054405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3787038,"threshold_uncertainty_score":0.3852702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008038836556261378,"score_gpt":0.2432668962526529,"score_spread":0.2352280596963915,"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."}}