{"id":"W4393436138","doi":"10.54097/39xpbb68","title":"Improve Unsupervised Machine Learning Model on Fruits by Using VGG-16","year":2024,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001940512,0.0001576111,0.0001454304,0.0001400676,0.0001814196,0.0001050907,0.0002576656,0.0001447603,0.000007374362],"category_scores_gemma":[0.00004612477,0.00005992897,0.00002308891,0.001586843,0.0001290467,0.0001686334,0.00009369632,0.0003015589,0.00001905471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006188001,"about_ca_system_score_gemma":0.00001128025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005108858,"about_ca_topic_score_gemma":0.00003635712,"domain_scores_codex":[0.9988531,0.000006238725,0.0001400959,0.0004593752,0.0001700363,0.000371149],"domain_scores_gemma":[0.9997823,0.00005867347,0.00001644838,0.00005215121,0.00002469546,0.00006578487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001568743,0.00001663694,0.0002643364,0.000006008404,0.000002507081,0.000008490638,0.00003564644,0.001516938,0.9785887,0.01115991,0.00007725413,0.008322019],"study_design_scores_gemma":[0.000160912,0.000311757,0.0005305666,0.0001774748,0.000009840594,0.00003443613,0.00009597087,0.7708588,0.1739799,0.0008470673,0.05249695,0.0004962649],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958075,0.001011261,0.00007014318,0.002187405,0.0002310351,0.00008863538,0.00001129364,0.0003947414,0.0001979917],"genre_scores_gemma":[0.9993092,0.0001389478,0.0002348525,0.00003100145,0.00006907254,0.000009476163,0.000005494342,0.000001678677,0.000200288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8046088,"threshold_uncertainty_score":0.2443832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008234634594700687,"score_gpt":0.196914436559094,"score_spread":0.1886798019643933,"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."}}