{"id":"W4311974999","doi":"10.3390/electronics11244100","title":"Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"King Saud University","keywords":"Convolutional neural network; Maturity (psychological); Deep learning; Artificial intelligence; Computer science; Machine learning; Crop; Capability Maturity Model; Agricultural engineering; Artificial neural network; Agronomy; Engineering; Biology","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":[0.0003025237,0.00008093158,0.0001324371,0.000008470474,0.0003667351,0.00002048476,0.0001089948,0.00004597544,0.0001909762],"category_scores_gemma":[0.00003472168,0.00003492827,0.00004798201,0.0002630161,0.00001233677,0.00007178758,0.0001415354,0.0002777011,0.000001340998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003862287,"about_ca_system_score_gemma":0.000011647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003966348,"about_ca_topic_score_gemma":0.00007260631,"domain_scores_codex":[0.9990775,0.0001263165,0.0001714079,0.0001733828,0.0002319983,0.0002194036],"domain_scores_gemma":[0.9997202,0.00006470459,0.0001026503,0.00002113981,0.00003833376,0.00005301582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000497087,0.0001918563,0.02129377,0.00002493292,0.00006556243,0.000002567073,0.0004598529,0.0005557257,0.9152287,0.002525573,0.0004667696,0.05913499],"study_design_scores_gemma":[0.0009273984,0.003473869,0.54826,0.00002907154,0.0001472663,0.0001560776,0.003471403,0.02005789,0.03238752,0.003603081,0.3865806,0.0009058253],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915565,0.007594764,0.000004918497,0.0004479403,0.00008045424,0.00009816005,0.00002003606,0.0000430941,0.0001541019],"genre_scores_gemma":[0.9990929,0.0003390411,0.00003228793,0.0001226831,0.0001994228,0.000007252313,0.00006135598,8.235397e-7,0.000144188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8828412,"threshold_uncertainty_score":0.2820667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04267632985990146,"score_gpt":0.2526148989790267,"score_spread":0.2099385691191252,"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."}}