{"id":"W4393717159","doi":"10.3390/iocag2023-16880","title":"Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Coconut Research and Applications","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Maturity (psychological); SIGNAL (programming language); Computer science; Deep learning; Artificial intelligence; Machine learning; Agricultural engineering; Engineering; Psychology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007546588,0.00008504871,0.0001357368,0.00004319468,0.00006439293,0.00003011931,0.0001729428,0.00005926845,0.004365909],"category_scores_gemma":[0.00002405552,0.00006910977,0.00004792212,0.0002195892,0.0001034591,0.00005296776,0.00006156728,0.0002993353,0.00001794042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002340413,"about_ca_system_score_gemma":0.00008340892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003552397,"about_ca_topic_score_gemma":0.0001612965,"domain_scores_codex":[0.999176,0.000008948925,0.0002124851,0.000219286,0.0002295697,0.0001537337],"domain_scores_gemma":[0.9991274,0.0004177858,0.00005899441,0.000222047,0.00009600042,0.00007771768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003552697,0.000134818,0.01613211,0.0008228922,0.0001872266,0.000003420781,0.0004285512,0.0006661084,0.9269168,0.002623504,0.0001630639,0.05188597],"study_design_scores_gemma":[0.0006842908,0.00009137658,0.002427636,0.0005368821,0.0002246927,0.00001240386,0.004914802,0.1780987,0.7981531,0.00428028,0.01020382,0.000371972],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7627188,0.001021741,0.1750968,0.0001281076,0.000006675563,0.0003418402,0.000191172,0.0003141038,0.06018082],"genre_scores_gemma":[0.9965669,0.00002025149,0.001692209,0.000003238897,0.00008261737,0.0002131594,0.00008331415,0.00001452641,0.001323816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2338481,"threshold_uncertainty_score":0.9965442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009465255635436091,"score_gpt":0.2639061623205632,"score_spread":0.2544409066851271,"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."}}