{"id":"W4395456018","doi":"10.18280/isi.290201","title":"Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Food and Agricultural Sciences","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitas Diponegoro","keywords":"Artificial intelligence; Computer science; Quality (philosophy); Pattern recognition (psychology); Set (abstract data type); Class (philosophy); Selection (genetic algorithm); Machine learning","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":[0.001619927,0.0001421947,0.0002046258,0.00003798334,0.0003721215,0.0002532351,0.0002065663,0.0001010253,0.00003077629],"category_scores_gemma":[0.0001620678,0.00005484521,0.0001042129,0.000575899,0.0000475973,0.001505331,0.00004708987,0.00006611814,0.000009498463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118775,"about_ca_system_score_gemma":0.00005986921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001672234,"about_ca_topic_score_gemma":0.0001474949,"domain_scores_codex":[0.9985085,0.00009440866,0.0007153134,0.0001799151,0.0002644543,0.0002374295],"domain_scores_gemma":[0.9990975,0.0003366668,0.0002847708,0.00004198954,0.0001771048,0.00006202763],"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":[0.000009801352,0.00000767978,0.0001517422,0.0001546256,0.00001427494,6.993905e-8,0.002792065,0.00004659094,0.2473497,0.001778304,0.00001992576,0.7476752],"study_design_scores_gemma":[0.0004482638,0.0005502775,0.2950606,0.001396264,0.0001172957,0.00006675714,0.03455907,0.3001687,0.2462391,0.008183863,0.1116754,0.001534454],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9045833,0.0001036434,0.09337459,0.0001329578,0.0002735577,0.0003888412,0.00003829327,0.0001287939,0.000976033],"genre_scores_gemma":[0.7715639,0.000004561892,0.228109,0.0000387538,0.00009562747,0.0000345466,0.0001246746,7.373714e-7,0.00002815895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7461407,"threshold_uncertainty_score":0.2862095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09585048966874582,"score_gpt":0.3431926269888351,"score_spread":0.2473421373200893,"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."}}