{"id":"W4415719661","doi":"10.18280/ts.420515","title":"Coffee Bean Defect Detection with Deep Learning: Khawlani Coffee Case Study from Jizan, Saudi Arabia","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Coffee research and impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Green coffee; Coffee bean; Deep frying; Arabica coffee","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003842051,0.0004142904,0.0002742132,0.0007154939,0.0007262262,0.0005290456,0.0006224295,0.001003752,0.0008656551],"category_scores_gemma":[0.001180719,0.0001521222,0.0002842175,0.0006057067,0.0004083478,0.0003274207,0.0004402712,0.0004335889,0.0002525173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008486114,"about_ca_system_score_gemma":0.0006724868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04435086,"about_ca_topic_score_gemma":0.07833958,"domain_scores_codex":[0.9997912,0.00002746663,0.00001547552,0.00004295883,0.00006926287,0.00005359698],"domain_scores_gemma":[0.9991817,0.0002553013,0.00009444627,0.00006035264,0.0002759552,0.0001322218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00198949,0.001688118,0.6142981,0.0007685718,0.0003253121,0.09877892,0.00718395,0.02431642,0.03246541,0.001783195,0.01858493,0.1978175],"study_design_scores_gemma":[0.0001776062,0.001832808,0.6282896,0.0002975312,0.0004230066,0.05732726,0.03559048,0.1844247,0.04491363,0.003741383,0.04277368,0.0002083896],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952448,0.0003204326,0.002152625,0.0004814787,0.00001800261,0.00003448983,0.0004423624,0.00006065709,0.001245099],"genre_scores_gemma":[0.9936391,0.0002794671,0.002835791,0.0001043146,0.00001644745,0.000006983602,0.0005512406,0.00002064649,0.002546089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04435086,"threshold_uncertainty_score":0.08818543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162279928682387,"score_gpt":0.2932693950956282,"score_spread":0.2716465958088043,"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."}}