{"id":"W4383647811","doi":"10.23977/acss.2023.070507","title":"Research Progress in Potato Bud Recognition","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Deep learning; Machine learning; Artificial neural network; Agriculture; Pattern recognition (psychology); Biology","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":[0.0008574299,0.00008826616,0.000169441,0.00005187623,0.00008900013,0.0001061249,0.0001268078,0.00006505368,0.000009850036],"category_scores_gemma":[0.000004780625,0.00003273738,0.00002202717,0.001056384,0.0000453152,0.000230242,0.00007234571,0.0001372496,0.00004059342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000129144,"about_ca_system_score_gemma":0.000002719066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001146446,"about_ca_topic_score_gemma":0.0004859416,"domain_scores_codex":[0.9987298,0.0002175679,0.0002283086,0.0002888295,0.0002200179,0.0003154798],"domain_scores_gemma":[0.99946,0.0003593871,0.00003616933,0.0000308897,0.00006289945,0.00005069005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002239809,0.00008709297,0.1599248,0.00007484724,0.000005060238,0.00009378571,0.0004596259,0.0002464668,0.001764614,0.0002260953,0.001698626,0.8353965],"study_design_scores_gemma":[0.0007723779,0.001028941,0.8579112,0.001507102,0.000003817469,0.0000527837,0.002830246,0.01724823,0.0004750547,0.005005307,0.112434,0.0007309147],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914058,0.007219742,0.00001165768,0.0004417576,0.0003389372,0.0002981856,0.000008175142,0.00005248415,0.0002231903],"genre_scores_gemma":[0.9981444,0.001060322,0.00004212739,0.00003720621,0.0005390607,0.00008700891,0.00003940671,7.222652e-7,0.00004973426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8346656,"threshold_uncertainty_score":0.1334992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0651305593930928,"score_gpt":0.3170458760470358,"score_spread":0.251915316653943,"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."}}