{"id":"W2107100889","doi":"10.1155/2013/613062","title":"Genetic Diversity Analysis of Sugarcane Parents in Chinese Breeding Programmes Using gSSR Markers","year":2013,"lang":"en","type":"article","venue":"The Scientific World JOURNAL","topic":"Sugarcane Cultivation and Processing","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"National High-tech Research and Development Program; Fujian Agriculture and Forestry University","keywords":"Genetic diversity; Biology; Microsatellite; Biotechnology; Cultivar; Plant breeding; Crop; Allele; Selection (genetic algorithm); Principal component analysis; Genetic variation; Genetic marker; Breeding program; Genotype; Agronomy; Genetics; Gene; Population; Medicine; Statistics","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":[0.00096602,0.00009191735,0.0001716099,0.0001760445,0.00092493,0.000384612,0.0004275092,0.00002111724,0.001206964],"category_scores_gemma":[0.0000710986,0.00003024334,0.0001345603,0.00413785,0.0001904584,0.0002137235,0.0001720199,0.0001533966,0.000004601573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004117786,"about_ca_system_score_gemma":0.0000145066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001170849,"about_ca_topic_score_gemma":0.003440268,"domain_scores_codex":[0.9987483,0.000121379,0.0002892775,0.0001743874,0.0004031021,0.0002635239],"domain_scores_gemma":[0.9993491,0.0001119514,0.0002292656,0.00005446896,0.000171414,0.00008376969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001026751,0.00003739593,0.9271139,0.000002580425,0.0000555225,0.00000136089,0.0005498437,0.0003776456,0.03016294,0.000002384867,0.0001025064,0.04158368],"study_design_scores_gemma":[0.00009207113,0.00001353764,0.9913341,0.00002616131,0.00006492696,0.00000617818,0.00128239,0.006587769,0.0000956462,0.0003080488,0.0001106454,0.00007848646],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988312,0.0001283372,0.000006343361,0.0005101174,0.0002522854,0.0001101591,0.00000396623,0.000008451239,0.0001491705],"genre_scores_gemma":[0.9993016,0.000005557264,0.0001896538,0.00003958834,0.00006941881,0.000001340372,0.000005569504,5.27259e-7,0.0003867385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06422026,"threshold_uncertainty_score":0.9997061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03683774190153909,"score_gpt":0.254952654791028,"score_spread":0.2181149128894889,"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."}}