{"id":"W2374303252","doi":"","title":"Applying Cluster Analysis to Screen SSR Markers with High Identifying Ability from SSR Fingerprint Data of Maize Hybrids","year":2010,"lang":"en","type":"article","venue":"Seed","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Molecular marker; Hybrid; Fingerprint (computing); Locus (genetics); Biology; Genetic marker; DNA profiling; Microsatellite; Genetics; Biotechnology; DNA; Artificial intelligence; Computer science; Botany; Gene; Allele","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":[0.0008206517,0.0004207087,0.0004644483,0.001556391,0.0005354612,0.0004327234,0.0002762198,0.0002997958,0.0009548381],"category_scores_gemma":[0.001605784,0.0001912832,0.0005605593,0.001137115,0.0002064802,0.0002666138,0.0004185464,0.0003441963,0.0003978888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002969698,"about_ca_system_score_gemma":0.0004212094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001809458,"about_ca_topic_score_gemma":0.002034164,"domain_scores_codex":[0.9992917,0.0001477449,0.00005992179,0.0002346474,0.0001694011,0.00009657131],"domain_scores_gemma":[0.9993553,0.0002375081,0.00007968723,0.00008549861,0.0001769182,0.00006516604],"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":[0.0004777463,0.00009393315,0.01385203,0.0001710165,0.0001244766,0.0001392561,0.0004902252,0.0010268,0.9389234,0.0003964414,0.0001725661,0.04413209],"study_design_scores_gemma":[0.00008407071,0.0008681145,0.1901028,0.00003723762,0.0004865805,0.001456609,0.001118423,0.02925778,0.7678841,0.001711425,0.006862179,0.0001306135],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8895442,0.0002533744,0.107732,0.00008668223,0.00001897847,0.0001438409,0.0006846305,0.0002679185,0.001268238],"genre_scores_gemma":[0.8730755,0.0001229625,0.1242545,0.00002327991,0.00000657989,0.0001205137,0.001304793,0.00005708636,0.001034701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001809458,"threshold_uncertainty_score":0.004340053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755347691604265,"score_gpt":0.2477061609737069,"score_spread":0.2301526840576642,"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."}}