{"id":"W4229862346","doi":"10.5376/mpb.cn.2012.10.0079","title":"云南松种质资源SSR标记的引物筛选及Hardy-Weinberg检测","year":2012,"lang":"zh","type":"article","venue":"分子植物育种","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Hardy–Weinberg principle; Biology; Genetics; Allele frequency","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":[0.01275625,0.0006892742,0.001452473,0.001138665,0.0008392852,0.003407331,0.001322297,0.00109336,0.01051689],"category_scores_gemma":[0.03697722,0.0004276005,0.0009360085,0.00200841,0.002846773,0.003270807,0.0008187473,0.00166359,0.001917447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001219582,"about_ca_system_score_gemma":0.001411678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002095595,"about_ca_topic_score_gemma":0.001386484,"domain_scores_codex":[0.990683,0.004272399,0.0005608202,0.002587137,0.001306795,0.0005899313],"domain_scores_gemma":[0.9742996,0.01889655,0.001692797,0.003024321,0.001788518,0.0002982343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001766856,0.00007988488,0.03121577,0.000355954,0.0009442841,0.0004558039,0.001378643,0.007943788,0.001669369,0.8552158,0.006684024,0.09387981],"study_design_scores_gemma":[0.00005030284,0.0001739346,0.02279188,0.00006094246,0.0003148581,0.0005958654,0.00103723,0.02157047,0.001448788,0.9424393,0.009448257,0.0000682114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2431032,0.003562224,0.6780708,0.007242075,0.0006983128,0.0003221192,0.002953778,0.0007682195,0.06327936],"genre_scores_gemma":[0.9308897,0.0008208833,0.03969799,0.0007738589,0.00021293,0.000343006,0.0005754403,0.00009145313,0.02659463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01275625,"threshold_uncertainty_score":0.06746227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131988439997453,"score_gpt":0.2104089759182072,"score_spread":0.1990890915182327,"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."}}