{"id":"W2009153021","doi":"10.1186/1471-2105-10-243","title":"Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures","year":2009,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":169,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; International Fund for Agricultural Development; European Commission","keywords":"Core (optical fiber); Genetic diversity; Representativeness heuristic; Computer science; Genetic algorithm; Sampling (signal processing); Preference; Data mining; Machine learning; Statistics; Mathematics; Population","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003473438,0.0002496156,0.0002597947,0.00009103748,0.0002136942,0.00004618826,0.0002985827,0.0002824263,0.000006250506],"category_scores_gemma":[0.0005003212,0.0002295017,0.0001654274,0.00008522618,0.00005569276,0.000005550759,0.00003110056,0.00008862529,0.00001095697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000266581,"about_ca_system_score_gemma":0.00009055092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008469146,"about_ca_topic_score_gemma":0.00004402499,"domain_scores_codex":[0.9984807,0.00006068562,0.0005540571,0.0002843609,0.0001752533,0.0004449359],"domain_scores_gemma":[0.9987845,0.0001293364,0.0002445582,0.0005412197,0.0001499101,0.0001504218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003065475,0.0005457242,0.08256928,0.0001263012,0.0001125459,0.000001370353,0.0006492482,0.2147556,0.006298016,0.00004025818,0.005497106,0.689098],"study_design_scores_gemma":[0.00109489,0.001447737,0.1214221,0.00001771845,0.00003569043,0.000006687315,0.0002008484,0.8621216,0.0005709499,0.0001876891,0.01257191,0.0003221089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2568383,0.0001687312,0.7419695,0.00006497792,0.0001281947,0.000455319,0.0001048754,0.00003194032,0.0002381204],"genre_scores_gemma":[0.2582824,0.00003930442,0.7393518,0.001576952,0.0002978743,0.00004111329,0.0003157044,0.00002257638,0.00007226477],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6887759,"threshold_uncertainty_score":0.9358809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06288921997742554,"score_gpt":0.3033216433372166,"score_spread":0.240432423359791,"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."}}