{"id":"W1978516023","doi":"10.1371/journal.pone.0000830","title":"Divide and Conquer: Enriching Environmental Sequencing Data","year":2007,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"National Center for Research Resources; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Biology; Evolutionary biology; Genome; Divide and conquer algorithms; DNA sequencing; Diversity (politics); Distribution (mathematics); Community structure; Sequence (biology); Computational biology; Ecology; Computer science; Genetics; Mathematics; DNA; Algorithm; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004368164,0.001198421,0.001149724,0.00269198,0.001077255,0.001378518,0.001677512,0.001645086,0.002209709],"category_scores_gemma":[0.01625115,0.0007531252,0.0008994456,0.003014895,0.001595408,0.002434854,0.002090483,0.002066512,0.0012663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001086862,"about_ca_system_score_gemma":0.001281212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002442569,"about_ca_topic_score_gemma":0.004380646,"domain_scores_codex":[0.9971344,0.0009108597,0.00009345081,0.0008180057,0.0008236406,0.0002196613],"domain_scores_gemma":[0.9900585,0.006684382,0.0006079406,0.001395905,0.0009539789,0.000299344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001067012,0.0004184587,0.04364781,0.0006072658,0.0003025853,0.0008453557,0.001183081,0.3468896,0.04343784,0.02602102,0.008642429,0.5269375],"study_design_scores_gemma":[0.00007649989,0.0001478773,0.004459311,0.00005447341,0.00004467867,0.0002860328,0.000285634,0.8883713,0.0190296,0.06741458,0.01979139,0.00003854324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1548139,0.001831999,0.8327321,0.001577311,0.00009263441,0.0003279127,0.001732794,0.003190661,0.003700756],"genre_scores_gemma":[0.287692,0.000669187,0.7028397,0.000541687,0.0001452736,0.0004399992,0.004912026,0.0005701577,0.002189964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004368164,"threshold_uncertainty_score":0.02310139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05289197953606532,"score_gpt":0.2284392249269482,"score_spread":0.1755472453908829,"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."}}