{"id":"W2404149918","doi":"10.1142/9789814447973_0031","title":"LSHPlace: Fast phylogenetic placement using locality-sensitive hashing","year":2012,"lang":"en","type":"article","venue":"","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Phylogenetic tree; Computer science; Tree (set theory); Inference; Locality; Sequence (biology); Phylogenetic network; Domain (mathematical analysis); Fidelity; Locality-sensitive hashing; Hash function; Theoretical computer science; Artificial intelligence; Hash table; Biology; Mathematics; Combinatorics; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001790066,0.0009805495,0.001482717,0.001240754,0.001252234,0.001716087,0.003216165,0.00170518,0.008143082],"category_scores_gemma":[0.006868512,0.0008898735,0.0008849954,0.001868181,0.00113785,0.00385675,0.003294515,0.002082687,0.005349943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007505422,"about_ca_system_score_gemma":0.001665338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002282069,"about_ca_topic_score_gemma":0.003308013,"domain_scores_codex":[0.9987144,0.0003062528,0.00006259123,0.0002897174,0.0004931103,0.0001337924],"domain_scores_gemma":[0.9976544,0.0008339486,0.0001651184,0.000892887,0.0002821172,0.0001716341],"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":[0.0008513866,0.0003913968,0.004963313,0.0006604611,0.0002654281,0.0004211781,0.0006551757,0.09967381,0.04946564,0.03194437,0.03716081,0.7735471],"study_design_scores_gemma":[0.0001501486,0.0002690583,0.0008095506,0.00004120315,0.00004136537,0.0004102454,0.0001353039,0.9017657,0.04176324,0.03314831,0.02134135,0.0001244853],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01561878,0.0003636723,0.9553557,0.0002392619,0.0001718242,0.0001021729,0.0003583036,0.02621878,0.001571492],"genre_scores_gemma":[0.1158238,0.0002754074,0.8771546,0.0001839395,0.0001023555,0.0001835766,0.001156174,0.001871459,0.003248723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008143082,"threshold_uncertainty_score":0.02724135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02295670670859513,"score_gpt":0.2653714624931588,"score_spread":0.2424147557845637,"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."}}