{"id":"W6948996878","doi":"10.5281/zenodo.1184231","title":"A New Fast Method For Inferring Multiple Consensus Trees Using K-Medoids","year":2018,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sequence (biology); Tree (set theory); Identification (biology); Feature selection","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004963574,0.002450698,0.004004171,0.004695231,0.002741251,0.003082628,0.006577335,0.004472245,0.009886374],"category_scores_gemma":[0.02095897,0.002536092,0.003886102,0.004234022,0.001545636,0.00360178,0.004664022,0.004707657,0.005963626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00140718,"about_ca_system_score_gemma":0.003161301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01275034,"about_ca_topic_score_gemma":0.0221538,"domain_scores_codex":[0.9953582,0.001294949,0.0004075613,0.001606452,0.001109934,0.0002228961],"domain_scores_gemma":[0.9853488,0.009699968,0.0006787704,0.001754164,0.002095863,0.000422424],"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":[0.0006259599,0.0001747294,0.002113959,0.0006211657,0.0008036037,0.000355944,0.0006352217,0.2574347,0.0110985,0.0152145,0.01421758,0.6967041],"study_design_scores_gemma":[0.0001209741,0.00005863597,0.0003797964,0.00005810941,0.00009871018,0.000269464,0.000110864,0.9606223,0.002721696,0.02895818,0.006528001,0.00007318928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001382811,0.0001506743,0.9965662,0.00006621641,0.00006606388,0.00006667728,0.0001810711,0.001306889,0.0002134041],"genre_scores_gemma":[0.01840526,0.00009802152,0.9791232,0.00009034688,0.00006070267,0.0001857218,0.0007731586,0.0003908283,0.0008727719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01275034,"threshold_uncertainty_score":0.03307325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0909434847139969,"score_gpt":0.3072197814181054,"score_spread":0.2162762967041085,"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."}}