{"id":"W3115467785","doi":"10.48550/arxiv.2012.13475","title":"Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Phylogenetic tree; Evolutionary biology; Computer science; Artificial intelligence; Psychology; Biology; 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":[0.001672611,0.0005026394,0.0004334346,0.0004542384,0.0003803308,0.001057318,0.001009955,0.001190803,0.002147733],"category_scores_gemma":[0.007071837,0.0003158591,0.0004918316,0.0004563368,0.002084832,0.002376539,0.002083521,0.002594306,0.0004975003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007209008,"about_ca_system_score_gemma":0.0004485349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004133112,"about_ca_topic_score_gemma":0.0007573903,"domain_scores_codex":[0.9994199,0.0003123852,0.00001742847,0.0001405567,0.00008647516,0.00002330539],"domain_scores_gemma":[0.9979464,0.001393784,0.0001326892,0.000315081,0.0001342982,0.00007773614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002291476,0.0001603927,0.002728629,0.0002825928,0.00010731,0.0002745238,0.0005929004,0.1810191,0.02262564,0.444991,0.006718086,0.3402706],"study_design_scores_gemma":[0.00001279031,0.00006765934,0.0003983195,0.0000391692,0.0000123246,0.00009638449,0.00003063515,0.7434074,0.004801105,0.2460241,0.00509437,0.00001576054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01882042,0.0006719778,0.9753097,0.001430637,0.00006324479,0.00002345184,0.0000621061,0.0003649353,0.003253588],"genre_scores_gemma":[0.5439776,0.0007413418,0.4495614,0.0009457077,0.0002678442,0.0001696571,0.0002631847,0.000257379,0.003815947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002147733,"threshold_uncertainty_score":0.008845687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05509754808927667,"score_gpt":0.2028304962640279,"score_spread":0.1477329481747512,"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."}}