{"id":"W6913223366","doi":"10.5683/sp2/b1xmmd","title":"Insights from Fisher's geometric model on the likelihood of speciation under different histories of environmental change","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Genetic algorithm; Environmental change; Geometric modeling; Maximum likelihood; Geometric shape; Code (set theory); Geometric data analysis","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":[0.003665124,0.001324834,0.001343118,0.002766975,0.001178672,0.002114398,0.004034633,0.002242481,0.03508384],"category_scores_gemma":[0.01752517,0.00065973,0.001345413,0.004197077,0.0007047474,0.001425653,0.001920019,0.001873256,0.01882721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00289331,"about_ca_system_score_gemma":0.001613515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04933856,"about_ca_topic_score_gemma":0.1148235,"domain_scores_codex":[0.9982181,0.0006172702,0.0001442939,0.0004626073,0.0003828605,0.0001748208],"domain_scores_gemma":[0.9937339,0.003406791,0.0004038307,0.00138874,0.0007921878,0.0002745574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001656617,0.00005153034,0.008428875,0.0006488503,0.0001217956,0.00005925163,0.00007251255,0.01164645,0.0001623324,0.004670473,0.9679703,0.006001969],"study_design_scores_gemma":[0.001358471,0.000083751,0.03753319,0.0007124533,0.0001654449,0.0004885788,0.0003843755,0.03777428,0.001185499,0.04086763,0.879263,0.0001833887],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002984396,0.0002611583,0.001116601,0.0005865272,0.00005772079,0.00002146267,0.991578,0.0009147423,0.00247948],"genre_scores_gemma":[0.01314367,0.0001252611,0.002538393,0.0002862246,0.00001994433,0.0001301186,0.9822604,0.0001954024,0.001300657],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04933856,"threshold_uncertainty_score":0.1173671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03887864131673077,"score_gpt":0.2247345105308854,"score_spread":0.1858558692141546,"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."}}