{"id":"W2622065699","doi":"10.1111/2041-210x.12737","title":"Inference of selection gradients using performance measures as fitness proxies","year":2017,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Society","keywords":"Selection (genetic algorithm); Proxy (statistics); Genetic Fitness; Fitness proportionate selection; Inference; Natural selection; Biology; Statistics; Econometrics; Ecology; Evolutionary biology; Computer science; Mathematics; Fitness function; Machine learning; Artificial intelligence; Genetic algorithm","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":[0.004785174,0.0004632081,0.0007789105,0.002123807,0.0004047912,0.001413219,0.0005284924,0.0004655418,0.001417051],"category_scores_gemma":[0.02317432,0.0002762538,0.0007784957,0.001540417,0.001159968,0.001104294,0.001106411,0.0009594498,0.0004190295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008126306,"about_ca_system_score_gemma":0.0004137947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00542994,"about_ca_topic_score_gemma":0.003555446,"domain_scores_codex":[0.9970233,0.001537506,0.0001721499,0.0007113535,0.0003889943,0.0001667074],"domain_scores_gemma":[0.9827459,0.01202062,0.002199066,0.001692495,0.0009708028,0.0003710692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002778537,0.0001030781,0.8563123,0.0003737779,0.001043159,0.0001385083,0.000593918,0.04460818,0.03561799,0.01145517,0.00114468,0.04833125],"study_design_scores_gemma":[0.00001902657,0.0001744273,0.8300756,0.00005727468,0.0001897508,0.0001583718,0.0004306207,0.1397504,0.008407434,0.01866462,0.001975244,0.0000972979],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9069985,0.0003975549,0.08824304,0.0001274704,0.00002367419,0.0000524344,0.0009735694,0.0002642766,0.00291937],"genre_scores_gemma":[0.9921026,0.00004905355,0.007133919,0.00004107492,0.000007192442,0.00002717142,0.0004331036,0.00005102874,0.000154957],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00542994,"threshold_uncertainty_score":0.02530676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1273598655888027,"score_gpt":0.3554987814154191,"score_spread":0.2281389158266164,"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."}}