{"id":"W2946364149","doi":"10.1101/641449","title":"Increasing plant group productivity through latent genetic variation for cooperation","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant Reproductive Biology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universität Zürich; AgreenSkills; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agroscope; Agence Nationale de la Recherche; National Science Foundation","keywords":"Biology; Monoculture; Productivity; Allele; Competition (biology); Selection (genetic algorithm); Group selection; Genetics; Evolutionary biology; Biotechnology; Gene; Ecology; Economics; Computer science","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":[0.001132334,0.0005026243,0.0004945415,0.001002185,0.0003552322,0.0005288757,0.0006904708,0.0005241488,0.003204423],"category_scores_gemma":[0.0009769092,0.0002866496,0.0006068572,0.0004727258,0.0006418105,0.0002833898,0.001015656,0.00109138,0.0002149179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005647183,"about_ca_system_score_gemma":0.0003402783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003949228,"about_ca_topic_score_gemma":0.001270877,"domain_scores_codex":[0.9993094,0.0002282408,0.00005393049,0.0002093555,0.0001286136,0.00007031328],"domain_scores_gemma":[0.9984321,0.0007243517,0.0003376438,0.0002407496,0.00005706499,0.0002081008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000290451,0.000213375,0.005396562,0.00005933471,0.0001345282,0.0002438665,0.00008085303,0.001735305,0.9815817,0.003185291,0.00007933412,0.00699944],"study_design_scores_gemma":[0.001019729,0.003132217,0.3097308,0.0001198411,0.0009565969,0.003678443,0.0003700197,0.1809478,0.4738261,0.01812557,0.007633353,0.0004595016],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625956,0.00008830425,0.03530547,0.0001084503,0.00001982091,0.00006882549,0.0002351389,0.0002673196,0.001310991],"genre_scores_gemma":[0.9884403,0.00003711861,0.01077524,0.00004768151,0.000007528161,0.0000488305,0.0001285643,0.00004851204,0.000466262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003204423,"threshold_uncertainty_score":0.0107199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0145496320331423,"score_gpt":0.2188564344553625,"score_spread":0.2043068024222202,"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."}}