{"id":"W4411348433","doi":"10.1101/2025.06.13.659410","title":"Environment-dependent selection impacts heritable developmental stability and trait canalization in rice","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"National Institute of Food and Agriculture; National Institutes of Health; Canada Research Chairs; Gordon and Betty Moore Foundation","keywords":"Trait; Selection (genetic algorithm); Biology; Stability (learning theory); Evolutionary biology; Genetics; Biotechnology; Computer science; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004059921,0.0002620688,0.0002445115,0.00004347016,0.0001404004,0.0001355583,0.0001638934,0.0002477986,0.0002431689],"category_scores_gemma":[0.00007498948,0.0001522389,0.00003921971,0.0003298077,0.00003169565,0.0001503893,0.0002103367,0.000277376,0.000004230842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005502655,"about_ca_system_score_gemma":0.0001242134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002098771,"about_ca_topic_score_gemma":0.0006037119,"domain_scores_codex":[0.9983747,0.0001026702,0.0003690791,0.0006119377,0.0002281577,0.0003134795],"domain_scores_gemma":[0.9995018,0.00004724175,0.0001655951,0.00008080115,0.0000773456,0.0001271843],"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":[0.00001388046,0.0001268013,0.04791394,0.00009012991,0.0000212488,0.000001466929,0.00001698157,0.00001568783,0.9516835,0.00004542008,0.00003787336,0.00003312171],"study_design_scores_gemma":[0.0002072053,0.00003907474,0.7965214,0.00008553739,0.00001593992,7.431898e-9,0.00003867465,0.000167652,0.2014109,0.00000243827,0.001201074,0.0003100902],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984593,0.0001733841,0.00007956882,0.0002762444,0.0001402785,0.00060683,0.0001573263,0.00007674294,0.00003028864],"genre_scores_gemma":[0.9989541,0.000193427,0.0004948234,0.0001706475,0.00006413537,0.00009946295,0.000002063917,0.000002436817,0.00001894478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7502725,"threshold_uncertainty_score":0.6208122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747597231485302,"score_gpt":0.1970862407719456,"score_spread":0.1796102684570926,"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."}}