{"id":"W4298162047","doi":"","title":"Effets de la sélection sur la diversité et la différenciation génétique moléculaire : résultats de simulations et application au pin maritime pour les gènes de la lignification","year":2004,"lang":"fr","type":"preprint","venue":"Prodinra (INRA Bordeaux-Aquitaine)","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Gene; Selection (genetic algorithm); Biology; Trait; Computational biology; Genetics; Gene expression; Evolutionary biology; Quantitative trait locus; Lignin; Botany; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003871849,0.0008180409,0.000644378,0.0001299537,0.001230054,0.0009689578,0.00080227,0.001650587,0.0001726785],"category_scores_gemma":[0.002605733,0.0004971297,0.0004326541,0.0009085635,0.0005892127,0.0007459024,0.0005784768,0.001703503,0.00008349184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774618,"about_ca_system_score_gemma":0.0009242569,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01650398,"about_ca_topic_score_gemma":0.00376799,"domain_scores_codex":[0.9912372,0.004612167,0.000820828,0.001301833,0.0008272043,0.001200746],"domain_scores_gemma":[0.9940736,0.003652916,0.0005698912,0.000292303,0.0008994385,0.0005118293],"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":[0.0003664973,0.002823619,0.1227419,0.0009313297,0.0003219914,0.00008626456,0.007683733,0.06174292,0.6093216,0.08087802,0.001011037,0.1120911],"study_design_scores_gemma":[0.001071967,0.0002745547,0.9279554,0.0005614637,0.0002703053,0.0001524336,0.001033861,0.02254712,0.01189009,0.02397968,0.009171821,0.001091312],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666395,0.0004725992,0.005874377,0.01868396,0.00009340026,0.001993187,0.0002940599,0.0003680606,0.005580926],"genre_scores_gemma":[0.9901075,0.001668598,0.001486642,0.0002556714,0.0006595597,0.0005970912,0.001838874,0.00002390644,0.003362134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8052135,"threshold_uncertainty_score":0.9997481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995483026855919,"score_gpt":0.314255529548607,"score_spread":0.2943006992800478,"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."}}