{"id":"W4410350959","doi":"10.1101/2025.05.07.652750","title":"Improving plant functional annotation from knowledge graphs using Graph Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Knowledge graph; Computer science; Annotation; Artificial neural network; Graph; Artificial intelligence; Theoretical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000412895,0.0005358853,0.0004470388,0.0004532087,0.0004622262,0.0007874834,0.001438693,0.0004230301,0.000006802293],"category_scores_gemma":[0.00006433879,0.0006019399,0.0001899859,0.001233716,0.00008927627,0.0005196456,0.001449315,0.0008575533,0.00001114839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001829464,"about_ca_system_score_gemma":0.0005853709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004340042,"about_ca_topic_score_gemma":0.000008886355,"domain_scores_codex":[0.9968981,0.0001279228,0.0005949523,0.001507909,0.0003280599,0.0005430809],"domain_scores_gemma":[0.996935,0.0001697732,0.0004807585,0.001746536,0.0004460387,0.0002219237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001907239,0.003431132,0.0227905,0.002029994,0.003109793,0.0003355465,0.000428499,0.1661241,0.5088599,0.2713951,0.0164607,0.004844099],"study_design_scores_gemma":[0.0002792288,0.00001505646,0.01862122,0.0002223643,0.00009002021,2.379797e-8,0.0000017049,0.9761398,0.003283361,0.00004425986,0.0006629574,0.0006399767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0954111,0.001886402,0.8966509,0.0001103433,0.003586479,0.0004663841,0.001177383,0.0007059372,0.000005114978],"genre_scores_gemma":[0.8018661,0.0001140188,0.1964999,0.0002493582,0.0009068238,0.0002760254,0.00001729575,0.00006486103,0.000005622619],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8100157,"threshold_uncertainty_score":0.9996432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143391006112888,"score_gpt":0.2279737122802846,"score_spread":0.2065398022191557,"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."}}