{"id":"W4387361408","doi":"10.1093/pcp/pcad117","title":"Harnessing Deep Learning to Analyze Cryptic Morphological Variability of <i>Marchantia polymorpha</i>","year":2023,"lang":"en","type":"article","venue":"Plant and Cell Physiology","topic":"Bryophyte Studies and Records","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute for Basic Biology; Institute of Genetics; Japan Society for the Promotion of Science; Monash University","keywords":"Biology; Inbred strain; Classifier (UML); Phenotype; Artificial intelligence; Genetics; Gene; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002125366,0.0001121006,0.0002740536,0.00001508649,0.0001995951,0.000008687857,0.0001113076,0.00007954123,0.0001813415],"category_scores_gemma":[0.00003236085,0.00004217409,0.00006423849,0.0003224785,0.0001012244,0.00002111667,0.0001924813,0.0001194624,0.00003626485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003966036,"about_ca_system_score_gemma":0.000002554691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001600194,"about_ca_topic_score_gemma":0.00004075167,"domain_scores_codex":[0.9990463,0.0001232534,0.0001680971,0.0002967965,0.00007002182,0.000295523],"domain_scores_gemma":[0.9994587,0.0003434722,0.00006221521,0.00003679342,0.00002369978,0.00007506407],"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":[0.00007451368,0.00004705087,0.009446231,0.00002498071,0.00001353078,0.00000860291,0.0001233781,0.00003322312,0.9754489,0.00004736308,0.0005906016,0.01414163],"study_design_scores_gemma":[0.0001538727,0.0005389837,0.9836194,0.00001666147,0.00002304449,0.00001027688,0.0006473764,0.001043648,0.002524116,0.0008710618,0.01032547,0.000226116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984778,0.0001247001,0.00000408846,0.0003232496,0.0002835474,0.000080019,0.00002242038,0.00004921391,0.0006349448],"genre_scores_gemma":[0.9990792,0.0002846948,0.00004619906,0.00009694707,0.0002895371,0.00000661247,0.00006987756,7.794749e-7,0.0001261862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9741731,"threshold_uncertainty_score":0.1985562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01262705731617115,"score_gpt":0.2051616997567258,"score_spread":0.1925346424405547,"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."}}