{"id":"W3205155635","doi":"10.3389/fpls.2021.757869","title":"Comparative Analysis of Machine Learning and Evolutionary Optimization Algorithms for Precision Micropropagation of Cannabis sativa: Prediction and Validation of in vitro Shoot Growth and Development Based on the Optimization of Light and Carbohydrate Sources","year":2021,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Plant tissue culture and regeneration","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Micropropagation; Shoot; Cannabis sativa; Computer science; Plant growth; Algorithm; Machine learning; Botany; Biology; Artificial intelligence; Biotechnology; Biochemical engineering; In vitro; Tissue culture; Engineering","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":[0.0002613061,0.00005499191,0.0001452083,0.0001908176,0.00005846107,0.000006467203,0.00002673369,0.00004127343,2.400271e-7],"category_scores_gemma":[0.00008316167,0.00004203662,0.00000844584,0.0003101461,0.0001102183,0.00001680746,0.00001989186,0.00002467264,6.085268e-10],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000112857,"about_ca_system_score_gemma":0.00007374276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003707456,"about_ca_topic_score_gemma":0.00003250439,"domain_scores_codex":[0.9994196,0.00006106874,0.0002001565,0.000168604,0.0001024641,0.0000481601],"domain_scores_gemma":[0.9996479,0.00002102295,0.0001627798,0.00003669443,0.0001180057,0.00001358225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001014382,0.00002038255,0.03461815,0.00002878272,0.00002244688,6.928599e-8,0.0006133463,0.3061091,0.6582956,0.000003205241,0.000005815552,0.0001816687],"study_design_scores_gemma":[0.000146117,0.00004773005,0.007363651,0.00002431317,0.00002385071,5.749852e-7,0.0001643267,0.4475968,0.5446016,0.000002044835,0.000004556642,0.00002442658],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.866203,0.0008860324,0.1326,0.00004867656,0.00002180054,0.0001577753,0.00007696938,7.529384e-7,0.000005040392],"genre_scores_gemma":[0.9632863,0.0002495103,0.0359533,0.000002482247,0.000003309462,0.000006850385,0.0004929852,0.000001443674,0.000003770014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1414877,"threshold_uncertainty_score":0.1714204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00877286990406522,"score_gpt":0.2204823501687989,"score_spread":0.2117094802647336,"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."}}