{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001986873,0.001153436,0.0009454302,0.001002754,0.0003539197,0.0006751296,0.0005790797,0.001171311,0.0006408924],"category_scores_gemma":[0.003183795,0.0003544651,0.001040826,0.0004942814,0.000288489,0.0004643649,0.0003728665,0.0007328273,0.0001107959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007552429,"about_ca_system_score_gemma":0.0009078316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01059793,"about_ca_topic_score_gemma":0.005918518,"domain_scores_codex":[0.9995534,0.0001854718,0.00004968319,0.00008777114,0.00006866011,0.0000550224],"domain_scores_gemma":[0.9981441,0.001423611,0.0001350189,0.00004857565,0.0002170943,0.0000314094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001521458,0.000134525,0.003064998,0.0001063299,0.00009492816,0.00003326262,0.00003999181,0.9522304,0.002852606,0.0003374375,0.0001255723,0.04082777],"study_design_scores_gemma":[0.000005405703,0.00007289971,0.0007740388,0.000005642568,0.00001444953,0.000004548296,0.000009801663,0.9979075,0.001088992,0.00005714297,0.00005536485,0.000004220313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7581829,0.001808152,0.2350634,0.000347354,0.00006418789,0.0001689607,0.0001629768,0.0006249698,0.003576931],"genre_scores_gemma":[0.9407539,0.0003283566,0.05787152,0.000048158,0.000008274493,0.0001633086,0.0001380814,0.00002561374,0.0006626289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01059793,"threshold_uncertainty_score":0.02107251,"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."}}