{"id":"W3189020595","doi":"10.1101/2021.08.09.455719","title":"Comparative analysis of machine learning and evolutionary optimization algorithms for precision tissue culture of <i>Cannabis sativa</i> : Prediction and validation of <i>in vitro</i> shoot growth and development based on the optimization of light and carbohydrate sources","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant tissue culture and regeneration","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Artificial intelligence; Computer science; Algorithm; Shoot; Multilayer perceptron; Artificial neural network; Adaptive neuro fuzzy inference system; Micropropagation; Biotechnology; Data mining; Biology; Biochemical engineering; Botany; Tissue culture; Fuzzy logic; Engineering; In vitro","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":[0.0026515,0.001413523,0.001301205,0.001232279,0.0004163512,0.0007816428,0.000727998,0.001346389,0.001154332],"category_scores_gemma":[0.004539826,0.0004511098,0.00115642,0.0006687351,0.000365965,0.0005692925,0.0005052018,0.001013717,0.0001976462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009375924,"about_ca_system_score_gemma":0.001108441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01278898,"about_ca_topic_score_gemma":0.006629845,"domain_scores_codex":[0.9993888,0.0002591678,0.00006331241,0.0001231927,0.00009254212,0.00007303822],"domain_scores_gemma":[0.9967898,0.002469089,0.0001879632,0.00008305359,0.0004171379,0.00005298758],"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":[0.0001364279,0.0001384725,0.002203789,0.0001062053,0.00009148565,0.00003335031,0.00003944412,0.9516652,0.001465125,0.0004254419,0.0002401428,0.04345481],"study_design_scores_gemma":[0.000004998212,0.00004618336,0.0003793151,0.00000515403,0.000008892822,0.000003365967,0.000009277158,0.9990374,0.0003827207,0.00006819433,0.00005128184,0.000003211198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5507797,0.003027513,0.4377764,0.0006601862,0.0001499418,0.0002821322,0.0002597961,0.001447801,0.005616575],"genre_scores_gemma":[0.8991585,0.0004057409,0.09876067,0.00008758192,0.00001569694,0.0002529956,0.0002371458,0.00005671137,0.001024902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01278898,"threshold_uncertainty_score":0.02542907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009107393044356704,"score_gpt":0.2172640929946062,"score_spread":0.2081566999502495,"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."}}