{"id":"W4310376384","doi":"10.3390/f13122020","title":"Machine Learning-Assisted In Vitro Rooting Optimization in Passiflora caerulea","year":2022,"lang":"en","type":"article","venue":"Forests","topic":"Plant tissue culture and regeneration","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Explant culture; Auxin; Micropropagation; Biology; Passiflora; Botany; In vitro; Caerulea; Machine learning; Biological system; Computer science; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003394299,0.0008405029,0.0005506806,0.0003256143,0.0001925535,0.0003696795,0.0004361772,0.0005156678,0.000239158],"category_scores_gemma":[0.0003271174,0.0001897925,0.0006114895,0.0002385997,0.0001372316,0.0001934897,0.000167931,0.0003352261,0.00007756746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006825363,"about_ca_system_score_gemma":0.0004146789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01716628,"about_ca_topic_score_gemma":0.01937419,"domain_scores_codex":[0.9998956,0.00001901264,0.000006643949,0.00004442839,0.00002199428,0.00001230628],"domain_scores_gemma":[0.9998703,0.00004648063,0.00003371884,0.000007896641,0.00003401533,0.000007618164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000120214,0.0001365016,0.004696427,0.0001776165,0.00007449573,0.0001910757,0.00006355486,0.7909713,0.1764638,0.0001373553,0.0001571628,0.02681052],"study_design_scores_gemma":[0.000009958709,0.0002261677,0.004544256,0.000005482696,0.0000465272,0.000027609,0.000021647,0.9680777,0.02667499,0.00008393855,0.0002658908,0.00001589873],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9707755,0.0005686201,0.02720139,0.00006937474,0.00001510918,0.00002838447,0.0001184646,0.0003080308,0.0009150919],"genre_scores_gemma":[0.9827536,0.0002298689,0.01595843,0.00001900338,0.000002379685,0.00004131953,0.000163531,0.00001774266,0.0008141879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01716628,"threshold_uncertainty_score":0.03413272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009158509951737672,"score_gpt":0.2247513133675842,"score_spread":0.2155928034158466,"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."}}