{"id":"W2746949165","doi":"10.1105/tpc.17.00073","title":"ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology","year":2017,"lang":"en","type":"article","venue":"The Plant Cell","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":515,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Waterloo; University of Toronto","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Workflow; Visualization; Biology; Computer science; Set (abstract data type); Interface (matter); Data visualization; Process (computing); Data science; Hierarchy; Web application; Computational biology; World Wide Web; Data mining; Database; Programming language","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":[0.008460591,0.002734921,0.001481877,0.00670351,0.001406766,0.005714135,0.004450205,0.001656091,0.05098841],"category_scores_gemma":[0.01429908,0.001768764,0.003298925,0.003860316,0.001552276,0.007242906,0.007234493,0.004077179,0.01435692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023597,"about_ca_system_score_gemma":0.00201605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002447099,"about_ca_topic_score_gemma":0.003797079,"domain_scores_codex":[0.9979425,0.0007519439,0.0001631538,0.0004073171,0.0005917636,0.000143353],"domain_scores_gemma":[0.9895198,0.007612633,0.0004431667,0.001143777,0.0006818199,0.0005987102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001346744,0.000324707,0.004442873,0.005664329,0.0009749446,0.002122439,0.004174559,0.009085239,0.03104334,0.04533489,0.5020781,0.3934079],"study_design_scores_gemma":[0.000931296,0.0003540425,0.01029449,0.002225725,0.0004134742,0.002201416,0.001505113,0.152101,0.04828135,0.2386563,0.542264,0.000771802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003916875,0.001185142,0.737662,0.001386344,0.0003388148,0.000568725,0.02150351,0.2262908,0.007147843],"genre_scores_gemma":[0.02821566,0.001295392,0.919282,0.0005914166,0.0001340336,0.001661988,0.0196165,0.02558576,0.003617277],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05098841,"threshold_uncertainty_score":0.1705732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.569053378128741,"score_gpt":0.2963328785291418,"score_spread":0.2727204995995993,"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."}}