{"id":"W4287586160","doi":"10.5281/zenodo.4296611","title":"Chemical Screening Pipeline for Identification of Specific Plant Autophagy Modulators","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Knut och Alice Wallenbergs Stiftelse; Svenska Forskningsrådet Formas; Sveriges Lantbruksuniversitet; Stiftelsen för Strategisk Forskning; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Autophagy; Identification (biology); Pipeline (software); Computational biology; Biochemical engineering; Computer science; Chemistry; Biology; Engineering; Biochemistry; Botany","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003443105,0.001192776,0.001186571,0.001576229,0.0005881612,0.0006105591,0.0005344837,0.0005926354,0.007146459],"category_scores_gemma":[0.0003204956,0.0004788332,0.0009406983,0.001052677,0.0002160285,0.0004138663,0.0006058946,0.0009332911,0.003170447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000581531,"about_ca_system_score_gemma":0.00136349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226074,"about_ca_topic_score_gemma":0.003458825,"domain_scores_codex":[0.9996831,0.00001993684,0.00002167803,0.00006317121,0.0001538139,0.00005817968],"domain_scores_gemma":[0.9998772,0.00002438927,0.00001724864,0.00001220433,0.00004684595,0.00002209118],"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":[0.0002425217,0.0001650602,0.0002715148,0.0004131763,0.00004792041,0.000191298,0.00002535791,0.0007165115,0.9826868,0.000300272,0.00119193,0.01374778],"study_design_scores_gemma":[0.0002634001,0.001509908,0.003133512,0.00004782352,0.0002184368,0.0005442429,0.00004424974,0.002588212,0.9578942,0.0002590844,0.03343855,0.00005832003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6754067,0.01498095,0.1668215,0.002036557,0.0003221751,0.009164258,0.07294572,0.01027514,0.04804702],"genre_scores_gemma":[0.6820425,0.02151367,0.1425042,0.001905021,0.0000989928,0.006549493,0.09560733,0.0005850412,0.04919378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007146459,"threshold_uncertainty_score":0.02390724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05936294357744007,"score_gpt":0.2219725149416807,"score_spread":0.1626095713642406,"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."}}