{"id":"W3123525417","doi":"10.20944/preprints201910.0034.v1","title":"Structural Diversity, Characterization and Toxicology of Microcystins","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; National Research Council Canada","funders":"","keywords":"Identification (biology); Computational biology; Diversity (politics); Isolation (microbiology); Characterization (materials science); Environmental chemistry; Chemical structure; Chemistry; Biology; Toxicology; Nanotechnology; Bioinformatics; Ecology; Organic chemistry; Materials science; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002327541,0.0001853007,0.0003394489,0.00003614242,0.00008448282,0.00000702044,0.0003744188,0.0002520454,0.001790307],"category_scores_gemma":[0.00003860516,0.0001878695,0.00006120617,0.00004903244,0.0001204179,0.0000718436,0.007073806,0.0002713565,0.0004168415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089598,"about_ca_system_score_gemma":0.00001785232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005594099,"about_ca_topic_score_gemma":0.0001270252,"domain_scores_codex":[0.9987532,0.00007693114,0.000307791,0.0005043173,0.000186051,0.0001716748],"domain_scores_gemma":[0.9989956,0.0000357378,0.0003964867,0.0004891073,0.0000186545,0.00006440198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001418665,0.00001558443,0.9552872,0.0001490293,0.00002478234,0.000001483177,0.000742607,0.0007941006,0.04257151,0.00007789223,0.000005954434,0.0003156179],"study_design_scores_gemma":[0.0001625978,0.00001549687,0.9912446,0.00004769136,0.00002825353,0.000007625286,0.00002384773,0.004561165,0.003062401,0.0003215745,0.0003422755,0.0001824883],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961674,0.000008883399,0.0002866588,0.00006240889,0.0004805781,0.0004572484,0.0001058744,0.00002321387,0.002407775],"genre_scores_gemma":[0.9986489,0.00003306978,0.00009559911,0.00005655015,0.00003338048,0.000007132029,0.0001024871,0.00001230735,0.001010621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03950911,"threshold_uncertainty_score":0.9991222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0527623456963591,"score_gpt":0.2752239277098855,"score_spread":0.2224615820135264,"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."}}