{"id":"W2564448208","doi":"10.1055/s-0036-1596714","title":"Application of a simple bioactivity profiling strategy to natural product discovery from endophytes of marine macroalgae","year":2016,"lang":"en","type":"article","venue":"Planta Medica","topic":"Seaweed-derived Bioactive Compounds","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Natural product; Antimicrobial; Biology; Profiling (computer programming); Drug discovery; Natural Product Research; Fractionation; Computational biology; Biological activity; Chemistry; Microbiology; Bioinformatics; Biochemistry; Pharmacognosy; Chromatography; In vitro; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001412757,0.0001250901,0.0002349767,0.00001696172,0.00004022617,0.00001184617,0.000278373,0.00004605455,0.00008922198],"category_scores_gemma":[0.0001582993,0.00004106637,0.00005360364,0.00021157,0.0001166867,0.0001590679,0.0001149091,0.00007173453,0.000007734757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002557568,"about_ca_system_score_gemma":0.00001863015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002480132,"about_ca_topic_score_gemma":0.0006245313,"domain_scores_codex":[0.9988931,0.00005382182,0.0002335972,0.0003415429,0.0002809934,0.0001968963],"domain_scores_gemma":[0.9990911,0.0004859329,0.0001735661,0.0001037254,0.00006753327,0.00007811887],"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":[0.0001392453,0.00006949947,0.06281899,0.000007750775,0.00002172562,8.556864e-7,0.00001372603,1.12127e-7,0.7600462,0.0000608681,0.00005832938,0.1767627],"study_design_scores_gemma":[0.0001152439,0.0001409348,0.3891163,0.00002067461,0.00000952653,0.000002138525,0.00006217037,0.00001319289,0.6095236,0.0005260956,0.0003857983,0.00008435456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977754,0.00007166164,0.00009791274,0.0006723944,0.00009221954,0.0003225083,0.0007088962,0.00002976328,0.000229252],"genre_scores_gemma":[0.9991606,0.00002963976,0.0002353288,0.00003183746,0.0002600779,0.00002629974,0.0002176137,0.000001188393,0.00003739713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3262973,"threshold_uncertainty_score":0.3749234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537520208301692,"score_gpt":0.2323308860627422,"score_spread":0.2169556839797253,"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."}}