{"id":"W2735858835","doi":"10.7490/f1000research.1112250.1","title":"CoDaSeq: Analyzing HTS using compositional data analysis","year":2016,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Open peer review; Plant biology; Neuroscience; Computational biology; Physiology; Computer science; Biology; Botany","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":[],"consensus_categories":[],"category_scores_codex":[0.002444471,0.0001031702,0.0002365599,0.000343312,0.0002668645,0.00008349524,0.002417483,0.00006887027,0.0002735935],"category_scores_gemma":[0.0005112141,0.00007232046,0.00009304866,0.001732446,0.0002572574,0.0004440062,0.001699819,0.0001676866,0.00005331036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000629209,"about_ca_system_score_gemma":0.0001608117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002195713,"about_ca_topic_score_gemma":0.00002067255,"domain_scores_codex":[0.9974858,0.0002481518,0.0002706497,0.0005920049,0.0009469184,0.0004564739],"domain_scores_gemma":[0.9970319,0.0004300722,0.00009510821,0.001377833,0.0009200989,0.0001449358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008133813,0.0005986564,0.08802774,0.0002288931,0.002251987,0.0001957644,0.0006646346,0.003581575,0.7992713,0.03091617,0.04394032,0.03024156],"study_design_scores_gemma":[0.002306428,0.0002699654,0.08575512,0.0007470705,0.0002986766,0.00008709096,0.0002521814,0.4978941,0.2490389,0.02441609,0.1376314,0.001302948],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1483897,0.0001709774,0.810268,0.0286132,0.00007022927,0.0002167166,0.0009137599,0.0001194992,0.01123791],"genre_scores_gemma":[0.9716155,0.000008806813,0.02524644,0.00001820422,0.0000538442,0.00000224191,0.0001991224,0.000001954692,0.002853887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8232258,"threshold_uncertainty_score":0.4492325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1391095139802823,"score_gpt":0.3991590283537207,"score_spread":0.2600495143734385,"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."}}