{"id":"W2004033830","doi":"10.2134/jeq2014.06.0273","title":"Characterization of Organic Phosphorus Form and Bioavailability in Lake Sediments using<sup>31</sup>P Nuclear Magnetic Resonance and Enzymatic Hydrolysis","year":2015,"lang":"en","type":"article","venue":"Journal of Environmental Quality","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Biotechnology and Biological Sciences Research Council; School of Medicine, Stanford University; National Science Foundation","keywords":"Sediment; Bloom; Phosphorus; Bay; Genetic algorithm; Eutrophication; Environmental chemistry; Algal bloom; Microcosm; Biogeochemical cycle; Organic matter; Sediment–water interface; Oceanography; Chemistry; Geology; Phytoplankton; Nutrient; Biology; Ecology; Geomorphology","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":[0.001233652,0.0001491593,0.0003746179,0.00004035918,0.0000433288,0.00001666741,0.0001250322,0.00008308758,0.0004279028],"category_scores_gemma":[0.00004987465,0.0001336445,0.00004375297,0.0001181597,0.0002325185,0.0003377535,0.0001498723,0.0001386145,0.00001247033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858464,"about_ca_system_score_gemma":0.00001394535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008977406,"about_ca_topic_score_gemma":0.0001168848,"domain_scores_codex":[0.9982415,0.0001550806,0.0008210324,0.0001970623,0.0004180809,0.0001672776],"domain_scores_gemma":[0.9990589,0.00004859409,0.0005395181,0.0001863412,0.000004887924,0.0001617719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001102064,0.0002901567,0.9437318,0.00004228043,0.00001181017,0.000006090325,0.002044696,0.00009238585,0.05057826,0.00001037077,0.00001231829,0.00306965],"study_design_scores_gemma":[0.001177116,0.0003289043,0.9816269,0.00005852085,0.00003819663,0.0000592474,0.0006465157,0.01368904,0.0005390756,0.0002920039,0.001361231,0.0001832319],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992347,0.0003269681,0.00003666955,0.00003743355,0.00003734429,0.0001828048,0.00007498974,0.000002618436,0.0000664839],"genre_scores_gemma":[0.9989831,0.0001909003,0.0007006006,0.00004982584,0.00001559372,9.650066e-7,0.000005304084,0.00001261615,0.0000411097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05003919,"threshold_uncertainty_score":0.5449863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378688019329294,"score_gpt":0.2219077923393554,"score_spread":0.2081209121460625,"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."}}