{"id":"W2041487367","doi":"10.1139/f06-070","title":"Characterization of phosphorus in sequential extracts from lake sediments using <sup>31</sup>P nuclear magnetic resonance spectroscopy","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vetenskapsrådet","keywords":"Extraction (chemistry); Phosphorus; Seston; Nuclear magnetic resonance spectroscopy; Chemistry; Pyrophosphate; Humic acid; Environmental chemistry; Phosphorus-31 NMR spectroscopy; Sediment; Spectroscopy; Humin; Nutrient; Geology; Chromatography; Stereochemistry; Organic chemistry; Phytoplankton","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002199863,0.00009238879,0.0001577489,0.00008530963,0.0001431942,0.0001061034,0.0001944058,0.0000395256,0.0001876706],"category_scores_gemma":[0.00001799216,0.00008079789,0.00002684682,0.0003010378,0.0005716491,0.0004645642,0.00001890243,0.00007441771,0.000002611638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008840425,"about_ca_system_score_gemma":0.0001200443,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03397989,"about_ca_topic_score_gemma":0.01122921,"domain_scores_codex":[0.999,0.00002931332,0.0003322703,0.0001485306,0.0002457575,0.0002441623],"domain_scores_gemma":[0.9995915,0.00002017283,0.0001782435,0.00006317228,0.000007307734,0.0001395826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001285288,0.00002007943,0.9903717,0.000002167199,0.000001540324,0.00003216066,0.0008182244,0.0002621715,0.005324783,0.00002020817,0.00006787566,0.003066236],"study_design_scores_gemma":[0.0007479406,0.0003449349,0.9276702,0.0001656958,0.00002391321,0.00004120306,0.0006656526,0.05564109,0.001990977,0.005229469,0.00723163,0.0002472534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988526,0.0002447612,0.00004306194,0.0001961925,0.0001353616,0.00005842264,0.00001814755,0.00000163208,0.0004497923],"genre_scores_gemma":[0.997658,0.00006075156,0.002113234,0.00007686736,0.00004585705,3.387063e-7,0.000003520918,0.000005470096,0.00003603412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06270146,"threshold_uncertainty_score":0.9724529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01015733705021133,"score_gpt":0.1920394477783103,"score_spread":0.181882110728099,"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."}}