{"id":"W2936777354","doi":"10.4224/40003211","title":"NOAA/NRC second intercomparison for nutrients in seawater","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Seawater; Environmental science; Nutrient; Oceanography; Meteorology; Remote sensing; Geology; Geography; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03509388,0.00160781,0.001647497,0.002904001,0.004247748,0.002520401,0.004462281,0.002205485,0.002336492],"category_scores_gemma":[0.0124801,0.0007362058,0.001619485,0.00296878,0.00102969,0.001189772,0.002264332,0.001942979,0.001143877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007964127,"about_ca_system_score_gemma":0.03585588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1719181,"about_ca_topic_score_gemma":0.2142273,"domain_scores_codex":[0.9829053,0.004619047,0.0008359618,0.001802923,0.009278474,0.0005582878],"domain_scores_gemma":[0.9817778,0.001117452,0.0009408949,0.002480955,0.01284187,0.0008410605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007973708,0.001850712,0.06969888,0.002215747,0.001704265,0.0009374452,0.003305743,0.03044637,0.3782228,0.01396823,0.1051633,0.3845128],"study_design_scores_gemma":[0.001090514,0.003408689,0.1128813,0.0004743168,0.001108031,0.0004774182,0.001734086,0.02503626,0.2880745,0.009022249,0.5563427,0.0003500481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4181964,0.01597274,0.3716586,0.008396866,0.01005219,0.008176383,0.03717671,0.002786984,0.1275832],"genre_scores_gemma":[0.4203709,0.005359928,0.4825847,0.002233564,0.0003385865,0.005472397,0.02360438,0.0009106585,0.05912483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1719181,"threshold_uncertainty_score":0.3418348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03461666754389069,"score_gpt":0.2523631577697436,"score_spread":0.2177464902258529,"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."}}