{"id":"W2983763085","doi":"","title":"Modeling Nutrients Dynamics in Nearshore Lake Ontario","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Nutrient; Oceanography; Environmental science; Geography; Geology; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000206578,0.000422178,0.0003318872,0.0002703289,0.001244515,0.001112448,0.001008606,0.0008943455,0.002543952],"category_scores_gemma":[0.001039263,0.0004644231,0.0004913103,0.0004554132,0.0005104065,0.0005611142,0.000549712,0.0004322067,0.0001707179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01010423,"about_ca_system_score_gemma":0.008111228,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9543775,"about_ca_topic_score_gemma":0.9610052,"domain_scores_codex":[0.9998977,0.0000125452,0.000004921269,0.00003002884,0.00001625611,0.00003865167],"domain_scores_gemma":[0.9996719,0.0001054614,0.00003551078,0.00001303827,0.0001115076,0.00006249662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000111534,0.00008589681,0.06548533,0.00002981992,0.00005442814,0.000116828,0.0002278429,0.9268248,0.00114072,0.0007525692,0.001104407,0.004065909],"study_design_scores_gemma":[0.00004183313,0.00003525038,0.02196791,0.000005413215,0.00002538005,0.000006684301,0.0002625713,0.9763858,0.0002403926,0.0002372082,0.0007776078,0.00001391872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958642,0.00003166623,0.0006744811,0.0001405481,0.000006138591,0.00001296608,0.0004605447,0.00003804704,0.002771437],"genre_scores_gemma":[0.9957727,0.00004017579,0.0008189611,0.00001623535,0.000002498615,0.0000142877,0.0003952131,0.00001204165,0.002927977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04562247,"threshold_uncertainty_score":0.09178221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800906218894477,"score_gpt":0.2346102155230451,"score_spread":0.2166011533341003,"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."}}