{"id":"W4396669845","doi":"10.1016/j.ocemod.2024.102374","title":"Multi-year three-dimensional simulation of seasonal variation in phytoplankton species composition in a large shallow lake","year":2024,"lang":"en","type":"article","venue":"Ocean Modelling","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Environment and Climate Change Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Phytoplankton; Seasonality; Variation (astronomy); Environmental science; Composition (language); Oceanography; Waves and shallow water; Climatology; Atmospheric sciences; Geology; Ecology; Nutrient; Biology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0002968263,0.0003854911,0.000379262,0.0003602174,0.0005871886,0.0006472653,0.0006317333,0.001044109,0.001230184],"category_scores_gemma":[0.001059845,0.0003208111,0.0006373865,0.0004573743,0.0005792282,0.0004614754,0.0006944775,0.0006310593,0.00008212292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001511536,"about_ca_system_score_gemma":0.0012402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09592262,"about_ca_topic_score_gemma":0.09883451,"domain_scores_codex":[0.999913,0.00002018032,0.000007472909,0.00001856821,0.00001294716,0.00002778469],"domain_scores_gemma":[0.9996334,0.0001588749,0.0000472114,0.00002492012,0.00005612035,0.00007944334],"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":[0.0001012496,0.0001167003,0.021702,0.00001889441,0.00005801353,0.0001075107,0.00007547452,0.9743044,0.001724291,0.0003245,0.0002342652,0.001232678],"study_design_scores_gemma":[0.00002964615,0.0000352306,0.006059477,0.0000027425,0.00001287195,0.000006934551,0.00006080235,0.9933786,0.0002184042,0.00008679519,0.00009888717,0.000009607631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982529,0.00001851443,0.0006803663,0.00006861539,0.000005767773,0.000007351089,0.0001952239,0.00003705323,0.0007342387],"genre_scores_gemma":[0.9982314,0.00002084956,0.001121252,0.00002662238,0.000001644109,0.00001872686,0.0002221598,0.000008773068,0.0003487136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09592262,"threshold_uncertainty_score":0.1907285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02472200522227971,"score_gpt":0.2446381815938311,"score_spread":0.2199161763715514,"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."}}