{"id":"W4416313466","doi":"10.1002/lno.70271","title":"Effects of temporal and spatial variability in energy fluxes on phytoplankton","year":2025,"lang":"en","type":"article","venue":"Limnology and Oceanography","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Phytoplankton; Eutrophication; Spatial variability; Nutrient; Chlorophyll a; Abundance (ecology); Spatial heterogeneity; Climate change","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":[0.000752537,0.0002007048,0.0001856423,0.0005389506,0.0002882009,0.000463449,0.000168623,0.0001803225,0.0004412477],"category_scores_gemma":[0.001216313,0.0001509286,0.0003028879,0.0007242792,0.0002794021,0.000475787,0.0005455025,0.0001635975,0.00005071857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004533823,"about_ca_system_score_gemma":0.0002880072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01124413,"about_ca_topic_score_gemma":0.01754405,"domain_scores_codex":[0.9996949,0.00008882706,0.00003196877,0.00008502694,0.00005470285,0.00004463124],"domain_scores_gemma":[0.99931,0.000298968,0.0001609027,0.00006860476,0.0001155782,0.00004594601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001263843,0.00002494886,0.9783734,0.000021081,0.0002024586,0.0000772757,0.0001161393,0.004010372,0.01284066,0.00007445158,0.00006990905,0.004062921],"study_design_scores_gemma":[0.000001284098,0.00001168077,0.9957962,0.00000158765,0.0000145842,0.00001234017,0.00006261306,0.003507501,0.0004655113,0.00003660532,0.00008697822,0.000003113969],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992888,0.00006107043,0.0003096229,0.00001517288,0.000003008189,0.000001315876,0.0001336,0.00000517066,0.0001822294],"genre_scores_gemma":[0.9997724,0.0000124028,0.00006315534,0.000003779626,0.000001684708,0.000002113978,0.0001071599,0.000001552693,0.00003580873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01124413,"threshold_uncertainty_score":0.02235734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001875079043429134,"score_gpt":0.1852834772931669,"score_spread":0.1834083982497378,"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."}}