{"id":"W2278221935","doi":"10.1016/j.limno.2016.01.006","title":"Physical conditions driving the spatial and temporal variability in aquatic metabolism of a subtropical coastal lake","year":2016,"lang":"en","type":"article","venue":"Limnologica","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Federal de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Global Lake Ecological Observatory Network","keywords":"Subtropics; Environmental science; Spatial variability; Aquatic environment; Aquatic ecosystem; Ecology; Oceanography; Geography; Fishery; Biology; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001310167,0.0001727784,0.000195185,0.0004419153,0.0002699881,0.0003588791,0.0001311669,0.0001634783,0.0003966421],"category_scores_gemma":[0.0003665765,0.0001444084,0.0001564904,0.0005092317,0.0002524513,0.0002289989,0.0003794377,0.0001161052,0.00006234944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003210748,"about_ca_system_score_gemma":0.0002411015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01733991,"about_ca_topic_score_gemma":0.03708154,"domain_scores_codex":[0.9999485,0.000008240059,0.000004195778,0.00001618042,0.00001121896,0.00001172427],"domain_scores_gemma":[0.9997979,0.00002891189,0.00008497125,0.000009924614,0.00003981751,0.00003851268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001798558,0.00003414722,0.9473814,0.00003032188,0.00005311515,0.0001731455,0.0009831699,0.0002480644,0.0473377,0.00004393145,0.0000354637,0.003499721],"study_design_scores_gemma":[9.655215e-7,0.00001453247,0.9995224,7.201314e-7,0.000003648023,0.0000153759,0.00010724,0.0001727545,0.000124533,0.000008160769,0.00002838661,0.000001253845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998372,0.00001652333,0.0000270782,0.000003893944,2.315226e-7,0.00000125649,0.00003274669,0.000001266171,0.00007983598],"genre_scores_gemma":[0.9998114,0.00001442702,0.00005519397,0.000002841103,9.009733e-7,0.000002988633,0.00006677643,8.509459e-7,0.00004465781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01733991,"threshold_uncertainty_score":0.03447795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044080095449027,"score_gpt":0.2041302746001082,"score_spread":0.1936894736456179,"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."}}