{"id":"W4415738525","doi":"10.1111/fwb.70121","title":"Seasonal Forest Resources Support Fish Biomass in Floodplain Lakes of an Amazonian Tributary","year":2025,"lang":"en","type":"article","venue":"Freshwater Biology","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Global Water Futures; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; National Geographic Society; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Nacional de Pesquisas da Amazônia; Fundação de Amparo à Pesquisa do Estado do Amazonas; University of Saskatchewan","keywords":"Floodplain; Tributary; Biomass (ecology); Amazonian; Ecosystem; Food web; River ecosystem; Hydrology (agriculture); Organic matter","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":[0.0001249341,0.0001162645,0.0001284606,0.0004890423,0.0003845532,0.0003563357,0.0001825608,0.0001477721,0.00187098],"category_scores_gemma":[0.0006857694,0.000133012,0.0001364115,0.0005218566,0.0003204348,0.0002838106,0.0003424966,0.0001208412,0.0001271237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003318675,"about_ca_system_score_gemma":0.0002290376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03842093,"about_ca_topic_score_gemma":0.0827352,"domain_scores_codex":[0.9999439,0.00001213669,0.000003905052,0.00001579159,0.0000078018,0.00001653357],"domain_scores_gemma":[0.9996272,0.00008288036,0.0001267527,0.00001793963,0.00005137979,0.0000937275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005621462,0.00001790108,0.9941705,0.00001162384,0.00001791546,0.00008451611,0.001024256,0.00005497597,0.002927501,0.00004637859,0.00004718047,0.0015411],"study_design_scores_gemma":[0.000001625647,0.0000111172,0.9990409,0.000001780125,0.000003608938,0.00002536287,0.0005970917,0.0001953013,0.00004754804,0.0000198126,0.00005453625,0.000001275631],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999809,0.00000994556,0.00001409112,0.00001102246,2.070012e-7,0.000001070127,0.00004587212,0.000001172947,0.0001076797],"genre_scores_gemma":[0.9999036,0.000005460284,0.00001879655,0.000002556815,4.829884e-7,0.000001291287,0.00002436106,5.299882e-7,0.00004296144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03842093,"threshold_uncertainty_score":0.07639462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009618561992609624,"score_gpt":0.244058133754771,"score_spread":0.2344395717621614,"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."}}