{"id":"W4210695229","doi":"10.1007/s10750-021-04789-2","title":"Bayesian two-part modeling of phytoplankton biomass and occurrence","year":2022,"lang":"en","type":"article","venue":"Hydrobiologia","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Environment Research Council; Sight Research UK; Simons Foundation","keywords":"Phytoplankton; Biomass (ecology); Covariate; Log-normal distribution; Environmental science; Ecology; Statistics; Prior probability; Abiotic component; Bayesian probability; Mathematics; Econometrics; Biology; Nutrient","routes":{"ca_aff":true,"ca_fund":false,"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":[0.005237937,0.001332311,0.002836429,0.001688256,0.0009276041,0.002875239,0.005003206,0.004240262,0.004070227],"category_scores_gemma":[0.01722758,0.003655716,0.002580213,0.002236086,0.002360449,0.003526311,0.002174762,0.002133059,0.00102485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002238043,"about_ca_system_score_gemma":0.00162377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0299766,"about_ca_topic_score_gemma":0.02268851,"domain_scores_codex":[0.9982443,0.0007751342,0.0001049619,0.0004634246,0.0001961119,0.0002161138],"domain_scores_gemma":[0.9897135,0.007825672,0.000832679,0.0006181749,0.0006431171,0.000366818],"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":[0.0000721164,0.00002298522,0.001049862,0.00001958343,0.00006009516,0.00004895058,0.0000381145,0.9904439,0.0002110437,0.005362784,0.0002933919,0.002377124],"study_design_scores_gemma":[0.000008270361,0.000006033985,0.0003673077,0.000002626836,0.0000117888,0.00001305862,0.000003729853,0.9967465,0.00003914364,0.002735965,0.00005540549,0.00001005398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2067589,0.0008324654,0.7835441,0.001570769,0.0001625986,0.00008698189,0.001608314,0.0007287024,0.004707027],"genre_scores_gemma":[0.9357218,0.0004887378,0.0444227,0.000270536,0.0001452351,0.0002279732,0.001287343,0.0002305659,0.01720509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0299766,"threshold_uncertainty_score":0.05960423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01312789522945838,"score_gpt":0.2164812460256592,"score_spread":0.2033533507962009,"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."}}