{"id":"W3197779575","doi":"10.22541/au.162799968.80128369/v1","title":"A general meta-ecosystem model to predict ecosystem function at landscape extents","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Concordia University; Fisheries and Oceans Canada; York University; University of Guelph; Toronto and Region Conservation Authority; Université de Montréal; University of New Brunswick; Université de Sherbrooke; University of Toronto; Memorial University of Newfoundland; McGill University","funders":"","keywords":"Ecosystem; Ecosystem services; Total human ecosystem; Environmental science; Spatial heterogeneity; Biodiversity; Ecology; Ecosystem model; Environmental resource management; Marine ecosystem; Ecosystem health; Biology","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":[0.0005949099,0.0006849823,0.0007553973,0.0007350672,0.0004843776,0.001081275,0.001804276,0.002188714,0.00237177],"category_scores_gemma":[0.001519345,0.0005069671,0.001754099,0.001102292,0.0006925902,0.002049842,0.001107402,0.00100974,0.0005280174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063367,"about_ca_system_score_gemma":0.000928257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008914716,"about_ca_topic_score_gemma":0.005805149,"domain_scores_codex":[0.9998405,0.00005093989,0.000009916447,0.00004492344,0.00003055629,0.0000232276],"domain_scores_gemma":[0.9996243,0.0001698839,0.00005168611,0.00004635729,0.00005785799,0.00004979962],"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":[0.00001051287,0.00001346901,0.0008731655,0.00002117015,0.00004090421,0.00004457944,0.00001410508,0.9806584,0.0009781236,0.01529864,0.0002929869,0.001753912],"study_design_scores_gemma":[0.000007424745,0.000007132623,0.0002333073,0.000004521108,0.00001098374,0.00001861567,0.000005122642,0.9849281,0.00007911189,0.014042,0.0006575532,0.000006025709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1164232,0.0009949998,0.8605211,0.001809772,0.0001417053,0.00007325139,0.001696787,0.0007242296,0.01761488],"genre_scores_gemma":[0.8538728,0.001171957,0.1299738,0.000552636,0.0001566013,0.0003447042,0.00114024,0.0003134347,0.01247376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008914716,"threshold_uncertainty_score":0.01772565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02833409771814063,"score_gpt":0.2359125742329334,"score_spread":0.2075784765147927,"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."}}