{"id":"W4386805188","doi":"10.32942/x2tw2s","title":"Spatially explicit predictions of food web structure from regional level data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Courtois Foundation; Wellcome Trust","keywords":"Ecoregion; Species richness; Downscaling; Variation (astronomy); Scale (ratio); Ecology; Diversity (politics); Probabilistic logic; Species distribution; Geography; Biodiversity; Macroecology; Environmental resource management; Climate change; Computer science; Environmental science; Habitat; Biology; Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009404056,0.0003772894,0.0003156752,0.001438896,0.0002409464,0.0009445969,0.0005812146,0.000420374,0.00145525],"category_scores_gemma":[0.004670836,0.0003152502,0.000708179,0.001089348,0.0003452236,0.001184333,0.0006284383,0.0006242358,0.0004256759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001303701,"about_ca_system_score_gemma":0.0007994259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09289812,"about_ca_topic_score_gemma":0.1547494,"domain_scores_codex":[0.9997986,0.00006477322,0.000008931277,0.00007651492,0.00002962306,0.00002147764],"domain_scores_gemma":[0.9986038,0.0006761918,0.000212967,0.0002303253,0.0001979497,0.00007876427],"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":[0.0001021723,0.00003865431,0.1628789,0.0001422579,0.0002793907,0.0001203697,0.0001977243,0.7982374,0.005744861,0.004977276,0.001215912,0.02606514],"study_design_scores_gemma":[0.000006820133,0.000007845913,0.04730698,0.00001520793,0.00002276257,0.00002553048,0.00007919469,0.9442241,0.0005612251,0.006906081,0.0008303848,0.00001384403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8147255,0.0004096378,0.1725634,0.0003776399,0.00001822029,0.00003841933,0.00784415,0.001201471,0.002821516],"genre_scores_gemma":[0.9620715,0.0001493542,0.03383688,0.00003571862,0.000008509051,0.00002675101,0.003459937,0.00009151024,0.0003198239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09289812,"threshold_uncertainty_score":0.1847148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.376000511493914,"score_gpt":0.2676170633868682,"score_spread":0.1083834481070458,"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."}}