{"id":"W4386348325","doi":"10.1101/2023.08.30.555604","title":"Foodscapes for Salmon and Other Mobile Consumers in River Networks","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Foraging; Abundance (ecology); Trophic level; Habitat; Population; Geography; Fish <Actinopterygii>; Optimal foraging theory; Ecology; Environmental resource management; Fishery; Biology; Environmental science","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":[0.0002528775,0.0001313411,0.000180952,0.001160121,0.0007934017,0.001539666,0.000272835,0.000381492,0.007140733],"category_scores_gemma":[0.001161666,0.0001312259,0.0002153938,0.000987735,0.0005824447,0.001034753,0.00115713,0.0002431121,0.0002212307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007194442,"about_ca_system_score_gemma":0.0003324686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01818463,"about_ca_topic_score_gemma":0.04821414,"domain_scores_codex":[0.9998672,0.00002723892,0.00000779581,0.00004660188,0.00002296446,0.00002820706],"domain_scores_gemma":[0.9992602,0.0001664718,0.0002301537,0.00004552188,0.0001085126,0.0001891058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002286125,0.00008903738,0.9067323,0.0002926906,0.0001473935,0.0007307233,0.00426911,0.00344437,0.00866827,0.01883671,0.007605149,0.0489555],"study_design_scores_gemma":[0.00001124262,0.00005016671,0.9652254,0.00009562188,0.00003579063,0.000360576,0.007959791,0.01037273,0.0004942596,0.00771663,0.007641803,0.00003600432],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885476,0.0002520015,0.002106858,0.0003762801,0.00001158642,0.00001404837,0.001001603,0.00007169654,0.007618328],"genre_scores_gemma":[0.9969997,0.00007293271,0.001644117,0.00002949166,0.000006659597,0.00001132077,0.0003648066,0.000008747141,0.0008621513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01818463,"threshold_uncertainty_score":0.03615755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424648057187252,"score_gpt":0.2167619884829818,"score_spread":0.2025155079111093,"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."}}