{"id":"W4402532198","doi":"10.1002/wat2.1752","title":"Food for fish: Challenges and opportunities for quantifying foodscapes in river networks","year":2024,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Water","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"","keywords":"Habitat; Environmental resource management; Climate change; Food security; Abiotic component; Foraging; Watershed; Environmental science; Fish <Actinopterygii>; Ecology; Fishery; Computer science; Biology; Agriculture","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006920046,0.0001945719,0.0003323255,0.00006078336,0.0001815529,0.00003307621,0.0001438719,0.00007092231,0.0001360571],"category_scores_gemma":[0.00001011429,0.0001279955,0.0001157641,0.00002973683,0.0001769844,0.0003060648,0.0008884588,0.00008878683,0.00002991125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004034995,"about_ca_system_score_gemma":0.000001370515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.32887e-7,"about_ca_topic_score_gemma":0.0009111036,"domain_scores_codex":[0.9988097,0.00005459593,0.0003202262,0.0004155708,0.00004900711,0.0003509056],"domain_scores_gemma":[0.9996612,0.0001230443,0.00003246378,0.0001415119,0.000004047414,0.0000377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002684677,0.0002052961,0.002605503,0.005059888,0.0003247993,0.00004860213,0.02604568,0.0002504001,0.00006228944,0.003142628,0.5141294,0.4478571],"study_design_scores_gemma":[0.0004683392,0.001177312,0.006052291,0.001203635,0.0001293058,0.00001188268,0.001683336,0.005132423,0.00004072985,0.01401115,0.969615,0.0004745485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4768379,0.2967955,0.01176833,0.1573465,0.005279504,0.01555009,0.000156448,0.0005359693,0.03572981],"genre_scores_gemma":[0.8724439,0.1203809,0.0008162213,0.001334665,0.0001964064,0.002628842,0.00005876114,0.00004495675,0.002095409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4554856,"threshold_uncertainty_score":0.5219507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.12820718659677,"score_gpt":0.315111769786137,"score_spread":0.186904583189367,"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."}}