{"id":"W4417101052","doi":"10.1111/fwb.70140","title":"Constructing Isoscapes of <scp> δ <sup>13</sup> C </scp> , <scp> δ <sup>15</sup> N </scp> and <scp> δ <sup>34</sup> S </scp> Baselines Within a River System: A Spatial Stream Network Modelling Approach","year":2025,"lang":"en","type":"article","venue":"Freshwater Biology","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"U.S. Geological Survey; U.S. Fish and Wildlife Service; U.S. Army Corps of Engineers; Michigan Department of Natural Resources; Great Lakes Fishery Commission","keywords":"Food web; Trophic level; Invertebrate; Spatial ecology; Spatial variability; Ecosystem; Stable isotope ratio; Isotope analysis","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":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002740311,0.0017048,0.002844842,0.0008371188,0.001045893,0.0002390982,0.002086101,0.001726561,0.0001994254],"category_scores_gemma":[0.001553209,0.001433454,0.0006736292,0.001709747,0.003346285,0.0006472534,0.002398009,0.001471943,0.000355342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006164545,"about_ca_system_score_gemma":0.000215923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005450097,"about_ca_topic_score_gemma":0.0027053,"domain_scores_codex":[0.9879457,0.002073915,0.00293511,0.003168103,0.0008166928,0.003060493],"domain_scores_gemma":[0.9923244,0.003690069,0.001328993,0.00184776,0.0002620869,0.0005466764],"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":[0.00002852337,0.0003431696,0.3085347,0.0003800445,0.0009213511,0.00006836236,0.005967268,0.6658735,0.0007278954,0.00139626,0.01448079,0.001278122],"study_design_scores_gemma":[0.002462691,0.0004915729,0.001014053,0.0002647244,0.0008714338,0.0002606384,0.01393513,0.9518594,0.002626923,0.002306587,0.02357135,0.0003354724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9357298,0.001146435,0.04911953,0.0001017038,0.0005174291,0.001786976,0.0005161394,0.0004238751,0.01065814],"genre_scores_gemma":[0.9530495,0.0002668165,0.03999148,0.0007874486,0.001038598,0.0003916453,0.0007581823,0.0002002416,0.003516029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3075206,"threshold_uncertainty_score":0.9995698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009826311301826176,"score_gpt":0.2105117501250383,"score_spread":0.2006854388232121,"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."}}