{"id":"W2612639656","doi":"10.3390/w9050346","title":"Summer Season Water Temperature Modeling under the Climate Change: Case Study for Fourchue River, Quebec, Canada","year":2017,"lang":"en","type":"article","venue":"Water","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"BC Cancer Agency; Ministry of Public Safety and Security","keywords":"Environmental science; Coupled model intercomparison project; Climate change; Salvelinus; Representative Concentration Pathways; Fontinalis; Hydrology (agriculture); Trout; Effects of global warming; Climate model; Climatology; Global warming; Atmospheric sciences; Oceanography; Fishery; Fish <Actinopterygii>; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002644152,0.0001421782,0.0001206398,0.000008416144,0.002047202,0.0000871637,0.0002775076,0.00004448278,0.0002848523],"category_scores_gemma":[0.000004424811,0.0000657884,0.00003289074,0.000008640755,0.0001157835,0.0002465154,0.0006596823,0.0001006145,0.00006973069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001039028,"about_ca_system_score_gemma":0.000003742285,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7357872,"about_ca_topic_score_gemma":0.9955108,"domain_scores_codex":[0.9989976,0.00003791176,0.0001010207,0.0002730294,0.000126202,0.0004642942],"domain_scores_gemma":[0.9995101,0.00001328776,0.0000235171,0.0004062668,0.000009350413,0.00003746344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001964367,0.0004722867,0.7016867,0.00009229346,0.000507534,0.002433988,0.03103034,0.01155413,0.0007141488,0.0001002956,0.2497527,0.001459172],"study_design_scores_gemma":[0.009912523,0.0006355083,0.7072511,0.00006867153,0.001388562,0.0002521913,0.06934051,0.0380339,0.005458571,0.002326259,0.1619119,0.003420334],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983329,0.000006817259,0.00001644774,0.01490665,0.0003826195,0.0008070279,0.0000080298,0.00001458478,0.0005288019],"genre_scores_gemma":[0.9933528,0.000009693657,0.00001598296,0.002458946,0.0000905874,0.0002557577,0.000007676785,0.00001354265,0.003794992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2597236,"threshold_uncertainty_score":0.999252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035264130341323,"score_gpt":0.2563122628775865,"score_spread":0.2210481325362635,"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."}}