{"id":"W1995493126","doi":"10.4236/jwarp.2015.74026","title":"Predicting Hourly Stream Temperatures Using the Equilibrium Temperature Model","year":2015,"lang":"en","type":"article","venue":"Journal of Water Resource and Protection","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Fisheries and Oceans Canada; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Snowmelt; Environmental science; Air temperature; Thermodynamic equilibrium; River ecosystem; Mean radiant temperature; Hydrology (agriculture); Atmospheric sciences; Snow; Meteorology; Ecosystem; Climate change; Thermodynamics; Ecology; Physics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002367513,0.0004094884,0.000342933,0.0002972166,0.0002245634,0.0005212496,0.0006124589,0.0005136475,0.0007609472],"category_scores_gemma":[0.0006825507,0.0002553049,0.0005734709,0.0004102886,0.0001162572,0.000428391,0.0002203709,0.0004345346,0.0001778982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006372435,"about_ca_system_score_gemma":0.0006898899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0262544,"about_ca_topic_score_gemma":0.02213108,"domain_scores_codex":[0.999877,0.0000280472,0.0000104301,0.00004163049,0.00002467666,0.0000181905],"domain_scores_gemma":[0.9997893,0.00009337295,0.0000270366,0.00001558955,0.0000625479,0.00001211081],"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":[0.00001743661,0.00001808768,0.006318492,0.00001235402,0.00001531338,0.00002339823,0.00001601742,0.9874858,0.001162121,0.0002156381,0.0001138838,0.004601446],"study_design_scores_gemma":[0.000002565477,0.000006188488,0.001492158,0.000001008079,0.00000278293,0.000003045748,0.00000424066,0.9980537,0.0002566076,0.00008826584,0.00008691722,0.000002670766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8344293,0.0001596306,0.1594391,0.00009840702,0.00004985741,0.00006625367,0.001476626,0.0006109336,0.003669862],"genre_scores_gemma":[0.9884285,0.00007189522,0.01023922,0.000007770181,0.000006718941,0.00003863328,0.0005272158,0.00001858924,0.0006613758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0262544,"threshold_uncertainty_score":0.05220312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347147768645257,"score_gpt":0.2240506080690816,"score_spread":0.200579130382629,"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."}}