{"id":"W3047400853","doi":"10.1002/rra.3680","title":"Large dam renewals and removals—Part 1: Building a science framework to support a decision‐making process","year":2020,"lang":"en","type":"article","venue":"River Research and Applications","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Energie NB Power (Canada); Environment and Climate Change Canada; University of Calgary; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; University of New Brunswick","keywords":"Dam removal; Hydroelectricity; Process (computing); Decision-making; Environmental resource management; Service (business); Decision support system; Environmental science; Computer science; Operations research; Environmental planning; Business; Engineering; Operations management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02730279,0.00147546,0.00100144,0.005196546,0.003947667,0.01361604,0.004101368,0.004444715,0.00523476],"category_scores_gemma":[0.01806368,0.001127892,0.002526883,0.002694348,0.008540562,0.009994326,0.006658711,0.004253971,0.001084678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01057484,"about_ca_system_score_gemma":0.02404436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02353965,"about_ca_topic_score_gemma":0.02005711,"domain_scores_codex":[0.9879153,0.006935559,0.001126428,0.001181618,0.002050607,0.0007904349],"domain_scores_gemma":[0.9768409,0.01491482,0.002097228,0.001633358,0.002870072,0.001643672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004604173,0.0003481541,0.002409338,0.0002651608,0.00005946844,0.0004173562,0.001811023,0.1771769,0.001363307,0.7605014,0.003590631,0.05201113],"study_design_scores_gemma":[0.00007145172,0.0002056942,0.001051674,0.000722557,0.00005960731,0.0001433582,0.002205178,0.3860449,0.002052974,0.5310389,0.07624797,0.0001556259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01409064,0.0007949056,0.9385105,0.01068426,0.0001499967,0.001899536,0.0003669717,0.0009846395,0.03251849],"genre_scores_gemma":[0.1821159,0.0007466943,0.812063,0.0004952961,0.0001291044,0.001162454,0.0004751495,0.00008574574,0.002726584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02730279,"threshold_uncertainty_score":0.1443927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04950598024980428,"score_gpt":0.3935278431327355,"score_spread":0.3440218628829312,"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."}}