{"id":"W2784032581","doi":"10.1016/j.enconman.2017.12.075","title":"The cost of conserved water for coal power generation with carbon capture and storage in Alberta, Canada","year":2018,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Energy Technology Laboratory","keywords":"Cost of electricity by source; Environmental science; Electricity generation; Coal; Carbon capture and storage (timeline); Life-cycle assessment; Environmental engineering; Waste management; Power (physics); Engineering; Production (economics); Geology; Oceanography; Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000730369,0.0003395307,0.0003421809,0.001189107,0.002139658,0.002662748,0.001393977,0.000711963,0.00418721],"category_scores_gemma":[0.002412052,0.0002863398,0.0005573303,0.002807521,0.0009205153,0.0008510724,0.0006128273,0.0008692026,0.0001306134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09609389,"about_ca_system_score_gemma":0.08012398,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9974388,"about_ca_topic_score_gemma":0.999025,"domain_scores_codex":[0.9990103,0.0001056926,0.00003092246,0.0000661997,0.0004408453,0.0003459182],"domain_scores_gemma":[0.9985697,0.0002572614,0.0000986966,0.00003404639,0.000777952,0.0002622989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002476712,0.000673441,0.4922183,0.0008265171,0.0006801504,0.00304686,0.001643512,0.1964518,0.004605838,0.07983436,0.05106133,0.1664813],"study_design_scores_gemma":[0.0003509664,0.0003379439,0.7573204,0.0003918312,0.0007480491,0.0006374983,0.02033891,0.1306987,0.004510902,0.00838743,0.07602387,0.0002535898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9569547,0.002285764,0.0006200441,0.003047375,0.00006905337,0.00007250311,0.003872354,0.00003541187,0.03304286],"genre_scores_gemma":[0.9873335,0.001085204,0.0004550079,0.0001011725,0.000007632719,0.000008731048,0.0007552102,0.000009224834,0.01024431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09609389,"threshold_uncertainty_score":0.6972133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005603426314057649,"score_gpt":0.1654279801682958,"score_spread":0.1598245538542381,"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."}}