{"id":"W2621993672","doi":"10.1016/j.ijhydene.2017.03.185","title":"Techno-economic assessment of a solar-geothermal multigeneration system for buildings","year":2017,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exergy; Cost of electricity by source; Environmental science; Renewable energy; Geothermal gradient; Process engineering; Exergy efficiency; Geothermal energy; Photovoltaic system; Electricity; Solar energy; Geothermal power; Electricity generation; Waste management; Power (physics); Engineering; Thermodynamics; Electrical engineering; 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":[0.001229212,0.0009719604,0.0007581543,0.00161228,0.0008210791,0.001983442,0.0009685996,0.001148899,0.004270864],"category_scores_gemma":[0.001351214,0.0005351846,0.001211626,0.001283508,0.0007295322,0.00179288,0.001060177,0.0006686192,0.0002821793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003983312,"about_ca_system_score_gemma":0.001300643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01673158,"about_ca_topic_score_gemma":0.02129139,"domain_scores_codex":[0.9992841,0.0003653368,0.00002575337,0.0000558914,0.0001616052,0.0001073141],"domain_scores_gemma":[0.9992825,0.000407574,0.00005190926,0.00003871331,0.0001491163,0.00007015277],"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":[0.0007257805,0.0001963669,0.00490176,0.00007671232,0.00007971375,0.0002479859,0.00002121399,0.9814606,0.003633044,0.001760017,0.0002251151,0.006671576],"study_design_scores_gemma":[0.00008366451,0.0009421082,0.01170457,0.00001645313,0.000126853,0.00006901517,0.0001817212,0.9814172,0.003507503,0.001383732,0.0005356853,0.00003143679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799293,0.0003072059,0.00704042,0.0002300982,0.00003120688,0.0002139256,0.0007793087,0.00006370168,0.01140498],"genre_scores_gemma":[0.9983578,0.0000621996,0.0005561677,0.000006591542,0.000003623038,0.00002646109,0.00008622121,0.00000591467,0.0008949391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01673158,"threshold_uncertainty_score":0.03326833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007534345110749909,"score_gpt":0.2573205758414215,"score_spread":0.2497862307306716,"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."}}