{"id":"W4225315677","doi":"10.1109/dasa54658.2022.9764965","title":"Selection of Sustainable Energy Alternatives from Indian Context","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Decision Aid Sciences and Applications (DASA)","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petroleum Technology Research Centre; University of Regina","funders":"Mae Fah Luang University","keywords":"Selection (genetic algorithm); Computer science; Context (archaeology); Sustainable energy; Energy (signal processing); Artificial intelligence; Engineering; Renewable energy; Biology; Electrical engineering; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005052711,0.0007474112,0.0006029805,0.004907832,0.0009773262,0.002886501,0.00045759,0.0003917799,0.004738478],"category_scores_gemma":[0.00121392,0.0001845417,0.0009826504,0.004331792,0.0003383165,0.0006084446,0.00098125,0.0004398165,0.0003057597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001398271,"about_ca_system_score_gemma":0.001861839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006455845,"about_ca_topic_score_gemma":0.0197738,"domain_scores_codex":[0.9993078,0.0002248927,0.00003274524,0.00004444218,0.0002473083,0.0001428398],"domain_scores_gemma":[0.999723,0.0001044961,0.0000264517,0.00001066619,0.00009986155,0.00003553475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001430964,0.000934214,0.03117225,0.002139253,0.0006033281,0.005615531,0.00210794,0.3421304,0.02951364,0.1347518,0.0109218,0.4386789],"study_design_scores_gemma":[0.0003193215,0.002106087,0.05457368,0.001131563,0.001426273,0.002309677,0.03902329,0.6733279,0.02971508,0.09482648,0.1009033,0.0003375043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7816816,0.002026995,0.03956273,0.0009803823,0.00009145617,0.0005104534,0.0009729101,0.0001157279,0.1740579],"genre_scores_gemma":[0.9709877,0.001104174,0.02351088,0.00007474584,0.0000109257,0.0001530396,0.0003270351,0.00001817208,0.003813452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006455845,"threshold_uncertainty_score":0.0158518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1234362030299689,"score_gpt":0.4160697405662962,"score_spread":0.2926335375363274,"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."}}