{"id":"W4400573448","doi":"10.3390/en17143493","title":"Optimal Placement of Multiple Sources in a Mesh-Type DC Microgrid Using Dijkstra’s Algorithm","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Angers Loire Métropole; Campus France","keywords":"Dijkstra's algorithm; Algorithm; Microgrid; Type (biology); Computer science; Mathematics; Shortest path problem; Theoretical computer science; Artificial intelligence; Geology; Control (management); Graph","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":[0.0004603046,0.0008249116,0.0006961616,0.0007511055,0.0004658063,0.0009834081,0.0009008208,0.0007301929,0.00309933],"category_scores_gemma":[0.001341573,0.0004026406,0.0005189934,0.001042185,0.0004190776,0.0008240577,0.0006275506,0.0003926529,0.0003623729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115483,"about_ca_system_score_gemma":0.001232464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01234679,"about_ca_topic_score_gemma":0.01058818,"domain_scores_codex":[0.9998268,0.0000526744,0.000009657821,0.0000389061,0.00004438151,0.00002763967],"domain_scores_gemma":[0.9997197,0.0001355578,0.00004166846,0.0000199052,0.00006343426,0.00001968181],"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":[0.00002281354,0.00001301427,0.0002615295,0.00003039014,0.00001345334,0.0000254343,0.00002637347,0.9772971,0.0006588313,0.005322415,0.0004947066,0.01583393],"study_design_scores_gemma":[0.000007509575,0.00001912228,0.0000593646,0.00000356367,0.000004525548,0.000008460265,0.00002447578,0.9962744,0.0002433117,0.00287212,0.0004802412,0.00000294825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02827137,0.0001488311,0.9644617,0.0001530668,0.0000314516,0.0001003247,0.00009572782,0.0001874564,0.006550075],"genre_scores_gemma":[0.5997358,0.0002841223,0.3929735,0.0000594778,0.00002287072,0.0001795328,0.000176545,0.00008508279,0.0064831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01234679,"threshold_uncertainty_score":0.0245499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008143895723293387,"score_gpt":0.2098006827762833,"score_spread":0.2016567870529899,"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."}}