{"id":"W7048569101","doi":"","title":"LUBANG Electronic Technology: Clean Energy Features and Applications – US and Canada Report","year":2024,"lang":"en","type":"other","venue":"","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Energy (signal processing); Clean energy; Field (mathematics); Government (linguistics); Clean technology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006432008,0.0005171317,0.0002796945,0.001894625,0.004093239,0.002552443,0.0008151263,0.001368748,0.04933016],"category_scores_gemma":[0.0006760187,0.000237311,0.0003554874,0.001917202,0.0007107855,0.0007124901,0.001060468,0.001224667,0.008271344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01597845,"about_ca_system_score_gemma":0.05698835,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9103291,"about_ca_topic_score_gemma":0.9672526,"domain_scores_codex":[0.9986104,0.00001527707,0.00001076587,0.00004957113,0.001039155,0.0002749285],"domain_scores_gemma":[0.9987747,0.00005290316,0.00001907402,0.00003512162,0.0009070594,0.0002111651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001984497,0.0002670698,0.004500978,0.0001795825,0.00001870227,0.0001962204,0.0001512625,0.0007082197,0.005155107,0.01827571,0.899086,0.07126272],"study_design_scores_gemma":[0.00002006931,0.00003230149,0.01134233,0.00005204252,0.00001280239,0.00005100168,0.0002944549,0.0006528361,0.006919265,0.001035888,0.9795625,0.00002432624],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02245651,0.002857238,0.001211691,0.0102325,0.00074122,0.0003894777,0.02811661,0.0007349826,0.93326],"genre_scores_gemma":[0.03240701,0.002032849,0.00140561,0.0007455439,0.00003735344,0.00004611386,0.008413394,0.0001347426,0.9547774],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.0896709,"threshold_uncertainty_score":0.1803979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002075687416190194,"score_gpt":0.2167494331017011,"score_spread":0.2146737456855109,"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."}}