{"id":"W2154340855","doi":"10.2528/pierb14110505","title":"GENERIC BUILDING BLOCKS FOR CONSTRUCTION OF ARTIFICIAL MAGNETIC MEDIA","year":2015,"lang":"en","type":"article","venue":"Progress In Electromagnetics Research B","topic":"Metamaterials and Metasurfaces Applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003519509,0.0001254451,0.0002853747,0.000283045,0.00009980662,0.00009962016,0.0004151801,0.0000986035,0.0001433862],"category_scores_gemma":[0.000584539,0.0001181084,0.00003257491,0.0006684422,0.0005714454,0.00007245761,0.0001094095,0.0001472497,0.00001464567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007484168,"about_ca_system_score_gemma":0.0002696665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003549991,"about_ca_topic_score_gemma":0.00003760294,"domain_scores_codex":[0.9974213,0.0002868217,0.0005187067,0.0003710406,0.000712182,0.0006899398],"domain_scores_gemma":[0.9984326,0.0002598485,0.0001218473,0.0003489311,0.0006672291,0.0001694806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001140707,0.0001187758,0.0002769196,0.00004720247,0.000002229078,0.000001243945,0.000161633,0.00002354295,0.9441164,0.01250348,0.0001877102,0.04244681],"study_design_scores_gemma":[0.0004487568,0.001025994,0.0002406426,0.00001832539,0.0000129123,0.000008051883,0.0001241733,0.001827811,0.9662542,0.02715483,0.002754516,0.0001297811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992878,0.004462027,0.0009984906,0.0002259189,0.0002843283,0.0009182306,0.00003563649,0.00003098932,0.0001664198],"genre_scores_gemma":[0.8207306,0.0001176583,0.1783312,0.000004231511,0.0001602987,0.0005961381,0.00001240097,0.00002183936,0.00002565153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1773327,"threshold_uncertainty_score":0.4816323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.12375615933063,"score_gpt":0.3925302779565223,"score_spread":0.2687741186258923,"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."}}