{"id":"W4391457419","doi":"10.1504/ijes.2023.136376","title":"<i>AR</i><SUP align=\"right\">2</SUP><i>PNET</i>: an adversarially robust re-weighting prototypical network for few-shot learning","year":2023,"lang":"en","type":"article","venue":"International Journal of Embedded Systems","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Weighting; Computer science; Physics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004973422,0.000938084,0.001336932,0.001150622,0.001244134,0.001549337,0.004341427,0.0007014996,0.0001947086],"category_scores_gemma":[0.0006357519,0.000886383,0.001023882,0.001631546,0.0002401248,0.002045299,0.000722033,0.001090422,0.0002222664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008368266,"about_ca_system_score_gemma":0.0007088563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001874008,"about_ca_topic_score_gemma":0.0001210481,"domain_scores_codex":[0.9899883,0.0009134026,0.003339315,0.001492917,0.002816373,0.001449648],"domain_scores_gemma":[0.9911821,0.001119795,0.002872267,0.001195689,0.00283489,0.0007952685],"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":[0.001075543,0.0009746822,0.0004308952,0.0002868231,0.001945651,0.0006630397,0.003160724,0.3751726,0.1343926,0.3571404,0.1114766,0.01328052],"study_design_scores_gemma":[0.002093501,0.00106705,0.000522278,0.0009775942,0.0001389983,0.0009360272,0.0001293569,0.535293,0.001355162,0.003627535,0.4527343,0.001125284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04026294,0.001026235,0.9361221,0.002507478,0.006028187,0.003740685,0.0001433109,0.001430037,0.008739001],"genre_scores_gemma":[0.9392458,0.0002511145,0.04386275,0.000302806,0.008585412,0.001002171,0.000189004,0.0002180566,0.006342888],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8989829,"threshold_uncertainty_score":0.9994872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03182528465471989,"score_gpt":0.2903528276723231,"score_spread":0.2585275430176032,"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."}}