{"meta":{"query_hash":"cd5af0cfa622","filters":{"topic":"Adversarial Robustness in Machine Learning"},"cohort_total":797,"direct_labels_cover":2,"predictions_cover":797,"exported":797,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/cd5af0cfa622","api":"https://metacan.xera.ac/api/v1/cohort?topic=Adversarial+Robustness+in+Machine+Learning"},"results":[{"id":"W1588602234","doi":"","title":"Reverse Capability Transfer","year":2007,"lang":"en","type":"article","venue":"ASAC","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Transfer (computing); Business; Operating system","score_opus":0.011292095512754152,"score_gpt":0.2611325694452569,"score_spread":0.24984047393250278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588602234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018285895,0.00048386515,0.85750073,0.001505297,0.0007705635,0.00021765237,0.0004953222,0.0028586283,0.11788198],"genre_scores_gemma":[0.72606444,0.0008291508,0.15175192,0.0012041291,0.0007393964,0.00034652566,0.001288532,0.0011016928,0.11667408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904543,0.0001857897,0.00004111656,0.00025904857,0.00032739007,0.00014124178],"domain_scores_gemma":[0.997341,0.00060652423,0.00014028323,0.0012792303,0.00045420782,0.00017876177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001155044,0.0013393953,0.00072296493,0.001182909,0.00084185024,0.0017475454,0.001425448,0.0019031425,0.035584304],"category_scores_gemma":[0.0062319944,0.00033299482,0.00090558204,0.0007158342,0.0018519722,0.003997826,0.004412276,0.0033554905,0.01176941],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002904609,0.00018460646,0.0004896847,0.00021563239,0.00006976918,0.00037634152,0.0001261168,0.08705439,0.021751089,0.6056464,0.029855024,0.2539405],"study_design_scores_gemma":[0.00003438239,0.00017836677,0.00041430723,0.00006656777,0.00003668069,0.0006195584,0.000055248125,0.4561918,0.030929947,0.47288516,0.038525593,0.00006246335],"about_ca_topic_score_codex":0.00057594053,"about_ca_topic_score_gemma":0.00044449943,"teacher_disagreement_score":0.035584304,"about_ca_system_score_codex":0.0005224347,"about_ca_system_score_gemma":0.0009977706,"threshold_uncertainty_score":0.11904132},"labels":[],"label_agreement":null},{"id":"W1762216642","doi":"10.1609/aaai.v30i1.10268","title":"Conservativeness of Untied Auto-Encoders","year":2016,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Encoder; Component (thermodynamics); Field (mathematics); Computer science; Function (biology); Algorithm; Unit vector; Mathematics; Pattern recognition (psychology); Control theory (sociology); Applied mathematics; Artificial intelligence; Pure mathematics; Mathematical analysis","score_opus":0.06925652489997468,"score_gpt":0.2969894329996154,"score_spread":0.2277329080996407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1762216642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075950205,0.00017658681,0.91707593,0.00039643975,0.000044738757,0.000032593613,0.00011087499,0.00028890293,0.005923764],"genre_scores_gemma":[0.92690337,0.00019360427,0.06424758,0.00031038444,0.00005962615,0.000112647846,0.00024179772,0.00017768447,0.0077532604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875474,0.00032093556,0.00008909866,0.00025653085,0.0004363383,0.00014242911],"domain_scores_gemma":[0.994181,0.0032865244,0.00060423044,0.00088941015,0.0007108326,0.00032802258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003266527,0.00086240313,0.0007735106,0.00067963044,0.00056419324,0.001387547,0.0013949889,0.001086748,0.0028807498],"category_scores_gemma":[0.014130922,0.00068690424,0.00047697418,0.0003036825,0.0025135034,0.0032192052,0.0024009414,0.0019764737,0.0005277573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001905985,0.00008871197,0.00196193,0.00012915445,0.000055611814,0.0005666263,0.0003595872,0.29692027,0.020955473,0.64751357,0.0011419753,0.030116467],"study_design_scores_gemma":[0.000016562753,0.00006181849,0.00045364007,0.000035165587,0.00000945492,0.0001428212,0.000035570305,0.7474727,0.00707402,0.2437791,0.0008964873,0.000022690778],"about_ca_topic_score_codex":0.00086506974,"about_ca_topic_score_gemma":0.0010216954,"teacher_disagreement_score":0.003266527,"about_ca_system_score_codex":0.0007573178,"about_ca_system_score_gemma":0.0008705591,"threshold_uncertainty_score":0.017275274},"labels":[],"label_agreement":null},{"id":"W2064064056","doi":"10.1109/isi.2008.4565023","title":"Subverting prediction in adversarial settings","year":2008,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence","score_opus":0.012166938733424103,"score_gpt":0.22271728010905095,"score_spread":0.21055034137562684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064064056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04350352,0.00028115962,0.94894874,0.00088776497,0.00010828003,0.000052903444,0.00008902864,0.00049595285,0.0056326436],"genre_scores_gemma":[0.9028685,0.00038767463,0.091727965,0.00048625548,0.0001730581,0.00012549343,0.00017814725,0.0001838573,0.0038691377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99585414,0.0018399676,0.00017336477,0.0006965431,0.0010254789,0.00041056244],"domain_scores_gemma":[0.9638587,0.026847694,0.0023846526,0.0056172116,0.0008689204,0.0004228672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045370637,0.0014739844,0.0010596985,0.0006360578,0.0008346726,0.0015572944,0.0018744887,0.0015778692,0.0026161876],"category_scores_gemma":[0.035973165,0.0005086741,0.0007398373,0.00049065903,0.0035836163,0.0042671524,0.0054274094,0.004133185,0.000668336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000331392,0.00011397795,0.0021720259,0.00008430037,0.00010882667,0.00047025233,0.0003449433,0.7407131,0.0071754516,0.20591097,0.0026679353,0.03990681],"study_design_scores_gemma":[0.000009705754,0.000046964433,0.00014794689,0.000015707712,0.000010631543,0.00008941364,0.00002015645,0.901323,0.0025779977,0.095030494,0.0007139526,0.000013982339],"about_ca_topic_score_codex":0.00094186957,"about_ca_topic_score_gemma":0.0006815815,"teacher_disagreement_score":0.0045370637,"about_ca_system_score_codex":0.00084868097,"about_ca_system_score_gemma":0.000691303,"threshold_uncertainty_score":0.023994565},"labels":[],"label_agreement":null},{"id":"W2077742958","doi":"10.1109/mis.2009.108","title":"Adversarial Knowledge Discovery","year":2009,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Adversarial system; Computer science; Normality; Face (sociological concept); Artificial intelligence; Data science; Knowledge extraction; Machine learning","score_opus":0.02210910896871352,"score_gpt":0.2880726922928005,"score_spread":0.265963583324087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077742958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053381776,0.0006247575,0.98443985,0.0018147223,0.00012975445,0.00011629118,0.00017338162,0.0003055997,0.0070574917],"genre_scores_gemma":[0.7116337,0.0025631113,0.26881447,0.0019982834,0.000771721,0.00046611644,0.0010580616,0.0002084522,0.012486093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9921017,0.0028848378,0.00037078722,0.0015871172,0.0025116259,0.0005439543],"domain_scores_gemma":[0.970949,0.020880368,0.0013101542,0.004928466,0.0014703687,0.00046176882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008459066,0.0011862924,0.0016138846,0.0018633815,0.0012584602,0.0035419466,0.0031974565,0.002603647,0.0034154577],"category_scores_gemma":[0.034902956,0.0007185387,0.0010053876,0.0016992625,0.00439896,0.0059899413,0.0069230786,0.0047105225,0.0012983691],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022983112,0.00015782042,0.0018862216,0.00034372145,0.0002869098,0.0003871986,0.00024469805,0.3447088,0.0033216025,0.49089712,0.013799095,0.14373699],"study_design_scores_gemma":[0.00001962595,0.00006440504,0.00022867369,0.00006605843,0.000037252743,0.000278766,0.000053496202,0.62357116,0.002477302,0.3644762,0.008695029,0.000032066895],"about_ca_topic_score_codex":0.0010521479,"about_ca_topic_score_gemma":0.0008420857,"teacher_disagreement_score":0.008459066,"about_ca_system_score_codex":0.0015196848,"about_ca_system_score_gemma":0.0019923854,"threshold_uncertainty_score":0.044736326},"labels":[],"label_agreement":null},{"id":"W2141473882","doi":"","title":"Shallow vs. Deep Sum-Product Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":237,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Deep learning; Product (mathematics); Computer science; Artificial neural network; Artificial intelligence; Deep neural networks; Computation; Layer (electronics); Deep water; Theoretical computer science; Mathematics; Algorithm; Engineering","score_opus":0.023778017402601877,"score_gpt":0.23183415635814,"score_spread":0.20805613895553812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141473882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07099721,0.0009167519,0.9142302,0.0012110333,0.000047397072,0.000037334175,0.00016111998,0.00029911226,0.01209984],"genre_scores_gemma":[0.8846572,0.0012315834,0.10684173,0.00045101997,0.00008584208,0.00010329056,0.00019718718,0.00012111747,0.0063110366],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990711,0.00030936924,0.00004456614,0.00021318167,0.0002489785,0.00011272186],"domain_scores_gemma":[0.9957766,0.0023970867,0.00048294594,0.0008705148,0.00024674818,0.00022610948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021963783,0.00094115094,0.00071069243,0.0005230201,0.000466776,0.0015124046,0.00167362,0.001492604,0.0037879362],"category_scores_gemma":[0.009682773,0.000452265,0.0007042875,0.00058794435,0.0026339605,0.0055952235,0.0034806826,0.002569983,0.0007045221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029374356,0.00005296042,0.0011299967,0.00017472498,0.00005652099,0.00013375412,0.0001661488,0.3427348,0.007775132,0.57326275,0.0018980756,0.072321445],"study_design_scores_gemma":[0.000009387854,0.00008451398,0.00020993233,0.000030414358,0.00001953036,0.00007729566,0.000023225592,0.623776,0.0024958996,0.37205154,0.0012091254,0.000013105238],"about_ca_topic_score_codex":0.00068844634,"about_ca_topic_score_gemma":0.0005288672,"teacher_disagreement_score":0.0037879362,"about_ca_system_score_codex":0.0010378852,"about_ca_system_score_gemma":0.00049327035,"threshold_uncertainty_score":0.012671888},"labels":[],"label_agreement":null},{"id":"W2153145027","doi":"10.1007/978-3-540-24840-8_21","title":"The Structural Model Interpretation of the NESS Test","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Causation; Counterfactual thinking; Overdetermination; Counterfactual conditional; Causality (physics); Counterintuitive; Test (biology); Interpretation (philosophy); Attribution; Epistemology; Psychology; Conformity; Causal model; Analogy; Mathematics; Social psychology; Philosophy; Statistics; Linguistics","score_opus":0.009563536490076534,"score_gpt":0.24650943021512847,"score_spread":0.23694589372505193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153145027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11489838,0.0008533451,0.7216152,0.015332932,0.0008684369,0.00016239838,0.0013599733,0.00064034824,0.144269],"genre_scores_gemma":[0.9560687,0.00041238635,0.026151454,0.0015464977,0.00061996473,0.00022625577,0.0008491509,0.00023455883,0.013890962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9932586,0.003672203,0.00027966662,0.0011312087,0.0011326574,0.00052563165],"domain_scores_gemma":[0.9552816,0.03394412,0.0026981877,0.005124046,0.0021908586,0.00076105184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011598931,0.0010021625,0.0018672459,0.0021164748,0.0011505587,0.0022428806,0.0027948031,0.0036878511,0.03191156],"category_scores_gemma":[0.073453106,0.0005472038,0.0013860506,0.0012564332,0.006896454,0.008919242,0.0035394027,0.004569971,0.0016261123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012544994,0.00003983456,0.0012522838,0.000080040256,0.000045224257,0.00017710571,0.00009694713,0.007923835,0.0004224556,0.9708307,0.003948636,0.0150575545],"study_design_scores_gemma":[0.000033088472,0.000053842105,0.00059211865,0.000022689968,0.00001167622,0.00009840369,0.000039057824,0.0339907,0.00044611588,0.9634933,0.0012061837,0.000012727713],"about_ca_topic_score_codex":0.00071738206,"about_ca_topic_score_gemma":0.00066646124,"teacher_disagreement_score":0.03191156,"about_ca_system_score_codex":0.0014976127,"about_ca_system_score_gemma":0.0015050826,"threshold_uncertainty_score":0.10675478},"labels":[],"label_agreement":null},{"id":"W2178128099","doi":"10.1109/pacrim.2015.7334893","title":"Robust adversarial learning and invariant measures","year":2015,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Adversarial system; Invariant (physics); Computer science; Artificial intelligence; Mathematics","score_opus":0.05185230491389536,"score_gpt":0.2597855883818426,"score_spread":0.20793328346794726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178128099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013806751,0.00091607886,0.9772963,0.0011458743,0.00007231657,0.000022576396,0.00009753743,0.00014776982,0.0064947824],"genre_scores_gemma":[0.8953581,0.0020182533,0.09364051,0.0006511585,0.00048719128,0.0001644726,0.0003962303,0.00017708316,0.007107084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961086,0.0016996805,0.00015046283,0.0008439471,0.00083406316,0.0003632332],"domain_scores_gemma":[0.9741101,0.019825675,0.0023454712,0.0021063902,0.0010607927,0.0005515544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058875512,0.0012994591,0.00152882,0.0017032502,0.0007367335,0.0024414281,0.0015921609,0.0017414453,0.0025428906],"category_scores_gemma":[0.023860564,0.00060496875,0.0011236864,0.0009968057,0.0058613527,0.00411319,0.0037700904,0.004119326,0.00042298602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049047638,0.00003243948,0.00092121237,0.00009073148,0.00009923896,0.00010881518,0.00009711869,0.3692429,0.0008440493,0.61155576,0.0014305533,0.01552805],"study_design_scores_gemma":[0.000007507003,0.00003139409,0.00025821204,0.000030297659,0.000010114599,0.000041384275,0.000022790622,0.5824693,0.00043365653,0.4157261,0.0009480127,0.000021245341],"about_ca_topic_score_codex":0.001845012,"about_ca_topic_score_gemma":0.0010136154,"teacher_disagreement_score":0.0058875512,"about_ca_system_score_codex":0.0020824494,"about_ca_system_score_gemma":0.0010824694,"threshold_uncertainty_score":0.031136751},"labels":[],"label_agreement":null},{"id":"W2178195001","doi":"10.60082/2563-4631.1001","title":"International Human Rights Fact-finding Praxis in its Living Forms: A TWAIL Perspective","year":2014,"lang":"en","type":"article","venue":"The Transnational Human Rights Review","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Praxis; Human rights; Salience (neuroscience); Political science; Perspective (graphical); Environmental ethics; Sociology; Power (physics); Law and economics; Law; Epistemology; Public relations; Psychology","score_opus":0.02577428534812014,"score_gpt":0.32630597994136723,"score_spread":0.3005316945932471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178195001","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005552722,0.1305319,0.060405895,0.43960539,0.004990508,0.00012215137,0.0001539977,0.000104878025,0.35853258],"genre_scores_gemma":[0.6878606,0.10222335,0.027056893,0.12437892,0.012013129,0.0004801131,0.00022659286,0.00024332922,0.04551701],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97766155,0.01443719,0.0010649048,0.0018899621,0.0039732787,0.00097312935],"domain_scores_gemma":[0.94897974,0.040997423,0.0023897125,0.0038764917,0.0030215294,0.0007351628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043638505,0.0006033772,0.000992423,0.004430443,0.005622814,0.022322414,0.0033457424,0.012234474,0.005837484],"category_scores_gemma":[0.051254712,0.00042704237,0.0006690131,0.0063284417,0.059608337,0.024542514,0.006586642,0.017435296,0.0011359315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023821906,0.0000028015686,0.000048806458,0.000080243335,0.0000032092466,0.000019458763,0.0013049651,0.00013340381,0.000016038812,0.98840696,0.004698121,0.005283562],"study_design_scores_gemma":[0.000004157641,0.00001981416,0.00029445338,0.0016908656,0.000009717169,0.00012969354,0.0036388428,0.000789818,0.00015280003,0.6724225,0.32082447,0.0000228631],"about_ca_topic_score_codex":0.005773293,"about_ca_topic_score_gemma":0.0052315984,"teacher_disagreement_score":0.043638505,"about_ca_system_score_codex":0.009534438,"about_ca_system_score_gemma":0.00835233,"threshold_uncertainty_score":0.23078525},"labels":[],"label_agreement":null},{"id":"W2179402106","doi":"10.48550/arxiv.1511.05122","title":"Adversarial Manipulation of Deep Representations","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Image (mathematics); Artificial intelligence; Representation (politics); Computer science; Class (philosophy); Natural (archaeology); Similarity (geometry); Pattern recognition (psychology); Space (punctuation); Computer vision; Geography","score_opus":0.09478518249713105,"score_gpt":0.23361884969032745,"score_spread":0.1388336671931964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179402106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084753156,0.00024175375,0.89781445,0.0008767948,0.00012428629,0.000044834986,0.00016417826,0.0004924341,0.015488117],"genre_scores_gemma":[0.9476651,0.00021422682,0.047105987,0.00028479408,0.00004555488,0.000059659702,0.000121484074,0.00009163123,0.004411721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999519,0.00014037402,0.0000150267715,0.00010044304,0.00014918302,0.00007597798],"domain_scores_gemma":[0.99889463,0.00056468847,0.00014420836,0.0002912383,0.000057480294,0.000047779296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063082157,0.00071322004,0.00032152634,0.00028651897,0.000273759,0.00060478714,0.000820925,0.0007491519,0.0022413277],"category_scores_gemma":[0.0038403596,0.0002442356,0.00041103322,0.00019868107,0.0015337958,0.0013690491,0.0018727141,0.0017188068,0.0003355977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019472605,0.000057001304,0.00076364283,0.00007682663,0.00006338913,0.00032752694,0.00011776315,0.7280047,0.033168864,0.20584556,0.002931645,0.028448354],"study_design_scores_gemma":[0.000009306393,0.000039859056,0.00020088883,0.00001450924,0.0000072885828,0.00008621133,0.00001620031,0.9329415,0.0070194937,0.057598267,0.0020565286,0.000010013127],"about_ca_topic_score_codex":0.0006635921,"about_ca_topic_score_gemma":0.000557992,"teacher_disagreement_score":0.0022413277,"about_ca_system_score_codex":0.00057392125,"about_ca_system_score_gemma":0.00031376688,"threshold_uncertainty_score":0.0074979663},"labels":[],"label_agreement":null},{"id":"W2179423374","doi":"10.48550/arxiv.1511.06297","title":"Conditional Computation in Neural Networks for faster models","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Computation; Artificial intelligence; Machine learning; Dropout (neural networks); Regularization (linguistics); Artificial neural network; Deep learning; Algorithm","score_opus":0.1124767320843273,"score_gpt":0.22950611236928345,"score_spread":0.11702938028495614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179423374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006302695,0.0009637537,0.98685,0.0009784808,0.00011241046,0.000041077103,0.000087346205,0.0012009406,0.003463376],"genre_scores_gemma":[0.45217472,0.0019245896,0.5297806,0.0009190315,0.00037480515,0.000593136,0.0006320758,0.0014031965,0.012197878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989686,0.00044089335,0.000046314304,0.00019798428,0.00026741126,0.0000787003],"domain_scores_gemma":[0.99554235,0.0029357253,0.00026401403,0.00092220365,0.00023544882,0.00010017574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024614423,0.0015627637,0.0010045815,0.0007685269,0.000547574,0.001507811,0.001797012,0.0016577219,0.008246746],"category_scores_gemma":[0.013700028,0.0008550513,0.0009137,0.0009550497,0.0015975647,0.0037796176,0.0026253436,0.0056516514,0.0018712734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000935812,0.000055704408,0.00045156543,0.0001223992,0.000043774962,0.000059529,0.00007134351,0.700303,0.0016146175,0.2501404,0.0052290265,0.041815024],"study_design_scores_gemma":[0.000005771729,0.0000071616573,0.000025157495,0.000009612006,0.0000035397604,0.000007637035,0.0000019818856,0.9385125,0.0003721863,0.059852477,0.0011983595,0.0000036292338],"about_ca_topic_score_codex":0.0039916695,"about_ca_topic_score_gemma":0.004710406,"teacher_disagreement_score":0.008246746,"about_ca_system_score_codex":0.0018306742,"about_ca_system_score_gemma":0.0012451119,"threshold_uncertainty_score":0.027588129},"labels":[],"label_agreement":null},{"id":"W2408022431","doi":"","title":"The structural model interpretation of the NESS test.","year":2004,"lang":"en","type":"article","venue":"Non-Monotonic Reasoning","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Causation; Overdetermination; Counterfactual conditional; Counterfactual thinking; Counterintuitive; Causality (physics); Test (biology); Interpretation (philosophy); Epistemology; Attribution; Causal model; Conformity; Computer science; Psychology; Cognitive psychology; Mathematics; Social psychology; Philosophy; Statistics","score_opus":0.0049075726543851735,"score_gpt":0.24631946958082315,"score_spread":0.24141189692643797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408022431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034004334,0.0006667143,0.8290972,0.02355344,0.00064135605,0.00027565076,0.0009898782,0.0005794252,0.11019204],"genre_scores_gemma":[0.89705914,0.00042456322,0.090730004,0.0040597366,0.0005770833,0.00067671057,0.0006526461,0.00014807742,0.005672116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9795386,0.012249218,0.0008937748,0.002803664,0.0037431775,0.000771552],"domain_scores_gemma":[0.9353983,0.047339182,0.004532885,0.008460593,0.0035057815,0.0007632105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017647613,0.001163659,0.0013555838,0.0029021644,0.0016089014,0.0023558282,0.0031753683,0.004228396,0.017992962],"category_scores_gemma":[0.09568851,0.00055035425,0.002884933,0.0015065568,0.015135384,0.00990892,0.004333022,0.005781932,0.0013974088],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037231188,0.00002603825,0.00057743536,0.000053663854,0.000055406737,0.00011333271,0.00011094147,0.0039886627,0.00015302112,0.9872329,0.0019468968,0.0057043983],"study_design_scores_gemma":[0.000024192253,0.00002734292,0.00021345672,0.000022462948,0.000013171854,0.000098268996,0.00003816487,0.0148048485,0.00023425485,0.982799,0.0017154551,0.000009448865],"about_ca_topic_score_codex":0.0009377019,"about_ca_topic_score_gemma":0.0009247943,"teacher_disagreement_score":0.017992962,"about_ca_system_score_codex":0.002805419,"about_ca_system_score_gemma":0.0022622265,"threshold_uncertainty_score":0.09333062},"labels":[],"label_agreement":null},{"id":"W2486457958","doi":"10.1016/j.scijus.2016.05.007","title":"What should a forensic practitioner's likelihood ratio be?","year":2016,"lang":"en","type":"article","venue":"Science & Justice","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Engineering and Physical Sciences Research Council","keywords":"Normative; Statistics; Range (aeronautics); Forensic science; Population; Computer science; Econometrics; Value (mathematics); Confidence interval; Interval (graph theory); Likelihood ratios in diagnostic testing; Psychology; Mathematics; Law; Engineering; Medicine; Demography; Sociology","score_opus":0.031263672058768927,"score_gpt":0.3127678718706677,"score_spread":0.2815041998118988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2486457958","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033378582,0.01771569,0.072961375,0.8843842,0.0070843287,0.00005072627,0.00015557242,0.00036604595,0.013944153],"genre_scores_gemma":[0.52220786,0.030348571,0.15569599,0.23844153,0.037388507,0.00042717005,0.00024267125,0.0007672345,0.014480416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9675437,0.01919424,0.002734934,0.0023094278,0.0074996273,0.0007179508],"domain_scores_gemma":[0.852094,0.10076398,0.008227666,0.007813475,0.025794746,0.0053061424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.064827055,0.0012970663,0.0029816139,0.0037115493,0.0025753325,0.012357591,0.0042386255,0.01881908,0.006753982],"category_scores_gemma":[0.34799632,0.00073005864,0.0009349986,0.0012516446,0.015653396,0.023026587,0.003940474,0.017750204,0.0051843165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003328434,0.00028052798,0.006006281,0.0010689953,0.00026065647,0.0006552032,0.0009998609,0.0032434352,0.00082584994,0.4040746,0.21634284,0.36590892],"study_design_scores_gemma":[0.00012186223,0.00016984549,0.0020326748,0.0027432137,0.00013706945,0.0025107136,0.0018129952,0.0099831065,0.0018230042,0.8494896,0.12892456,0.00025138285],"about_ca_topic_score_codex":0.0016433954,"about_ca_topic_score_gemma":0.0013747502,"teacher_disagreement_score":0.064827055,"about_ca_system_score_codex":0.0025447202,"about_ca_system_score_gemma":0.0045426735,"threshold_uncertainty_score":0.34284234},"labels":[],"label_agreement":null},{"id":"W2508070696","doi":"10.6084/m9.figshare.3817398.v1","title":"WAR-ALGORITHM ACCOUNTABILITY","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Standards and Technology; McGill University; Fudan University; Army Research Laboratory; Eidgenössisches Departement für Auswärtige Angelegenheiten; China University of Political Science and Law; Harvard University; Silicon Valley Community Foundation","keywords":"Accountability; Sketch; Computer science; Expansive; Algorithm; Relation (database); Normative; Code (set theory); Law; Law and economics; Political science; Sociology; Set (abstract data type); Programming language","score_opus":0.026786041744843878,"score_gpt":0.27659189896840664,"score_spread":0.24980585722356274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508070696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023499701,0.00037025384,0.7536037,0.012554234,0.0013161248,0.00025659282,0.0006169129,0.005658836,0.20212364],"genre_scores_gemma":[0.8272873,0.0003314641,0.09811992,0.0034478651,0.0004948034,0.00030876385,0.00073721167,0.002791688,0.066481024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99049824,0.0038747701,0.0004345353,0.0013720192,0.002793543,0.0010268309],"domain_scores_gemma":[0.9650111,0.0136624975,0.0018160832,0.013909463,0.0046491423,0.00095161446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008469881,0.00075008697,0.0008256064,0.0010554006,0.002053005,0.006038634,0.0018832684,0.0032393287,0.038716197],"category_scores_gemma":[0.06298201,0.0005594179,0.0007932422,0.0010255416,0.004005902,0.009556439,0.005875448,0.0051852735,0.007559008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019238917,0.0000668664,0.0018474009,0.000074764925,0.000034716802,0.0001947803,0.00028105153,0.028575014,0.0024721136,0.85412097,0.027900593,0.08423935],"study_design_scores_gemma":[0.000038897506,0.00009804241,0.0010868764,0.00008188899,0.000029613288,0.00035255554,0.00019072962,0.16190052,0.010497862,0.73708856,0.08858146,0.00005305516],"about_ca_topic_score_codex":0.0010916262,"about_ca_topic_score_gemma":0.0012476922,"teacher_disagreement_score":0.038716197,"about_ca_system_score_codex":0.0017298384,"about_ca_system_score_gemma":0.0030442076,"threshold_uncertainty_score":0.12951857},"labels":[],"label_agreement":null},{"id":"W2592303957","doi":"10.48550/arxiv.1702.06856","title":"Robustness to Adversarial Examples through an Ensemble of Specialists","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Adversarial system; Confusion; Computer science; Robustness (evolution); Confusion matrix; Entropy (arrow of time); Class (philosophy); Artificial intelligence; Interpretation (philosophy); Machine learning; Theoretical computer science; Psychology","score_opus":0.10741449934091224,"score_gpt":0.24203039722085953,"score_spread":0.1346158978799473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592303957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22224283,0.00041292672,0.77012056,0.0014115422,0.00010573626,0.00013645862,0.00007238393,0.00097122695,0.004526391],"genre_scores_gemma":[0.91847634,0.00010448234,0.07846249,0.00057329534,0.00009542429,0.000076568875,0.00015914236,0.00009538116,0.001956794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950363,0.0017859796,0.00023411632,0.0012324239,0.0012223365,0.0004888012],"domain_scores_gemma":[0.98695916,0.005655966,0.0014526913,0.0033835985,0.001816796,0.0007318201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074307495,0.001285289,0.0019490274,0.0010547442,0.0010719636,0.0019969114,0.0020111555,0.0022831648,0.0012954978],"category_scores_gemma":[0.019607026,0.0005798783,0.0011118551,0.0004723177,0.0018491087,0.0030157533,0.00390258,0.0025460785,0.0006395229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010420235,0.00039235855,0.029265216,0.00025898038,0.00068165344,0.000700264,0.001081866,0.6214958,0.084985256,0.025131641,0.004502963,0.2304619],"study_design_scores_gemma":[0.000017235248,0.00027895923,0.0023239446,0.00003127449,0.00007225843,0.00039290928,0.00010013651,0.9614555,0.016369417,0.017094357,0.0018207419,0.000043205288],"about_ca_topic_score_codex":0.000761044,"about_ca_topic_score_gemma":0.0009813346,"teacher_disagreement_score":0.0074307495,"about_ca_system_score_codex":0.0007311054,"about_ca_system_score_gemma":0.00069493864,"threshold_uncertainty_score":0.039297998},"labels":[],"label_agreement":null},{"id":"W2618318883","doi":"10.48550/arxiv.1705.08551","title":"Safe Model-based Reinforcement Learning with Stability Guarantees","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":337,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Reinforcement learning; Inverted pendulum; Stability (learning theory); Computer science; State space; Artificial neural network; Lyapunov function; Gaussian process; Process (computing); State (computer science); Artificial intelligence; Control (management); Control theory (sociology); Machine learning; Gaussian; Algorithm; Mathematics; Nonlinear system","score_opus":0.0607400630856931,"score_gpt":0.19952215720877162,"score_spread":0.13878209412307851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618318883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034034792,0.00007352766,0.9634,0.00026484136,0.000018196422,0.00004874494,0.000036275636,0.00048010435,0.0016434843],"genre_scores_gemma":[0.94926226,0.000061140796,0.049090046,0.000109335066,0.000019301915,0.00012958131,0.000062982734,0.00007015083,0.0011950713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876285,0.00039278693,0.0000648069,0.00027487526,0.00033425898,0.0001705319],"domain_scores_gemma":[0.99389714,0.004151972,0.00064617605,0.0005899097,0.00049012847,0.00022463399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025607967,0.0012602606,0.0010536069,0.0005568001,0.0005661238,0.0008758652,0.0012748562,0.0011375243,0.0016820729],"category_scores_gemma":[0.01141073,0.0005065722,0.0006158674,0.00030344576,0.002176057,0.001184562,0.0023136737,0.0021462005,0.00031552382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006941459,0.000034209723,0.00054372154,0.000032630196,0.000018038403,0.000055067583,0.000051889074,0.97039527,0.0013160289,0.017663008,0.00026133514,0.009559456],"study_design_scores_gemma":[0.00000851464,0.000018714365,0.000026665552,0.0000030552835,0.0000022771687,0.0000054490424,0.0000022016206,0.9907759,0.00038476125,0.008706854,0.00006332902,0.0000022772208],"about_ca_topic_score_codex":0.0031871444,"about_ca_topic_score_gemma":0.0021979199,"teacher_disagreement_score":0.0031871444,"about_ca_system_score_codex":0.0011927595,"about_ca_system_score_gemma":0.0021230055,"threshold_uncertainty_score":0.01354295},"labels":[],"label_agreement":null},{"id":"W2619064626","doi":"10.1016/j.asoc.2017.04.052","title":"Optimization under uncertainty: A perspective of soft computing","year":2017,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Exponential family; Robustness (evolution); Inference; Artificial neural network; Computation; Leverage (statistics); Gaussian; Algorithm; Bayesian probability; Mathematical optimization; Approximate inference; Divergence (linguistics); Exponential function; Posterior probability; Artificial intelligence; Bayesian inference; Gaussian process; Machine learning; Mathematics","score_opus":0.017414957745257812,"score_gpt":0.2898115176817402,"score_spread":0.2723965599364824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619064626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005543579,0.009402659,0.9527556,0.0070090867,0.0004579886,0.000024579735,0.0000653862,0.00006002006,0.024681175],"genre_scores_gemma":[0.69519526,0.02547479,0.25901523,0.0021771507,0.0065145027,0.00024848027,0.00012471265,0.00017137626,0.011078495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99675983,0.001771669,0.00014921637,0.00034863033,0.0008355657,0.00013509534],"domain_scores_gemma":[0.99326116,0.0052071325,0.00043417135,0.00047374083,0.00043027196,0.00019342988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004854689,0.0015012888,0.0020791178,0.0022973532,0.00093291956,0.0063997204,0.002206575,0.0035454216,0.0025441125],"category_scores_gemma":[0.010932165,0.00071894185,0.0015508742,0.002678027,0.008706996,0.006615017,0.0031832613,0.0056854514,0.00037199297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009629814,0.000014000023,0.00007075626,0.00010191253,0.0000371127,0.00003086452,0.00005542727,0.036853753,0.000221506,0.9562082,0.0005935575,0.0058032973],"study_design_scores_gemma":[0.000004416393,0.000013830988,0.00004736086,0.00003920389,0.000009374987,0.000016780325,0.000027194303,0.11940239,0.00011466657,0.878245,0.0020685624,0.000011253482],"about_ca_topic_score_codex":0.0012687275,"about_ca_topic_score_gemma":0.0008647717,"teacher_disagreement_score":0.0063997204,"about_ca_system_score_codex":0.0023138083,"about_ca_system_score_gemma":0.0012825142,"threshold_uncertainty_score":0.025674403},"labels":[],"label_agreement":null},{"id":"W2734426634","doi":"10.1609/aaai.v32i1.11634","title":"Adversarial Dropout for Supervised and Semi-Supervised Learning","year":2018,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Generality; Dropout (neural networks); MNIST database; Adversarial system; Computer science; Machine learning; Artificial intelligence; Artificial neural network; Generalization; Set (abstract data type); Deep neural networks; Supervised learning; Mathematics; Psychology","score_opus":0.06696556916244889,"score_gpt":0.3084728003732138,"score_spread":0.2415072312107649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734426634","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055763656,0.00042243322,0.9920568,0.00029675243,0.00004154814,0.000038322618,0.000060298804,0.00035183132,0.0011556367],"genre_scores_gemma":[0.7004999,0.0012340737,0.2915252,0.0005711348,0.00034669365,0.0004270204,0.00058483,0.00027759362,0.004533532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969825,0.0014007996,0.0001582321,0.0006131011,0.00068247196,0.00016296233],"domain_scores_gemma":[0.99263304,0.004863449,0.00064418255,0.0011823593,0.000493239,0.00018372099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005549772,0.001430365,0.0013910102,0.0008272806,0.0005790681,0.0011028233,0.002071774,0.0017908334,0.0017581126],"category_scores_gemma":[0.013429455,0.0005861729,0.0010847735,0.0007858415,0.002858663,0.0023532307,0.0026007784,0.0039757597,0.0004160445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015331707,0.000083845065,0.00080389495,0.00023308229,0.00013360442,0.00014886298,0.00013812711,0.8105924,0.0035949291,0.115380645,0.0033209883,0.065416284],"study_design_scores_gemma":[0.0000060460766,0.000023133236,0.00009636726,0.0000111694135,0.0000052361574,0.000020792302,0.0000034023944,0.9668929,0.00096589833,0.031353284,0.00061491923,0.000006881591],"about_ca_topic_score_codex":0.0013739524,"about_ca_topic_score_gemma":0.0013545938,"teacher_disagreement_score":0.005549772,"about_ca_system_score_codex":0.0016974993,"about_ca_system_score_gemma":0.0011213537,"threshold_uncertainty_score":0.02935034},"labels":[],"label_agreement":null},{"id":"W2765368425","doi":"10.1007/978-3-319-70096-0_13","title":"Combating Adversarial Inputs Using a Predictive-Estimator Network","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Estimator; Adversarial system; Construct (python library); Artificial intelligence; Machine learning; Feed forward; Process (computing); Mathematics; Statistics; Control engineering","score_opus":0.02150898241578336,"score_gpt":0.27714648231756617,"score_spread":0.2556374999017828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765368425","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069902334,0.00033407073,0.98945904,0.00019184616,0.000089647634,0.000022067286,0.000032898162,0.00030679948,0.0025734105],"genre_scores_gemma":[0.7758413,0.00080823805,0.20674473,0.0004453528,0.00024321796,0.00013960415,0.00021686991,0.00014184925,0.015418893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994894,0.000114544906,0.00001862883,0.00015001047,0.00015944292,0.00006794993],"domain_scores_gemma":[0.99872404,0.00076905504,0.00009359148,0.00016143346,0.00020882954,0.00004308989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010480763,0.0009842423,0.00093970215,0.00051841215,0.00046231944,0.000870506,0.0017354764,0.0017144507,0.0029875222],"category_scores_gemma":[0.0035573435,0.0005420669,0.00058675307,0.00046520086,0.0010775919,0.0015935685,0.0025214732,0.0022935385,0.00064942084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013192395,0.000052288087,0.00036052027,0.00007002287,0.000047575173,0.00011603032,0.000053179734,0.8388254,0.0055458485,0.03040457,0.0023469874,0.12204565],"study_design_scores_gemma":[0.0000017928453,0.000019976702,0.000036710488,0.000006355291,0.000007708465,0.000025952508,0.0000028521865,0.9937104,0.0009685272,0.004876888,0.00033860916,0.0000042020374],"about_ca_topic_score_codex":0.0021781186,"about_ca_topic_score_gemma":0.0025351788,"teacher_disagreement_score":0.0029875222,"about_ca_system_score_codex":0.0006849696,"about_ca_system_score_gemma":0.0006426622,"threshold_uncertainty_score":0.009994268},"labels":[],"label_agreement":null},{"id":"W2770024286","doi":"","title":"Predict Responsibly: Increasing Fairness by Learning To Defer","year":2017,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Pipeline (software); Computer science; Decision maker; Downstream (manufacturing); Scheme (mathematics); Work (physics); Artificial intelligence; Machine learning; Risk analysis (engineering); Operations research; Economics; Business; Engineering; Operations management","score_opus":0.012157375804348603,"score_gpt":0.280750064011967,"score_spread":0.2685926882076184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770024286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1872712,0.0004345848,0.80262077,0.002518232,0.0001230398,0.00014518939,0.00009776349,0.0011593841,0.005629816],"genre_scores_gemma":[0.96200436,0.00009322331,0.03554998,0.00044402262,0.00005907262,0.000059946535,0.000042318436,0.00008822981,0.001658799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936743,0.003242112,0.00020832743,0.0011384509,0.0011434517,0.0005933473],"domain_scores_gemma":[0.95745426,0.025547003,0.0034627384,0.009318332,0.002012157,0.0022055448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014111311,0.0013472539,0.0017052523,0.00068771513,0.0015833864,0.002339271,0.002908224,0.0021183416,0.0023898885],"category_scores_gemma":[0.053235114,0.0007423189,0.00088997657,0.00048162517,0.0037495042,0.0056738206,0.00482897,0.0043358617,0.0006342445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013379124,0.0005167962,0.011125808,0.00014236402,0.00014761252,0.00045495026,0.0014030779,0.7092003,0.0077692396,0.14088112,0.0046691946,0.12235159],"study_design_scores_gemma":[0.00005017272,0.00014571096,0.00032006178,0.000019488902,0.000024765159,0.000071991686,0.00004244562,0.9074362,0.0025309168,0.088470474,0.000860602,0.00002717123],"about_ca_topic_score_codex":0.0025961476,"about_ca_topic_score_gemma":0.0020960316,"teacher_disagreement_score":0.014111311,"about_ca_system_score_codex":0.001871164,"about_ca_system_score_gemma":0.0033269313,"threshold_uncertainty_score":0.07462865},"labels":[],"label_agreement":null},{"id":"W2778289287","doi":"","title":"VTG - Vulnerability Test Generator, a Plug-in for Rodin","year":2012,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Plug-in; Vulnerability (computing); Generator (circuit theory); Computer science; Test (biology); Spark plug; Computer security; Engineering; Physics; Geology; Operating system; Mechanical engineering","score_opus":0.02098789055237157,"score_gpt":0.2624652259792398,"score_spread":0.24147733542686825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2778289287","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018284747,0.00035135445,0.19652927,0.0003603386,0.00039957342,0.0005172487,0.0050553638,0.7696434,0.00885868],"genre_scores_gemma":[0.56846863,0.00062389503,0.16198695,0.00208115,0.00028426925,0.001489548,0.026534284,0.19723396,0.041297317],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838984,0.00036505892,0.00012200953,0.00043812627,0.00046594327,0.0002190321],"domain_scores_gemma":[0.9954817,0.0019713016,0.0002657477,0.0015137985,0.00046607718,0.00030147124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022332922,0.003168948,0.0013009326,0.0020248408,0.0004015415,0.0015582592,0.003780713,0.0019374258,0.052495923],"category_scores_gemma":[0.009126957,0.0013346035,0.0012349649,0.0006060353,0.0009333752,0.0031750784,0.002746401,0.0020788843,0.018193142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006567264,0.0011261062,0.016182352,0.0019653863,0.000634681,0.0029571396,0.00067247945,0.04317241,0.049980164,0.015231982,0.4602522,0.40125778],"study_design_scores_gemma":[0.0021144832,0.0013318465,0.006956493,0.00042771836,0.00023421236,0.0032520546,0.00018265353,0.5966015,0.19796933,0.028666342,0.16181439,0.00044900872],"about_ca_topic_score_codex":0.0010287282,"about_ca_topic_score_gemma":0.0010824106,"teacher_disagreement_score":0.052495923,"about_ca_system_score_codex":0.00081024447,"about_ca_system_score_gemma":0.00088894175,"threshold_uncertainty_score":0.17561638},"labels":[],"label_agreement":null},{"id":"W2788235277","doi":"10.1109/smc.2019.8913861","title":"Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; MNIST database; Machine learning; Artificial intelligence; Deep neural networks; Artificial neural network; Set (abstract data type); Focus (optics); Binary classification; Adversarial machine learning; Deep learning","score_opus":0.02639344613223639,"score_gpt":0.27563289567800203,"score_spread":0.24923944954576566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788235277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04437294,0.00026016383,0.9522867,0.0004290665,0.00005049353,0.0000658872,0.00009121633,0.0011055964,0.0013378938],"genre_scores_gemma":[0.88620836,0.00014900816,0.11019993,0.00040294288,0.00006601957,0.00007556149,0.00030524973,0.00011760611,0.0024753874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809796,0.0005516618,0.00008390258,0.000542829,0.0005512868,0.00017247688],"domain_scores_gemma":[0.99569833,0.0022837634,0.000633484,0.00087422744,0.00032721838,0.00018300492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024689988,0.001079301,0.0011811969,0.00070593145,0.00031974728,0.0009937476,0.0017152138,0.0015637835,0.0016803528],"category_scores_gemma":[0.010645754,0.0005454318,0.00070172484,0.00043941822,0.001331339,0.002187042,0.0028527812,0.002353486,0.0005638688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004998484,0.00014682942,0.00436041,0.00012504213,0.00015977035,0.0003764853,0.00016738648,0.7672186,0.015948556,0.028181132,0.004446988,0.17836897],"study_design_scores_gemma":[0.0000056702775,0.00003418089,0.00025024035,0.000005521595,0.000005986631,0.00007948766,0.0000051271168,0.9881633,0.003007428,0.008108287,0.00032785238,0.0000069994817],"about_ca_topic_score_codex":0.0010437096,"about_ca_topic_score_gemma":0.0009521985,"teacher_disagreement_score":0.0024689988,"about_ca_system_score_codex":0.00087818503,"about_ca_system_score_gemma":0.0007068366,"threshold_uncertainty_score":0.01305747},"labels":[],"label_agreement":null},{"id":"W2788416960","doi":"10.48550/arxiv.1802.06309","title":"Learning Adversarially Fair and Transferable Representations","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":190,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Odds; Computer science; Representation (politics); Key (lock); Downstream (manufacturing); Feature learning; Artificial intelligence; Machine learning; Theoretical computer science; Computer security; Business; Law; Political science; Marketing","score_opus":0.04717142227532687,"score_gpt":0.2071062386372224,"score_spread":0.15993481636189555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788416960","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03173955,0.00015426362,0.96329266,0.0011615942,0.000057533547,0.00008731852,0.00010170477,0.0003582829,0.0030470155],"genre_scores_gemma":[0.8979471,0.00016208472,0.09724899,0.0005604026,0.00008948573,0.00024371044,0.00018962477,0.00011095272,0.0034477066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942147,0.0029051537,0.0001559774,0.001042154,0.001101633,0.00058043725],"domain_scores_gemma":[0.97844976,0.014636405,0.0014476781,0.0041421056,0.0007853299,0.00053875556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009499679,0.0014838998,0.0012847859,0.00067026174,0.0010169584,0.0026163245,0.0026303625,0.0026679605,0.0027687792],"category_scores_gemma":[0.039803714,0.00051933434,0.0007559745,0.00064279634,0.0044574416,0.0054355566,0.004670974,0.005369911,0.000711193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028469757,0.00017350067,0.0017157781,0.00009063046,0.00009104679,0.00016893014,0.00022283495,0.7186547,0.0026293974,0.23658192,0.0022253413,0.037161287],"study_design_scores_gemma":[0.000019108158,0.000056916266,0.00011435781,0.000017140615,0.00000867342,0.000033801014,0.000024456003,0.78430694,0.0012978092,0.21358195,0.0005266559,0.000012158144],"about_ca_topic_score_codex":0.0010661493,"about_ca_topic_score_gemma":0.0010054973,"teacher_disagreement_score":0.009499679,"about_ca_system_score_codex":0.0019655575,"about_ca_system_score_gemma":0.0021545026,"threshold_uncertainty_score":0.05023974},"labels":[],"label_agreement":null},{"id":"W2790331160","doi":"10.1109/iros.2018.8594018","title":"Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Artificial neural network; Artificial intelligence; Benchmark (surveying); Dropout (neural networks); Curse of dimensionality; Probabilistic logic; Machine learning; Controller (irrigation)","score_opus":0.018737602604474605,"score_gpt":0.2489554853752059,"score_spread":0.2302178827707313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790331160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008049778,0.00006832758,0.98992026,0.00011227003,0.00002494731,0.00003927755,0.000025652229,0.0005400864,0.0012194812],"genre_scores_gemma":[0.6881567,0.00010929296,0.30853912,0.00019449992,0.00003723048,0.00031640354,0.00015358781,0.00021526715,0.0022778166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996611,0.00007825295,0.000019549938,0.00009239781,0.0001065353,0.000042203832],"domain_scores_gemma":[0.99888164,0.0006892351,0.00013859132,0.00012936759,0.00011751732,0.000043568205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009689803,0.00094848976,0.00077526615,0.0003794274,0.00029174727,0.00067978847,0.0010327426,0.0009872899,0.0018691231],"category_scores_gemma":[0.0041825976,0.00060192513,0.00053538615,0.00030478992,0.0008940192,0.0008758757,0.001296986,0.0015828587,0.00040560414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002046958,0.000015395577,0.00017322633,0.000023864653,0.000013693293,0.000020619089,0.000017143047,0.9758892,0.0010028284,0.004882019,0.00040393675,0.017537598],"study_design_scores_gemma":[0.0000041039943,0.000008259101,0.000011080583,0.0000017725653,0.0000011756323,0.000003230414,0.0000011084595,0.99792445,0.0002495609,0.001666953,0.00012692538,0.000001334419],"about_ca_topic_score_codex":0.003240813,"about_ca_topic_score_gemma":0.0034027225,"teacher_disagreement_score":0.003240813,"about_ca_system_score_codex":0.0007903806,"about_ca_system_score_gemma":0.0012736929,"threshold_uncertainty_score":0.0064439178},"labels":[],"label_agreement":null},{"id":"W2792331414","doi":"10.1186/s12859-018-2391-z","title":"Valection: design optimization for validation and verification studies","year":2018,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Prostate Cancer Canada; Government of Ontario; Canadian Institutes of Health Research; Genome Canada; National Cancer Institute; National Institutes of Health; Ontario Institute for Cancer Research; Terry Fox Research Institute; Movember Foundation","keywords":"Computer science; Software; Selection (genetic algorithm); Inference; Data mining; Machine learning; Artificial intelligence; Programming language","score_opus":0.09419023023291122,"score_gpt":0.3327364530797012,"score_spread":0.23854622284678995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792331414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004406358,0.000059177568,0.9906222,0.000058448317,0.000021319209,0.00013882355,0.00008743667,0.0035221698,0.0010841537],"genre_scores_gemma":[0.16136068,0.00009869419,0.83406293,0.00016610461,0.0000286444,0.0009386245,0.00040872546,0.0015946568,0.0013409174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960573,0.0018815988,0.00022452015,0.00053925684,0.0010777441,0.00021958997],"domain_scores_gemma":[0.9881626,0.0084610805,0.00084675173,0.0011857157,0.0011684762,0.00017540563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009349621,0.0021930982,0.0007798381,0.0012189925,0.00049004535,0.0011596545,0.0015972728,0.0010414831,0.0078230435],"category_scores_gemma":[0.022928597,0.0009091032,0.0014611245,0.00047004188,0.0012324831,0.0010173382,0.0019157003,0.0021112093,0.0016410579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000704032,0.00021353549,0.0039338856,0.0006229305,0.000267252,0.0002819506,0.00022572993,0.71956336,0.020712757,0.025107747,0.0092077525,0.21915914],"study_design_scores_gemma":[0.000078516714,0.00022048905,0.00031179667,0.000061831925,0.000039240596,0.00009641708,0.000024341281,0.9696835,0.012459593,0.0121890055,0.0048151086,0.000020181382],"about_ca_topic_score_codex":0.0007930476,"about_ca_topic_score_gemma":0.0009384606,"teacher_disagreement_score":0.009349621,"about_ca_system_score_codex":0.0007622943,"about_ca_system_score_gemma":0.0018670381,"threshold_uncertainty_score":0.049446106},"labels":[],"label_agreement":null},{"id":"W2799491151","doi":"","title":"Maintaining Intruder Detection Capability in a Rectangular Domain with Sensors","year":2016,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Rectangle; Domain (mathematical analysis); Computer science; Dimension (graph theory); Set (abstract data type); Grid; Real-time computing; Mathematics; Geometry; Mathematical analysis","score_opus":0.005818852663139768,"score_gpt":0.22086013331258636,"score_spread":0.21504128064944658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799491151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29531974,0.0007539929,0.6960884,0.0003979708,0.00008122046,0.00013775281,0.00029847087,0.0011301856,0.005792322],"genre_scores_gemma":[0.7474616,0.00044625366,0.24807768,0.00012812295,0.0000306509,0.00010664834,0.00032017287,0.00006741066,0.00336139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844533,0.0002986429,0.00012438346,0.00047042506,0.00030260094,0.00035863166],"domain_scores_gemma":[0.99318755,0.0026297378,0.00082879944,0.0021372542,0.0007754564,0.00044122656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016328053,0.0006536757,0.0011019119,0.0005009324,0.0006064976,0.0014052204,0.0025449402,0.0007381117,0.0014949271],"category_scores_gemma":[0.007145116,0.0006989477,0.00057285797,0.0007024736,0.0013473514,0.0025608796,0.0024120617,0.00085958466,0.00093497057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014654443,0.00026937213,0.010335873,0.000462734,0.00012716693,0.0012176811,0.00046978952,0.7934821,0.080950096,0.02946974,0.0029703465,0.07877968],"study_design_scores_gemma":[0.00008774566,0.00047931934,0.0016226225,0.000060432736,0.000048445654,0.0005832184,0.00035181642,0.9379613,0.0355361,0.017369004,0.005844113,0.00005586461],"about_ca_topic_score_codex":0.0026349726,"about_ca_topic_score_gemma":0.0020615347,"teacher_disagreement_score":0.0026349726,"about_ca_system_score_codex":0.0005343072,"about_ca_system_score_gemma":0.0011958781,"threshold_uncertainty_score":0.008635223},"labels":[],"label_agreement":null},{"id":"W2805329444","doi":"10.1109/mmsp.2018.8547128","title":"Adversarial Attacks on Face Detectors Using Neural Net Based Constrained Optimization","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Adversarial system; Generator (circuit theory); Robustness (evolution); Artificial intelligence; Detector; Face (sociological concept); Scalability; Artificial neural network; Machine learning; Pattern recognition (psychology)","score_opus":0.033870231934047285,"score_gpt":0.29842978305982815,"score_spread":0.2645595511257809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805329444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06503863,0.000233473,0.9278754,0.00055560406,0.0000562406,0.00007922758,0.000083752224,0.0014606265,0.0046170093],"genre_scores_gemma":[0.8296281,0.00016819051,0.16434085,0.00047637778,0.00004062187,0.00011319875,0.00017308188,0.00019604947,0.0048634624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909306,0.0002932017,0.000028672164,0.00018763368,0.0002818658,0.000115464994],"domain_scores_gemma":[0.99847645,0.0009406596,0.00014661612,0.00025749998,0.0001256351,0.000053021693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015303844,0.0011406096,0.00080568774,0.0005012813,0.00035261086,0.00064567383,0.0010725234,0.0012830946,0.0021430738],"category_scores_gemma":[0.004588115,0.0004468164,0.00068016985,0.00028511725,0.0016753664,0.0014453977,0.0017773685,0.0021062153,0.00046098256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011130388,0.00005568149,0.0005508778,0.000029796645,0.000048477872,0.00009413566,0.000027572169,0.9403465,0.00864111,0.014692583,0.0016646687,0.033737272],"study_design_scores_gemma":[0.000004163331,0.000015819405,0.000050752857,0.0000022980435,0.0000016386236,0.000015163299,0.0000018931497,0.9948933,0.0019216712,0.0029289078,0.00016145567,0.0000029257237],"about_ca_topic_score_codex":0.002468193,"about_ca_topic_score_gemma":0.0026796206,"teacher_disagreement_score":0.002468193,"about_ca_system_score_codex":0.001255692,"about_ca_system_score_gemma":0.00075702305,"threshold_uncertainty_score":0.009110689},"labels":[],"label_agreement":null},{"id":"W2805869754","doi":"10.1007/978-3-319-92058-0_83","title":"Meticulous Transparency—An Evaluation Process for an Agile AI Regulatory Scheme","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; Douglas Mental Health University Institute; McGill University","funders":"","keywords":"Transparency (behavior); Pace; Computer science; Agile software development; Ethical issues; Process (computing); Engineering ethics; Conformity; Risk analysis (engineering); Management science; Computer security; Political science; Software engineering; Engineering; Business; Law","score_opus":0.042468450693073236,"score_gpt":0.3341469633492444,"score_spread":0.2916785126561711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805869754","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041374736,0.00022403368,0.8644917,0.008826227,0.00038193204,0.0012902358,0.00012712911,0.0010407773,0.08224325],"genre_scores_gemma":[0.70940447,0.00010180964,0.27712288,0.001186521,0.00009574086,0.000786656,0.000079885926,0.00032765802,0.010894388],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92896575,0.039408393,0.0025260055,0.0041830214,0.021565948,0.0033508334],"domain_scores_gemma":[0.8667753,0.07549203,0.006419206,0.027970226,0.019376285,0.0039669406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06634583,0.00086110696,0.0010291478,0.0018072788,0.0034796027,0.011206771,0.0038764293,0.0059183026,0.012158527],"category_scores_gemma":[0.16675039,0.00068249734,0.001012032,0.001103385,0.009101881,0.011665413,0.009647338,0.010321419,0.0017505952],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025543277,0.00012901382,0.00072749506,0.00009240941,0.00003862181,0.000120406614,0.001051899,0.012482217,0.0019034385,0.93362224,0.0033255243,0.04625138],"study_design_scores_gemma":[0.00016789444,0.0005104755,0.0006347032,0.00031956562,0.00004859097,0.00012524231,0.0010432187,0.12916726,0.0064421096,0.8394191,0.02201487,0.000106936386],"about_ca_topic_score_codex":0.0017929844,"about_ca_topic_score_gemma":0.0015269332,"teacher_disagreement_score":0.06634583,"about_ca_system_score_codex":0.005290254,"about_ca_system_score_gemma":0.011369746,"threshold_uncertainty_score":0.35087448},"labels":[],"label_agreement":null},{"id":"W2809263290","doi":"10.48550/arxiv.1807.00942","title":"Stochastic Layer-Wise Precision in Deep Neural Networks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"MNIST database; Computer science; Memory footprint; Regularization (linguistics); Artificial neural network; Convolutional neural network; Deep neural networks; Generalization; Artificial intelligence; Implementation; Deep learning; Machine learning; Mathematics","score_opus":0.05264411588032774,"score_gpt":0.21228099033909492,"score_spread":0.15963687445876718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809263290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05917863,0.0008089924,0.93654925,0.0005033403,0.000049302427,0.000032265707,0.000077744146,0.0007438252,0.0020566604],"genre_scores_gemma":[0.9125718,0.0006067601,0.0840953,0.00022353794,0.000083026905,0.00008959875,0.00011093224,0.00017627861,0.0020427625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837,0.00052059116,0.00011329099,0.00036596422,0.00047524588,0.00015492782],"domain_scores_gemma":[0.99587774,0.0024814897,0.00054999284,0.00070752273,0.00027963365,0.000103577004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034504987,0.0011074872,0.0010026103,0.00071955804,0.0005880427,0.0014682114,0.0016195015,0.0015798496,0.0010515542],"category_scores_gemma":[0.0142700225,0.0008938378,0.0006748356,0.00076241454,0.002768928,0.0027769976,0.0026643265,0.0027961351,0.00029297118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013326114,0.000022259384,0.0006516694,0.00006691021,0.000042714688,0.00007609824,0.00006835937,0.91615874,0.005524013,0.052904908,0.00055670284,0.023794334],"study_design_scores_gemma":[0.000015164897,0.000058347883,0.00019242587,0.000023067672,0.0000122913825,0.000035465837,0.000005766242,0.9413017,0.0037520886,0.054158423,0.00043297806,0.000012251523],"about_ca_topic_score_codex":0.0018206335,"about_ca_topic_score_gemma":0.0016026862,"teacher_disagreement_score":0.0034504987,"about_ca_system_score_codex":0.0014498485,"about_ca_system_score_gemma":0.0009158637,"threshold_uncertainty_score":0.0182482},"labels":[],"label_agreement":null},{"id":"W2828141535","doi":"","title":"Fooling the classifier: Ligand antagonism and adversarial examples","year":2018,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Decision boundary; Computer science; Machine learning; Adversarial system; Analogy; Artificial neural network; Classifier (UML)","score_opus":0.020481633103381063,"score_gpt":0.26161342027708795,"score_spread":0.2411317871737069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2828141535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13543046,0.00044347227,0.8493003,0.0022572868,0.00010443912,0.00006744588,0.00006528244,0.0003154063,0.012015864],"genre_scores_gemma":[0.9519892,0.0002235273,0.0436224,0.00047620042,0.000052201816,0.0000922929,0.00004766311,0.00008074881,0.0034157352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987509,0.00054567686,0.00004799156,0.00021782664,0.00028310422,0.00015449125],"domain_scores_gemma":[0.9924029,0.005492514,0.0006943675,0.0008173342,0.0002766809,0.000316268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020999608,0.000601893,0.00072967785,0.0005604556,0.0006556951,0.0015081003,0.0010211782,0.0019127531,0.0021082307],"category_scores_gemma":[0.014720673,0.0003684017,0.00056623144,0.00022832613,0.004004144,0.0021075527,0.0022238914,0.0023088239,0.0003867819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001723003,0.000054517255,0.0013523987,0.00010366941,0.00006768838,0.0002613409,0.00018792522,0.5856803,0.017836658,0.37200686,0.0017585595,0.020517845],"study_design_scores_gemma":[0.000015271495,0.000064591986,0.00022254344,0.000020308882,0.000008876229,0.000085270454,0.000026311893,0.8218396,0.0038102479,0.17275967,0.0011300805,0.000017173052],"about_ca_topic_score_codex":0.00052272156,"about_ca_topic_score_gemma":0.0003804606,"teacher_disagreement_score":0.0021082307,"about_ca_system_score_codex":0.0010807677,"about_ca_system_score_gemma":0.00055088877,"threshold_uncertainty_score":0.011105776},"labels":[],"label_agreement":null},{"id":"W2888401638","doi":"10.1007/978-3-319-99229-7_37","title":"Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Perception; Function (biology); Measure (data warehouse); Key (lock); Artificial intelligence; Work (physics); Position (finance); Machine learning; Human–computer interaction; Data mining; Computer security; Engineering; Psychology","score_opus":0.016406251456040553,"score_gpt":0.28984445296704636,"score_spread":0.2734382015110058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888401638","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011194497,0.00011614908,0.99724257,0.000072174545,0.000026840875,0.00001867574,0.000022411681,0.00020078814,0.0011809157],"genre_scores_gemma":[0.2773947,0.0006730534,0.7130984,0.00021259398,0.00019076766,0.00020677224,0.00026531174,0.00039113467,0.0075672306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99914074,0.00015817574,0.000042532087,0.00019632868,0.0003363771,0.00012584115],"domain_scores_gemma":[0.9992041,0.000266747,0.00008019156,0.00015765458,0.00021868017,0.000072619216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001233641,0.0010299654,0.0010358766,0.00071413384,0.00072272134,0.0023120323,0.003136274,0.0021050882,0.0039027839],"category_scores_gemma":[0.0029252116,0.0006312245,0.0012188439,0.00059524365,0.0012963761,0.0029642293,0.0042836415,0.0030936513,0.0011164056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007395737,0.00010472718,0.0002953484,0.00012819847,0.00006976946,0.00015208707,0.00017495401,0.62901044,0.009794667,0.24686767,0.0047100773,0.108618125],"study_design_scores_gemma":[0.0000034309476,0.00002131723,0.000038735598,0.0000116314895,0.000007678574,0.000026762773,0.000016005872,0.93203765,0.0011073973,0.06448878,0.0022302165,0.000010495196],"about_ca_topic_score_codex":0.0036414303,"about_ca_topic_score_gemma":0.003488647,"teacher_disagreement_score":0.0039027839,"about_ca_system_score_codex":0.0009239959,"about_ca_system_score_gemma":0.0014502082,"threshold_uncertainty_score":0.013056159},"labels":[],"label_agreement":null},{"id":"W2899135085","doi":"10.1109/camad.2018.8514982","title":"Toward Intelligent Detection Modelling for Adversarial Samples in Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; KDDI Foundation; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Adversarial system; Margin (machine learning); MNIST database; Artificial intelligence; Machine learning; Artificial neural network; Classifier (UML); Convolutional neural network; Deep learning; Deep neural networks; Pattern recognition (psychology)","score_opus":0.05873592529830224,"score_gpt":0.2827011199057792,"score_spread":0.22396519460747694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899135085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02005949,0.00014132717,0.9789514,0.00014633982,0.000017082195,0.000019628098,0.000024783509,0.00020654529,0.00043351552],"genre_scores_gemma":[0.8514937,0.00032571924,0.1453224,0.00016634811,0.00006491358,0.00009577635,0.00014451913,0.000102122714,0.0022845084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988017,0.0003696202,0.00006376188,0.0003206099,0.0003170134,0.00012724043],"domain_scores_gemma":[0.9941121,0.0036153987,0.0009669414,0.0005268163,0.0006186138,0.0001601374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030057286,0.0012531494,0.0010817199,0.001037177,0.00042874657,0.0012243919,0.0014756115,0.0012276991,0.00060549664],"category_scores_gemma":[0.011974381,0.0007484519,0.0010614657,0.0004979809,0.0016891636,0.0022389211,0.0020094889,0.0028090964,0.00018849436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006368724,0.000018264074,0.00096719974,0.00002195065,0.000025709944,0.00004428279,0.000054264066,0.96502876,0.0016727499,0.015273289,0.0003610279,0.016468909],"study_design_scores_gemma":[8.118438e-7,0.0000049570563,0.00004428486,0.000001727043,0.00000182338,0.0000053898425,0.0000014717327,0.9973699,0.00037963767,0.0021401288,0.000047677255,0.0000022311435],"about_ca_topic_score_codex":0.0034002464,"about_ca_topic_score_gemma":0.0024099937,"teacher_disagreement_score":0.0034002464,"about_ca_system_score_codex":0.0015534009,"about_ca_system_score_gemma":0.00094702607,"threshold_uncertainty_score":0.015896022},"labels":[],"label_agreement":null},{"id":"W2900818629","doi":"10.1145/3243734.3278486","title":"Spartan Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Preprocessor; Spartan; Robustness (evolution); Adversarial system; Deep learning; Artificial intelligence; Artificial neural network; Deep neural networks; Machine learning","score_opus":0.009361430731714776,"score_gpt":0.25150469843854634,"score_spread":0.24214326770683156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900818629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030150415,0.0016999788,0.9310432,0.00096199324,0.00044090455,0.000118208256,0.0006363125,0.0032444028,0.031704586],"genre_scores_gemma":[0.7233667,0.002538633,0.21102124,0.00148533,0.00025684375,0.00033607153,0.0020686155,0.00071691297,0.058209676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944204,0.00010555847,0.000029078537,0.00015856196,0.00018230172,0.0000824812],"domain_scores_gemma":[0.9988844,0.0004084445,0.00011642455,0.00032982347,0.00021413955,0.00004682194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082441216,0.0010254307,0.000602175,0.0005372308,0.00047235686,0.0012887261,0.0017522449,0.0012891042,0.010233174],"category_scores_gemma":[0.0038041326,0.00052977883,0.0006348886,0.00040696067,0.0011532003,0.0023866403,0.0016246701,0.0020466812,0.0027230782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023182094,0.000068973524,0.0012987326,0.00027102744,0.00015818338,0.00017213733,0.0000832016,0.6151858,0.0077876975,0.18296012,0.016469536,0.17531282],"study_design_scores_gemma":[0.000015046031,0.00007870621,0.00016598744,0.00004694996,0.000028001808,0.00012952757,0.0000149938905,0.8861593,0.0064199124,0.08664441,0.020277148,0.000020076906],"about_ca_topic_score_codex":0.0019597628,"about_ca_topic_score_gemma":0.0031939673,"teacher_disagreement_score":0.010233174,"about_ca_system_score_codex":0.00086925103,"about_ca_system_score_gemma":0.00097596773,"threshold_uncertainty_score":0.03423333},"labels":[],"label_agreement":null},{"id":"W2901440361","doi":"10.1109/cvpr.2019.00445","title":"Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"MNIST database; Adversarial system; Norm (philosophy); Computer science; Decoupling (probability); Artificial intelligence; Robustness (evolution); Perturbation (astronomy); Algorithm; Pattern recognition (psychology); Mathematical optimization; Machine learning; Deep learning; Mathematics; Engineering","score_opus":0.016978691342172704,"score_gpt":0.274125652303942,"score_spread":0.25714696096176926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901440361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01078756,0.00030389833,0.9852475,0.00029985726,0.00005470293,0.000058643138,0.000039702947,0.00064482546,0.002563365],"genre_scores_gemma":[0.6437012,0.0005992425,0.34816703,0.0005605956,0.0001712365,0.00033080616,0.00028445284,0.000395804,0.005789571],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808705,0.00074709277,0.00010256003,0.00027044117,0.00060763734,0.00018522705],"domain_scores_gemma":[0.99752337,0.0013414721,0.0002451903,0.0005423768,0.00022750326,0.000120108954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023267881,0.0019026824,0.0012603081,0.00095300627,0.00047797093,0.0010720632,0.0011589272,0.0016546777,0.0025972086],"category_scores_gemma":[0.008274626,0.00056577753,0.0008483604,0.00056226546,0.002094205,0.0022971053,0.0037557795,0.00363627,0.0013322738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036639548,0.00015216216,0.0006551816,0.00013800534,0.00008982782,0.00017167804,0.00012000396,0.73222697,0.02320919,0.09243124,0.005543672,0.14489572],"study_design_scores_gemma":[0.000014923292,0.0000680811,0.0000853851,0.000011609156,0.000006295539,0.00007567962,0.000011025752,0.9691692,0.0039632022,0.025422772,0.0011585228,0.000013333606],"about_ca_topic_score_codex":0.00061455806,"about_ca_topic_score_gemma":0.00075337867,"teacher_disagreement_score":0.0025972086,"about_ca_system_score_codex":0.00066827936,"about_ca_system_score_gemma":0.00088092766,"threshold_uncertainty_score":0.012305379},"labels":[],"label_agreement":null},{"id":"W2901827878","doi":"10.48550/arxiv.1811.05381","title":"Sorting out Lipschitz function approximation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lipschitz continuity; Affine transformation; Robustness (evolution); Norm (philosophy); Mathematics; Mathematical optimization; Applied mathematics; Computer science; Pure mathematics","score_opus":0.0708494643085336,"score_gpt":0.20537134867007006,"score_spread":0.13452188436153645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901827878","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029388856,0.00021522825,0.9642023,0.0008699437,0.000052484986,0.000042029053,0.000116541916,0.0009237763,0.0041887606],"genre_scores_gemma":[0.77195466,0.00048862456,0.21413612,0.0009832667,0.00014333555,0.0002839701,0.00055166526,0.0007299092,0.01072848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99758923,0.0007391025,0.00015070803,0.00056291505,0.0007369484,0.00022112817],"domain_scores_gemma":[0.99432904,0.002976641,0.0003964421,0.001666677,0.00046927063,0.00016191011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034324932,0.0014610132,0.0012985002,0.00071213784,0.00083333434,0.001780754,0.0018045022,0.0022400634,0.0042744405],"category_scores_gemma":[0.019595958,0.000751576,0.00109874,0.00055217044,0.0029041185,0.004770013,0.0042124204,0.0037112883,0.0010402908],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032621008,0.00007334782,0.0017693118,0.00021871224,0.000101243844,0.0002877833,0.0002258321,0.54027325,0.012130325,0.3578428,0.0058117,0.08093964],"study_design_scores_gemma":[0.000013606139,0.00005365963,0.00015750971,0.000028677128,0.0000125354,0.000078759775,0.00002103906,0.80055565,0.0057221255,0.19129403,0.0020483297,0.000014042812],"about_ca_topic_score_codex":0.001284406,"about_ca_topic_score_gemma":0.0014270229,"teacher_disagreement_score":0.0042744405,"about_ca_system_score_codex":0.001753605,"about_ca_system_score_gemma":0.0013633118,"threshold_uncertainty_score":0.018153012},"labels":[],"label_agreement":null},{"id":"W2902833081","doi":"10.1609/aaai.v34i07.6722","title":"SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"King Abdullah University of Science and Technology","keywords":"Adversarial system; Adversary; Task (project management); Focus (optics); Deep neural networks; Object (grammar); Autonomous agent; Deep learning","score_opus":0.06996422985742493,"score_gpt":0.3038931894540672,"score_spread":0.23392895959664226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902833081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02405339,0.0004616194,0.9648657,0.0013177883,0.00016387436,0.00013828735,0.00022505881,0.0023989705,0.006375347],"genre_scores_gemma":[0.86656445,0.00030656197,0.12630548,0.0009503683,0.000086794134,0.00024864386,0.00039903718,0.00020678887,0.0049319817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989335,0.00038020583,0.00004660763,0.00016002782,0.0003510472,0.00012870178],"domain_scores_gemma":[0.9977724,0.0013564661,0.00019384513,0.00040296325,0.00018212793,0.00009213427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016004088,0.0010096312,0.00068529765,0.0005284592,0.00047977193,0.000818585,0.0011854989,0.0015770707,0.002825774],"category_scores_gemma":[0.0057031056,0.0003582117,0.00072930247,0.00022526228,0.001919559,0.0016133923,0.0034234864,0.0026724301,0.0006064267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046308574,0.000105305284,0.0015706545,0.00016733159,0.00012397385,0.0002814852,0.000117770425,0.8043097,0.017333722,0.079196535,0.012285943,0.08404448],"study_design_scores_gemma":[0.00001990312,0.00005013417,0.00013864881,0.000014501207,0.0000071036507,0.00007092384,0.000009386589,0.968286,0.0036708864,0.025583532,0.0021387774,0.000010232496],"about_ca_topic_score_codex":0.0009956814,"about_ca_topic_score_gemma":0.001274339,"teacher_disagreement_score":0.002825774,"about_ca_system_score_codex":0.00096210523,"about_ca_system_score_gemma":0.00097004534,"threshold_uncertainty_score":0.009453118},"labels":[],"label_agreement":null},{"id":"W2904990262","doi":"10.1609/aaai.v33i01.33014699","title":"Adversarial Dropout for Recurrent Neural Networks","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Dropout (neural networks); Recurrent neural network; Computer science; MNIST database; Adversarial system; Generalization; Inference; Artificial intelligence; Machine learning; Artificial neural network; Mathematics","score_opus":0.04608459895551688,"score_gpt":0.2972818511481127,"score_spread":0.2511972521925958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904990262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011150923,0.00031311242,0.98650265,0.00035761666,0.00003400896,0.000027275362,0.00006877262,0.0004072077,0.0011384576],"genre_scores_gemma":[0.83532435,0.00070371176,0.15473375,0.00038918378,0.00013072682,0.0002925317,0.0004997886,0.00024376936,0.007682154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987739,0.00048539293,0.00006586235,0.00025324692,0.0003013439,0.000120251265],"domain_scores_gemma":[0.9947916,0.0038645747,0.0004038669,0.00043431646,0.00037916165,0.0001265233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035290483,0.0012397357,0.001134825,0.0005984865,0.0005029148,0.0008381587,0.0015285894,0.0012437814,0.0014318703],"category_scores_gemma":[0.012791566,0.0005611695,0.0007768273,0.0004995762,0.001685598,0.0016356924,0.0018030179,0.0026185936,0.00042491156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000869179,0.000024280684,0.0004305726,0.000050053706,0.000035187135,0.00007314092,0.000044868877,0.95005375,0.0016073412,0.031098265,0.0010522972,0.015443277],"study_design_scores_gemma":[0.0000023338243,0.000008154404,0.00003459528,0.0000027501005,0.0000022421427,0.0000048680613,0.0000014357748,0.9921272,0.00031142373,0.0073514897,0.00015125232,0.0000023286086],"about_ca_topic_score_codex":0.003946432,"about_ca_topic_score_gemma":0.0036176115,"teacher_disagreement_score":0.003946432,"about_ca_system_score_codex":0.0019690401,"about_ca_system_score_gemma":0.0010212867,"threshold_uncertainty_score":0.018663585},"labels":[],"label_agreement":null},{"id":"W2905619401","doi":"10.15353/jcvis.v4i1.336","title":"Guarding Against Adversarial Attacks using Biologically Inspired Contour Integration","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Adversarial system; Robustness (evolution); Computer science; Artificial intelligence; System integration; Computer vision; Biology","score_opus":0.019343367401511623,"score_gpt":0.3128733315685395,"score_spread":0.2935299641670279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905619401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086065866,0.00037782552,0.9073999,0.00048013165,0.00008268132,0.00005271849,0.00002390893,0.0007259485,0.004790993],"genre_scores_gemma":[0.93637747,0.00018519454,0.061158616,0.00023059946,0.000036172343,0.000037391113,0.000038547478,0.00008708615,0.0018489665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993899,0.00014269077,0.00003072159,0.00011912803,0.00023453553,0.00008291824],"domain_scores_gemma":[0.996959,0.0015464019,0.0005118548,0.0005615477,0.00027318244,0.00014796673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013599551,0.0008489289,0.00076969847,0.0005897996,0.0005186415,0.0012183802,0.0011167225,0.0015180157,0.0016670545],"category_scores_gemma":[0.006295442,0.00042008937,0.00054014724,0.00030336823,0.0020922776,0.0017766639,0.0026454306,0.002084329,0.00037700034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020342416,0.00008410202,0.001261211,0.00005047424,0.000082548686,0.00021633419,0.0001401621,0.8562254,0.044543136,0.03370107,0.0012908871,0.062201347],"study_design_scores_gemma":[0.0000069874686,0.00004970843,0.00012712185,0.0000059974195,0.000006690879,0.00004744496,0.000008157439,0.98647493,0.0045213643,0.00836232,0.00038121082,0.00000812831],"about_ca_topic_score_codex":0.00077303126,"about_ca_topic_score_gemma":0.0006098549,"teacher_disagreement_score":0.0016670545,"about_ca_system_score_codex":0.00070515886,"about_ca_system_score_gemma":0.0005324896,"threshold_uncertainty_score":0.0071921945},"labels":[],"label_agreement":null},{"id":"W2906501467","doi":"10.15353/jcvis.v4i1.329","title":"On Robustness of Deep Neural Networks: A Comprehensive Study on the Effect of Architecture and Weight Initialization to Susceptibility and Transferability of Adversarial Attacks","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Robustness (evolution); Initialization; Transferability; Computer science; Adversarial system; Artificial neural network; Network architecture; Artificial intelligence; Network model; Architecture; Machine learning; Data mining; Computer security","score_opus":0.009030126548489809,"score_gpt":0.2898208874482386,"score_spread":0.28079076089974875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906501467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55192065,0.013833754,0.41952714,0.0018514636,0.00035030165,0.0003096478,0.0005405101,0.0017069549,0.009959643],"genre_scores_gemma":[0.97967505,0.0026136388,0.01572123,0.0001517984,0.00007179063,0.000066029424,0.00025392152,0.00016202108,0.0012844583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964574,0.001344149,0.00033812516,0.000525247,0.0009764778,0.00035867942],"domain_scores_gemma":[0.9580013,0.031992782,0.0035273694,0.004369487,0.0016558198,0.00045320878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065094107,0.0022321534,0.0011224974,0.001639439,0.00062065653,0.0012930072,0.0010798733,0.0015460425,0.0018317795],"category_scores_gemma":[0.041471265,0.00065760413,0.0011926645,0.0008387279,0.0018016762,0.0033808707,0.0021706964,0.0026567348,0.00031271062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038990597,0.00015123638,0.003817236,0.00034892996,0.00033432298,0.0001428484,0.00007870051,0.9287296,0.009087333,0.004518242,0.00065832067,0.051743254],"study_design_scores_gemma":[0.000019528881,0.0007374762,0.0031471325,0.00016734032,0.00016526665,0.00022415278,0.000066600915,0.95739007,0.02753893,0.009426382,0.0010626998,0.000054368964],"about_ca_topic_score_codex":0.0022433726,"about_ca_topic_score_gemma":0.0018223458,"teacher_disagreement_score":0.0065094107,"about_ca_system_score_codex":0.0014377862,"about_ca_system_score_gemma":0.0007594603,"threshold_uncertainty_score":0.034425437},"labels":[],"label_agreement":null},{"id":"W2909244445","doi":"10.1145/3278721.3278728","title":"Towards Provably Moral AI Agents in Bottom-up Learning Frameworks","year":2018,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Top-down and bottom-up design; Artificial intelligence; Cognitive science; Programming language; Psychology","score_opus":0.026013956701540056,"score_gpt":0.3166067087152982,"score_spread":0.29059275201375817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909244445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01834635,0.00019590536,0.9643753,0.0033904351,0.00006774145,0.000084789695,0.000051376664,0.0003338608,0.013154308],"genre_scores_gemma":[0.71391314,0.00023977934,0.27792767,0.001211734,0.00017286064,0.00035509808,0.000104936305,0.00024246608,0.005832323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9891509,0.0065364502,0.00033593664,0.001203351,0.0020714852,0.00070191704],"domain_scores_gemma":[0.9680233,0.021832986,0.002338304,0.004293746,0.0023635456,0.0011481289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015674409,0.0012883052,0.0012464046,0.001414686,0.002099861,0.0051106806,0.0032799481,0.004241579,0.0042339307],"category_scores_gemma":[0.049046494,0.00081415445,0.0014436824,0.00077813346,0.0111943865,0.006615051,0.009155268,0.0065032435,0.00079961715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029063143,0.000033250686,0.00034280846,0.00006261203,0.000037601465,0.00007521997,0.0003834596,0.06528025,0.0004574484,0.9264922,0.0005872163,0.006218803],"study_design_scores_gemma":[0.000013583609,0.000020134059,0.000041271138,0.000021464832,0.000009922468,0.000014127494,0.000046728564,0.16707122,0.0004086887,0.8311692,0.0011718988,0.0000116905885],"about_ca_topic_score_codex":0.0014207804,"about_ca_topic_score_gemma":0.0014635504,"teacher_disagreement_score":0.015674409,"about_ca_system_score_codex":0.0031842312,"about_ca_system_score_gemma":0.0026308156,"threshold_uncertainty_score":0.08289522},"labels":[],"label_agreement":null},{"id":"W2918598147","doi":"10.1109/cvpr.2019.01160","title":"A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Convolutional neural network; Artificial intelligence; MNIST database; Pattern recognition (psychology); Segmentation; Computer science; Robustness (evolution); Decision boundary; Deep learning; Support vector machine","score_opus":0.01664981280280637,"score_gpt":0.26650835769509873,"score_spread":0.24985854489229237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918598147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031268768,0.00015889664,0.96550494,0.00021977414,0.00005634092,0.000051235485,0.000039625447,0.001107949,0.0015926143],"genre_scores_gemma":[0.8128466,0.00021533348,0.18105799,0.00020761213,0.00006310376,0.000114102484,0.00014575403,0.00021372864,0.00513577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994524,0.00014266607,0.000026726368,0.00013688226,0.0001867983,0.000054557207],"domain_scores_gemma":[0.998965,0.0003431794,0.00014017358,0.00034611998,0.00014498345,0.000060619463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073401234,0.0010024853,0.00057173736,0.0005066603,0.00033048843,0.00044372134,0.00082291744,0.001073085,0.0019562896],"category_scores_gemma":[0.0037095116,0.00025212206,0.00059088017,0.00029923124,0.0010939763,0.0012245044,0.0017185573,0.0015908183,0.0006674862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024912227,0.00021006227,0.0014336777,0.00014833045,0.00012764394,0.00029029718,0.00020611474,0.60670006,0.093447536,0.047551062,0.005866911,0.24376914],"study_design_scores_gemma":[0.000009270781,0.00009402431,0.00034962615,0.0000082602355,0.000010061861,0.0001518084,0.000011289681,0.9750046,0.015904304,0.0068466673,0.0015958635,0.000014330877],"about_ca_topic_score_codex":0.0006877673,"about_ca_topic_score_gemma":0.00091693376,"teacher_disagreement_score":0.0019562896,"about_ca_system_score_codex":0.0004280758,"about_ca_system_score_gemma":0.0006304423,"threshold_uncertainty_score":0.006544411},"labels":[],"label_agreement":null},{"id":"W2921997616","doi":"10.48550/arxiv.1903.03234","title":"Dyna-AIL : Adversarial Imitation Learning by Planning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Imitation; Computer science; Convergence (economics); Differentiable function; Artificial intelligence; Machine learning; Control (management); State (computer science); Algorithm; Mathematics; Psychology","score_opus":0.04934010721025346,"score_gpt":0.20255240407676814,"score_spread":0.15321229686651466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921997616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040313294,0.00012898567,0.9927816,0.00013580018,0.00003420467,0.000034879897,0.000035406272,0.0009632049,0.001854652],"genre_scores_gemma":[0.662885,0.00030682137,0.3279972,0.00031040134,0.000063135274,0.00033574458,0.00024407823,0.0004667646,0.00739093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994247,0.00021337491,0.000027230608,0.00012387113,0.0001608155,0.000049947186],"domain_scores_gemma":[0.9984187,0.001047945,0.00013688342,0.00020599984,0.000115999515,0.00007442854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013127633,0.00090251025,0.00092165836,0.00044087326,0.0003910176,0.0007461748,0.0018573047,0.0011584355,0.0033919653],"category_scores_gemma":[0.0037528314,0.00047449226,0.00051983306,0.00038614072,0.0014688309,0.0011953999,0.0020941184,0.0021805656,0.0007604934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005209644,0.00003307629,0.00025230338,0.000060479106,0.00003888218,0.00006269251,0.00003756702,0.93429583,0.0015468912,0.02579883,0.001441518,0.036379732],"study_design_scores_gemma":[0.000003471528,0.000011179736,0.00001680016,0.0000026414777,0.0000017742349,0.00000848514,0.0000014914406,0.99364555,0.0003544939,0.0055354135,0.00041595122,0.0000027748902],"about_ca_topic_score_codex":0.002659917,"about_ca_topic_score_gemma":0.0023652462,"teacher_disagreement_score":0.0033919653,"about_ca_system_score_codex":0.0007113771,"about_ca_system_score_gemma":0.001099309,"threshold_uncertainty_score":0.011347234},"labels":[],"label_agreement":null},{"id":"W2923912983","doi":"10.1109/iccv.2019.00496","title":"The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Air Force Office of Scientific Research","keywords":"Adversarial system; MNIST database; Image (mathematics); Computer science; Decision boundary; Logarithm; Benchmark (surveying); Norm (philosophy); Artificial intelligence; Boundary (topology); Perturbation (astronomy); Minification; Machine learning; Pattern recognition (psychology); Mathematics; Deep learning; Support vector machine","score_opus":0.018742412752189692,"score_gpt":0.29572751806008557,"score_spread":0.2769851053078959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923912983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023994602,0.00043338453,0.9683934,0.0008578089,0.00010559152,0.000080747544,0.000078577614,0.0011878923,0.00486798],"genre_scores_gemma":[0.8108175,0.0004569527,0.17975599,0.0009028589,0.0001395361,0.00019243994,0.00023543004,0.00039249513,0.007106769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983279,0.0005971976,0.000059401304,0.00031671074,0.0005410689,0.00015776875],"domain_scores_gemma":[0.9963528,0.0020432826,0.00037573278,0.00088214176,0.00018906387,0.00015704003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019009861,0.0014158512,0.0010167423,0.00068878784,0.00055775564,0.0011622842,0.0016606182,0.0021400729,0.0031550033],"category_scores_gemma":[0.0080780955,0.00045304513,0.00085653993,0.00041173463,0.0026986673,0.0031904473,0.0036634316,0.00357985,0.0008856648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005615087,0.00017148638,0.0012595623,0.00016647586,0.00013810642,0.00031032012,0.0001875378,0.7102653,0.033834707,0.104329206,0.01062009,0.13815577],"study_design_scores_gemma":[0.000020863876,0.000082073224,0.0001536376,0.000021387143,0.000010741493,0.0001253493,0.0000135685905,0.962962,0.007674839,0.026710544,0.0022085982,0.000016464752],"about_ca_topic_score_codex":0.0009973367,"about_ca_topic_score_gemma":0.000921434,"teacher_disagreement_score":0.0031550033,"about_ca_system_score_codex":0.0009335321,"about_ca_system_score_gemma":0.0007734623,"threshold_uncertainty_score":0.0105544925},"labels":[],"label_agreement":null},{"id":"W2927780566","doi":"10.48550/arxiv.1904.00438","title":"Understanding Neural Architecture Search Techniques","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Interpretability; Computer science; Controller (irrigation); Architecture; Computation; Graph; Artificial neural network; Artificial intelligence; Similarity (geometry); Machine learning; ENCODE; Theoretical computer science; Algorithm","score_opus":0.16851964173281778,"score_gpt":0.2283406863077185,"score_spread":0.05982104457490073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927780566","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02545905,0.0010142862,0.96169966,0.0014776057,0.000038262442,0.0000633385,0.0001674745,0.0003707747,0.009709528],"genre_scores_gemma":[0.621035,0.0017747182,0.36924478,0.00041141346,0.00013304086,0.0002872885,0.00050466985,0.0002332269,0.0063758865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990871,0.0002957936,0.000055935514,0.00023608738,0.00024730113,0.00007782173],"domain_scores_gemma":[0.99686664,0.0020070919,0.00026717197,0.0005288376,0.00027293948,0.000057339144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018616758,0.0007731292,0.00050796376,0.0014097496,0.0005158547,0.0016973788,0.0014921466,0.0015983012,0.0054265317],"category_scores_gemma":[0.010010669,0.0005490224,0.0009627619,0.0007108498,0.001756513,0.0042878417,0.0015848593,0.0022397235,0.00066142133],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053764128,0.00005639679,0.0023712346,0.00028693242,0.00009827983,0.00013964796,0.0003857187,0.40795648,0.0043753465,0.47650015,0.0029263531,0.10484965],"study_design_scores_gemma":[0.000009798629,0.000022751989,0.00041146626,0.000035264755,0.000011682885,0.000047467223,0.000042474665,0.69029915,0.0011115831,0.30536327,0.0026348522,0.000010130779],"about_ca_topic_score_codex":0.0020148004,"about_ca_topic_score_gemma":0.001850159,"teacher_disagreement_score":0.0054265317,"about_ca_system_score_codex":0.0012403163,"about_ca_system_score_gemma":0.0007079492,"threshold_uncertainty_score":0.018153548},"labels":[],"label_agreement":null},{"id":"W2935694433","doi":"10.1007/978-3-030-18305-9_24","title":"Sparseout: Controlling Sparsity in Deep Networks","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Dropout (neural networks); Computer science; Overfitting; Regularization (linguistics); Artificial neural network; Artificial intelligence; Deep neural networks; Contextual image classification; Source code; Image (mathematics); Machine learning; Property (philosophy); Code (set theory); Deep learning; Simple (philosophy)","score_opus":0.013196336258663689,"score_gpt":0.23320492622154437,"score_spread":0.2200085899628807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935694433","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055703633,0.00046689832,0.9895845,0.00021277742,0.00012084444,0.000019528914,0.00011015517,0.0012321103,0.0026827254],"genre_scores_gemma":[0.3735291,0.0019408634,0.587787,0.0005692032,0.0004991635,0.00026421304,0.0010027941,0.002009811,0.03239787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999747,0.000073753276,0.000009054636,0.000056971898,0.00008270142,0.000030518277],"domain_scores_gemma":[0.9991709,0.0005619156,0.000048204525,0.00009921072,0.00007379863,0.00004591802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008621998,0.00097100093,0.00073071854,0.00037793393,0.00026475405,0.00089856674,0.0013342528,0.00109497,0.0055983495],"category_scores_gemma":[0.0030923777,0.00057072897,0.0004153621,0.0005658026,0.00089691667,0.0017521698,0.0016062969,0.0024567798,0.0009905569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022336224,0.00012568296,0.0003597961,0.00019136365,0.00006936009,0.0000736338,0.000065929096,0.5654289,0.011344117,0.08036343,0.021999646,0.31975484],"study_design_scores_gemma":[0.000009101171,0.00001953751,0.000033925357,0.000008103932,0.0000050367335,0.000009738603,0.0000032343362,0.96913034,0.0017432328,0.027588466,0.0014448786,0.000004393025],"about_ca_topic_score_codex":0.0015452566,"about_ca_topic_score_gemma":0.0025989185,"teacher_disagreement_score":0.0055983495,"about_ca_system_score_codex":0.00047363213,"about_ca_system_score_gemma":0.0004938963,"threshold_uncertainty_score":0.018728316},"labels":[],"label_agreement":null},{"id":"W2937447982","doi":"10.1109/icassp.2019.8682202","title":"Attacks on Digital Watermarks for Deep Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Digital watermarking; Computer science; Watermark; Artificial intelligence; Artificial neural network; Countermeasure; Deep learning; Embedding; Property (philosophy); Deep neural networks; Task (project management); Scheme (mathematics); Data mining; Machine learning; Computer security; Image (mathematics); Mathematics","score_opus":0.007538449835468789,"score_gpt":0.24131169253418217,"score_spread":0.23377324269871338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937447982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13865903,0.0007516105,0.8511213,0.0011348125,0.00018677003,0.00008210263,0.00016704398,0.0015111364,0.0063863075],"genre_scores_gemma":[0.93588835,0.00051037036,0.060321618,0.00023028719,0.00006133468,0.00004745927,0.00012833327,0.00012128896,0.0026909492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986981,0.00033184854,0.00008888704,0.00019376371,0.00054863235,0.00013873616],"domain_scores_gemma":[0.99516535,0.0026355663,0.0005801523,0.0012545962,0.00029572815,0.00006866407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017466273,0.00079967506,0.0005752561,0.0010332084,0.00039341862,0.0010535312,0.00073508616,0.0015855415,0.0015582192],"category_scores_gemma":[0.014336195,0.000438997,0.0008848335,0.00055217964,0.0017056495,0.0034849308,0.0019637723,0.0016708538,0.00039565194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085791235,0.0001859374,0.0023687426,0.00031274845,0.000201396,0.00067611376,0.0002350654,0.5140906,0.097934455,0.20272256,0.003437083,0.1769773],"study_design_scores_gemma":[0.000020599986,0.000067942725,0.00031217223,0.000036188594,0.000017839368,0.00014017412,0.000013701965,0.9423977,0.02701096,0.028611518,0.0013551624,0.000015915763],"about_ca_topic_score_codex":0.0005642336,"about_ca_topic_score_gemma":0.00045029476,"teacher_disagreement_score":0.0017466273,"about_ca_system_score_codex":0.0011106213,"about_ca_system_score_gemma":0.0003335092,"threshold_uncertainty_score":0.00923717},"labels":[],"label_agreement":null},{"id":"W2943008967","doi":"10.48550/arxiv.1904.13310","title":"Survey of Dropout Methods for Deep Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dropout (neural networks); Artificial neural network; Computer science; Artificial intelligence; Inference; Regularization (linguistics); Convolutional neural network; Machine learning; Deep neural networks; Deep learning","score_opus":0.10044227397292577,"score_gpt":0.27352352266819013,"score_spread":0.17308124869526437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943008967","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002790048,0.067272775,0.91986775,0.0016052179,0.00045676407,0.00009039117,0.00032959195,0.0009016041,0.0066857394],"genre_scores_gemma":[0.21269777,0.20540966,0.5381707,0.0028549416,0.003852626,0.00076302775,0.0028956113,0.0018171982,0.031538446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980773,0.00049521966,0.00016674525,0.00027128684,0.00088940415,0.000100101875],"domain_scores_gemma":[0.99770457,0.0013193508,0.0001561876,0.00029524125,0.00045136252,0.00007333705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004356591,0.0017777965,0.0022302957,0.0014203165,0.0005773574,0.0018335902,0.0027750165,0.001981816,0.004206546],"category_scores_gemma":[0.009062241,0.0009871019,0.0015503096,0.0018284521,0.0013202727,0.0030647626,0.002776826,0.0039344393,0.0018607706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027328773,0.00013684685,0.0010902093,0.0020471897,0.00034256064,0.0001710677,0.00016294746,0.15878522,0.0028277393,0.14328177,0.023856783,0.6670243],"study_design_scores_gemma":[0.000058057474,0.00017456063,0.00083363237,0.0008190747,0.00013140876,0.0003027324,0.000039242463,0.7604631,0.0061749723,0.1356641,0.09527036,0.000068677866],"about_ca_topic_score_codex":0.002765696,"about_ca_topic_score_gemma":0.0022045707,"teacher_disagreement_score":0.004356591,"about_ca_system_score_codex":0.001815746,"about_ca_system_score_gemma":0.0017411147,"threshold_uncertainty_score":0.023040116},"labels":[],"label_agreement":null},{"id":"W2944839767","doi":"10.1007/978-3-030-18305-9_36","title":"Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Overfitting; Adversarial system; Computer science; Regularization (linguistics); Deep neural networks; Artificial intelligence; Machine learning; Artificial neural network; Sensitivity (control systems); Mathematical optimization; Mathematics; Engineering","score_opus":0.018819987075397386,"score_gpt":0.2516263882658365,"score_spread":0.2328064011904391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944839767","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015615808,0.0004966689,0.9795806,0.00041894897,0.00007260446,0.000024535211,0.000030568714,0.00042043027,0.0033398308],"genre_scores_gemma":[0.7778766,0.0010287107,0.20658424,0.0005360892,0.00029452294,0.00014069935,0.00021346188,0.00038956615,0.012936115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986511,0.00049606327,0.000041439118,0.00027855436,0.00040704408,0.00012580657],"domain_scores_gemma":[0.9946831,0.0035667026,0.00041895328,0.00081052794,0.0003886123,0.00013217305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027765897,0.0015624334,0.0012517292,0.0009250402,0.00047268567,0.0011251384,0.0018257031,0.0021339192,0.0018989053],"category_scores_gemma":[0.010392345,0.0006824599,0.0008290799,0.00058832724,0.0019830936,0.0022797382,0.0034795532,0.0043751854,0.0006642102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012112228,0.00006853251,0.00058725243,0.00011498859,0.0001126985,0.00010549944,0.00009690965,0.82075745,0.011972338,0.058303237,0.0039666076,0.10379345],"study_design_scores_gemma":[0.0000032543066,0.0000344141,0.0000808048,0.000011621712,0.000009728193,0.000043620614,0.000005601326,0.97767866,0.0014969697,0.020009467,0.00061921845,0.00000662026],"about_ca_topic_score_codex":0.0007355108,"about_ca_topic_score_gemma":0.00089456065,"teacher_disagreement_score":0.0027765897,"about_ca_system_score_codex":0.0007820257,"about_ca_system_score_gemma":0.0004816481,"threshold_uncertainty_score":0.0146842},"labels":[],"label_agreement":null},{"id":"W2947994715","doi":"10.48550/arxiv.1905.12797","title":"Bandlimiting Neural Networks Against Adversarial Attacks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Adversarial system; Computer security; Computer science; Artificial neural network; Deep neural networks; Artificial intelligence","score_opus":0.04896313561997109,"score_gpt":0.19816384203607032,"score_spread":0.14920070641609923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947994715","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06072768,0.0004868328,0.9338112,0.00043151298,0.00007620991,0.00003434673,0.000043771262,0.0008210342,0.0035674048],"genre_scores_gemma":[0.9277029,0.00029565167,0.068751566,0.00021662541,0.00005884697,0.00006249941,0.00006823836,0.000101023805,0.0027427052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906033,0.00026315782,0.000041526615,0.00017562954,0.00030954293,0.00014977528],"domain_scores_gemma":[0.99716455,0.0018122058,0.00025267564,0.0005127397,0.00018212933,0.00007571787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018577069,0.0010694325,0.00094356126,0.0006803277,0.0003926539,0.0007617815,0.00097421644,0.0013182567,0.0017600952],"category_scores_gemma":[0.008020779,0.00035834234,0.0004761474,0.00031877297,0.0019703938,0.0018083097,0.0022993817,0.0019835522,0.00047377817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021208782,0.000048988535,0.0007083003,0.00010312153,0.00007445863,0.00019202738,0.00008594428,0.8485298,0.023844413,0.046510614,0.0015502764,0.07813992],"study_design_scores_gemma":[0.0000044483886,0.00003378556,0.000113959424,0.000008817164,0.0000053521408,0.000047081394,0.000008794968,0.9805797,0.0049543725,0.013818066,0.00041948684,0.000006120596],"about_ca_topic_score_codex":0.00062297285,"about_ca_topic_score_gemma":0.0005420594,"teacher_disagreement_score":0.0018577069,"about_ca_system_score_codex":0.00072067184,"about_ca_system_score_gemma":0.00044806523,"threshold_uncertainty_score":0.009824634},"labels":[],"label_agreement":null},{"id":"W2950088694","doi":"10.48550/arxiv.1807.02905","title":"Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Pooling; Adversarial system; Deep learning; Computer science; Artificial intelligence; Vulnerability (computing); Task (project management); Image (mathematics); Pattern recognition (psychology); Contextual image classification; Machine learning; Computer security; Engineering","score_opus":0.0740751252021232,"score_gpt":0.24118066841364488,"score_spread":0.16710554321152168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950088694","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79144585,0.0017126715,0.19991755,0.0015110451,0.00016470355,0.00010889397,0.00043700798,0.0012477387,0.003454444],"genre_scores_gemma":[0.98844236,0.00020352956,0.010453669,0.0000967119,0.000024051395,0.000018810117,0.00018033767,0.0000345052,0.00054589123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856156,0.00052684033,0.00008064262,0.00021686265,0.0004380495,0.00017606109],"domain_scores_gemma":[0.99336445,0.0043794215,0.00071259565,0.00084374985,0.0005111899,0.00018855596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023199255,0.0007032707,0.00050074473,0.0009295417,0.00028345606,0.00049640797,0.00054766325,0.0008272238,0.00076577556],"category_scores_gemma":[0.010969309,0.00022588772,0.0005274528,0.00034097355,0.0010563985,0.0010112793,0.001183756,0.0011353808,0.00019896617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008278426,0.00011632492,0.013666577,0.00016905495,0.00025539027,0.00040788332,0.00013480525,0.8838604,0.021859689,0.0056615197,0.0030768088,0.06996368],"study_design_scores_gemma":[0.000005478093,0.00008671348,0.0030827797,0.000015488948,0.000021625487,0.00012878948,0.000018035216,0.9842827,0.01009429,0.0019319638,0.00032023693,0.00001200976],"about_ca_topic_score_codex":0.0013300355,"about_ca_topic_score_gemma":0.00085047213,"teacher_disagreement_score":0.0023199255,"about_ca_system_score_codex":0.00088526064,"about_ca_system_score_gemma":0.00037106816,"threshold_uncertainty_score":0.012269139},"labels":[],"label_agreement":null},{"id":"W2951873722","doi":"","title":"Learning deep representations by mutual information estimation and maximization","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":514,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Infomax; Artificial intelligence; Mutual information; Computer science; Unsupervised learning; Locality; Representation (politics); Machine learning; Feature learning; Matching (statistics); Maximization; Artificial neural network; Deep learning; Autoencoder; Pattern recognition (psychology); Mathematics","score_opus":0.025279241044255663,"score_gpt":0.1959921560152496,"score_spread":0.17071291497099395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951873722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067611244,0.00014669047,0.99136084,0.0002575588,0.000015138653,0.000028017812,0.000053182463,0.00035276372,0.0010246576],"genre_scores_gemma":[0.6374412,0.0004898856,0.3554456,0.0005271453,0.00014557397,0.00031169032,0.00051810226,0.0004684558,0.0046522645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983045,0.0008109275,0.00007632128,0.0003617293,0.00032464066,0.00012187652],"domain_scores_gemma":[0.99601847,0.0025075122,0.0005171882,0.0005667158,0.00027606214,0.000114009505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034246647,0.0016357874,0.0012761281,0.00088872324,0.00048045957,0.0014978731,0.0020897274,0.0017080659,0.0017681968],"category_scores_gemma":[0.010800257,0.00080354855,0.0009151546,0.0008778258,0.0025007415,0.0034393328,0.0033561906,0.0029241394,0.0007477746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012127494,0.00008604168,0.0007184874,0.00014650846,0.0001206589,0.00005768869,0.000090738446,0.8244452,0.0044143507,0.081319444,0.0029789396,0.08550062],"study_design_scores_gemma":[0.000005523752,0.000024920582,0.00006725837,0.000011513641,0.0000070372485,0.000014154446,0.0000052529467,0.9590052,0.0016617833,0.03877568,0.00041327244,0.000008466964],"about_ca_topic_score_codex":0.0011691655,"about_ca_topic_score_gemma":0.0014186815,"teacher_disagreement_score":0.0034246647,"about_ca_system_score_codex":0.0015765205,"about_ca_system_score_gemma":0.0013627717,"threshold_uncertainty_score":0.018111527},"labels":[],"label_agreement":null},{"id":"W2952193948","doi":"10.1109/icpr48806.2021.9412010","title":"Meta Learning via Learned Loss","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"International Max Planck Research School for Advanced Methods in Process and Systems Engineering; York University; European Commission; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; National Science Foundation","keywords":"Computer science; Reinforcement learning; Machine learning; Artificial intelligence; Parametric statistics; Regularization (linguistics); Pipeline (software); Code (set theory); Set (abstract data type); Meta learning (computer science); Process (computing); Source code; Function (biology); Task (project management); Engineering","score_opus":0.05842071702153865,"score_gpt":0.3002474851927516,"score_spread":0.24182676817121293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952193948","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009954039,0.0005150357,0.9845947,0.00048018884,0.000056254048,0.00007357946,0.0001297911,0.0016635368,0.0025328232],"genre_scores_gemma":[0.64529806,0.0007204275,0.34356824,0.00095484743,0.00021960738,0.0007461486,0.0009485102,0.0011490572,0.0063951523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972288,0.0012407758,0.00014175152,0.00055725273,0.0005829264,0.00024846202],"domain_scores_gemma":[0.9936892,0.0034790935,0.0005074064,0.0015824984,0.0005241871,0.0002176315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054207677,0.00269876,0.0019986378,0.0013005697,0.0005781074,0.0025616297,0.003942176,0.0030155398,0.00450277],"category_scores_gemma":[0.018700313,0.0012521311,0.0013478113,0.0008851869,0.0024134538,0.00449426,0.0048344336,0.005230918,0.0020229425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017105303,0.00011958064,0.001113616,0.00014724629,0.00014542982,0.00008944551,0.000057978603,0.8832222,0.0021558097,0.032843772,0.0038143694,0.07611933],"study_design_scores_gemma":[0.000014063787,0.00005071369,0.000059633046,0.00002570123,0.000012507875,0.000026443462,0.0000063084026,0.9728032,0.0011024895,0.025285814,0.00060491427,0.000008289772],"about_ca_topic_score_codex":0.00087691535,"about_ca_topic_score_gemma":0.0012399306,"teacher_disagreement_score":0.0054207677,"about_ca_system_score_codex":0.0018690118,"about_ca_system_score_gemma":0.0015027677,"threshold_uncertainty_score":0.028668165},"labels":[],"label_agreement":null},{"id":"W2952348804","doi":"10.1145/3437880.3460401","title":"On the Robustness of Backdoor-based Watermarking in Deep Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backdoor; Digital watermarking; Watermark; Robustness (evolution); Computer science; Deep learning; Black box; Artificial neural network; Artificial intelligence; White box; Deep neural networks; Set (abstract data type); Computer security; Data mining; Machine learning; Embedding; Image (mathematics)","score_opus":0.018353766674160808,"score_gpt":0.24953403892629938,"score_spread":0.23118027225213858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952348804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19794779,0.0019242568,0.79136574,0.0014135218,0.00013819666,0.00007826197,0.00013898703,0.0012592763,0.0057340357],"genre_scores_gemma":[0.9618256,0.00062815414,0.035473302,0.0001436479,0.00007614189,0.00003932111,0.000071687195,0.00011813359,0.0016239893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99651647,0.0012608137,0.00020156347,0.00058718224,0.0010047503,0.00042916054],"domain_scores_gemma":[0.9708911,0.020482976,0.00242493,0.004922808,0.00096565473,0.0003124876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048492257,0.0013045607,0.00095850363,0.0013171053,0.0006650181,0.0018529252,0.0013481638,0.0023525758,0.001972025],"category_scores_gemma":[0.03498083,0.00066639995,0.0009360451,0.00078153884,0.0049615083,0.005744402,0.0038156267,0.0033088024,0.0004277992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000873549,0.00012400682,0.0016160257,0.00024967975,0.0002056579,0.00024535143,0.00024730168,0.7677126,0.031759623,0.11361815,0.0013644114,0.08198364],"study_design_scores_gemma":[0.000020842628,0.0001331405,0.00021052579,0.000037539816,0.000023916324,0.00009057564,0.000024785628,0.9352448,0.019407224,0.04425884,0.00052087574,0.000026956808],"about_ca_topic_score_codex":0.0005377549,"about_ca_topic_score_gemma":0.00033726086,"teacher_disagreement_score":0.0048492257,"about_ca_system_score_codex":0.001253683,"about_ca_system_score_gemma":0.0006221377,"threshold_uncertainty_score":0.025645435},"labels":[],"label_agreement":null},{"id":"W2952911150","doi":"10.48550/arxiv.1808.02651","title":"Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically\\n Differentiable Renderer","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Differentiable function; Norm (philosophy); Parametric statistics; Computer science; Pixel; Mathematics; Pure mathematics; Computer vision; Political science; Law","score_opus":0.1084304749016775,"score_gpt":0.233256151275525,"score_spread":0.12482567637384749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952911150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008210461,0.00018059854,0.9854801,0.000500764,0.00006003025,0.000036962512,0.000040131035,0.0007817986,0.004709061],"genre_scores_gemma":[0.70854753,0.0006007477,0.27421793,0.0007383135,0.00015332935,0.0001835674,0.00019497126,0.0007436529,0.0146200545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868554,0.0004493825,0.00004067888,0.0002504723,0.00047728268,0.00009659819],"domain_scores_gemma":[0.997778,0.0011970461,0.00021866315,0.0005909414,0.00011479895,0.00010058797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017300863,0.0010292287,0.0007125027,0.0003774567,0.00048614925,0.0016468993,0.0014679933,0.0014819235,0.003424712],"category_scores_gemma":[0.0065581105,0.0004697633,0.00075020996,0.00023657997,0.003185035,0.002616617,0.0043393853,0.0040286668,0.0008395519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019087641,0.000055917015,0.00073324813,0.00009684073,0.0000490335,0.00015174792,0.00017253523,0.70497644,0.013953401,0.19927411,0.0037987668,0.076547034],"study_design_scores_gemma":[0.000009236511,0.000043399556,0.000100100246,0.000017616836,0.0000052093797,0.00005339016,0.000012355339,0.9453445,0.0045307647,0.046973743,0.0028975261,0.000012141314],"about_ca_topic_score_codex":0.0010096495,"about_ca_topic_score_gemma":0.0009614038,"teacher_disagreement_score":0.003424712,"about_ca_system_score_codex":0.0011911052,"about_ca_system_score_gemma":0.0006906096,"threshold_uncertainty_score":0.011456788},"labels":[],"label_agreement":null},{"id":"W2953355478","doi":"10.1016/j.neunet.2022.07.012","title":"Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy","year":2022,"lang":"en","type":"article","venue":"Neural Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Mathematical Sciences; Compute Canada; Harvard University","keywords":"Adversarial system; Robustness (evolution); Computer science; Artificial intelligence; Deep neural networks; Machine learning; Adversary; Artificial neural network; Training set; Generalization; Algorithm; Mathematics; Computer security","score_opus":0.029092826850136415,"score_gpt":0.2639222751859434,"score_spread":0.234829448335807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953355478","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01412594,0.00028262628,0.98201513,0.00026707022,0.00005249474,0.000033639113,0.00006927932,0.0008131367,0.002340599],"genre_scores_gemma":[0.76537853,0.0004135202,0.22933581,0.00051351934,0.0000937332,0.00013669742,0.00027831833,0.00026758123,0.0035822925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999065,0.00028260562,0.000045115976,0.00021292517,0.0002707147,0.00012371535],"domain_scores_gemma":[0.9975292,0.0013145463,0.00024256294,0.0005849712,0.0002367172,0.000091947346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019112625,0.0016029113,0.0009728043,0.00053526164,0.00050551,0.0006211737,0.0018452245,0.0012774598,0.002412307],"category_scores_gemma":[0.0071955314,0.0004325527,0.0007170987,0.00047729755,0.0017985471,0.0018708309,0.002807238,0.0030105452,0.00066075724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001457501,0.00004612764,0.00077468116,0.000074685064,0.00006155788,0.000117876465,0.00005807012,0.9039257,0.0086277705,0.019421687,0.0022735633,0.06447252],"study_design_scores_gemma":[0.0000050799235,0.000039733375,0.00009827288,0.000011837167,0.0000076051256,0.000051092473,0.0000054191864,0.98577684,0.0040009697,0.009409828,0.0005846783,0.000008628559],"about_ca_topic_score_codex":0.0021587526,"about_ca_topic_score_gemma":0.0022788784,"teacher_disagreement_score":0.002412307,"about_ca_system_score_codex":0.00076596683,"about_ca_system_score_gemma":0.0007491676,"threshold_uncertainty_score":0.010107875},"labels":[],"label_agreement":null},{"id":"W2954903132","doi":"10.1109/icse.2019.00107","title":"CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries","year":2019,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":191,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; MNIST database; Implementation; Deep learning; Artificial intelligence; Software bug; Software; Reliability (semiconductor); Machine learning; Anomaly detection; Key (lock); Software engineering; Programming language; Operating system","score_opus":0.007898350116497276,"score_gpt":0.26343674162655745,"score_spread":0.2555383915100602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954903132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33158746,0.0044218805,0.53758353,0.0013031362,0.0005909204,0.0005423759,0.003085834,0.11668042,0.004204518],"genre_scores_gemma":[0.82132816,0.00026041205,0.16449963,0.0011540699,0.00007515705,0.00035520355,0.0075225825,0.002526735,0.0022781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99409,0.0022612456,0.00047512425,0.0015436572,0.0011993981,0.00043058707],"domain_scores_gemma":[0.9759797,0.014088956,0.0015722725,0.0049936967,0.0027774486,0.00058793963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010932743,0.0033693288,0.0011102136,0.0028366498,0.0007240705,0.0013928468,0.004515148,0.002304416,0.0023671472],"category_scores_gemma":[0.038485005,0.00097176264,0.0013419598,0.0011024042,0.0020736824,0.0027724141,0.0031234554,0.0030459578,0.0012748531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011038673,0.0007789735,0.038275126,0.0006850408,0.0009051965,0.0004257143,0.00021735212,0.6536398,0.01172243,0.0039424314,0.032385062,0.25591892],"study_design_scores_gemma":[0.00006694255,0.00014283147,0.000840531,0.000033831526,0.000035221587,0.00004635313,0.000023039674,0.9912566,0.0043060365,0.0023198612,0.00091368647,0.0000150369215],"about_ca_topic_score_codex":0.010359188,"about_ca_topic_score_gemma":0.015158402,"teacher_disagreement_score":0.010932743,"about_ca_system_score_codex":0.001726898,"about_ca_system_score_gemma":0.0035164692,"threshold_uncertainty_score":0.05781859},"labels":[],"label_agreement":null},{"id":"W2963068442","doi":"10.1109/cvpr.2019.00443","title":"Adversarial Attacks Beyond the Image Space","year":2019,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Rendering (computer graphics); Computer science; Artificial intelligence; Differentiable function; Adversarial system; Pixel; Computer vision; Space (punctuation); Artificial neural network; Image (mathematics); Image translation; Translation (biology); Mathematics","score_opus":0.004341876323211905,"score_gpt":0.2323319089576488,"score_spread":0.2279900326344369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963068442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03290681,0.00039275692,0.9561478,0.0014206921,0.00014086085,0.0000699239,0.00013905844,0.00066009775,0.008121971],"genre_scores_gemma":[0.9016285,0.00047855303,0.089765966,0.00093626423,0.00014632443,0.00012842665,0.0002442755,0.00021100158,0.0064607724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985752,0.00053033186,0.000054644177,0.00027230993,0.0004127917,0.0001546663],"domain_scores_gemma":[0.99540037,0.0031118956,0.00033362294,0.0008879345,0.00017114267,0.000095043186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015940404,0.001198782,0.00084969454,0.0003119709,0.0004468739,0.0010829355,0.0011461246,0.0014150802,0.0026844023],"category_scores_gemma":[0.007538892,0.0004056863,0.0007326391,0.00024370525,0.002546586,0.0025602838,0.003379828,0.004079331,0.00055186974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019869085,0.000051552972,0.00081017666,0.000096539115,0.00009569983,0.00024078261,0.00011114064,0.80461407,0.011128712,0.14301418,0.0041738916,0.035464507],"study_design_scores_gemma":[0.0000127802095,0.00004843884,0.00018371455,0.000023672877,0.000009909622,0.00009862006,0.000020523483,0.91028446,0.0043770866,0.08232595,0.0026009134,0.000013992635],"about_ca_topic_score_codex":0.00095058867,"about_ca_topic_score_gemma":0.0008345163,"teacher_disagreement_score":0.0026844023,"about_ca_system_score_codex":0.0007677023,"about_ca_system_score_gemma":0.000509955,"threshold_uncertainty_score":0.008980274},"labels":[],"label_agreement":null},{"id":"W2963080758","doi":"10.1609/aaai.v32i1.11634","title":"Adversarial Dropout for Supervised and Semi-Supervised Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Adversarial system; Regularization (linguistics); Generality; MNIST database; Computer science; Machine learning; Artificial intelligence; Artificial neural network; Dropout (neural networks); Generalization; Supervised learning; Mathematics","score_opus":0.015018980569141726,"score_gpt":0.2662634749676445,"score_spread":0.25124449439850277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963080758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057377173,0.0004280373,0.9921251,0.00024490218,0.00003929208,0.000036088742,0.000053122447,0.0003417669,0.0009940166],"genre_scores_gemma":[0.6982292,0.0012576331,0.29464588,0.000500353,0.0003000113,0.00038802656,0.0005143444,0.00024264303,0.0039218906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973551,0.0011988963,0.00015184804,0.00054291403,0.00060392765,0.00014734075],"domain_scores_gemma":[0.9936387,0.0041370606,0.00057763717,0.0010174746,0.00046174147,0.00016737988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00517153,0.0014431635,0.0013760902,0.00078526733,0.00057268713,0.0009905711,0.002042722,0.0016211856,0.0015455134],"category_scores_gemma":[0.011591155,0.0005640553,0.0010521745,0.00076468434,0.0026625355,0.0023154092,0.0023695966,0.0036893426,0.00036355524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014914734,0.000082290586,0.0007694911,0.00022438212,0.00012300085,0.00013943181,0.00012679584,0.83147466,0.004043713,0.0925639,0.0028286178,0.067474544],"study_design_scores_gemma":[0.000005333024,0.00002417324,0.00009067476,0.000009967269,0.000005370665,0.000019939833,0.000003147997,0.9754954,0.0010526942,0.022737931,0.0005486041,0.0000068071804],"about_ca_topic_score_codex":0.0014618426,"about_ca_topic_score_gemma":0.0014684547,"teacher_disagreement_score":0.00517153,"about_ca_system_score_codex":0.0015993387,"about_ca_system_score_gemma":0.001090352,"threshold_uncertainty_score":0.027349949},"labels":[],"label_agreement":null},{"id":"W2963096987","doi":"","title":"A closer look at memorization in deep networks","year":2017,"lang":"en","type":"article","venue":"Jagiellonian University Repository (Jagiellonian University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":654,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Canadian Institute for Advanced Research; McGill University; Université de Montréal; Concordia University","funders":"Samsung; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Samsung Advanced Institute of Technology; Canadian Institute for Advanced Research","keywords":"Memorization; Deep neural networks; Computer science; Artificial intelligence; Generalization; Deep learning; Robustness (evolution); Regularization (linguistics); Artificial neural network; Machine learning; Adversarial system; Noise (video); Mathematics; Cognitive psychology; Psychology","score_opus":0.006018646023883206,"score_gpt":0.18839845671215769,"score_spread":0.18237981068827447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963096987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2759846,0.0054545146,0.67357713,0.0114311855,0.00038898172,0.00012952086,0.00034090437,0.0010553679,0.031637862],"genre_scores_gemma":[0.95801914,0.0013370602,0.03444114,0.00060513214,0.00021745838,0.000057795856,0.00009200166,0.00016145346,0.00506886],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989477,0.00033873136,0.000049797814,0.00023193732,0.00027539744,0.00015649933],"domain_scores_gemma":[0.99142414,0.0049044564,0.0010971682,0.0016588761,0.00061221945,0.00030319582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025435453,0.0007706702,0.00066510844,0.0010979578,0.0006081768,0.0021632225,0.00169985,0.0012047517,0.004687249],"category_scores_gemma":[0.018686365,0.00045336696,0.0007231068,0.00058454234,0.003581737,0.0093623055,0.0025896379,0.0040681898,0.0003588406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048221953,0.00030889284,0.0078862235,0.000603525,0.0002268379,0.00063356094,0.0023207697,0.35233578,0.04811896,0.4395513,0.005027211,0.14250472],"study_design_scores_gemma":[0.00003713558,0.000551046,0.0057932483,0.00027499194,0.000061753875,0.0005095727,0.00045015698,0.51408106,0.02767316,0.44000027,0.010477426,0.00009024544],"about_ca_topic_score_codex":0.0011606175,"about_ca_topic_score_gemma":0.0009420256,"teacher_disagreement_score":0.004687249,"about_ca_system_score_codex":0.0013730433,"about_ca_system_score_gemma":0.00046343822,"threshold_uncertainty_score":0.015680432},"labels":[],"label_agreement":null},{"id":"W2963489463","doi":"","title":"GibbsNet: Iterative Adversarial Inference for Deep Graphical Models","year":2017,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Latent variable; Inference; Computer science; Joint probability distribution; Sampling (signal processing); Latent class model; Graphical model; Conditional probability distribution; Algorithm; Variable (mathematics); Artificial intelligence; Gibbs sampling; Flexibility (engineering); Theoretical computer science; Mathematical optimization; Machine learning; Mathematics; Statistics","score_opus":0.032470172596978826,"score_gpt":0.3099848913903102,"score_spread":0.2775147187933314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963489463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020319489,0.00011883745,0.9929811,0.00015784701,0.000029988758,0.000038701666,0.00015726777,0.002923288,0.0015609451],"genre_scores_gemma":[0.27544484,0.00042017383,0.70904857,0.00071353663,0.00012522725,0.00053793273,0.0017933289,0.0021904048,0.009726034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992674,0.00029580103,0.000026570679,0.00014731976,0.00019153158,0.00007140957],"domain_scores_gemma":[0.9985127,0.0010413316,0.000070650596,0.00020427177,0.000096452044,0.00007457502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001563211,0.0015743934,0.001125765,0.0007943775,0.000524969,0.001235379,0.0030704513,0.0016736742,0.008497616],"category_scores_gemma":[0.0054847184,0.001115917,0.0013003424,0.0006716698,0.0015554746,0.0022359937,0.002732596,0.003969386,0.0021066975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108221204,0.00005623466,0.00052839797,0.00009924318,0.000079487574,0.00009579934,0.00006997048,0.84773546,0.0010454106,0.07418851,0.007904351,0.068088904],"study_design_scores_gemma":[0.000006517313,0.000004574652,0.0000160787,0.000005723264,0.0000025408872,0.000007272197,0.0000025109496,0.9763566,0.00024620243,0.022550132,0.0007990397,0.0000027442063],"about_ca_topic_score_codex":0.0062325792,"about_ca_topic_score_gemma":0.016379561,"teacher_disagreement_score":0.008497616,"about_ca_system_score_codex":0.0017686371,"about_ca_system_score_gemma":0.0015791429,"threshold_uncertainty_score":0.028427362},"labels":[],"label_agreement":null},{"id":"W2963564844","doi":"10.1145/3128572.3140444","title":"Adversarial Examples Are Not Easily Detected","year":2017,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1415,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Multidisciplinary University Research Initiative; Canadian Institute for Advanced Research; William and Flora Hewlett Foundation","keywords":"Adversarial system; Computer science; Artificial intelligence; Simple (philosophy); Deep neural networks; Space (punctuation); Artificial neural network; Machine learning; Theoretical computer science; Epistemology","score_opus":0.036496187256260824,"score_gpt":0.2836542017279437,"score_spread":0.24715801447168287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963564844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07236969,0.0015683437,0.9037754,0.004134302,0.00044074576,0.00020572152,0.0002583293,0.0014852447,0.015762232],"genre_scores_gemma":[0.8898156,0.0011150099,0.10280018,0.0016739687,0.00029323404,0.0002123997,0.00036924146,0.0002659829,0.0034542617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918064,0.0028911668,0.00039905042,0.0014739452,0.0028485018,0.00058104587],"domain_scores_gemma":[0.93902,0.04324694,0.0046774196,0.010270053,0.0021003229,0.0006853228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007169367,0.0013584801,0.0015445175,0.0010403234,0.0010894227,0.0028059965,0.00226083,0.0037000477,0.0024797914],"category_scores_gemma":[0.058585007,0.0008152102,0.0011671826,0.00060162373,0.0047345036,0.00600921,0.004297606,0.0058389446,0.0010672706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006970602,0.0002157993,0.009345509,0.0008474126,0.0005104699,0.00091303006,0.00043627544,0.40027052,0.02665229,0.33508182,0.015764311,0.2092655],"study_design_scores_gemma":[0.00005892958,0.0003443548,0.0021677846,0.0002823569,0.000102405764,0.0020918217,0.00018003704,0.66021115,0.026936198,0.29494557,0.012590988,0.00008837481],"about_ca_topic_score_codex":0.00033897153,"about_ca_topic_score_gemma":0.0003532658,"teacher_disagreement_score":0.007169367,"about_ca_system_score_codex":0.0010335727,"about_ca_system_score_gemma":0.0007646628,"threshold_uncertainty_score":0.037915707},"labels":[],"label_agreement":null},{"id":"W2963682248","doi":"","title":"Reinterpreting Importance-Weighted Autoencoders","year":2017,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Upper and lower bounds; Interpretation (philosophy); Computer science; Marginal distribution; Artificial intelligence; Distribution (mathematics); Algorithm; Mathematics; Pattern recognition (psychology); Statistics; Random variable","score_opus":0.04670941002473038,"score_gpt":0.3662012404771157,"score_spread":0.31949183045238533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963682248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006738915,0.00018447192,0.98935086,0.00026261812,0.0000686601,0.000015787877,0.000034692464,0.00010415002,0.0032398594],"genre_scores_gemma":[0.60883147,0.00085235114,0.37683627,0.0005100702,0.00033602904,0.00014452994,0.00024041769,0.00044834326,0.011800517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864227,0.00044841095,0.00007432475,0.00025621982,0.0004487104,0.0001301474],"domain_scores_gemma":[0.99684334,0.0017483091,0.0002735024,0.0005307128,0.0004816942,0.00012236173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031273137,0.0011955468,0.0007647404,0.0010203287,0.00038166466,0.0018109736,0.0016959397,0.0015993835,0.0032047294],"category_scores_gemma":[0.011908533,0.00068870455,0.00062027626,0.0007149316,0.0019781422,0.003371305,0.0030203264,0.0031047713,0.0006458681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052104926,0.000045720426,0.0005308836,0.00011123766,0.000054079836,0.00020079323,0.00012765508,0.35830978,0.0053682355,0.5853602,0.0017269829,0.048112266],"study_design_scores_gemma":[0.000008286458,0.000021148875,0.00013444896,0.000027499336,0.000012615041,0.000050814913,0.000011222339,0.7692196,0.0015593711,0.22741166,0.0015324987,0.0000109542225],"about_ca_topic_score_codex":0.0015205931,"about_ca_topic_score_gemma":0.0027087631,"teacher_disagreement_score":0.0032047294,"about_ca_system_score_codex":0.0009877862,"about_ca_system_score_gemma":0.0008854298,"threshold_uncertainty_score":0.016538978},"labels":[],"label_agreement":null},{"id":"W2963693747","doi":"10.1109/cvpr.2019.00445","title":"Decoupling direction and norm for efficient gradient-based L2 adversarial attacks and defenses","year":2019,"lang":"","type":"article","venue":"Espace ÉTS (ETS)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":252,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"MNIST database; Adversarial system; Norm (philosophy); Computer science; Decoupling (probability); Robustness (evolution); Artificial intelligence; Perturbation (astronomy); Algorithm; Pattern recognition (psychology); Machine learning; Mathematical optimization; Deep learning; Mathematics; Engineering","score_opus":0.011180411051507836,"score_gpt":0.2603883496451301,"score_spread":0.24920793859362225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963693747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010024589,0.00027193464,0.9863316,0.00025290038,0.000050088343,0.00005539058,0.000035710495,0.0006297137,0.0023481185],"genre_scores_gemma":[0.65034854,0.0005172004,0.34202757,0.0005140351,0.00016035557,0.00031347616,0.0002736983,0.00034630927,0.0054987776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981201,0.0007376198,0.000099017496,0.00026889678,0.00059486297,0.000179497],"domain_scores_gemma":[0.9976476,0.001298534,0.00024052028,0.00047968116,0.00022190735,0.00011177618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002323513,0.0018455216,0.0012124556,0.00096034195,0.00046709154,0.0010367602,0.0011452738,0.0015813035,0.0025724594],"category_scores_gemma":[0.007901757,0.00054787286,0.00082822476,0.000538032,0.0019797375,0.002181844,0.0036852502,0.0034005176,0.0013538006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036508357,0.00015109214,0.000639899,0.00012405787,0.00008522471,0.0001651962,0.00010885187,0.743817,0.023520866,0.07868282,0.005073653,0.14726622],"study_design_scores_gemma":[0.000013730368,0.000069109694,0.00008252312,0.000010862775,0.0000058173864,0.000071774426,0.000010006226,0.97428346,0.0037724879,0.02065257,0.0010147723,0.000012949415],"about_ca_topic_score_codex":0.00062633597,"about_ca_topic_score_gemma":0.0007486698,"teacher_disagreement_score":0.0025724594,"about_ca_system_score_codex":0.0006405483,"about_ca_system_score_gemma":0.0008994113,"threshold_uncertainty_score":0.012288034},"labels":[],"label_agreement":null},{"id":"W2964155212","doi":"","title":"Adversarial Distillation of Bayesian Neural Network Posteriors","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Bayesian probability; Artificial neural network; Posterior probability; Distillation; Artificial intelligence; Machine learning; Markov chain Monte Carlo; Langevin dynamics; Adversarial system; Variance (accounting); Mathematics; Statistics; Chemistry","score_opus":0.023221817081329713,"score_gpt":0.29423572523240576,"score_spread":0.27101390815107607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964155212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012624708,0.00016158375,0.9838864,0.0003878003,0.000044388184,0.000029676963,0.00014145118,0.00052081095,0.0022030473],"genre_scores_gemma":[0.7288518,0.0004001719,0.26302096,0.0005102659,0.00010847086,0.00021549981,0.00079772936,0.00043383046,0.005661298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986921,0.00050891505,0.000046624547,0.00027637873,0.00034914765,0.0001267464],"domain_scores_gemma":[0.9957438,0.0030598827,0.0002658136,0.0004776359,0.00030958376,0.00014328702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002709325,0.0012209226,0.0012309777,0.0007589463,0.0006637704,0.0012659518,0.002168828,0.0015386115,0.0032782555],"category_scores_gemma":[0.012136292,0.00088344776,0.0009267658,0.0006819905,0.002329669,0.0026742748,0.0027797602,0.0045099235,0.00066111377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008319427,0.000021566208,0.00043676735,0.000038275695,0.000027069618,0.000056070134,0.000048556416,0.91613793,0.0010528739,0.061248742,0.0011162545,0.019732686],"study_design_scores_gemma":[0.0000035339854,0.000004610078,0.000029844361,0.000005283515,0.0000017613673,0.000007867558,0.000002012581,0.9820734,0.0003085485,0.017300239,0.0002584168,0.0000044065837],"about_ca_topic_score_codex":0.005161356,"about_ca_topic_score_gemma":0.0059989905,"teacher_disagreement_score":0.005161356,"about_ca_system_score_codex":0.0017617733,"about_ca_system_score_gemma":0.0018723748,"threshold_uncertainty_score":0.01432848},"labels":[],"label_agreement":null},{"id":"W2964330541","doi":"10.1109/iiswc.2018.8573476","title":"Benchmarking and Analyzing Deep Neural Network Training","year":2018,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Computer science; Toolchain; Artificial intelligence; Machine learning; Benchmarking; Deep learning; Artificial neural network; Inference; Benchmark (surveying); Reinforcement learning; Convolutional neural network; Profiling (computer programming); Deep neural networks; Workspace; Robot; Software","score_opus":0.01980527962653514,"score_gpt":0.26218563530868305,"score_spread":0.2423803556821479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964330541","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7716248,0.006981686,0.15127431,0.0012325577,0.0010249186,0.00046445694,0.012166057,0.032545973,0.022685302],"genre_scores_gemma":[0.82461005,0.002091275,0.12904924,0.0003952017,0.00011004769,0.00055299874,0.03593342,0.0028629026,0.0043948777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924672,0.0018428082,0.0008762531,0.001068394,0.0028989124,0.0008464473],"domain_scores_gemma":[0.9899573,0.004117709,0.0004659208,0.002555425,0.0025494345,0.00035423203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055208886,0.002708723,0.0011049849,0.0021519165,0.00064231106,0.0015946757,0.0037336089,0.0010171743,0.0022395018],"category_scores_gemma":[0.017728426,0.0006681816,0.00094318466,0.0042745173,0.001101042,0.0027949533,0.0020630637,0.0020041228,0.0010841928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001359621,0.0008282031,0.014414055,0.0014523163,0.000492462,0.0004487226,0.00026544562,0.664059,0.015658239,0.012941391,0.04862656,0.23945405],"study_design_scores_gemma":[0.000101036836,0.00044090877,0.005131257,0.00008106723,0.00004740408,0.000095705924,0.00014491475,0.94094825,0.03788908,0.0064877975,0.008588867,0.000043817945],"about_ca_topic_score_codex":0.010117275,"about_ca_topic_score_gemma":0.0093272645,"teacher_disagreement_score":0.010117275,"about_ca_system_score_codex":0.002227509,"about_ca_system_score_gemma":0.0021046896,"threshold_uncertainty_score":0.029197633},"labels":[],"label_agreement":null},{"id":"W2964370683","doi":"10.1103/physrevx.9.031012","title":"Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning","year":2019,"lang":"en","type":"article","venue":"Physical Review X","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Samsung; Natural Sciences and Engineering Research Council of Canada; Samsung Advanced Institute of Technology; McGill University","keywords":"Adversarial system; Analogy; Artificial neural network; Point (geometry); Simple (philosophy); Adversarial machine learning; Deep learning","score_opus":0.020734131005613214,"score_gpt":0.3337855526453463,"score_spread":0.3130514216397331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964370683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11484213,0.0032375834,0.84390116,0.010684907,0.0002731534,0.000046017594,0.000119333126,0.0002203637,0.026675336],"genre_scores_gemma":[0.95479846,0.0019280954,0.03909397,0.00074942224,0.00020503759,0.0000775365,0.00005013797,0.00006329908,0.003034088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992095,0.00035219698,0.000036054065,0.00015225564,0.00015759173,0.00009233581],"domain_scores_gemma":[0.9950983,0.0035203253,0.0004154376,0.00053662196,0.00020348921,0.00022588803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016704155,0.00061392435,0.0007570043,0.00043260818,0.0006173271,0.0018790558,0.0009200774,0.0021627902,0.0020005552],"category_scores_gemma":[0.0087647755,0.0003158859,0.00072869525,0.0002733086,0.005438674,0.0029735507,0.0016756615,0.0032440631,0.00033131748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034005607,0.000026740174,0.0009364769,0.00011166642,0.00004467583,0.00011073426,0.0001412071,0.22918735,0.0026405354,0.7522448,0.001196571,0.0133253],"study_design_scores_gemma":[0.000011324753,0.000024499708,0.00032176767,0.000020622996,0.0000058011938,0.000045186312,0.000029851239,0.24932158,0.0007088533,0.7481235,0.0013698315,0.00001703944],"about_ca_topic_score_codex":0.0008148696,"about_ca_topic_score_gemma":0.00057316467,"teacher_disagreement_score":0.0021627902,"about_ca_system_score_codex":0.001348311,"about_ca_system_score_gemma":0.00067395606,"threshold_uncertainty_score":0.009782791},"labels":[],"label_agreement":null},{"id":"W2964434332","doi":"","title":"Low Frequency Adversarial Perturbation","year":2018,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Computer science; Black box; Image (mathematics); Perturbation (astronomy); Cloud computing; Frequency domain; Transformation (genetics); Artificial intelligence; Theoretical computer science; Computer vision; Algorithm","score_opus":0.02908829838875739,"score_gpt":0.2972562858240615,"score_spread":0.2681679874353041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964434332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024030473,0.0003207396,0.96734846,0.00049133116,0.000072209885,0.00010256127,0.0001174013,0.00078470784,0.006732159],"genre_scores_gemma":[0.86821777,0.00031838776,0.12386676,0.00054154726,0.0001069012,0.00017203795,0.00026602368,0.00015455364,0.006356018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989737,0.00033896253,0.000031594314,0.00019780676,0.00031560214,0.0001424314],"domain_scores_gemma":[0.9972752,0.0017533929,0.00019951406,0.0005524317,0.0001392575,0.00008031921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010362857,0.0010597465,0.0010214502,0.0004652585,0.00047910147,0.0009486263,0.0012409757,0.0013475451,0.0042627417],"category_scores_gemma":[0.0053897672,0.00031614953,0.00058621145,0.00039181032,0.0014962736,0.0019518399,0.0024413501,0.002203305,0.00083102164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035175154,0.00016366041,0.0009163631,0.0001405031,0.000103663246,0.00030863273,0.000085966574,0.7764242,0.025695609,0.09586233,0.008232464,0.09171478],"study_design_scores_gemma":[0.000015013191,0.000067588844,0.00015827242,0.00000989516,0.000009810321,0.00011080283,0.000018395845,0.95943105,0.0047975047,0.03334489,0.0020238922,0.000012906632],"about_ca_topic_score_codex":0.0008388974,"about_ca_topic_score_gemma":0.0008412664,"teacher_disagreement_score":0.0042627417,"about_ca_system_score_codex":0.0007488971,"about_ca_system_score_gemma":0.0005245146,"threshold_uncertainty_score":0.014260292},"labels":[],"label_agreement":null},{"id":"W2965889373","doi":"10.48550/arxiv.1908.01667","title":"A principled approach for generating adversarial images under non-smooth dissimilarity metrics","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"MNIST database; Adversarial system; Differentiable function; Norm (philosophy); Exploit; Metric (unit); Pixel; Variation (astronomy); Class (philosophy); Artificial intelligence; Mathematics; Image (mathematics); Convolutional neural network; Deep neural networks; Computer science; Pattern recognition (psychology); Algorithm; Artificial neural network; Pure mathematics","score_opus":0.07390901883819734,"score_gpt":0.22660335766432765,"score_spread":0.15269433882613032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965889373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003743685,0.0000410849,0.99488515,0.00015920488,0.000023195444,0.00003997564,0.000021877619,0.00022044635,0.00086532236],"genre_scores_gemma":[0.432766,0.00023590685,0.5590499,0.0005789447,0.00016931341,0.00033346514,0.0002172767,0.00030248816,0.006346722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987796,0.00038574025,0.000049987644,0.00026774235,0.00044546556,0.00007147886],"domain_scores_gemma":[0.9983367,0.00080360135,0.00018389538,0.00039895315,0.00017143223,0.00010549926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023431813,0.0010764581,0.0007556853,0.00077222317,0.00048737513,0.0009331071,0.0016682167,0.0015430284,0.00244186],"category_scores_gemma":[0.0055716075,0.0005264477,0.0009774235,0.00052041904,0.0020813202,0.0016220032,0.0039469795,0.0035666146,0.0006140068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016709675,0.000073475145,0.000658687,0.00007480387,0.000094033974,0.00012000433,0.00010579242,0.7353566,0.0132620875,0.13991284,0.0035887286,0.106585816],"study_design_scores_gemma":[0.000010431024,0.00005387823,0.00007118556,0.0000073166466,0.0000063254697,0.00006191974,0.000005037273,0.9678173,0.0029001317,0.02778952,0.0012683176,0.000008605691],"about_ca_topic_score_codex":0.00062592153,"about_ca_topic_score_gemma":0.0007895928,"teacher_disagreement_score":0.00244186,"about_ca_system_score_codex":0.0009501925,"about_ca_system_score_gemma":0.00074051763,"threshold_uncertainty_score":0.012392044},"labels":[],"label_agreement":null},{"id":"W2966777314","doi":"","title":"SANE: Towards Improved Prediction Robustness via Stochastically Activated Network Ensembles","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence","score_opus":0.011896650592169308,"score_gpt":0.23962906397666625,"score_spread":0.22773241338449696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966777314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021185573,0.0003337785,0.97537416,0.00029464503,0.00010695646,0.00002919262,0.00008201395,0.0008377326,0.001755926],"genre_scores_gemma":[0.79186046,0.00030559435,0.20040122,0.00048701034,0.00022250542,0.00015332675,0.00044076078,0.00037558912,0.0057534613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987832,0.0004773205,0.000049292117,0.00028013592,0.00029634082,0.00011368395],"domain_scores_gemma":[0.9964709,0.002173177,0.00019230405,0.00058007304,0.00044365897,0.00013985147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030141182,0.0014853411,0.0016816949,0.0006520122,0.0005947661,0.0010225713,0.002053939,0.0020083513,0.0024272464],"category_scores_gemma":[0.008808861,0.00079098897,0.0009321678,0.0004701526,0.0012929812,0.0022608172,0.003502851,0.0035319452,0.00072586857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016631032,0.00006419005,0.00041593937,0.000044990695,0.00012020435,0.000052343243,0.000041552565,0.9348456,0.0031124703,0.013213927,0.0018184561,0.046103943],"study_design_scores_gemma":[0.0000027018582,0.000012065154,0.000022122795,0.0000019700783,0.0000037522032,0.0000045557713,0.000001276948,0.99610764,0.00040669,0.0033492593,0.00008565145,0.0000022736351],"about_ca_topic_score_codex":0.0017266315,"about_ca_topic_score_gemma":0.0023794735,"teacher_disagreement_score":0.0030141182,"about_ca_system_score_codex":0.00060313195,"about_ca_system_score_gemma":0.00086107745,"threshold_uncertainty_score":0.015940368},"labels":[],"label_agreement":null},{"id":"W2968215148","doi":"10.1007/978-3-030-29729-9_2","title":"Using Honeypots in a Decentralized Framework to Defend Against Adversarial Machine-Learning Attacks","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Adversarial system; Honeypot; Adversary; Computer science; Adversarial machine learning; Computer security; Threat model; Artificial intelligence; Machine learning","score_opus":0.02523049076188772,"score_gpt":0.2877179076421205,"score_spread":0.2624874168802328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968215148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06373237,0.00084742694,0.9197423,0.0007713468,0.00037361376,0.00013674286,0.00005176012,0.0022013667,0.012143122],"genre_scores_gemma":[0.9307145,0.0002638947,0.062857814,0.00018708777,0.00015388556,0.00011829257,0.000056625697,0.00012229224,0.00552565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988814,0.00033283245,0.0000475825,0.0002314813,0.00034438897,0.00016225908],"domain_scores_gemma":[0.9977323,0.00087837153,0.00024623956,0.000826783,0.00017854954,0.00013777043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013017277,0.0006589229,0.0009230377,0.0005169649,0.0007072675,0.001625771,0.0014522432,0.001788324,0.0023269167],"category_scores_gemma":[0.003266496,0.0004999828,0.0005499893,0.0003451306,0.0013617689,0.0030942012,0.0031878734,0.002521351,0.00067397015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008440738,0.00038615614,0.0016041003,0.00031771275,0.00033730431,0.0007145691,0.00037645327,0.4329333,0.08736541,0.26715255,0.012819241,0.19514905],"study_design_scores_gemma":[0.00006303734,0.0002159592,0.00039106765,0.000021046348,0.000035705027,0.00023730719,0.000044083896,0.8870628,0.008207463,0.09727394,0.006420507,0.000027115488],"about_ca_topic_score_codex":0.00015120419,"about_ca_topic_score_gemma":0.00022984855,"teacher_disagreement_score":0.0023269167,"about_ca_system_score_codex":0.0003949591,"about_ca_system_score_gemma":0.00041715868,"threshold_uncertainty_score":0.0077842474},"labels":[],"label_agreement":null},{"id":"W2968990382","doi":"10.1145/3339252.3340520","title":"Near-optimal Evasion of Randomized Convex-inducing Classifiers in Adversarial Environments","year":2019,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Adversarial system; Computer science; Evasion (ethics); Regular polygon; Time complexity; Artificial intelligence; Machine learning; Mathematics; Algorithm","score_opus":0.00756131610559369,"score_gpt":0.23016174090909167,"score_spread":0.22260042480349798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968990382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092404425,0.000352542,0.90083385,0.0009320294,0.00007901952,0.00020256198,0.00013501929,0.001070482,0.003990092],"genre_scores_gemma":[0.86747706,0.00013367458,0.12947737,0.0003288725,0.000055660388,0.00016418341,0.00024883787,0.00016641417,0.001948009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958689,0.0017161681,0.00022683025,0.0007830757,0.0007746205,0.00063041464],"domain_scores_gemma":[0.97718096,0.016952718,0.0013590308,0.0027692136,0.0010733157,0.0006646591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046502165,0.0013096231,0.0020296855,0.00055487803,0.0007175413,0.0015271642,0.0027533143,0.0018787392,0.0019112581],"category_scores_gemma":[0.024297332,0.0005992685,0.0011430151,0.0005078546,0.0022125272,0.002798206,0.0030969984,0.0033958845,0.0005007044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035878588,0.00015054949,0.0011740761,0.000070747636,0.00005358217,0.00009368633,0.00009453727,0.9302725,0.0024148214,0.027990142,0.0019952147,0.03533127],"study_design_scores_gemma":[0.00001692239,0.000061845596,0.00009032503,0.0000070552237,0.0000064005008,0.00003288814,0.000016564754,0.9804458,0.0010677298,0.018014371,0.00023321039,0.0000068010927],"about_ca_topic_score_codex":0.0015516453,"about_ca_topic_score_gemma":0.0017278412,"teacher_disagreement_score":0.0046502165,"about_ca_system_score_codex":0.0018360066,"about_ca_system_score_gemma":0.0017023854,"threshold_uncertainty_score":0.024592996},"labels":[],"label_agreement":null},{"id":"W2970615870","doi":"10.48550/arxiv.1911.00937","title":"Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lipschitz continuity; Robustness (evolution); Computer science; Norm (philosophy); Mathematical optimization; Convolution (computer science); Algorithm; Mathematics; Artificial intelligence; Artificial neural network; Pure mathematics","score_opus":0.028433213585069873,"score_gpt":0.1780032827735786,"score_spread":0.14957006918850874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970615870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034575,0.00020078229,0.9597374,0.00053978193,0.00003502886,0.00004886707,0.00008850675,0.0011411664,0.0036334947],"genre_scores_gemma":[0.8429547,0.0004475342,0.15030932,0.0006166655,0.00008491714,0.00029205586,0.00037601398,0.00059254107,0.004326328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974909,0.0009002296,0.00013566842,0.00040987702,0.00080947875,0.00025395412],"domain_scores_gemma":[0.99251455,0.004710585,0.00060564216,0.0014351668,0.0005186948,0.00021527246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003177044,0.0014945819,0.0010665334,0.0005705413,0.00066814304,0.0013225494,0.001717007,0.0017383049,0.0023207024],"category_scores_gemma":[0.021805594,0.0008546428,0.00082177576,0.0004827614,0.0029066824,0.003958192,0.005353268,0.0039256676,0.0005815801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035520527,0.000079244724,0.0012342124,0.00019423672,0.000055489185,0.00024388572,0.00019357011,0.7552347,0.016370853,0.16466221,0.0036051483,0.05777123],"study_design_scores_gemma":[0.000019600431,0.000058829984,0.00012913975,0.00002491313,0.0000082731485,0.000053639942,0.00001415915,0.9238441,0.0070490027,0.06733013,0.0014566757,0.000011510341],"about_ca_topic_score_codex":0.0014540767,"about_ca_topic_score_gemma":0.0015733705,"teacher_disagreement_score":0.003177044,"about_ca_system_score_codex":0.0015019974,"about_ca_system_score_gemma":0.0012480839,"threshold_uncertainty_score":0.016802013},"labels":[],"label_agreement":null},{"id":"W2970987521","doi":"10.1007/978-3-030-26250-1_23","title":"Improving ML Safety with Partial Specifications","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Workflow; Software engineering; Key (lock); Component (thermodynamics); Safety assurance; Software; Partial evaluation; Programming language; Reliability engineering; Operating system; Database; Engineering","score_opus":0.01840434861093901,"score_gpt":0.2362812111301523,"score_spread":0.2178768625192133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970987521","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01334218,0.0003269618,0.97561556,0.0005475804,0.00010539324,0.000041916774,0.00012363198,0.0036453146,0.006251452],"genre_scores_gemma":[0.69110596,0.00057026726,0.29327866,0.00096910493,0.00038286299,0.00016560167,0.0008442718,0.0024314388,0.010251835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99184954,0.002741053,0.0005128754,0.0009092209,0.0033231366,0.00066412106],"domain_scores_gemma":[0.9750591,0.015019278,0.00087161944,0.0071714697,0.0015852774,0.0002932415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047881273,0.00146034,0.0012317308,0.0009434241,0.0006746562,0.0026141638,0.002154695,0.0017916673,0.008451843],"category_scores_gemma":[0.026825951,0.0011150914,0.0017594611,0.0006480427,0.0034683857,0.0050229346,0.005919332,0.0053940583,0.0025525289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008191855,0.00018769495,0.00159013,0.000546834,0.00016883985,0.00036329476,0.00035676986,0.26726234,0.0231271,0.3726676,0.010312984,0.3225972],"study_design_scores_gemma":[0.000056258916,0.000201276,0.00014094464,0.000093631585,0.00006182217,0.00019243611,0.000048568294,0.51866424,0.022734562,0.45173576,0.006030155,0.000040289036],"about_ca_topic_score_codex":0.000541391,"about_ca_topic_score_gemma":0.000788825,"teacher_disagreement_score":0.008451843,"about_ca_system_score_codex":0.0008366418,"about_ca_system_score_gemma":0.0017175825,"threshold_uncertainty_score":0.028274238},"labels":[],"label_agreement":null},{"id":"W2971547406","doi":"","title":"SANE: Exploring Adversarial Robustness With Stochastically Activated Network Ensembles","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Waterloo","funders":"","keywords":"Robustness (evolution); Adversarial system; Computer science; Artificial intelligence","score_opus":0.02462234266608005,"score_gpt":0.23527263681102872,"score_spread":0.21065029414494868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971547406","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028218463,0.0003532625,0.96633273,0.00037232458,0.00007771678,0.000043474392,0.00009643422,0.00057028874,0.003935242],"genre_scores_gemma":[0.86308575,0.0003229107,0.12989861,0.00034399013,0.00015075969,0.00018884198,0.00030724457,0.00036864655,0.005333413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915016,0.00042208203,0.000022511735,0.00013927575,0.00019313119,0.00007286732],"domain_scores_gemma":[0.99556834,0.0035390214,0.00019554429,0.0003321201,0.00024593502,0.0001190787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027095464,0.0012372442,0.0012359716,0.0007941717,0.0005262252,0.001161015,0.002016761,0.0018615944,0.0030632156],"category_scores_gemma":[0.008876719,0.0007478651,0.001078787,0.00047518802,0.0016638435,0.0021572402,0.0030294259,0.002396325,0.00042341606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035556222,0.000020886902,0.00019761815,0.000024268405,0.00004751451,0.000024358047,0.000017265902,0.9784872,0.0004686927,0.013562352,0.00046924394,0.0066451505],"study_design_scores_gemma":[0.0000021516755,0.000008599335,0.000016800303,0.0000018380524,0.0000027839733,0.0000035967764,0.0000015454075,0.9930703,0.00011919353,0.0066985553,0.000072896226,0.0000017106086],"about_ca_topic_score_codex":0.0021497328,"about_ca_topic_score_gemma":0.0019287752,"teacher_disagreement_score":0.0030632156,"about_ca_system_score_codex":0.0007197994,"about_ca_system_score_gemma":0.00071103044,"threshold_uncertainty_score":0.014329672},"labels":[],"label_agreement":null},{"id":"W2972204217","doi":"10.1109/icsme.2019.00078","title":"DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Fuzz testing; Computer science; Machine learning; Artificial intelligence; Code coverage; Artificial neural network; Random testing; Deep neural networks; Model-based testing; Training set; Test case; Data mining; Software; Programming language","score_opus":0.04957674376864774,"score_gpt":0.28519897419551643,"score_spread":0.2356222304268687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972204217","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06546822,0.00046999872,0.92724854,0.0004935559,0.000049994873,0.00012484538,0.00017029938,0.0032738994,0.0027005512],"genre_scores_gemma":[0.6758232,0.00020003764,0.3205097,0.00034039945,0.00003139029,0.0003083036,0.0003644327,0.00044007256,0.001982355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998928,0.00044638425,0.000065501285,0.00017679142,0.00026352954,0.00011975753],"domain_scores_gemma":[0.99529016,0.0035290874,0.00029242373,0.00033710388,0.00042537285,0.00012580793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023994197,0.0013069336,0.0008857648,0.0013286752,0.00047584908,0.0008954953,0.0026362361,0.0016873545,0.0025638249],"category_scores_gemma":[0.008732006,0.00071353314,0.0009778081,0.0005254203,0.0017095389,0.0014765925,0.0017819513,0.0018872395,0.00027807843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009177344,0.00007674777,0.002279875,0.000088336135,0.000068775196,0.00013494649,0.00008425364,0.93078506,0.0040988787,0.009381711,0.0009973134,0.0519124],"study_design_scores_gemma":[0.0000075390362,0.000027730002,0.00007651346,0.000007731493,0.0000056416925,0.000013828838,0.000006454885,0.99443346,0.0009967063,0.0041978373,0.00022351439,0.000003102586],"about_ca_topic_score_codex":0.006103616,"about_ca_topic_score_gemma":0.0059340107,"teacher_disagreement_score":0.006103616,"about_ca_system_score_codex":0.001662492,"about_ca_system_score_gemma":0.0013179799,"threshold_uncertainty_score":0.012689471},"labels":[],"label_agreement":null},{"id":"W2972233065","doi":"10.1109/qrs.2019.00059","title":"TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Implementation; Machine learning; Code (set theory); Training set; Artificial intelligence; Training (meteorology); Process (computing); Artificial neural network; Focus (optics); Software engineering; Data mining; Programming language; Set (abstract data type)","score_opus":0.047381581540811686,"score_gpt":0.29621450341650685,"score_spread":0.24883292187569517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972233065","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019156506,0.00022224817,0.78958684,0.00031154734,0.00010840501,0.00023330959,0.0013626692,0.18766277,0.0013557097],"genre_scores_gemma":[0.29966334,0.00039812253,0.6683045,0.0005908904,0.000111822614,0.0009027781,0.0047887284,0.021726219,0.0035136654],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99576306,0.0011835939,0.000555779,0.00080287195,0.001346185,0.00034848682],"domain_scores_gemma":[0.97779536,0.013373973,0.003019159,0.0037043013,0.0017338224,0.00037331248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007551754,0.0024821444,0.0010180498,0.0029872367,0.0011712712,0.0023363114,0.003851538,0.0017242301,0.008617418],"category_scores_gemma":[0.038060818,0.0016445004,0.0019272161,0.0013562345,0.0028523973,0.0055054924,0.0028705948,0.0028337233,0.0019759808],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024016402,0.00057678187,0.029974902,0.0026602906,0.0005263722,0.0015477462,0.0017799896,0.26599836,0.047095962,0.06687333,0.07649351,0.50407106],"study_design_scores_gemma":[0.000098155775,0.00019648997,0.0013565063,0.00015784061,0.00005267989,0.00026476954,0.00006620796,0.89957106,0.049902927,0.037522018,0.010721427,0.00008992501],"about_ca_topic_score_codex":0.0049374322,"about_ca_topic_score_gemma":0.004877382,"teacher_disagreement_score":0.008617418,"about_ca_system_score_codex":0.0022678387,"about_ca_system_score_gemma":0.0040834867,"threshold_uncertainty_score":0.039937973},"labels":[],"label_agreement":null},{"id":"W2975819162","doi":"10.48550/arxiv.1909.11835","title":"GAMIN: An Adversarial Approach to Black-Box Model Inversion","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; MNIST database; Adversarial system; Inversion (geology); Convolutional neural network; Artificial intelligence; Deep learning; Deep neural networks; Adversary; Artificial neural network; Machine learning; Generative adversarial network; Black box; Pattern recognition (psychology); Computer security","score_opus":0.07598808535223112,"score_gpt":0.20763665810285478,"score_spread":0.13164857275062367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975819162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01146406,0.00019874072,0.98386997,0.00040972236,0.00005683318,0.00008983311,0.00006937939,0.0010251425,0.0028163448],"genre_scores_gemma":[0.80535287,0.00043466425,0.18708582,0.0007888187,0.0001354555,0.00030196318,0.00026386962,0.0004099415,0.0052265828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782085,0.0010277963,0.000074201394,0.000319592,0.00054615637,0.00021139097],"domain_scores_gemma":[0.9952087,0.0031050688,0.0003797926,0.0010039005,0.00017754339,0.00012504443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030931672,0.0013919014,0.0009165185,0.000768398,0.00048595003,0.0011157778,0.0018397946,0.0019856612,0.0029035457],"category_scores_gemma":[0.009102926,0.0005931135,0.0012803961,0.00036535467,0.0032148028,0.0022678194,0.0052968804,0.003957053,0.0005635996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002554845,0.00009146282,0.0008922407,0.00009977685,0.00014269997,0.00027420968,0.00014016764,0.80447626,0.008534779,0.139971,0.003157388,0.041964523],"study_design_scores_gemma":[0.000016819582,0.00006407467,0.000072620656,0.000017404705,0.00000980291,0.00007898738,0.000009976883,0.9393566,0.002609733,0.056461047,0.0012904769,0.000012369275],"about_ca_topic_score_codex":0.00064707134,"about_ca_topic_score_gemma":0.0006779068,"teacher_disagreement_score":0.0030931672,"about_ca_system_score_codex":0.0007966931,"about_ca_system_score_gemma":0.0008768301,"threshold_uncertainty_score":0.016358435},"labels":[],"label_agreement":null},{"id":"W2978190445","doi":"10.1109/qrs.2019.00059","title":"TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs","year":2019,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Implementation; Machine learning; Artificial intelligence; Training set; Artificial neural network; Code (set theory); Training (meteorology); Process (computing); Focus (optics); Software; Software engineering; Programming language; Set (abstract data type)","score_opus":0.016575728784407117,"score_gpt":0.24781041259946435,"score_spread":0.23123468381505724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978190445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018564103,0.00019841977,0.81321436,0.0002748113,0.00009156739,0.00024039633,0.0012181235,0.16489996,0.0012982386],"genre_scores_gemma":[0.2871821,0.0003510394,0.6861417,0.0005181297,0.000089938476,0.0008674308,0.004232688,0.017282221,0.0033347104],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963671,0.0009787563,0.00046987162,0.0007037113,0.0011823436,0.0002983011],"domain_scores_gemma":[0.9817224,0.011059886,0.0026086387,0.0027656835,0.001531251,0.0003121463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006916519,0.0023576645,0.0009343673,0.0027778323,0.0010913762,0.002008921,0.003516638,0.0015386356,0.008394517],"category_scores_gemma":[0.033444047,0.0015042559,0.0017919316,0.0011970059,0.002545961,0.004755071,0.0026286282,0.0025969688,0.0017475412],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021393644,0.0005270307,0.028348638,0.0023685417,0.0004777138,0.0014119671,0.0015038406,0.29203093,0.046361547,0.059441756,0.06851997,0.49686867],"study_design_scores_gemma":[0.000078591955,0.00017291194,0.0012328984,0.000127244,0.000042252876,0.00022818784,0.00005214365,0.9174751,0.04377905,0.027876684,0.008861961,0.00007289358],"about_ca_topic_score_codex":0.005447205,"about_ca_topic_score_gemma":0.005729823,"teacher_disagreement_score":0.008394517,"about_ca_system_score_codex":0.0022759347,"about_ca_system_score_gemma":0.004099425,"threshold_uncertainty_score":0.036578536},"labels":[],"label_agreement":null},{"id":"W2978447704","doi":"10.1109/ijcnn.2019.8852298","title":"Learning Adaptive Weight Masking for Adversarial Examples","year":2019,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Sigmoid function; Pointwise; Convolutional neural network; Masking (illustration); Artificial intelligence; Algorithm; Pattern recognition (psychology); Convolution (computer science); Pixel; Layer (electronics); Artificial neural network; Mathematics","score_opus":0.014299846893670587,"score_gpt":0.24333758271703518,"score_spread":0.2290377358233646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978447704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13476065,0.00017873073,0.8622002,0.000333469,0.000043991156,0.00004783513,0.0000439465,0.0006139377,0.0017771714],"genre_scores_gemma":[0.9484777,0.00010389793,0.049100146,0.00014330969,0.00002118896,0.000048161342,0.00005521246,0.000054154883,0.0019963009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960965,0.000115449366,0.000015235414,0.00010770326,0.00008823172,0.00006374078],"domain_scores_gemma":[0.9983871,0.0009616188,0.00022530429,0.0002569243,0.000092511975,0.00007658812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013057323,0.0009260574,0.00067448744,0.00036497714,0.00022431367,0.0005129742,0.0010953143,0.0011254047,0.001388058],"category_scores_gemma":[0.0052957875,0.00047153223,0.00052052917,0.00020876463,0.0014446368,0.0013284936,0.0012817071,0.0014299033,0.00024558246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016925503,0.00003610352,0.00063709274,0.0000328006,0.000035700432,0.00007689789,0.0000459223,0.9475823,0.011763466,0.015122765,0.0004277783,0.024069788],"study_design_scores_gemma":[0.0000037082234,0.00002054353,0.00007305589,0.0000028982697,0.0000033438955,0.000014575063,0.0000016133239,0.9938445,0.0015217916,0.004417343,0.00009410041,0.0000025634574],"about_ca_topic_score_codex":0.0013138909,"about_ca_topic_score_gemma":0.0013846805,"teacher_disagreement_score":0.001388058,"about_ca_system_score_codex":0.00079643726,"about_ca_system_score_gemma":0.0005786998,"threshold_uncertainty_score":0.0069054365},"labels":[],"label_agreement":null},{"id":"W2979332623","doi":"10.48550/arxiv.1910.04241","title":"Out-of-distribution Detection in Classifiers via Generation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"MNIST database; Autoencoder; Classifier (UML); Artificial intelligence; Computer science; Inference; Artificial neural network; Machine learning; Pattern recognition (psychology); Detector","score_opus":0.07386632659431568,"score_gpt":0.20775211754979414,"score_spread":0.13388579095547848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979332623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025445057,0.00016772126,0.9722464,0.00023211738,0.000026551814,0.000052246512,0.000055178316,0.00090970844,0.0008649708],"genre_scores_gemma":[0.6830594,0.00018939139,0.31357545,0.00036105313,0.000067548004,0.00018211405,0.00031711935,0.0002381751,0.0020096868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983176,0.000543776,0.000075379954,0.00046940256,0.0004486552,0.00014522274],"domain_scores_gemma":[0.99358046,0.003803278,0.0006795985,0.0011889598,0.0006272492,0.000120367535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028037105,0.0009601919,0.0011064558,0.0010930378,0.000539859,0.00094181066,0.0017320762,0.001473054,0.0013873376],"category_scores_gemma":[0.01207053,0.0006446618,0.0007874956,0.0006324376,0.0020982379,0.0024413979,0.0021919112,0.0021868495,0.00053354405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003158414,0.00011129186,0.006082469,0.00013857127,0.00009514541,0.00020517921,0.00026209943,0.6304005,0.015440592,0.0390332,0.004425981,0.30348918],"study_design_scores_gemma":[0.000008022361,0.00003069396,0.00032252708,0.000008610528,0.000006376986,0.00005413376,0.000010547778,0.9831655,0.004753538,0.011016538,0.00061535364,0.000008195885],"about_ca_topic_score_codex":0.001863425,"about_ca_topic_score_gemma":0.0019521567,"teacher_disagreement_score":0.0028037105,"about_ca_system_score_codex":0.0013299969,"about_ca_system_score_gemma":0.000850939,"threshold_uncertainty_score":0.014827609},"labels":[],"label_agreement":null},{"id":"W2981692241","doi":"10.1109/icassp40776.2020.9052913","title":"Detection of Adversarial Attacks and Characterization of Adversarial Subspace","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Adversarial system; Subspace topology; Computer science; Linear subspace; Detector; Spectrogram; Artificial intelligence; Relation (database); Pattern recognition (psychology); Algorithm; Mathematics; Data mining; Telecommunications","score_opus":0.016414346668203697,"score_gpt":0.2532152622154554,"score_spread":0.2368009155472517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981692241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062459506,0.00025122004,0.9346585,0.00032528432,0.000045353252,0.000054169865,0.00007986219,0.000525292,0.0016007727],"genre_scores_gemma":[0.9120693,0.00025167817,0.08542585,0.00017785282,0.00006384318,0.00007439989,0.00028318854,0.00009335255,0.0015605941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732745,0.0009116544,0.00012824952,0.0005261054,0.0008889123,0.00021754086],"domain_scores_gemma":[0.99202627,0.0043559805,0.0010132485,0.0015875656,0.0007113859,0.00030550407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024456251,0.0008459673,0.0010027278,0.0009889586,0.0004699487,0.0012956922,0.0009925937,0.0012263363,0.001134991],"category_scores_gemma":[0.015314722,0.00035169686,0.0007086479,0.0004666701,0.002308726,0.0025117644,0.0025162175,0.0025615825,0.00046742495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032024278,0.00015193733,0.010304398,0.0001530922,0.00015708375,0.00035705618,0.00022198717,0.71410125,0.03433052,0.08079123,0.0026663481,0.15644482],"study_design_scores_gemma":[0.000002557463,0.000055680965,0.0007189631,0.000010071252,0.0000051694647,0.000121221936,0.000025594136,0.9776977,0.0061518527,0.014651065,0.0005444504,0.000015651382],"about_ca_topic_score_codex":0.0005583916,"about_ca_topic_score_gemma":0.00037775116,"teacher_disagreement_score":0.0024456251,"about_ca_system_score_codex":0.0005711159,"about_ca_system_score_gemma":0.00058454485,"threshold_uncertainty_score":0.01293385},"labels":[],"label_agreement":null},{"id":"W2982044490","doi":"","title":"Detecting Extrapolation with Local Ensembles","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Extrapolation; Spurious relationship; Hessian matrix; Computer science; Curvature; Focus (optics); Component (thermodynamics); Artificial intelligence; Algorithm; Mathematics; Pattern recognition (psychology); Machine learning; Applied mathematics; Statistics; Physics","score_opus":0.04736355427099913,"score_gpt":0.18548513931229774,"score_spread":0.13812158504129862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982044490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12767765,0.00029948915,0.8682308,0.00029448434,0.00006401284,0.000050356313,0.00016859348,0.0016489304,0.0015656981],"genre_scores_gemma":[0.9021903,0.00010134333,0.094552554,0.00027201648,0.00010493972,0.000091921254,0.0005934336,0.00032292126,0.0017704468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976131,0.00074950367,0.00011900234,0.0005771756,0.0007049596,0.00023621795],"domain_scores_gemma":[0.98864007,0.0052663633,0.0013481826,0.0028315594,0.0013309307,0.000582973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003793571,0.0012324824,0.0015177123,0.0013435839,0.0008168432,0.0012982121,0.0021839265,0.0016597805,0.0014568946],"category_scores_gemma":[0.019878414,0.00071988656,0.0009156003,0.0007710158,0.0013834066,0.0024580725,0.0037529848,0.0031271651,0.0006907231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077099155,0.00028890214,0.041892767,0.00013203746,0.00036024486,0.0008476067,0.0005640963,0.7093035,0.02860894,0.014450074,0.005117534,0.1976634],"study_design_scores_gemma":[0.0000064780693,0.0000828992,0.0015362506,0.000014027355,0.000014715439,0.00011863353,0.000027328993,0.98377407,0.0060476563,0.007835895,0.0005221735,0.000019854127],"about_ca_topic_score_codex":0.0017025726,"about_ca_topic_score_gemma":0.0026172278,"teacher_disagreement_score":0.003793571,"about_ca_system_score_codex":0.00068177446,"about_ca_system_score_gemma":0.0007931035,"threshold_uncertainty_score":0.020062566},"labels":[],"label_agreement":null},{"id":"W2982677911","doi":"10.1109/icc40277.2020.9149117","title":"Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Intrusion detection system; Deep learning; Robustness (evolution); Machine learning; Artificial neural network; Deep neural networks; Pattern recognition (psychology)","score_opus":0.02749599193211109,"score_gpt":0.2714477163220017,"score_spread":0.24395172438989063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982677911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41533938,0.0017493663,0.5732813,0.0020295908,0.000109317145,0.00014146324,0.00020451685,0.0010503381,0.0060946457],"genre_scores_gemma":[0.97992367,0.0002238161,0.018857682,0.00012588948,0.000019849942,0.00004590812,0.000064269116,0.000042863034,0.00069599197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831057,0.0008218878,0.00008483967,0.0002630273,0.00030041838,0.00021931254],"domain_scores_gemma":[0.9874743,0.010044279,0.0009884998,0.00075628987,0.00047231172,0.00026440824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046430337,0.0014730653,0.0010741681,0.0008293233,0.0004110511,0.0008609175,0.00094892684,0.0014220536,0.00095173146],"category_scores_gemma":[0.015014875,0.00049969077,0.0006947651,0.00044431913,0.0017982338,0.0018942676,0.0016122828,0.0020455234,0.00015482905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015414043,0.000044303117,0.0008998241,0.000048491944,0.00005020604,0.000023348373,0.00002162826,0.9874402,0.0011945923,0.003690714,0.00036157708,0.0060709696],"study_design_scores_gemma":[0.000002837681,0.000042503903,0.00010564164,0.0000036036647,0.0000045267548,0.0000067442725,0.000004560627,0.99734336,0.0008503708,0.0015905036,0.00004263918,0.000002700096],"about_ca_topic_score_codex":0.0015600764,"about_ca_topic_score_gemma":0.0007930864,"teacher_disagreement_score":0.0046430337,"about_ca_system_score_codex":0.0017219614,"about_ca_system_score_gemma":0.0006262342,"threshold_uncertainty_score":0.024555027},"labels":[],"label_agreement":null},{"id":"W2990091474","doi":"10.48550/arxiv.1911.11195","title":"A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Robustness (evolution); Computer science; Calibration; Artificial intelligence; Range (aeronautics); Scaling; Pattern recognition (psychology); Machine learning; Statistics; Mathematics; Engineering","score_opus":0.05045522398792803,"score_gpt":0.22742400202263638,"score_spread":0.17696877803470834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990091474","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008673914,0.00013635178,0.9895739,0.00009613805,0.00003780882,0.000032304888,0.000040033596,0.0008199415,0.0005897068],"genre_scores_gemma":[0.3627172,0.00026533817,0.6325988,0.00032211727,0.0001258266,0.00017451758,0.00047355308,0.00046444687,0.0028580786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991518,0.00017217179,0.000055250533,0.0002663965,0.00028707055,0.00006725141],"domain_scores_gemma":[0.99847347,0.00035433756,0.0002511033,0.0003971067,0.00044673547,0.000077184144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019181023,0.0013085752,0.00092423195,0.0007671286,0.0005833647,0.0008853231,0.002029202,0.0013411982,0.0016976731],"category_scores_gemma":[0.005555694,0.0005591915,0.0010319769,0.0008109802,0.0012723628,0.0020011342,0.0025425847,0.0027130675,0.00068752904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019202221,0.000113465416,0.001843795,0.00012566714,0.00013206176,0.00011808229,0.0002156621,0.53506476,0.036053184,0.015771193,0.0036750792,0.406695],"study_design_scores_gemma":[0.000005022182,0.000028086864,0.00022111504,0.000008421134,0.000009514062,0.00003337994,0.00000804472,0.98693955,0.008758286,0.0030980867,0.00087896566,0.000011568674],"about_ca_topic_score_codex":0.0018367026,"about_ca_topic_score_gemma":0.0027886704,"teacher_disagreement_score":0.002029202,"about_ca_system_score_codex":0.0010207561,"about_ca_system_score_gemma":0.0013808222,"threshold_uncertainty_score":0.010144055},"labels":[],"label_agreement":null},{"id":"W2990378874","doi":"10.48550/arxiv.1912.00888","title":"Deep Neural Network Fingerprinting by Conferrable Adversarial Examples","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Adversarial system; Fingerprint (computing); Transfer of learning; Artificial neural network; Machine learning; Weighting; Adversary; Data mining; Pattern recognition (psychology); Computer security","score_opus":0.05084058482975618,"score_gpt":0.19489839878680304,"score_spread":0.14405781395704687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990378874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08415513,0.00035878533,0.9104511,0.00055833906,0.00006631483,0.000078171135,0.00011665652,0.0017188364,0.0024966693],"genre_scores_gemma":[0.884611,0.00013346784,0.113005795,0.00023828,0.000033058142,0.00008156523,0.00015909925,0.00012265288,0.0016150933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678963,0.0013626245,0.0001458715,0.0004941838,0.0010047783,0.00020278769],"domain_scores_gemma":[0.98823303,0.006335481,0.0009836712,0.0036167246,0.000656862,0.0001742573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003993873,0.0009831177,0.0007825016,0.00071152253,0.0004117625,0.00091289374,0.0015430637,0.0015154941,0.0015432392],"category_scores_gemma":[0.02191655,0.00046000825,0.0007355844,0.000478145,0.002328109,0.0027964355,0.002600839,0.0029448855,0.0004009947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048210312,0.00011217386,0.003121897,0.00010846332,0.00012615536,0.00024333832,0.0001494235,0.8360673,0.014665638,0.034793578,0.0023853285,0.10774453],"study_design_scores_gemma":[0.000007844766,0.00005827892,0.00024825145,0.000010872614,0.000007913051,0.00007202845,0.000008438403,0.9794534,0.0089115575,0.010666648,0.0005452294,0.000009563696],"about_ca_topic_score_codex":0.0008420123,"about_ca_topic_score_gemma":0.00070586655,"teacher_disagreement_score":0.003993873,"about_ca_system_score_codex":0.0011893059,"about_ca_system_score_gemma":0.00054201623,"threshold_uncertainty_score":0.02112186},"labels":[],"label_agreement":null},{"id":"W2991005916","doi":"10.1109/icst46399.2020.00019","title":"Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study","year":2020,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Context (archaeology); Complement (music); Online and offline; Deep neural networks; Statistical hypothesis testing; Artificial neural network; Test data","score_opus":0.06495997125813802,"score_gpt":0.299791241828165,"score_spread":0.234831270570027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991005916","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767879,0.0003326706,0.01971918,0.0002842258,0.00006490632,0.00012307968,0.0004841675,0.00052827457,0.0016756534],"genre_scores_gemma":[0.983284,0.000087202556,0.015035508,0.00009188913,0.000017386521,0.000079922946,0.0007593128,0.00005793032,0.00058685004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958454,0.0017228312,0.00031314403,0.0007547538,0.0011261248,0.00023773084],"domain_scores_gemma":[0.96915734,0.020935914,0.0015444396,0.0035943473,0.0040569007,0.0007110491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040049544,0.00094527,0.00046834434,0.00097017526,0.0003739881,0.0008169802,0.0019779587,0.0014465628,0.00072683726],"category_scores_gemma":[0.019637292,0.00031041217,0.00047183168,0.000740896,0.0011573717,0.0014004955,0.00095431786,0.0010514215,0.00028243742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002785537,0.0043853456,0.124171466,0.0009636615,0.0005322748,0.0026081516,0.0017179295,0.6124148,0.021452963,0.0056373808,0.0089703165,0.21436024],"study_design_scores_gemma":[0.00022319278,0.0027721957,0.030607456,0.000093518924,0.0000970205,0.0006794909,0.0011071998,0.9244458,0.030044546,0.0051053516,0.004736901,0.000087268876],"about_ca_topic_score_codex":0.0070304885,"about_ca_topic_score_gemma":0.00966085,"teacher_disagreement_score":0.0070304885,"about_ca_system_score_codex":0.0012093189,"about_ca_system_score_gemma":0.000844403,"threshold_uncertainty_score":0.02118051},"labels":[],"label_agreement":null},{"id":"W2991311696","doi":"","title":"Trust Region Sequential Variational Inference","year":2019,"lang":"en","type":"article","venue":"Asian Conference on Machine Learning","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Computer science; Artificial intelligence; Algorithm","score_opus":0.024625190974588887,"score_gpt":0.2771333908033532,"score_spread":0.2525081998287643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991311696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034116851,0.0004444693,0.9924529,0.0005343197,0.00015283251,0.000024955623,0.000067277775,0.00023937268,0.002672232],"genre_scores_gemma":[0.49666435,0.0014118847,0.45981976,0.00080041366,0.0007753067,0.00027767805,0.0009358881,0.00084951735,0.03846512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852884,0.0006439529,0.00006724371,0.0003666167,0.0002900293,0.000103216466],"domain_scores_gemma":[0.99408984,0.0043087476,0.00024786568,0.0006731176,0.00046936853,0.00021098412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042580734,0.0013996456,0.0025127192,0.0009135528,0.0005835955,0.0019076604,0.001907347,0.0025702189,0.0071386783],"category_scores_gemma":[0.0136428345,0.0015281519,0.001709696,0.0009744889,0.0024585298,0.0025952603,0.003249313,0.0043950295,0.0013522009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002981824,0.0000924045,0.0007976587,0.00021366034,0.00028548576,0.00012796321,0.00012680645,0.63066226,0.0021330742,0.26331952,0.008775468,0.09316746],"study_design_scores_gemma":[0.000009445767,0.000018026745,0.000048586837,0.000007107493,0.000011234363,0.000017825174,0.0000032349533,0.9587078,0.0003467925,0.039797917,0.0010256638,0.0000063213843],"about_ca_topic_score_codex":0.0056577725,"about_ca_topic_score_gemma":0.0047119176,"teacher_disagreement_score":0.0071386783,"about_ca_system_score_codex":0.0014383792,"about_ca_system_score_gemma":0.0017685648,"threshold_uncertainty_score":0.023881257},"labels":[],"label_agreement":null},{"id":"W2991523350","doi":"10.1109/tmi.2020.3006437","title":"Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":341,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Dice; Segmentation; Normalization (sociology); Image segmentation; Calibration; Convolutional neural network; Pattern recognition (psychology); Medical imaging","score_opus":0.011863829040864641,"score_gpt":0.28847102604041774,"score_spread":0.2766071969995531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991523350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054170568,0.00073305663,0.94266087,0.00040497325,0.000034040804,0.000039075345,0.000086084925,0.00087337435,0.0009979378],"genre_scores_gemma":[0.87965167,0.00033816745,0.118447095,0.00022565426,0.00005967712,0.00006023926,0.0002599909,0.00020207003,0.0007554645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979997,0.0007541005,0.0001218349,0.00043160314,0.00058055925,0.00011209804],"domain_scores_gemma":[0.98964477,0.006235453,0.0014009398,0.0012704035,0.0011992718,0.0002491875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006254786,0.0010709486,0.0008013769,0.0014673629,0.0005195004,0.001395204,0.0016239857,0.0016808802,0.0008864488],"category_scores_gemma":[0.033693973,0.00070293364,0.00059671054,0.0006442248,0.001994256,0.0022950452,0.0023324348,0.0019757375,0.00019727736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019511148,0.000032691092,0.0028612341,0.00008670505,0.00006986761,0.000079027806,0.00015613505,0.914376,0.005642503,0.011141529,0.0007539318,0.06460529],"study_design_scores_gemma":[0.0000032154069,0.000024853056,0.0005311672,0.000020062038,0.0000074577088,0.000038691323,0.0000076168553,0.98893213,0.0038727687,0.0063343267,0.00021516868,0.0000124478],"about_ca_topic_score_codex":0.0029521454,"about_ca_topic_score_gemma":0.0024692547,"teacher_disagreement_score":0.006254786,"about_ca_system_score_codex":0.0019451058,"about_ca_system_score_gemma":0.0011853651,"threshold_uncertainty_score":0.03307891},"labels":[],"label_agreement":null},{"id":"W2994150880","doi":"10.1109/icsme.2019.00078","title":"DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks","year":2019,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Fuzz testing; Computer science; Machine learning; Artificial intelligence; Code coverage; Random testing; Model-based testing; Artificial neural network; Test case; Test strategy; Data mining; Software","score_opus":0.01641999231786189,"score_gpt":0.23972657710143006,"score_spread":0.22330658478356816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994150880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06611568,0.00045044272,0.9268087,0.00047555903,0.00004898323,0.000127399,0.00015519389,0.0031577763,0.0026601416],"genre_scores_gemma":[0.68296444,0.00018657508,0.3135835,0.00032579436,0.000028348188,0.00030792187,0.00032965312,0.00038880468,0.0018849563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990088,0.00040684867,0.0000608555,0.00016818885,0.00024050598,0.000114744405],"domain_scores_gemma":[0.99563843,0.0032716454,0.00028185747,0.00028848732,0.0004015312,0.00011804861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023111084,0.0012975964,0.0008695845,0.0013254427,0.00048043873,0.0008509706,0.00261861,0.0015494631,0.002491567],"category_scores_gemma":[0.008017203,0.00069870404,0.00094064133,0.0004888104,0.0016139838,0.0013826261,0.001708283,0.0017764821,0.00026067684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007835346,0.00006869147,0.0021308076,0.000075515025,0.000063416875,0.00012233731,0.000072186594,0.9388082,0.0036110373,0.007898171,0.0008656647,0.04620571],"study_design_scores_gemma":[0.000006362556,0.000025580164,0.00007014687,0.000006671012,0.000005076558,0.000011965765,0.0000056986555,0.99550974,0.0008472862,0.0033121842,0.00019652752,0.0000028639695],"about_ca_topic_score_codex":0.006749374,"about_ca_topic_score_gemma":0.0067639286,"teacher_disagreement_score":0.006749374,"about_ca_system_score_codex":0.0016913265,"about_ca_system_score_gemma":0.0013765306,"threshold_uncertainty_score":0.013420165},"labels":[],"label_agreement":null},{"id":"W2994896922","doi":"","title":"Thieves of Sesame Street: Model Extraction on BERT-based APIs","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Heuristics; Adversary; Language model; Set (abstract data type); Artificial intelligence; Task (project management); Exploit; Inference; Natural language processing; Computer security; Programming language","score_opus":0.08559918538337095,"score_gpt":0.20274484910803137,"score_spread":0.11714566372466041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994896922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24280669,0.00054261077,0.7302661,0.006139916,0.00016027754,0.00016910367,0.00095031055,0.008409563,0.010555543],"genre_scores_gemma":[0.8838344,0.00023786817,0.10573909,0.0008757089,0.00007063011,0.00008524119,0.0013190993,0.00076074165,0.0070772246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979989,0.0007594268,0.00007741632,0.00031939763,0.00068545836,0.00015926937],"domain_scores_gemma":[0.9928542,0.0040967064,0.0003689968,0.0023062453,0.00025880872,0.00011499133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002730815,0.0006549297,0.00060550636,0.000655083,0.00085756317,0.001435213,0.0011564239,0.0018112701,0.0029515014],"category_scores_gemma":[0.0155404825,0.00043811282,0.0012521072,0.0006387919,0.0021762613,0.0057440917,0.0032033143,0.0027123827,0.0012613571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010671639,0.0003639528,0.007951781,0.00029364435,0.0001847172,0.0021483572,0.0012407157,0.517179,0.026587786,0.21826929,0.027275166,0.19743852],"study_design_scores_gemma":[0.00001762656,0.000046387,0.00039116698,0.00001444011,0.000012888957,0.00024153788,0.000065126194,0.9205621,0.011701462,0.06176965,0.005159678,0.000017928332],"about_ca_topic_score_codex":0.0023312909,"about_ca_topic_score_gemma":0.0022163633,"teacher_disagreement_score":0.0029515014,"about_ca_system_score_codex":0.000984663,"about_ca_system_score_gemma":0.0005527352,"threshold_uncertainty_score":0.014442086},"labels":[],"label_agreement":null},{"id":"W2995755899","doi":"10.48550/arxiv.1912.06409","title":"Potential adversarial samples for white-box attacks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; White (mutation); Computer security; White box; Computer science; Artificial intelligence; Machine learning","score_opus":0.057950468732276404,"score_gpt":0.21335514485556598,"score_spread":0.15540467612328956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995755899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049542647,0.00033950654,0.9457589,0.00043495707,0.000059924845,0.00007603719,0.000089460955,0.00067966944,0.0030189862],"genre_scores_gemma":[0.87808686,0.00023594502,0.117826164,0.00030747688,0.000059074413,0.00016340676,0.00021227915,0.00020892828,0.0028999392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988991,0.000405635,0.00004120073,0.00019637257,0.00035061856,0.00010714505],"domain_scores_gemma":[0.9954672,0.0032021492,0.00029511226,0.00074657676,0.0001836246,0.00010535727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017154351,0.0010229567,0.00086347165,0.0005865671,0.00055708835,0.0009148991,0.00084781944,0.0013564135,0.0034014955],"category_scores_gemma":[0.010484954,0.0004836065,0.0007327547,0.00030012723,0.0019078909,0.0023706215,0.0026229513,0.0029110543,0.0007797758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065447367,0.00012340852,0.0021955145,0.00014119848,0.0001276671,0.00035514243,0.00015856867,0.8035771,0.033799242,0.07165922,0.0033281697,0.08388035],"study_design_scores_gemma":[0.000017457245,0.00011284511,0.0003317789,0.000027116035,0.000017446975,0.00017079466,0.000029189498,0.9376806,0.014241566,0.045577075,0.0017774442,0.00001661316],"about_ca_topic_score_codex":0.000311363,"about_ca_topic_score_gemma":0.0004272704,"teacher_disagreement_score":0.0034014955,"about_ca_system_score_codex":0.0005545465,"about_ca_system_score_gemma":0.00043144432,"threshold_uncertainty_score":0.011379123},"labels":[],"label_agreement":null},{"id":"W2997532515","doi":"10.1016/j.eng.2019.12.012","title":"Adversarial Attacks and Defenses in Deep Learning","year":2020,"lang":"en","type":"article","venue":"Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":660,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Adversarial system; Adversarial machine learning; Computer science; Robustness (evolution); Vulnerability (computing); Deep learning; Artificial intelligence; Implementation; Computer security; Frontier; Cover (algebra); Data science; Field (mathematics); Attack surface; Machine learning; Engineering; Political science; Software engineering; Mathematics","score_opus":0.007379679572347504,"score_gpt":0.2004817178216978,"score_spread":0.1931020382493503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997532515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014178156,0.0038257248,0.97040164,0.0031357207,0.00015781468,0.000044622608,0.0000619715,0.0002758015,0.007918449],"genre_scores_gemma":[0.8861864,0.0054554087,0.100540765,0.0012409777,0.00056178,0.00019956104,0.00012941041,0.00012772222,0.00555788],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644,0.0014899767,0.00014398078,0.00048140288,0.0010909016,0.00035370345],"domain_scores_gemma":[0.9866584,0.010501771,0.0008635991,0.0012034903,0.00052636047,0.00024641096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045733973,0.0010684904,0.0011323931,0.001395374,0.00086678716,0.0022278333,0.001658666,0.002690666,0.0013720125],"category_scores_gemma":[0.015973814,0.0007117487,0.00084172713,0.00090515666,0.004957999,0.004144968,0.0043225777,0.0054313308,0.00026562178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008840835,0.000048007143,0.00082033407,0.0001594755,0.00008015339,0.0001270652,0.00015071793,0.34400034,0.0020872296,0.61095893,0.0029111532,0.03856817],"study_design_scores_gemma":[0.000008869015,0.000035328045,0.0001378557,0.000050570063,0.000009224771,0.00006450713,0.000018755107,0.72992927,0.0008398257,0.2668916,0.001998837,0.000015325117],"about_ca_topic_score_codex":0.000950691,"about_ca_topic_score_gemma":0.00067353225,"teacher_disagreement_score":0.0045733973,"about_ca_system_score_codex":0.0018826608,"about_ca_system_score_gemma":0.0010280528,"threshold_uncertainty_score":0.02418667},"labels":[],"label_agreement":null},{"id":"W2998116579","doi":"10.1609/aaai.v34i04.6086","title":"Reinforcement Learning with Perturbed Rewards","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Reinforcement learning; Computer science; Noise (video); Confusion matrix; Artificial intelligence; Convergence (economics); Confusion; Set (abstract data type); Gaussian; Matrix (chemical analysis); Mathematical optimization; Machine learning; Algorithm; Mathematics","score_opus":0.060282473032491304,"score_gpt":0.27529961439256584,"score_spread":0.21501714136007455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998116579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024872988,0.00030713016,0.97186637,0.00036534833,0.00005015177,0.000068075984,0.00005753352,0.0005145414,0.0018978504],"genre_scores_gemma":[0.9246601,0.00017345576,0.07204021,0.0002539973,0.00005685918,0.00016827982,0.00012357396,0.00008477795,0.0024386405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975473,0.0010410146,0.0001274863,0.00055338716,0.00047272857,0.00025812173],"domain_scores_gemma":[0.9928646,0.004687838,0.00086045754,0.0005263369,0.0007574988,0.00030323034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030724138,0.0014337711,0.001550443,0.00049449224,0.00045545504,0.0011936896,0.0014780106,0.0013432397,0.0018978321],"category_scores_gemma":[0.015459612,0.0006311451,0.00054151326,0.0003646695,0.0021038724,0.0015865603,0.0016936444,0.002321545,0.00040410765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001179932,0.000043578537,0.0007508854,0.000053802098,0.00003744979,0.00006317751,0.000056282417,0.9641032,0.000748848,0.016173853,0.00060020725,0.01725067],"study_design_scores_gemma":[0.00001146515,0.000022964825,0.00006585637,0.0000057702377,0.0000044652215,0.000009374304,0.0000036258782,0.99184364,0.00027452954,0.007569454,0.00018446747,0.00000443109],"about_ca_topic_score_codex":0.0035924262,"about_ca_topic_score_gemma":0.002476167,"teacher_disagreement_score":0.0035924262,"about_ca_system_score_codex":0.0015907609,"about_ca_system_score_gemma":0.0015621128,"threshold_uncertainty_score":0.016248643},"labels":[],"label_agreement":null},{"id":"W3003092138","doi":"10.4271/2020-01-0738","title":"Bridging the Gap between ISO 26262 and Machine Learning: A Survey of Techniques for Developing Confidence in Machine Learning Systems","year":2020,"lang":"en","type":"article","venue":"SAE International Journal of Advances and Current Practices in Mobility","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Critical Systems Labs","funders":"","keywords":"Bridging (networking); Computer science; Functional safety; Class (philosophy); Engineering; Artificial intelligence; Systems engineering; Reliability engineering; Computer security","score_opus":0.10356721390516128,"score_gpt":0.393370896460878,"score_spread":0.2898036825557167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003092138","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005382535,0.10286211,0.8668613,0.004264993,0.00036886224,0.00023595887,0.00014766322,0.00083178555,0.019044887],"genre_scores_gemma":[0.22903934,0.15194227,0.6085262,0.0016537305,0.0013997247,0.00067543687,0.00089699577,0.00049766654,0.0053686546],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98501486,0.0040619266,0.0021685828,0.0014155172,0.0069980887,0.00034110207],"domain_scores_gemma":[0.9690258,0.020571435,0.0017008252,0.0025201568,0.005951144,0.00023051359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013750951,0.0011826064,0.001428943,0.006484778,0.00072897,0.0048534567,0.0021144373,0.0023887805,0.0020704996],"category_scores_gemma":[0.03737455,0.00096347986,0.0013734788,0.0052233874,0.003083138,0.0069554616,0.0021614055,0.0031450822,0.0013473987],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058114943,0.000100182326,0.0020847525,0.002417998,0.00008336697,0.000096412696,0.0006029237,0.015083354,0.0015592083,0.1294129,0.0044430215,0.84405774],"study_design_scores_gemma":[0.00004858089,0.000668993,0.007868511,0.008072479,0.00023129505,0.0013264328,0.0016071693,0.20449533,0.016286561,0.35568857,0.40337482,0.00033121754],"about_ca_topic_score_codex":0.0020920536,"about_ca_topic_score_gemma":0.0010584363,"teacher_disagreement_score":0.013750951,"about_ca_system_score_codex":0.0025445893,"about_ca_system_score_gemma":0.0030070078,"threshold_uncertainty_score":0.07272285},"labels":[],"label_agreement":null},{"id":"W3003526064","doi":"10.1007/978-3-030-45371-8_6","title":"Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Interpretability; Malware; Machine learning; Computer science; False positive paradox; Artificial intelligence; Leverage (statistics); False positives and false negatives; Domain (mathematical analysis); Computer security; Data mining","score_opus":0.01488328139086341,"score_gpt":0.27162062821451793,"score_spread":0.2567373468236545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003526064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18398686,0.0004891271,0.8019543,0.00094496313,0.00038899222,0.00012428244,0.000398882,0.0052322512,0.00648041],"genre_scores_gemma":[0.8728167,0.00017524627,0.122912854,0.00022407925,0.00017220681,0.000041295552,0.0004202344,0.00040087462,0.0028364377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992404,0.0002043232,0.000044844273,0.00016026947,0.00025160215,0.000098633835],"domain_scores_gemma":[0.99687505,0.0015200482,0.0003845812,0.0005299882,0.0005569589,0.00013334354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014605083,0.0012963747,0.00078127376,0.0016116351,0.0005367901,0.0019704415,0.0007549069,0.0019814738,0.0022230463],"category_scores_gemma":[0.009230287,0.00044309153,0.00067513407,0.0005738991,0.0008073947,0.0023622664,0.001381974,0.0013540295,0.0013180238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018521763,0.00046111352,0.018642224,0.00033987607,0.00021922046,0.0029196183,0.00043067615,0.18148977,0.35469815,0.0443182,0.011732674,0.38289633],"study_design_scores_gemma":[0.000014318713,0.000094726835,0.0018461529,0.00002085396,0.000038233375,0.0003313561,0.00007192026,0.9170504,0.042229522,0.036849473,0.0014294264,0.00002349047],"about_ca_topic_score_codex":0.0009358645,"about_ca_topic_score_gemma":0.0013199319,"teacher_disagreement_score":0.0022230463,"about_ca_system_score_codex":0.00048298138,"about_ca_system_score_gemma":0.0006262634,"threshold_uncertainty_score":0.007723987},"labels":[],"label_agreement":null},{"id":"W3003953721","doi":"10.59275/j.melba.2021-8678","title":"A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality","year":2021,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Nvidia; F. Hoffmann-La Roche; Alzheimer's Society; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Artificial intelligence; Probabilistic logic; Segmentation; Data mining; Machine learning; Heteroscedasticity; Uncertainty quantification; Robustness (evolution); Image quality; Noise (video); Quality (philosophy); Pattern recognition (psychology); Image (mathematics)","score_opus":0.02763975736512384,"score_gpt":0.3395162783419846,"score_spread":0.31187652097686075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003953721","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018356092,0.00018456277,0.979916,0.00026470068,0.000026149944,0.00003107996,0.00010668073,0.00017322219,0.0009415229],"genre_scores_gemma":[0.8826954,0.00045652455,0.110441506,0.00031827082,0.00011497527,0.00023681877,0.00040110035,0.000150882,0.00518449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851817,0.0004752142,0.000078956284,0.00044761377,0.00036546428,0.00011468401],"domain_scores_gemma":[0.9942404,0.0035648278,0.00094424665,0.00051300554,0.0006341404,0.00010337378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034109503,0.0012916543,0.00079734856,0.00070744,0.0003048175,0.0011373141,0.0016753657,0.0014988763,0.0011975071],"category_scores_gemma":[0.012589446,0.00070113095,0.0010150785,0.0004696874,0.0021912896,0.0018589727,0.0017163223,0.0023131918,0.00029571625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080516176,0.00002131707,0.0011351131,0.00005563741,0.00005419175,0.00007407855,0.000090494745,0.95943075,0.004509275,0.016099926,0.0004671795,0.017981559],"study_design_scores_gemma":[0.0000033417566,0.000024194473,0.00047661192,0.000010168302,0.0000132067225,0.000036147772,0.0000035185276,0.99051297,0.0014348272,0.007221269,0.00025082054,0.000012905737],"about_ca_topic_score_codex":0.0033329418,"about_ca_topic_score_gemma":0.0029832388,"teacher_disagreement_score":0.0034109503,"about_ca_system_score_codex":0.0014493245,"about_ca_system_score_gemma":0.0007746812,"threshold_uncertainty_score":0.018039048},"labels":[],"label_agreement":null},{"id":"W3005856724","doi":"10.1109/issre.2019.00013","title":"A Safety Analysis Method for Perceptual Components in Automated Driving","year":2019,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Perception; Artificial intelligence; Failure mode and effects analysis; Segmentation; Machine learning; Linkage (software); Component (thermodynamics); Reliability engineering; Engineering","score_opus":0.018525362988978285,"score_gpt":0.313929882371201,"score_spread":0.29540451938222273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005856724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03149819,0.00013565406,0.96540916,0.00020330885,0.000040555424,0.00010577957,0.0002718737,0.0012136891,0.0011218517],"genre_scores_gemma":[0.66942304,0.0001280162,0.32617092,0.00014122008,0.00006922038,0.00023320976,0.0011527031,0.00025465133,0.0024271067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990601,0.00018971557,0.000055818047,0.0002801986,0.00032035197,0.00009373179],"domain_scores_gemma":[0.9973828,0.0012011636,0.00028415036,0.00034569998,0.0006993475,0.00008674795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024353096,0.0008833973,0.00046254095,0.0020084535,0.00051187765,0.00092810934,0.0012485185,0.001181027,0.0024404016],"category_scores_gemma":[0.0072037494,0.00030047307,0.0010950968,0.0006847496,0.0010197457,0.0014313216,0.0015681126,0.0018316095,0.00054738537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035829496,0.00027718762,0.014427201,0.00019743374,0.00013793504,0.00017040731,0.0004102066,0.5115239,0.01817665,0.023032028,0.005553852,0.42573485],"study_design_scores_gemma":[0.000008306201,0.00007006509,0.0019163311,0.0000143832185,0.000015289057,0.000043270913,0.000047959475,0.9792733,0.004476723,0.012401263,0.001719865,0.000013243861],"about_ca_topic_score_codex":0.004571963,"about_ca_topic_score_gemma":0.0040914766,"teacher_disagreement_score":0.004571963,"about_ca_system_score_codex":0.0009799779,"about_ca_system_score_gemma":0.0013374997,"threshold_uncertainty_score":0.012879312},"labels":[],"label_agreement":null},{"id":"W3008392744","doi":"10.1049/iet-cvi.2019.0378","title":"Adversarial examples detection through the sensitivity in space mappings","year":2020,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); MNIST database; Detector; Computer science; Artificial intelligence; Adversarial system; Pattern recognition (psychology); Sensitivity (control systems); Deep neural networks; Feature extraction; False positive rate; Overhead (engineering); Feature vector; Feature (linguistics); Machine learning; Artificial neural network; Engineering","score_opus":0.01888495229284658,"score_gpt":0.2560280036806812,"score_spread":0.2371430513878346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008392744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06784257,0.00028371476,0.9278458,0.000402992,0.000067742425,0.000073912386,0.00005469139,0.0007868612,0.0026417181],"genre_scores_gemma":[0.9061246,0.0001943007,0.09092806,0.0002895108,0.000045416597,0.0000618488,0.0000754735,0.00009204596,0.0021887803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972945,0.0009521437,0.00012252548,0.00049147237,0.0009307075,0.00020852903],"domain_scores_gemma":[0.9893042,0.0075751687,0.0010717814,0.0011899174,0.00066886086,0.000190115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029700215,0.0008930163,0.0008139423,0.0012003096,0.000333301,0.0012137272,0.0011742265,0.0014311232,0.0015312553],"category_scores_gemma":[0.018944437,0.0005061371,0.0007141842,0.0004818685,0.001961459,0.0026774285,0.003077644,0.0021426987,0.0003865731],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004290514,0.000083937324,0.0034075002,0.00014341678,0.00016624917,0.00036076814,0.00023040856,0.74308,0.02771072,0.054004546,0.0021379953,0.1682454],"study_design_scores_gemma":[0.0000065718395,0.00006835649,0.00052498607,0.000011342663,0.000011810576,0.00016373539,0.000013476093,0.975804,0.0093285665,0.013433423,0.00061814283,0.000015672862],"about_ca_topic_score_codex":0.00064890797,"about_ca_topic_score_gemma":0.00037151983,"teacher_disagreement_score":0.0029700215,"about_ca_system_score_codex":0.0009869218,"about_ca_system_score_gemma":0.0004558681,"threshold_uncertainty_score":0.015707195},"labels":[],"label_agreement":null},{"id":"W3010028534","doi":"10.1109/cvpr42600.2020.00132","title":"Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Deep neural networks; Leverage (statistics); Computer science; Artificial neural network; Artificial intelligence; Robustness (evolution); Deep learning; Machine learning; Perturbation (astronomy); End-to-end principle; Inference","score_opus":0.018961371945602483,"score_gpt":0.2761250068037797,"score_spread":0.2571636348581772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010028534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01252318,0.0002442081,0.9809471,0.00019257268,0.00008443067,0.000115486604,0.00008955658,0.004541304,0.001262149],"genre_scores_gemma":[0.61592716,0.00034749022,0.37396532,0.0008778764,0.00015693427,0.00037551197,0.0008187141,0.0008990624,0.0066319928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884295,0.00026931334,0.000057660694,0.00029746967,0.00040391568,0.00012870462],"domain_scores_gemma":[0.99844676,0.00061904127,0.00016558923,0.0004275682,0.00024015667,0.00010093165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017081422,0.0018369202,0.0013503175,0.0006022049,0.00045068315,0.0007819165,0.0021190806,0.0017297668,0.0030210314],"category_scores_gemma":[0.0059309932,0.00053103565,0.00080726465,0.0003575631,0.0015775709,0.002354487,0.0034425538,0.0033419775,0.0011500968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035566228,0.00024831633,0.001261384,0.000121387864,0.00012771068,0.00025758613,0.00009815412,0.7365919,0.020526566,0.011708656,0.008714533,0.21998827],"study_design_scores_gemma":[0.00001236079,0.000089161476,0.000098104385,0.000006079331,0.000008162585,0.000048056325,0.0000057123098,0.9894818,0.0052574584,0.0042372327,0.0007466617,0.000009092518],"about_ca_topic_score_codex":0.0009889939,"about_ca_topic_score_gemma":0.0013678636,"teacher_disagreement_score":0.0030210314,"about_ca_system_score_codex":0.00073547504,"about_ca_system_score_gemma":0.0010084746,"threshold_uncertainty_score":0.010106385},"labels":[],"label_agreement":null},{"id":"W3013856963","doi":"","title":"PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.","year":2020,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Perception; Computer science; Psychology; Neuroscience","score_opus":0.11260758439789258,"score_gpt":0.34128833553054494,"score_spread":0.22868075113265235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013856963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009282565,0.00043435235,0.9780506,0.00090131705,0.00026359077,0.000116903124,0.00019671935,0.0053863,0.0053676604],"genre_scores_gemma":[0.7537093,0.00035809958,0.23566173,0.0012220503,0.00036337477,0.00021257652,0.0005064453,0.0009027883,0.007063804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995223,0.0019312605,0.00020425669,0.0006020062,0.0015986909,0.00044083025],"domain_scores_gemma":[0.9929056,0.0034796107,0.00040780968,0.0018110869,0.0010694894,0.0003264055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005101282,0.001364564,0.0017166759,0.000822299,0.00076621975,0.0029659225,0.003050273,0.0021029096,0.006646869],"category_scores_gemma":[0.020491721,0.0006381944,0.0009946238,0.0004602855,0.0028531922,0.004490418,0.009356244,0.004482346,0.002371313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012949884,0.0004551823,0.0020614967,0.0004113724,0.00026331397,0.00036138235,0.00043157735,0.5230284,0.01510619,0.11625418,0.033464793,0.30686727],"study_design_scores_gemma":[0.000038799946,0.00011974567,0.00014921461,0.000035373367,0.000021485479,0.00005571501,0.00006801983,0.9119438,0.0051843403,0.07870712,0.003655326,0.00002115492],"about_ca_topic_score_codex":0.0011564691,"about_ca_topic_score_gemma":0.0013618052,"teacher_disagreement_score":0.006646869,"about_ca_system_score_codex":0.00085061835,"about_ca_system_score_gemma":0.0021456643,"threshold_uncertainty_score":0.026978433},"labels":[],"label_agreement":null},{"id":"W3014636579","doi":"10.48550/arxiv.2004.00353","title":"SUMO: Unbiased Estimation of Log Marginal Probability for Latent\\n Variable Models","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Latent variable; Estimator; Mathematics; Latent variable model; Statistics; Parameterized complexity; Truncation (statistics); Minimum-variance unbiased estimator; Bias of an estimator; Efficient estimator; Variable (mathematics); Algorithm","score_opus":0.12111552536380138,"score_gpt":0.21569416183493006,"score_spread":0.09457863647112869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014636579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025140175,0.0001294072,0.99567455,0.00016661214,0.000028879273,0.000029298046,0.000106429085,0.00032620732,0.0010246128],"genre_scores_gemma":[0.37180284,0.001387148,0.6084797,0.0009775688,0.00037514072,0.00080423965,0.0020369892,0.0012683981,0.012867995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973556,0.0012189958,0.00009266014,0.00040883827,0.0007884687,0.00013552459],"domain_scores_gemma":[0.99374455,0.004069341,0.00044062771,0.0010842218,0.00046912226,0.00019213835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005758507,0.0015459507,0.0014003096,0.0012357574,0.0006386072,0.0016976529,0.0027411778,0.001662769,0.005372673],"category_scores_gemma":[0.027586306,0.0010305758,0.0011085701,0.000883191,0.002487094,0.0034664301,0.004786646,0.0038554517,0.0017755335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011958044,0.00009223205,0.0020817814,0.0002468813,0.00013680905,0.00015898304,0.00014041568,0.59793687,0.0033003686,0.28117332,0.008087045,0.10652575],"study_design_scores_gemma":[0.0000063981734,0.000017667811,0.00015223715,0.00002696505,0.000007128848,0.000038110426,0.0000069105054,0.91302985,0.0011313644,0.084222615,0.0013502605,0.00001042901],"about_ca_topic_score_codex":0.002085344,"about_ca_topic_score_gemma":0.003626615,"teacher_disagreement_score":0.005758507,"about_ca_system_score_codex":0.0019143177,"about_ca_system_score_gemma":0.0023271192,"threshold_uncertainty_score":0.030454278},"labels":[],"label_agreement":null},{"id":"W3016970897","doi":"10.1038/s42256-020-00257-z","title":"Shortcut learning in deep neural networks","year":2020,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1811,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Deep learning; Transferability; Benchmarking; Machine learning; Artificial neural network; Deep neural networks; Robustness (evolution); Perspective (graphical); Management","score_opus":0.010150698655579661,"score_gpt":0.26864828031207677,"score_spread":0.2584975816564971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016970897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015856558,0.003508316,0.9758809,0.0009303553,0.00015833705,0.000022986625,0.00008350192,0.0004705571,0.0030883914],"genre_scores_gemma":[0.7363018,0.0039363233,0.23700686,0.00065903313,0.0005801727,0.00021113458,0.0005286282,0.00059550663,0.020180656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922335,0.00032715118,0.000036225098,0.00014356177,0.00020190253,0.00006780428],"domain_scores_gemma":[0.9965669,0.002485292,0.00023435551,0.00027514907,0.00032142334,0.00011678823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002423513,0.00080032076,0.0012698935,0.00067076494,0.00041228373,0.001301654,0.0017704346,0.00202814,0.0024924513],"category_scores_gemma":[0.010467604,0.0007720911,0.00053935806,0.00077459303,0.0019325239,0.0027028853,0.0018836206,0.0031303521,0.00043930093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011476395,0.000057553098,0.00057371904,0.00020419997,0.00007641122,0.00006714857,0.00007367194,0.68385917,0.0021271552,0.21345189,0.005166075,0.09422823],"study_design_scores_gemma":[0.000005949112,0.00001538335,0.000075965996,0.0000146835,0.0000056770177,0.000009908348,0.0000037202897,0.89273095,0.00045858903,0.10596836,0.0007052091,0.000005505336],"about_ca_topic_score_codex":0.0033616405,"about_ca_topic_score_gemma":0.0029266234,"teacher_disagreement_score":0.0033616405,"about_ca_system_score_codex":0.0012868544,"about_ca_system_score_gemma":0.0007839862,"threshold_uncertainty_score":0.012816906},"labels":[],"label_agreement":null},{"id":"W3017343191","doi":"10.48550/arxiv.2004.07213","title":"Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Verifiable secret sharing; Trustworthiness; Computer science; Development (topology); Computer security; Data science; Programming language; Mathematics","score_opus":0.09684774672380762,"score_gpt":0.22389249999076927,"score_spread":0.12704475326696163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017343191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012469605,0.00075026334,0.96286553,0.008873081,0.00018593729,0.00030720644,0.00009999333,0.0011009887,0.013347368],"genre_scores_gemma":[0.66687083,0.0011641112,0.3216436,0.002382811,0.0005044136,0.0007313641,0.00021812676,0.00035442188,0.006130377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9577882,0.022363607,0.002280483,0.0050866674,0.010498174,0.0019829378],"domain_scores_gemma":[0.71198356,0.1854524,0.02133536,0.06373761,0.01387198,0.0036190706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049371194,0.0015761063,0.0016005436,0.004519685,0.0039479,0.011972914,0.007035494,0.009292577,0.00880966],"category_scores_gemma":[0.19954605,0.0019447496,0.0023181096,0.0018385699,0.021375818,0.025710324,0.018555302,0.012819579,0.0025666824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017751448,0.000111052344,0.0016190879,0.00025930206,0.000111723064,0.00028154036,0.0010914584,0.027820086,0.0027488042,0.9194474,0.003614341,0.042717673],"study_design_scores_gemma":[0.00006664107,0.00007305116,0.00030186572,0.00017685253,0.000052416544,0.0001910287,0.00021550091,0.1580373,0.0040899217,0.8269403,0.009794343,0.000060785238],"about_ca_topic_score_codex":0.0016971608,"about_ca_topic_score_gemma":0.0009336949,"teacher_disagreement_score":0.049371194,"about_ca_system_score_codex":0.004188198,"about_ca_system_score_gemma":0.005759572,"threshold_uncertainty_score":0.26110297},"labels":[],"label_agreement":null},{"id":"W3023083014","doi":"10.54813/fltl8789","title":"War-Algorithm Accountability","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Standards and Technology; McGill University; Fudan University; Army Research Laboratory; Eidgenössisches Departement für Auswärtige Angelegenheiten; China University of Political Science and Law; Harvard University; Silicon Valley Community Foundation","keywords":"Accountability; Computer science; Algorithm; Sketch; Law; Sociology; Political science","score_opus":0.018947514436825852,"score_gpt":0.2930098471104213,"score_spread":0.2740623326735954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023083014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006349979,0.0038025244,0.4731872,0.056989018,0.0029891164,0.00024809883,0.00041518238,0.0017817587,0.45423716],"genre_scores_gemma":[0.61652344,0.0052531026,0.1534337,0.034446836,0.0048114997,0.0013993304,0.0008814793,0.0025672028,0.18068337],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9804026,0.008346794,0.0013253659,0.0042039044,0.004247072,0.001474306],"domain_scores_gemma":[0.97490114,0.011856366,0.0020655615,0.0068693142,0.0034068576,0.0009007799],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01569869,0.0013413758,0.0007483695,0.0018567035,0.0056345616,0.015197903,0.0029471307,0.00824861,0.028249737],"category_scores_gemma":[0.049257595,0.00077567995,0.0014752046,0.0019493947,0.021574952,0.026558336,0.009042134,0.009652514,0.006867966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070690926,0.000005906831,0.0001386535,0.00002849279,0.000003861751,0.000016481683,0.00032204657,0.0005069807,0.00005959177,0.9840206,0.005872269,0.009018055],"study_design_scores_gemma":[0.000009779665,0.000021272179,0.00018249721,0.00013685561,0.000008688738,0.00007292558,0.00021659538,0.0026888708,0.0005246785,0.7851123,0.21099938,0.000026090025],"about_ca_topic_score_codex":0.003667212,"about_ca_topic_score_gemma":0.0028896346,"teacher_disagreement_score":0.99436545,"about_ca_system_score_codex":0.007253233,"about_ca_system_score_gemma":0.0075083706,"threshold_uncertainty_score":0.09450477},"labels":[],"label_agreement":null},{"id":"W3025658176","doi":"10.48550/arxiv.2005.05750","title":"Evaluating Ensemble Robustness Against Adversarial Attacks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Transferability; Robustness (evolution); Computer science; Measure (data warehouse); Artificial intelligence; Deep neural networks; Ensemble learning; Ensemble forecasting; Machine learning; Artificial neural network; Data mining","score_opus":0.153122486827989,"score_gpt":0.2652537893861667,"score_spread":0.11213130255817771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025658176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68318546,0.001892289,0.3041091,0.0007566941,0.00018743458,0.0001285801,0.0004982129,0.0012019464,0.008040362],"genre_scores_gemma":[0.9832281,0.00016682895,0.0153237805,0.000056716537,0.000035829908,0.00003358184,0.00038209694,0.00006252769,0.000710526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977919,0.00076985074,0.00012738202,0.00037264792,0.0006946842,0.00024352648],"domain_scores_gemma":[0.9866615,0.008672515,0.0010737965,0.0020698563,0.0009988737,0.0005235072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006388233,0.0010426075,0.0010557455,0.0013517992,0.00044791083,0.0010497001,0.00094760227,0.0016451731,0.0011925107],"category_scores_gemma":[0.021999368,0.0002829072,0.0006131545,0.00066451886,0.0010229435,0.0016076517,0.0019475687,0.001311318,0.00033375985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020103733,0.000049514543,0.0048787287,0.00004471185,0.0001480665,0.000048814858,0.00002790846,0.96737087,0.0025382352,0.0016486002,0.0005685168,0.022474945],"study_design_scores_gemma":[0.0000056197377,0.000150297,0.0017515093,0.000012294177,0.000025060492,0.000040704395,0.000018945482,0.9926536,0.0028722738,0.0022140562,0.00024596023,0.000009731525],"about_ca_topic_score_codex":0.0018336978,"about_ca_topic_score_gemma":0.00125026,"teacher_disagreement_score":0.006388233,"about_ca_system_score_codex":0.00089193496,"about_ca_system_score_gemma":0.00052732707,"threshold_uncertainty_score":0.033784628},"labels":[],"label_agreement":null},{"id":"W3032588483","doi":"10.5539/jpl.v13n2p115","title":"General Legal Limits of the Application of the Lethal Autonomous Weapons Systems within the Purview of International Humanitarian Law","year":2020,"lang":"en","type":"article","venue":"Journal of Politics and Law","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Russian Foundation for Basic Research","keywords":"International humanitarian law; Proportionality (law); Law; International law; Political science; Independence (probability theory); Humanitarian intervention; Law and economics; Sociology","score_opus":0.021418619018311675,"score_gpt":0.2672416622766636,"score_spread":0.24582304325835194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032588483","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03672429,0.0073779877,0.05454497,0.021156788,0.00052003755,0.0002798821,0.00024852622,0.00015076852,0.87899673],"genre_scores_gemma":[0.9224015,0.0061470736,0.028527468,0.011131883,0.0011214811,0.0013378772,0.00030989808,0.000093428585,0.028929405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9660652,0.00816054,0.0035198736,0.0039484734,0.015502968,0.0028029848],"domain_scores_gemma":[0.9644465,0.020938873,0.003267941,0.00478698,0.005724404,0.0008352969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021258967,0.0007614118,0.0013984198,0.0033944098,0.005038025,0.015133889,0.0043653096,0.010038385,0.0036411919],"category_scores_gemma":[0.039015412,0.0010066619,0.0014787589,0.0018414126,0.037875626,0.0097002145,0.0069479793,0.010463708,0.00076312904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004854258,0.000016343562,0.00025020633,0.00003702395,0.000005482391,0.00005148931,0.0006670613,0.00065228966,0.0001453142,0.9953708,0.00086785894,0.0019312378],"study_design_scores_gemma":[0.000019481275,0.000043137337,0.0012982254,0.0006138515,0.000029073613,0.00028315166,0.0011917526,0.0016343922,0.0005666591,0.9395593,0.05470757,0.000053494707],"about_ca_topic_score_codex":0.008108478,"about_ca_topic_score_gemma":0.005326998,"teacher_disagreement_score":0.021258967,"about_ca_system_score_codex":0.0058557456,"about_ca_system_score_gemma":0.009800914,"threshold_uncertainty_score":0.1124295},"labels":[],"label_agreement":null},{"id":"W3033072407","doi":"10.1007/s10898-020-00949-1","title":"Advances in verification of ReLU neural networks","year":2020,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"MNIST database; Artificial neural network; Solver; Computer science; Deep neural networks; Artificial intelligence; Set (abstract data type); Integer programming; Theoretical computer science; Machine learning; Algorithm; Programming language","score_opus":0.008913140916029237,"score_gpt":0.25727068776575346,"score_spread":0.24835754684972422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033072407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03633248,0.0011809038,0.9495678,0.0013668931,0.00019604492,0.00012331497,0.00029720395,0.0026610098,0.008274287],"genre_scores_gemma":[0.5624532,0.0009194614,0.42954198,0.00090810185,0.00018054697,0.0003279579,0.00086588855,0.00091723166,0.0038856112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9915725,0.003658331,0.00047485618,0.0013002526,0.0023271304,0.00066698587],"domain_scores_gemma":[0.97072953,0.022100331,0.0014446062,0.003116219,0.002252213,0.00035701366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059233652,0.0015719659,0.0013974181,0.0013316199,0.0009308117,0.0029067737,0.0029704692,0.002161965,0.0073100687],"category_scores_gemma":[0.038062703,0.0010807738,0.002842907,0.0010626247,0.0036311734,0.0039071944,0.004184741,0.004032359,0.0010774272],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032157762,0.00016754291,0.0023881001,0.0008173129,0.00015644921,0.00037479386,0.000295362,0.712724,0.0052732164,0.19633475,0.0034770288,0.077669874],"study_design_scores_gemma":[0.000028286046,0.000034282275,0.000103535545,0.00007248012,0.000017130784,0.000054914,0.000030405652,0.9140067,0.0027415336,0.08042602,0.0024714647,0.000013281929],"about_ca_topic_score_codex":0.00388209,"about_ca_topic_score_gemma":0.0036600516,"teacher_disagreement_score":0.0073100687,"about_ca_system_score_codex":0.0022222616,"about_ca_system_score_gemma":0.0028688344,"threshold_uncertainty_score":0.031326115},"labels":[],"label_agreement":null},{"id":"W3033311119","doi":"10.1109/eurosp51992.2021.00024","title":"Sponge Examples: Energy-Latency Attacks on Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Energy consumption; Latency (audio); Exploit; Application-specific integrated circuit; Artificial neural network; Efficient energy use; Software portability; Energy (signal processing); Embedded system; Artificial intelligence; Computer security; Operating system; Engineering; Telecommunications","score_opus":0.026208116300472147,"score_gpt":0.268355225118932,"score_spread":0.24214710881845986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033311119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4390687,0.0010994236,0.5358031,0.003157687,0.0003192137,0.00018599018,0.00041503762,0.00700354,0.0129473545],"genre_scores_gemma":[0.9789337,0.00016398537,0.01898968,0.00033614627,0.000025375686,0.00005286362,0.0000858771,0.00014177129,0.0012705812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857855,0.00054553116,0.00008639568,0.0001562882,0.00042636468,0.00020685776],"domain_scores_gemma":[0.99458647,0.003129927,0.00044900048,0.0014625321,0.00028007574,0.000091992784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011181617,0.0007939931,0.0005565492,0.00037820046,0.00047033746,0.0007482529,0.0010277467,0.0010989703,0.002141821],"category_scores_gemma":[0.008417485,0.00034261993,0.000750614,0.00035901205,0.0015244547,0.0033174704,0.0019413456,0.0019312993,0.0003961014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018508934,0.00020735932,0.0051036472,0.00036859943,0.00024883356,0.0012244537,0.00038887665,0.68217677,0.10363074,0.09295192,0.015468135,0.0963798],"study_design_scores_gemma":[0.000036268797,0.00020780743,0.0005523381,0.000025023044,0.000023505405,0.00033865852,0.000049770104,0.93385476,0.035409946,0.02622483,0.0032499873,0.000027049062],"about_ca_topic_score_codex":0.000651015,"about_ca_topic_score_gemma":0.0006149427,"teacher_disagreement_score":0.002141821,"about_ca_system_score_codex":0.000868938,"about_ca_system_score_gemma":0.00039790114,"threshold_uncertainty_score":0.007165134},"labels":[],"label_agreement":null},{"id":"W3034631124","doi":"10.48550/arxiv.2008.02883","title":"Stronger and Faster Wasserstein Adversarial Attacks","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wasserstein metric; Oracle; Adversarial system; Residual; Minification; Metric (unit); Robustness (evolution); Mathematics; Mathematical optimization; Computer science; Algorithm; Artificial intelligence; Applied mathematics","score_opus":0.05949320945209478,"score_gpt":0.18100704333347828,"score_spread":0.12151383388138351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034631124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017195517,0.00039346123,0.97443455,0.00093885814,0.00015511124,0.00009383635,0.00010552498,0.002474411,0.0042087412],"genre_scores_gemma":[0.6038164,0.0006279418,0.38272014,0.0014761988,0.0002482806,0.00028261906,0.0005942591,0.0008573579,0.009376801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662614,0.00095261924,0.00016438997,0.0006866067,0.0012210445,0.0003491773],"domain_scores_gemma":[0.99495333,0.0025124804,0.00041585648,0.001644491,0.00028935616,0.0001843708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031152042,0.0024375976,0.0019601367,0.00064832595,0.0006182224,0.0013572993,0.0021790948,0.002233664,0.0039603096],"category_scores_gemma":[0.011399779,0.00074193673,0.0013688854,0.0005318624,0.0022397735,0.0054449583,0.0057688905,0.0069059045,0.0014323334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027589026,0.0001958808,0.0006650855,0.00014647629,0.00012380455,0.00015300365,0.00013402163,0.75753295,0.016330611,0.108868346,0.009221297,0.106352635],"study_design_scores_gemma":[0.00001871383,0.00004614805,0.000073114745,0.000011804985,0.0000084641715,0.000049586037,0.000008442083,0.9676023,0.0033881802,0.027443573,0.0013378982,0.000011869387],"about_ca_topic_score_codex":0.0014958377,"about_ca_topic_score_gemma":0.002115781,"teacher_disagreement_score":0.0039603096,"about_ca_system_score_codex":0.0012888949,"about_ca_system_score_gemma":0.0011621137,"threshold_uncertainty_score":0.016474962},"labels":[],"label_agreement":null},{"id":"W3034672496","doi":"","title":"Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Adversarial system; Computer science; Invariant (physics); Sensitivity (control systems); Artificial intelligence; Robustness (evolution); Class (philosophy); Machine learning; Theoretical computer science; Mathematics","score_opus":0.06015761417466957,"score_gpt":0.29790528748645295,"score_spread":0.23774767331178337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034672496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06984034,0.0005690632,0.91463035,0.0030454386,0.00011406891,0.00015708758,0.00014754325,0.00079907913,0.0106971385],"genre_scores_gemma":[0.9324486,0.00041984298,0.06419825,0.00056733936,0.000156584,0.00020591612,0.00014815126,0.00017965223,0.0016756156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9875832,0.005193582,0.00060933235,0.0020266806,0.0037205347,0.00086658896],"domain_scores_gemma":[0.9171763,0.057716314,0.004501977,0.017419359,0.002132424,0.0010536431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013583453,0.001697897,0.0011433461,0.0008600819,0.00085636316,0.0026644634,0.00264255,0.0027565788,0.002193448],"category_scores_gemma":[0.08327256,0.0009040793,0.0010750075,0.000496593,0.006087805,0.006152884,0.0060723084,0.0065030926,0.0005203863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042840553,0.00019238036,0.004060602,0.00024820317,0.00018390952,0.00044332613,0.0004579003,0.5643301,0.023723556,0.34296337,0.0025752904,0.06039296],"study_design_scores_gemma":[0.000038252143,0.00030919875,0.001133386,0.000082789324,0.000044783083,0.00045088877,0.000087640066,0.6697973,0.012917203,0.3131266,0.0019555488,0.00005641285],"about_ca_topic_score_codex":0.00040906822,"about_ca_topic_score_gemma":0.00034891846,"teacher_disagreement_score":0.013583453,"about_ca_system_score_codex":0.001669867,"about_ca_system_score_gemma":0.0008832967,"threshold_uncertainty_score":0.07183701},"labels":[],"label_agreement":null},{"id":"W3036098004","doi":"10.1609/aaai.v34i10.7272","title":"Combating False Negatives in Adversarial Imitation Learning (Student Abstract)","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Adversarial system; Imitation; Computer science; Sample (material); Artificial intelligence; Machine learning; Psychology; Social psychology","score_opus":0.07769480420664557,"score_gpt":0.31635178777044354,"score_spread":0.23865698356379797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036098004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042491388,0.0004933256,0.94928324,0.0021458298,0.0002346077,0.00008556908,0.000083226856,0.0009577102,0.0042251362],"genre_scores_gemma":[0.8623354,0.00020275328,0.13166466,0.0009895943,0.00023334622,0.00017723422,0.00014650669,0.00022633003,0.004024194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99548596,0.0022889993,0.00013153152,0.0007195928,0.001141521,0.0002324395],"domain_scores_gemma":[0.96062994,0.02905349,0.002019247,0.00535026,0.002268956,0.0006780997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074203745,0.001061806,0.001362062,0.00054207875,0.0006958224,0.0012994467,0.0019614757,0.0017292358,0.0030009144],"category_scores_gemma":[0.047425445,0.00048201648,0.0005420992,0.00037446865,0.0032035229,0.0029238127,0.0034126248,0.0045885006,0.00087811024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009858788,0.0003284843,0.007928221,0.00032512055,0.00020887649,0.00042818536,0.00039617557,0.5636884,0.016849915,0.086445704,0.01801456,0.3044005],"study_design_scores_gemma":[0.000023902257,0.00014310534,0.0005082416,0.000021058013,0.00001334263,0.00017399923,0.00001984731,0.95920485,0.0060974774,0.032564577,0.00121144,0.000018214305],"about_ca_topic_score_codex":0.0011045533,"about_ca_topic_score_gemma":0.0010671731,"teacher_disagreement_score":0.0074203745,"about_ca_system_score_codex":0.0008646849,"about_ca_system_score_gemma":0.0008730257,"threshold_uncertainty_score":0.03924316},"labels":[],"label_agreement":null},{"id":"W3037553603","doi":"","title":"Stochastic Neural Network with Kronecker Flow.","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Computer science; Generalization; Kronecker product; Kronecker delta; Artificial neural network; Inference; Scalability; Artificial intelligence; Noise (video); Machine learning; Mathematical optimization; Mathematics","score_opus":0.024166058657566172,"score_gpt":0.1636484482311515,"score_spread":0.13948238957358533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037553603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068772775,0.00072045415,0.98866487,0.0003790212,0.00009441248,0.00003877427,0.000117147356,0.0002112932,0.0028966377],"genre_scores_gemma":[0.6335885,0.0015350799,0.3490652,0.0004890595,0.00022420775,0.0002908595,0.0006416664,0.00022539802,0.013940121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993305,0.00033821806,0.000030889216,0.00010533361,0.00015306787,0.00004199535],"domain_scores_gemma":[0.9981178,0.0011536736,0.0002010437,0.00024326754,0.00019696442,0.000087269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024216324,0.0010074765,0.0008157515,0.0005860011,0.0003652756,0.0010277776,0.0013451319,0.0013187169,0.002782409],"category_scores_gemma":[0.006998084,0.0005202549,0.000558183,0.0006344066,0.0014563272,0.0022481093,0.0016050693,0.002470272,0.0006775958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008353577,0.000034885677,0.0005734497,0.00009956143,0.000053690517,0.00006690169,0.00005290906,0.71920586,0.0012025933,0.23564488,0.002532622,0.040449068],"study_design_scores_gemma":[0.0000035541361,0.000010431254,0.000043570966,0.000007209715,0.0000032790672,0.000013278993,0.0000022909353,0.95179194,0.00018539348,0.047351036,0.0005842484,0.0000037039733],"about_ca_topic_score_codex":0.0032227046,"about_ca_topic_score_gemma":0.004502496,"teacher_disagreement_score":0.0032227046,"about_ca_system_score_codex":0.0010423621,"about_ca_system_score_gemma":0.001220762,"threshold_uncertainty_score":0.012806952},"labels":[],"label_agreement":null},{"id":"W3037846902","doi":"10.48550/arxiv.2006.14584","title":"The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Biological system; Mathematics; Chemistry; Biology","score_opus":0.10888581058485364,"score_gpt":0.23955181973882247,"score_spread":0.13066600915396884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037846902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11237636,0.0036752848,0.87539655,0.0016503717,0.00023536122,0.00013734734,0.00015403588,0.0013744764,0.005000211],"genre_scores_gemma":[0.83413726,0.001239995,0.16113317,0.0005566584,0.000210526,0.00013687774,0.00031849867,0.000624462,0.0016426175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9880783,0.0066167386,0.00078340655,0.0016053447,0.0023695698,0.000546624],"domain_scores_gemma":[0.9225353,0.061618354,0.005298932,0.006624284,0.003022885,0.0009002202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020135928,0.0027403631,0.0014499306,0.0026442623,0.0009789986,0.0021452236,0.00180047,0.0027760325,0.0015484587],"category_scores_gemma":[0.080735184,0.000704691,0.0014891175,0.0011217501,0.0030874675,0.0034726704,0.0036022214,0.0037226104,0.00047578398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071513676,0.00013126303,0.0062721674,0.00034692264,0.00041842888,0.00018811908,0.00016561174,0.86119324,0.0073303753,0.021922894,0.0018827496,0.0994331],"study_design_scores_gemma":[0.000021992619,0.00019315438,0.0015068996,0.000091889786,0.000053214906,0.00012258627,0.000052104082,0.9754371,0.00894238,0.012793765,0.0007439138,0.000040847495],"about_ca_topic_score_codex":0.001672013,"about_ca_topic_score_gemma":0.0010849998,"teacher_disagreement_score":0.020135928,"about_ca_system_score_codex":0.001429035,"about_ca_system_score_gemma":0.00095996866,"threshold_uncertainty_score":0.106490195},"labels":[],"label_agreement":null},{"id":"W3039588859","doi":"10.32920/24132885","title":"Towards Robust Deep Learning With Ensemble Networks and Noisy Layers","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Vector Institute; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Adversarial system; Computer science; Artificial intelligence; Deep learning; Machine learning","score_opus":0.02459965081981148,"score_gpt":0.2520518520576505,"score_spread":0.22745220123783905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039588859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009115912,0.00031599312,0.9874835,0.00049689045,0.000044656896,0.000017267133,0.00004104009,0.0006992686,0.001785426],"genre_scores_gemma":[0.6886373,0.0006096948,0.29929838,0.0011280258,0.00028037734,0.00014992841,0.0003095537,0.0005411269,0.009045618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973815,0.0009036953,0.000091172806,0.00050832867,0.00086988293,0.0002454357],"domain_scores_gemma":[0.99417394,0.0029692154,0.0005655377,0.0016389302,0.00047281967,0.00017954188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043283626,0.0019392208,0.0014081884,0.0008731959,0.0005937729,0.0016714926,0.002345934,0.002801914,0.0021286001],"category_scores_gemma":[0.011918896,0.0010021209,0.0009260751,0.0006903901,0.0027360753,0.0040142033,0.006409163,0.006144789,0.0008210787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025219013,0.00007400482,0.00084805075,0.00008720663,0.00015926227,0.00015193464,0.00010468548,0.79840386,0.010020584,0.11143613,0.0036419707,0.074820146],"study_design_scores_gemma":[0.000005765298,0.000026381427,0.000052230796,0.000008383988,0.000007027902,0.000025053512,0.000005627187,0.954328,0.0028860655,0.041977406,0.0006718964,0.000006173246],"about_ca_topic_score_codex":0.0013354556,"about_ca_topic_score_gemma":0.0012348484,"teacher_disagreement_score":0.0043283626,"about_ca_system_score_codex":0.0013040135,"about_ca_system_score_gemma":0.0007669879,"threshold_uncertainty_score":0.022890806},"labels":[],"label_agreement":null},{"id":"W3040741943","doi":"10.1109/isncc49221.2020.9297344","title":"Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Artificial intelligence; Deep learning; Robustness (evolution); Convolutional neural network; Machine learning; Deep neural networks; Benchmark (surveying); Artificial neural network; Intrusion detection system; Focus (optics)","score_opus":0.06360988534417131,"score_gpt":0.31290737357594894,"score_spread":0.24929748823177764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040741943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82253647,0.004767308,0.15737453,0.0016963753,0.00058908353,0.000327589,0.0004952247,0.002483115,0.009730248],"genre_scores_gemma":[0.97899526,0.0004302898,0.019043712,0.00021947456,0.000038240865,0.000051088744,0.00036031794,0.000050510494,0.0008111005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748296,0.0011147518,0.00019996444,0.00042779432,0.0004983325,0.0002761742],"domain_scores_gemma":[0.9906379,0.006261367,0.0007246406,0.0011362863,0.0008713842,0.0003684467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006477041,0.0018148879,0.0010243865,0.00084625924,0.00048049988,0.000830659,0.0015896116,0.0018485535,0.0010463977],"category_scores_gemma":[0.015471751,0.00036207246,0.0007341302,0.00047409016,0.0015678484,0.0024667506,0.0015639106,0.0017600182,0.00027642658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083569565,0.00036192115,0.002906582,0.0002007077,0.00015937361,0.00006737647,0.000035806075,0.9559505,0.0022610591,0.002414786,0.0012312966,0.03357494],"study_design_scores_gemma":[0.00002462786,0.00033579374,0.0005189948,0.000018105493,0.000024074845,0.000035280675,0.000015515629,0.99413043,0.003817886,0.0008631554,0.00020772258,0.000008315634],"about_ca_topic_score_codex":0.0026622051,"about_ca_topic_score_gemma":0.001959482,"teacher_disagreement_score":0.006477041,"about_ca_system_score_codex":0.0015450389,"about_ca_system_score_gemma":0.0007782184,"threshold_uncertainty_score":0.034254313},"labels":[],"label_agreement":null},{"id":"W3042404696","doi":"10.1109/access.2020.2988736","title":"A Pornographic Images Recognition Model based on Deep One-Class Classification With Visual Attention Mechanism","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Contextual image classification; Pooling; Preprocessor; Machine learning; Image (mathematics)","score_opus":0.06191490408255139,"score_gpt":0.3025050836419398,"score_spread":0.24059017955938838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042404696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04152609,0.0010200279,0.94668883,0.0006670682,0.00024987018,0.00010905031,0.00024421045,0.002712068,0.006782773],"genre_scores_gemma":[0.85013175,0.0012219622,0.12671031,0.00077464845,0.00021057259,0.00021818747,0.00078181527,0.00011763028,0.019833019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980456,0.0000126614095,0.000007998301,0.00008805119,0.000047077043,0.00003964171],"domain_scores_gemma":[0.99987495,0.000020395235,0.00001833754,0.000018674304,0.000053721476,0.000013819769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021360611,0.00073078746,0.0007630283,0.00057831244,0.00029435568,0.0006667789,0.0019244839,0.00084720645,0.002693097],"category_scores_gemma":[0.00043800572,0.0003715542,0.00089974765,0.0004526346,0.0004379072,0.0013109734,0.00078152696,0.0013474738,0.000747261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047022148,0.00032915262,0.003233382,0.00024812165,0.00016045304,0.00048010022,0.0001847203,0.28210425,0.057523828,0.017639885,0.010055067,0.62757075],"study_design_scores_gemma":[0.00000666332,0.000037623227,0.0003160322,0.0000052853957,0.00001814622,0.00006515477,0.000005987933,0.9927927,0.0040053884,0.0017554164,0.0009821426,0.000009418769],"about_ca_topic_score_codex":0.00892148,"about_ca_topic_score_gemma":0.0058071585,"teacher_disagreement_score":0.00892148,"about_ca_system_score_codex":0.0007529505,"about_ca_system_score_gemma":0.0006825386,"threshold_uncertainty_score":0.017739117},"labels":[],"label_agreement":null},{"id":"W3046102592","doi":"10.48550/arxiv.2007.14321","title":"Label-Only Membership Inference Attacks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Inference; Computer science; Exploit; Outlier; Adversary; Robustness (evolution); Machine learning; Data mining; Low Confidence; Artificial intelligence; Computer security; Psychology","score_opus":0.14059286758067197,"score_gpt":0.24122680054319423,"score_spread":0.10063393296252227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046102592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11846615,0.00049261993,0.8596359,0.0042683864,0.00018238275,0.00035441192,0.0009199105,0.0037625362,0.011917663],"genre_scores_gemma":[0.9348824,0.00012928803,0.060192756,0.001184279,0.000114336144,0.00018652291,0.0003759394,0.00021497687,0.0027195003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9805468,0.0065556723,0.0010908941,0.0034020387,0.0067129056,0.0016916775],"domain_scores_gemma":[0.9486164,0.02149781,0.00393222,0.023313124,0.0019449617,0.0006955282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010279721,0.0013645237,0.0017353576,0.000975725,0.0017784662,0.0035135648,0.0033681314,0.004170316,0.0030305162],"category_scores_gemma":[0.053369295,0.00081372977,0.0021583736,0.0011533694,0.0036054337,0.010514823,0.009472922,0.007512052,0.0013729726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025996922,0.00062463875,0.020150235,0.0005020075,0.0006001452,0.0012043029,0.002720339,0.23202856,0.04140522,0.43021643,0.01935941,0.24858893],"study_design_scores_gemma":[0.00009992302,0.00022011122,0.0017824394,0.00009127078,0.00009053683,0.000826146,0.00026274842,0.7126625,0.03485723,0.23856498,0.010456978,0.000085070795],"about_ca_topic_score_codex":0.0008840966,"about_ca_topic_score_gemma":0.00072712713,"teacher_disagreement_score":0.010279721,"about_ca_system_score_codex":0.0024560087,"about_ca_system_score_gemma":0.0015768141,"threshold_uncertainty_score":0.05436498},"labels":[],"label_agreement":null},{"id":"W3046426875","doi":"10.1109/dsn-w50199.2020.00012","title":"On The Generation of Unrestricted Adversarial Examples","year":2020,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Adversarial system; Computer science; MNIST database; Artificial intelligence; Classifier (UML); Adversary; Subspace topology; Machine learning; Transferability; Generative grammar; Deep learning; Computer security","score_opus":0.07875363385284133,"score_gpt":0.2581256882771781,"score_spread":0.17937205442433674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046426875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023411768,0.00025043823,0.9673671,0.0006224108,0.00006688113,0.000091094545,0.000054042033,0.00043135916,0.0077048903],"genre_scores_gemma":[0.8186241,0.00042542312,0.17089248,0.00071442075,0.000099494595,0.0002732074,0.00020738093,0.0002557396,0.008507789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974874,0.0010626696,0.00009329206,0.00040062107,0.00075785746,0.00019815998],"domain_scores_gemma":[0.9902694,0.0070928046,0.0005373395,0.0014512416,0.00045839985,0.00019082204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002692965,0.0010398118,0.0007700653,0.00057862676,0.0005752619,0.0010472649,0.0011804338,0.0016743977,0.0029232705],"category_scores_gemma":[0.012597321,0.00051771716,0.0008152424,0.00025051375,0.0035785185,0.0022850076,0.0038031768,0.0035886324,0.0007103102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002423182,0.000054790133,0.00089837826,0.00010975205,0.00005198316,0.00031080286,0.00025250303,0.7212353,0.011302283,0.20898461,0.0026686569,0.0538887],"study_design_scores_gemma":[0.000015408805,0.00006584591,0.0001654042,0.000039949868,0.000009363523,0.00015483024,0.000018784687,0.91775024,0.0049167466,0.074629076,0.002216549,0.000017764658],"about_ca_topic_score_codex":0.0005139369,"about_ca_topic_score_gemma":0.0005202075,"teacher_disagreement_score":0.0029232705,"about_ca_system_score_codex":0.0009710274,"about_ca_system_score_gemma":0.00054082624,"threshold_uncertainty_score":0.014241934},"labels":[],"label_agreement":null},{"id":"W3048511744","doi":"10.1145/3383313.3412243","title":"Revisiting Adversarially Learned Injection Attacks Against Recommender Systems","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transferability; Recommender system; Relevance (law); Computer security; Attack model; Machine learning","score_opus":0.05730140208896777,"score_gpt":0.3096711468692656,"score_spread":0.25236974478029783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048511744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0712906,0.0012098188,0.918158,0.0015745369,0.00016515324,0.00020276917,0.00015939656,0.0009278939,0.0063118567],"genre_scores_gemma":[0.941842,0.0005104009,0.0538484,0.00043826018,0.0001513091,0.00010797219,0.00014312123,0.00009904174,0.0028594811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99248534,0.0039164834,0.00035593956,0.0010410638,0.0015273304,0.0006739585],"domain_scores_gemma":[0.9611725,0.028362863,0.0025509396,0.005882574,0.001422411,0.0006087741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067903344,0.0015639635,0.0017876669,0.0008485512,0.000897734,0.002133249,0.0021366288,0.0032846297,0.002270514],"category_scores_gemma":[0.035170726,0.00085847883,0.001367148,0.0005779832,0.0030620862,0.005008604,0.0037327933,0.0044333045,0.0008005366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004013785,0.00021449593,0.003970547,0.00029746065,0.0002827987,0.00042708477,0.0003554379,0.8394274,0.0076320414,0.08804135,0.003988068,0.05496194],"study_design_scores_gemma":[0.000015185243,0.000081582344,0.00025228685,0.000025204625,0.000018163804,0.00011349618,0.000020419446,0.9778546,0.0013854806,0.019394636,0.0008202492,0.000018574501],"about_ca_topic_score_codex":0.0015865746,"about_ca_topic_score_gemma":0.0011894674,"teacher_disagreement_score":0.0067903344,"about_ca_system_score_codex":0.0013225935,"about_ca_system_score_gemma":0.0011533442,"threshold_uncertainty_score":0.035911202},"labels":[],"label_agreement":null},{"id":"W3080189561","doi":"10.1109/icpr48806.2021.9413263","title":"Adversarially Training for Audio Classifiers","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Adversary; Deep neural networks; Artificial intelligence; Benchmarking; Robustness (evolution); Machine learning; Artificial neural network; Pattern recognition (psychology); Computer security","score_opus":0.07006534765257547,"score_gpt":0.3117161311778678,"score_spread":0.24165078352529232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080189561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114696406,0.0010594889,0.87217665,0.00084336556,0.0002630078,0.00010687413,0.00024801347,0.0013125581,0.009293709],"genre_scores_gemma":[0.94399834,0.00028917598,0.05097989,0.00028039928,0.0000700503,0.00007815198,0.0002190936,0.00009707942,0.0039878264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988417,0.00032798978,0.000045679863,0.00026982458,0.0003327986,0.00018201195],"domain_scores_gemma":[0.9965321,0.0022623183,0.00027709597,0.00060336426,0.00022578351,0.00009921253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001780026,0.00090673723,0.00064630306,0.0004122955,0.0003842349,0.00074527756,0.0009858147,0.0011188634,0.0028046293],"category_scores_gemma":[0.008041612,0.0003017175,0.00057051715,0.0002397111,0.0013491682,0.0014260428,0.0017796134,0.0019363251,0.00071509206],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002245774,0.00005253721,0.0012347753,0.00006981741,0.00007175098,0.00010447036,0.00003868653,0.924302,0.008362823,0.014857865,0.0015036748,0.049177125],"study_design_scores_gemma":[0.000005936791,0.00005192222,0.00022021671,0.000012166469,0.000009266262,0.0000499901,0.000007665075,0.9884006,0.00483724,0.0055882824,0.0008095682,0.0000071024406],"about_ca_topic_score_codex":0.0012138757,"about_ca_topic_score_gemma":0.0012382811,"teacher_disagreement_score":0.0028046293,"about_ca_system_score_codex":0.00072696677,"about_ca_system_score_gemma":0.0005015107,"threshold_uncertainty_score":0.009413838},"labels":[],"label_agreement":null},{"id":"W3087068985","doi":"10.48550/arxiv.2009.09155","title":"SecDD: Efficient and Secure Method for Remotely Training Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overfitting; Leverage (statistics); Computer science; Deep neural networks; Adversarial system; Artificial neural network; Training (meteorology); Artificial intelligence; Machine learning; Vulnerability (computing); Deep learning; Distributed computing; Computer security","score_opus":0.1009419717924897,"score_gpt":0.23538453979533397,"score_spread":0.13444256800284426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087068985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041882535,0.000113428796,0.9910322,0.00031339488,0.000091118,0.000046876252,0.00010155685,0.0028977604,0.001215303],"genre_scores_gemma":[0.40223768,0.00030510535,0.5848613,0.00064807356,0.00021693869,0.00046455357,0.00082036474,0.0012297893,0.009216272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984465,0.0005008665,0.000095419804,0.00026461305,0.0005753426,0.00011715991],"domain_scores_gemma":[0.99689263,0.0011506339,0.00017524612,0.0013938708,0.00029635616,0.00009122203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024455157,0.0011014285,0.00096578716,0.0005659919,0.0005471749,0.0011526083,0.0018810314,0.0018128692,0.006590259],"category_scores_gemma":[0.00906082,0.0006309539,0.00083485676,0.00039231827,0.0018744968,0.0023797296,0.0043466063,0.004043727,0.002896942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067845744,0.00017792742,0.0016236104,0.0002970406,0.0001823822,0.0004181159,0.00019662322,0.43320197,0.030132612,0.13220404,0.026579544,0.37430772],"study_design_scores_gemma":[0.000044948003,0.000066589004,0.000104024584,0.000023778637,0.000010951466,0.00015951857,0.000014127582,0.92947495,0.013915356,0.051401228,0.004765744,0.000018780589],"about_ca_topic_score_codex":0.0005113871,"about_ca_topic_score_gemma":0.0010401437,"teacher_disagreement_score":0.006590259,"about_ca_system_score_codex":0.000840458,"about_ca_system_score_gemma":0.0014951548,"threshold_uncertainty_score":0.022046566},"labels":[],"label_agreement":null},{"id":"W3089548822","doi":"","title":"Stable Policy Optimization via Off-Policy Divergence Regularization.","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Reinforcement learning; Benchmark (surveying); Regularization (linguistics); Computer science; Divergence (linguistics); Stability (learning theory); Trust region; Mathematical optimization; Adversarial system; Optimization problem; Range (aeronautics); Term (time); Artificial intelligence; Machine learning; Algorithm; Mathematics; Engineering","score_opus":0.03451898926511433,"score_gpt":0.2957511071451971,"score_spread":0.26123211788008277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089548822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0140274195,0.00020846489,0.9835998,0.00019675454,0.000037689242,0.00004380046,0.000026254284,0.00034260904,0.0015173109],"genre_scores_gemma":[0.8231747,0.0001956484,0.17255744,0.0002789431,0.000056439563,0.0001921453,0.00016945979,0.00020832305,0.003166912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916196,0.00033342352,0.000042159434,0.00017202599,0.00020857132,0.000081834885],"domain_scores_gemma":[0.9963697,0.002504192,0.00037259655,0.0002939764,0.00029656876,0.00016296828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022324976,0.0010066902,0.0011540272,0.00057180406,0.00045947867,0.00093646295,0.0012012606,0.001405468,0.0016931817],"category_scores_gemma":[0.011412296,0.00054598576,0.0006003701,0.00045700872,0.0018532067,0.0014321029,0.0020701636,0.0024932458,0.0004611632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010559199,0.00006238483,0.00057914434,0.000056053606,0.000039922943,0.000047679816,0.000063781714,0.94112915,0.0014600838,0.021189235,0.0011646682,0.034102336],"study_design_scores_gemma":[0.000005127191,0.000016264914,0.000029074925,0.0000037982184,0.0000019619015,0.0000069695157,0.0000027873875,0.9945575,0.00026626902,0.0049690963,0.00013896187,0.0000022790666],"about_ca_topic_score_codex":0.0027084167,"about_ca_topic_score_gemma":0.0022417232,"teacher_disagreement_score":0.0027084167,"about_ca_system_score_codex":0.0010724049,"about_ca_system_score_gemma":0.0017058345,"threshold_uncertainty_score":0.011806667},"labels":[],"label_agreement":null},{"id":"W3089673743","doi":"","title":"C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mutual information; Estimator; Minimax; Conditional independence; Conditional mutual information; Computer science; Mathematical optimization; Focus (optics); Independence (probability theory); Estimation; Conditional expectation; Artificial intelligence; Machine learning; Mathematics; Statistics; Engineering","score_opus":0.05566346586866754,"score_gpt":0.3117709213048471,"score_spread":0.2561074554361796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089673743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059137014,0.00044899207,0.99030113,0.0002781805,0.00003360998,0.00006957438,0.0002041853,0.00074388616,0.00200673],"genre_scores_gemma":[0.5025173,0.00091971894,0.48431414,0.001152638,0.00030946138,0.00075704686,0.0024111937,0.0009997346,0.006618893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761665,0.0011967465,0.00008272992,0.00047344205,0.00047563217,0.00015482819],"domain_scores_gemma":[0.993536,0.0046231695,0.00043581164,0.00069579034,0.00053587114,0.00017334695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051979693,0.0017724702,0.0019381025,0.0011465887,0.0005007295,0.0015464792,0.0034415252,0.0017883716,0.0037610857],"category_scores_gemma":[0.013992425,0.00081281847,0.0010790434,0.0010355366,0.0018643253,0.0025088917,0.0030020517,0.003904469,0.0010375179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000275939,0.0001409755,0.002724814,0.00024780296,0.00026234373,0.00016337716,0.00013142091,0.766364,0.0036652307,0.09171764,0.009724064,0.12458241],"study_design_scores_gemma":[0.0000054280094,0.000022404458,0.00019393241,0.000015342495,0.000008027987,0.000035586603,0.000005510849,0.98184824,0.00091895903,0.01621634,0.00071863167,0.000011526636],"about_ca_topic_score_codex":0.0028688796,"about_ca_topic_score_gemma":0.0035021254,"teacher_disagreement_score":0.0051979693,"about_ca_system_score_codex":0.0013254699,"about_ca_system_score_gemma":0.0017695113,"threshold_uncertainty_score":0.027489841},"labels":[],"label_agreement":null},{"id":"W3090770992","doi":"10.3233/faia200169","title":"Necessary and Sufficient Conditions for Actual Root Causes","year":2020,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Root (linguistics); Mathematics; Philosophy; Linguistics","score_opus":0.03865173486200417,"score_gpt":0.29159644682123753,"score_spread":0.25294471195923335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090770992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052655164,0.0011532552,0.8817655,0.002591923,0.00041645428,0.00029203662,0.0014124897,0.0007914337,0.05892174],"genre_scores_gemma":[0.82548714,0.0016845588,0.14770015,0.0007010977,0.00061848,0.000734886,0.0015099949,0.00048749297,0.02107612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99701524,0.00051567884,0.00024808143,0.00088559394,0.000780426,0.00055504125],"domain_scores_gemma":[0.95762444,0.03228789,0.0018714993,0.0033141703,0.004013362,0.00088874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00310868,0.0011391785,0.0016689316,0.0018519536,0.0018085937,0.0025960873,0.0021500825,0.0034479303,0.021313904],"category_scores_gemma":[0.033256114,0.0014028068,0.0021705963,0.0008617826,0.0055115605,0.0061165546,0.0041163573,0.0060011204,0.0028500857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013734764,0.0000678587,0.0010936746,0.00033807097,0.00004799482,0.0006354295,0.00041974505,0.028580725,0.0046806764,0.9501065,0.0038779702,0.010014029],"study_design_scores_gemma":[0.00006577234,0.000045011904,0.00060470746,0.00009060998,0.00003901315,0.0005877289,0.00023501074,0.050159033,0.0031222561,0.9400759,0.004927331,0.000047691585],"about_ca_topic_score_codex":0.0009586776,"about_ca_topic_score_gemma":0.0013174929,"teacher_disagreement_score":0.021313904,"about_ca_system_score_codex":0.000980934,"about_ca_system_score_gemma":0.002266096,"threshold_uncertainty_score":0.071302116},"labels":[],"label_agreement":null},{"id":"W3090870145","doi":"10.15353/jcvis.v6i1.3539","title":"Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities","year":2021,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Leverage (statistics); Computer science; Oracle; Deep learning; Artificial intelligence; Artificial neural network; Trustworthiness; Computational trust; Deep neural networks; Set (abstract data type); Metric (unit); Data science; Computer security; Political science; Reputation; Business","score_opus":0.008017419130933056,"score_gpt":0.292259089003264,"score_spread":0.28424166987233096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090870145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079990335,0.0007513948,0.91314906,0.0025844204,0.00005347054,0.000060931823,0.00015516071,0.00017204396,0.0030832433],"genre_scores_gemma":[0.96442235,0.00040417627,0.033456877,0.00014347733,0.000059885533,0.00006697683,0.00008751309,0.00006900879,0.0012898111],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937168,0.0031811579,0.00033899015,0.0010236987,0.0012529959,0.00048638185],"domain_scores_gemma":[0.9503368,0.0341982,0.0064197113,0.0036522625,0.0038602701,0.0015326397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009458561,0.0008696155,0.001183136,0.0014353469,0.0009821684,0.0036199703,0.0015845288,0.0018604632,0.002534428],"category_scores_gemma":[0.07750756,0.00090319896,0.00096343487,0.00097000884,0.006081905,0.0121093495,0.0032075348,0.004509018,0.00031835688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006050545,0.00008841956,0.007985642,0.0002627386,0.00023380516,0.00044501823,0.0014664596,0.29475406,0.0037253692,0.64118856,0.0028696295,0.046375282],"study_design_scores_gemma":[0.000012064906,0.000045786255,0.00090497686,0.000039724287,0.000025653993,0.00007360441,0.00012112988,0.7136319,0.0010357049,0.2834048,0.0006662392,0.00003833243],"about_ca_topic_score_codex":0.005517013,"about_ca_topic_score_gemma":0.0026741999,"teacher_disagreement_score":0.009458561,"about_ca_system_score_codex":0.003839796,"about_ca_system_score_gemma":0.0015090198,"threshold_uncertainty_score":0.050022244},"labels":[],"label_agreement":null},{"id":"W3092397959","doi":"10.48550/arxiv.2010.02508","title":"Adversarial Boot Camp: label free certified robustness in one epoch","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Certification; Computer science; Robustness (evolution); Machine learning; Artificial intelligence; Retraining; Adversarial system; Equivalence (formal languages); Mathematics","score_opus":0.12933512399401004,"score_gpt":0.21497172767689768,"score_spread":0.08563660368288764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092397959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044710517,0.00035205513,0.9428519,0.0015621838,0.00017327446,0.00014877226,0.0002270203,0.0040844856,0.0058897138],"genre_scores_gemma":[0.90614986,0.00015416625,0.088182315,0.00062187505,0.000120457946,0.00016967604,0.00032482934,0.00066085317,0.0036158692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995895,0.0014430595,0.00017249252,0.00085347943,0.0011520659,0.0004838406],"domain_scores_gemma":[0.9808801,0.009613069,0.0012475769,0.0067762774,0.0010187483,0.00046433212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060363966,0.0014149306,0.0015163558,0.00069529243,0.0011328754,0.0019245581,0.0031159858,0.003257773,0.0037736436],"category_scores_gemma":[0.028798081,0.0008157301,0.0013861799,0.0005029629,0.004666106,0.0048788725,0.007230063,0.0061492,0.001173821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006213839,0.00016933763,0.001390618,0.00012988396,0.00012628069,0.0003674822,0.0002166882,0.83777237,0.009598069,0.09300905,0.0073305373,0.049268343],"study_design_scores_gemma":[0.000027998063,0.00008714416,0.0001125663,0.000020670699,0.0000107587075,0.00006740378,0.000016701722,0.9308708,0.003982638,0.06392472,0.0008595978,0.000019012292],"about_ca_topic_score_codex":0.0013401927,"about_ca_topic_score_gemma":0.0014538338,"teacher_disagreement_score":0.0060363966,"about_ca_system_score_codex":0.001728179,"about_ca_system_score_gemma":0.002459379,"threshold_uncertainty_score":0.03192395},"labels":[],"label_agreement":null},{"id":"W3094535217","doi":"10.48550/arxiv.2010.12638","title":"Posterior Differential Regularization with f-divergence for Improving Model Robustness","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Jacobian matrix and determinant; Regularization (linguistics); Computer science; Robustness (evolution); Adversarial system; Artificial intelligence; Regularization perspectives on support vector machines; Algorithm; Mathematics; Mathematical optimization; Applied mathematics; Inverse problem; Tikhonov regularization; Mathematical analysis","score_opus":0.055481607003347984,"score_gpt":0.19611928769262965,"score_spread":0.14063768068928167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094535217","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011228037,0.00021061646,0.98625517,0.00033804224,0.000030046824,0.000024862486,0.0000671827,0.000585671,0.0012604082],"genre_scores_gemma":[0.73117214,0.00048212177,0.26137605,0.0008304847,0.00023141415,0.0001811101,0.00074971543,0.000657579,0.0043193987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975331,0.001148787,0.00009376908,0.00051557895,0.0005507269,0.00015803032],"domain_scores_gemma":[0.99402666,0.003861925,0.00047482472,0.001069758,0.00037575825,0.00019103107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055881757,0.0021194022,0.001529056,0.0013173348,0.00075500406,0.0012196817,0.002311853,0.002218086,0.0019606377],"category_scores_gemma":[0.016289422,0.00062424387,0.0015211978,0.0008682411,0.002594917,0.0028038425,0.003577604,0.004275871,0.00066349783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008213391,0.000058500515,0.000977791,0.000094508505,0.00010233103,0.00009893833,0.00009165208,0.91666526,0.003929545,0.035526622,0.0022360364,0.04013669],"study_design_scores_gemma":[0.0000035781732,0.000020354237,0.00008835594,0.0000070355645,0.0000045495776,0.000025961763,0.000003970998,0.9811989,0.0006606765,0.017633706,0.00034676268,0.0000060883613],"about_ca_topic_score_codex":0.0029024815,"about_ca_topic_score_gemma":0.0022722797,"teacher_disagreement_score":0.0055881757,"about_ca_system_score_codex":0.0015601974,"about_ca_system_score_gemma":0.0012710177,"threshold_uncertainty_score":0.029553413},"labels":[],"label_agreement":null},{"id":"W3098136764","doi":"","title":"Interpreting Deep Learning-Based Networking Systems","year":2020,"lang":"en","type":"preprint","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","keywords":"Metis; Interpretability; Computer science; Hypergraph; Artificial intelligence; Debugging; Machine learning; Distributed computing; World Wide Web; Programming language","score_opus":0.01701487068478677,"score_gpt":0.25746588016755656,"score_spread":0.2404510094827698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098136764","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047843162,0.00023381224,0.94146276,0.00077234866,0.000090405665,0.000081730184,0.0003264741,0.0028420615,0.006347232],"genre_scores_gemma":[0.83488286,0.0003012998,0.16049929,0.00044114186,0.00004729258,0.00010183099,0.00048110113,0.00034358507,0.0029017031],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980716,0.00075593987,0.00012648199,0.0004133456,0.00049302285,0.0001396529],"domain_scores_gemma":[0.99627256,0.0020497858,0.00035026154,0.0008221317,0.0004263321,0.00007889375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025334035,0.000982393,0.0004628303,0.00066502,0.00045993135,0.0020477255,0.0014580633,0.0010960668,0.0031813597],"category_scores_gemma":[0.009326211,0.00038064143,0.0005714783,0.00037347432,0.001989279,0.0030250712,0.0020418193,0.0022084382,0.00044623943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027704722,0.000076871125,0.0028468834,0.000191438,0.00006811047,0.00053617376,0.0005138398,0.80894697,0.011414194,0.092741966,0.0033356491,0.07905076],"study_design_scores_gemma":[0.00001273149,0.000030759373,0.0001895819,0.000020850777,0.000014199983,0.000043568,0.00005729162,0.9381595,0.0065704035,0.05258106,0.0023085421,0.000011474957],"about_ca_topic_score_codex":0.0023200326,"about_ca_topic_score_gemma":0.0031276231,"teacher_disagreement_score":0.0031813597,"about_ca_system_score_codex":0.0014296978,"about_ca_system_score_gemma":0.0007727079,"threshold_uncertainty_score":0.013398111},"labels":[],"label_agreement":null},{"id":"W3099026386","doi":"10.1101/366724","title":"Attack and defence in cellular decision-making: lessons from machine learning","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Samsung; Samsung Advanced Institute of Technology; McGill University","keywords":"Artificial intelligence; Computer science; Decision boundary; Machine learning; Analogy; Artificial neural network; Adversarial system; Support vector machine","score_opus":0.019334153701698734,"score_gpt":0.26393329043171665,"score_spread":0.24459913673001793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099026386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11596102,0.0025033646,0.84847206,0.010259576,0.00023564325,0.00004681218,0.00013359071,0.00024095018,0.022146957],"genre_scores_gemma":[0.9515018,0.0014730281,0.042621206,0.0007908808,0.0001963365,0.00008632444,0.00006102568,0.000078659665,0.0031906802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990433,0.00042272406,0.000045192766,0.00019340968,0.0001903102,0.00010515371],"domain_scores_gemma":[0.99411523,0.0041668336,0.000523912,0.00065997546,0.0002524802,0.00028154894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018650212,0.0006542547,0.00085270643,0.00046095665,0.00065241684,0.0019085848,0.000994664,0.002447826,0.0020133166],"category_scores_gemma":[0.010556679,0.0003360297,0.0007939148,0.00029697747,0.0054975585,0.003098387,0.0019409788,0.003623386,0.00037362622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044884913,0.00003202587,0.001258255,0.00012887988,0.00005554098,0.00012659522,0.00017068422,0.30131078,0.0030557811,0.6776659,0.0014130916,0.014737612],"study_design_scores_gemma":[0.000013094527,0.000024345309,0.00032408632,0.000021792672,0.000005790696,0.000045582856,0.000028766326,0.31500503,0.0007734984,0.6825729,0.001167436,0.000017694081],"about_ca_topic_score_codex":0.00085494213,"about_ca_topic_score_gemma":0.00064312853,"teacher_disagreement_score":0.002447826,"about_ca_system_score_codex":0.0013712764,"about_ca_system_score_gemma":0.000729884,"threshold_uncertainty_score":0.009949386},"labels":[],"label_agreement":null},{"id":"W3101435147","doi":"10.48550/arxiv.2011.09364","title":"Self-Gradient Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Computer science; Artificial neural network; Exploit; Artificial intelligence; Leverage (statistics); Deep learning; Deep neural networks; Network architecture; Vulnerability (computing); Machine learning; Computer security","score_opus":0.05168721045477298,"score_gpt":0.18598449321891988,"score_spread":0.1342972827641469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101435147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048956294,0.0010671455,0.9330153,0.0007325922,0.00022252084,0.00009280172,0.0001664882,0.0013095762,0.014437219],"genre_scores_gemma":[0.90907896,0.00066623354,0.07592107,0.00058392406,0.000084140054,0.00013578344,0.00028397213,0.0001530259,0.013092857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996544,0.00008113184,0.000016984195,0.00008642545,0.000105259285,0.0000558052],"domain_scores_gemma":[0.99939835,0.00023416405,0.000085920314,0.000093064824,0.00014871998,0.00003983688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063758396,0.00084067305,0.000577579,0.00043231528,0.00035490992,0.000787878,0.001202528,0.0011645018,0.0034674432],"category_scores_gemma":[0.0026331714,0.00037680616,0.0005332029,0.0002483662,0.0011092299,0.0015877148,0.0012528003,0.0012821467,0.00084475667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001454117,0.00005840745,0.0011145216,0.00012459152,0.000067484594,0.000135176,0.00007824648,0.84660006,0.007760626,0.054076,0.0047099963,0.085129514],"study_design_scores_gemma":[0.000004854139,0.000029033376,0.000106635256,0.000008654099,0.0000063666716,0.000032239546,0.0000048345646,0.98623395,0.0013972269,0.010878855,0.0012921888,0.0000051694074],"about_ca_topic_score_codex":0.0019040104,"about_ca_topic_score_gemma":0.0022221094,"teacher_disagreement_score":0.0034674432,"about_ca_system_score_codex":0.00083327916,"about_ca_system_score_gemma":0.0005714311,"threshold_uncertainty_score":0.0115997195},"labels":[],"label_agreement":null},{"id":"W3102502161","doi":"","title":"Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization","year":2020,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University; Université de Montréal","funders":"","keywords":"Discriminator; Adversarial system; Reinforcement learning; Computer science; Generator (circuit theory); Imitation; Artificial intelligence; Machine learning; Optimization problem; Mathematical optimization; Algorithm; Power (physics); Mathematics","score_opus":0.011281012560074345,"score_gpt":0.25060416627886095,"score_spread":0.2393231537187866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102502161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010167588,0.00017110245,0.9843343,0.0003276392,0.00004658995,0.0000598861,0.000041707797,0.0008857191,0.0039655464],"genre_scores_gemma":[0.7687337,0.00021860974,0.22018644,0.00056416204,0.000093274364,0.000271738,0.00018788647,0.00045403512,0.009290138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886394,0.00037458268,0.000055568846,0.00024264255,0.00034235674,0.0001208956],"domain_scores_gemma":[0.99601436,0.0026362997,0.0002938643,0.00064786634,0.00022810495,0.0001795065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025950281,0.0012472627,0.0013531148,0.0005886242,0.0004474133,0.0011236881,0.0024361312,0.0019223391,0.0050332663],"category_scores_gemma":[0.010506215,0.0005635112,0.00065483735,0.00050211366,0.0022165088,0.0022806148,0.003523013,0.0030211348,0.0012176278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012015492,0.00007325972,0.00061515765,0.00008113242,0.00004972933,0.00013019664,0.00007135916,0.8901227,0.0024760906,0.04543509,0.0019514026,0.058873788],"study_design_scores_gemma":[0.000007690638,0.00001892199,0.000034743272,0.0000063258135,0.0000033428896,0.000018869172,0.0000032589558,0.9864044,0.0006219011,0.012483644,0.0003918574,0.0000050935173],"about_ca_topic_score_codex":0.0017053027,"about_ca_topic_score_gemma":0.0018085324,"teacher_disagreement_score":0.0050332663,"about_ca_system_score_codex":0.0009835806,"about_ca_system_score_gemma":0.0013382273,"threshold_uncertainty_score":0.016837895},"labels":[],"label_agreement":null},{"id":"W3104862198","doi":"","title":"The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification","year":2020,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Relaxation (psychology); Univariate; Computer science; Artificial neural network; Algorithm; Convex optimization; Linear programming relaxation; Linear programming; Exponential function; Regular polygon; Activation function; Mathematical optimization; Mathematics; Multivariate statistics; Artificial intelligence; Mathematical analysis; Geometry; Machine learning","score_opus":0.017689051050070764,"score_gpt":0.23722445815598733,"score_spread":0.21953540710591657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104862198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058428356,0.000107746084,0.99176437,0.0002343543,0.000029289442,0.000051388393,0.000047117985,0.0004119484,0.0015108124],"genre_scores_gemma":[0.40145093,0.00035930623,0.5926426,0.0004156272,0.00009596501,0.00030518405,0.00026477632,0.00059245684,0.0038731482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967507,0.00096977653,0.00015469514,0.00072759506,0.0009327758,0.0004644965],"domain_scores_gemma":[0.9912163,0.005741432,0.00067561975,0.0014586512,0.0006967618,0.00021120769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043152347,0.0017446688,0.0015850238,0.0008018891,0.00074703066,0.0020277135,0.0030230342,0.0015934432,0.0059601306],"category_scores_gemma":[0.020299492,0.0010180799,0.0020290667,0.0008372874,0.0029349644,0.0051979665,0.004441322,0.0069032954,0.0012214984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002830435,0.0001081485,0.00052498654,0.00030106716,0.000089063244,0.00021066146,0.00020093293,0.7740711,0.009220288,0.116125435,0.0029605546,0.09590471],"study_design_scores_gemma":[0.000016094762,0.000047710273,0.000041680083,0.000022669217,0.000009215056,0.000037524725,0.000012066474,0.9610812,0.003560252,0.034475014,0.00068463734,0.000011907042],"about_ca_topic_score_codex":0.0024470738,"about_ca_topic_score_gemma":0.003068569,"teacher_disagreement_score":0.0059601306,"about_ca_system_score_codex":0.0018488351,"about_ca_system_score_gemma":0.0030962392,"threshold_uncertainty_score":0.022821426},"labels":[],"label_agreement":null},{"id":"W3106679794","doi":"10.2139/ssrn.3750914","title":"Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Adversarial system; Computer science; Machine learning; Artificial intelligence; Psychology; Engineering ethics; Engineering","score_opus":0.04798838117388857,"score_gpt":0.33045967291156436,"score_spread":0.2824712917376758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106679794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7629716,0.0019409126,0.1944822,0.006447864,0.0011008808,0.000670559,0.00057405746,0.0004549213,0.031356953],"genre_scores_gemma":[0.9851777,0.0001920611,0.012577034,0.00047644295,0.00008824945,0.00019669202,0.00017882184,0.000046825055,0.0010660982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.97620684,0.018457554,0.0006264013,0.0010890008,0.0031664665,0.00045361064],"domain_scores_gemma":[0.7241746,0.23648533,0.012232748,0.016376961,0.0077272025,0.0030031763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024975866,0.00077847426,0.00050261116,0.00095344905,0.000637673,0.0017255988,0.0018754061,0.0024357894,0.0058817933],"category_scores_gemma":[0.24093977,0.00022941921,0.00047122626,0.0004806345,0.0055893552,0.002835761,0.0031579516,0.0019870137,0.00072670565],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008276388,0.0034994953,0.07999549,0.0011454187,0.00078247883,0.0010429301,0.0016063241,0.49264395,0.0054194857,0.16565086,0.0180836,0.22185358],"study_design_scores_gemma":[0.0009919185,0.005186962,0.023614146,0.00057133654,0.00018772346,0.0012146635,0.0009368851,0.7419912,0.007249222,0.20738275,0.010539134,0.00013400763],"about_ca_topic_score_codex":0.0011381584,"about_ca_topic_score_gemma":0.00085689535,"teacher_disagreement_score":0.024975866,"about_ca_system_score_codex":0.0011289932,"about_ca_system_score_gemma":0.0012654894,"threshold_uncertainty_score":0.13208658},"labels":[],"label_agreement":null},{"id":"W3107688556","doi":"10.1109/iccv48922.2021.00764","title":"Augmented Lagrangian Adversarial Attacks","year":2021,"lang":"en","type":"preprint","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Generality; Adversarial system; Computer science; Set (abstract data type); Computational complexity theory; Augmented Lagrangian method; Lagrangian; Theoretical computer science; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Applied mathematics","score_opus":0.03655309252292711,"score_gpt":0.32929353885504725,"score_spread":0.29274044633212015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107688556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074227946,0.0002649103,0.98652947,0.00031254932,0.000098859746,0.00009068247,0.00010275808,0.0006148363,0.004563186],"genre_scores_gemma":[0.543684,0.00060798205,0.43904433,0.0008648822,0.00021616818,0.00044204458,0.00073737104,0.0004800028,0.013923141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742895,0.0010101332,0.000119241755,0.00038576656,0.00086667575,0.0001891993],"domain_scores_gemma":[0.99606127,0.0020600155,0.0003278938,0.0011166227,0.00031275317,0.00012152707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022128967,0.0015155316,0.0013134911,0.00073292333,0.00063774013,0.0013298494,0.0017857612,0.0018426768,0.005314524],"category_scores_gemma":[0.00866087,0.00045511196,0.001019343,0.0006191092,0.0020500254,0.0023625202,0.004137002,0.0034350795,0.0016595577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023638661,0.00008246765,0.00069522427,0.00018574219,0.00012452825,0.00015975386,0.00009770439,0.7160202,0.007532064,0.14183177,0.010637819,0.12239629],"study_design_scores_gemma":[0.00001644747,0.000049848895,0.000082346196,0.00001986306,0.000008182706,0.000098952,0.000010467198,0.9542122,0.0026508786,0.038919542,0.0039190263,0.000012338255],"about_ca_topic_score_codex":0.00067652337,"about_ca_topic_score_gemma":0.0008219265,"teacher_disagreement_score":0.005314524,"about_ca_system_score_codex":0.0007286154,"about_ca_system_score_gemma":0.0007473536,"threshold_uncertainty_score":0.017778873},"labels":[],"label_agreement":null},{"id":"W3109774525","doi":"10.1145/3416013.3426461","title":"Adversarial Patches-based Attacks on Automated Vehicle Make and Model Recognition Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Artificial intelligence; Convolutional neural network; Machine learning; Deep learning; Work (physics); Recall; Adversarial machine learning; Computer security; Engineering","score_opus":0.039820711275048395,"score_gpt":0.26615129158747625,"score_spread":0.22633058031242786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109774525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41312835,0.0008723934,0.5715671,0.0011238722,0.00023490711,0.00013314499,0.00029338922,0.004347946,0.00829881],"genre_scores_gemma":[0.9758306,0.000095838266,0.021679899,0.00012974908,0.000030261892,0.000018066547,0.0002055106,0.00005884637,0.0019513444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990314,0.00026966643,0.00003433207,0.00019458644,0.00033009922,0.00013988641],"domain_scores_gemma":[0.99837273,0.0007695932,0.00017193782,0.00048925064,0.00012903844,0.00006749203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094545603,0.00071271695,0.000670453,0.0002930522,0.00032462613,0.00045854386,0.0006805761,0.00065723696,0.0012892644],"category_scores_gemma":[0.0036655369,0.00021667065,0.00043425467,0.00022204722,0.00093843386,0.0009594237,0.001476068,0.0011652668,0.00038956542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041174234,0.00010236018,0.0028426838,0.00005034298,0.000064026535,0.0002602822,0.00007642159,0.8893819,0.015302419,0.008606026,0.0036353797,0.07926643],"study_design_scores_gemma":[0.000006914347,0.00007666016,0.0005603151,0.000004226857,0.0000059804797,0.00007667647,0.0000107518645,0.989125,0.0066165784,0.0027352474,0.00077420106,0.0000074606505],"about_ca_topic_score_codex":0.0017893397,"about_ca_topic_score_gemma":0.0014509928,"teacher_disagreement_score":0.0017893397,"about_ca_system_score_codex":0.0005066893,"about_ca_system_score_gemma":0.00033309008,"threshold_uncertainty_score":0.005000055},"labels":[],"label_agreement":null},{"id":"W3113880833","doi":"10.1109/access.2022.3153036","title":"RAILS: A Robust Adversarial Immune-Inspired Learning System","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; Advanced Research Projects Agency; Medical School, University of Michigan; U.S. Department of Defense; University of Calgary; University of Michigan; Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research","keywords":"Adversarial system; Computer science; Artificial intelligence; Machine learning","score_opus":0.025259709139478972,"score_gpt":0.2699883069838443,"score_spread":0.24472859784436532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113880833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024203919,0.0004358806,0.9652187,0.000523747,0.00013312028,0.000114432056,0.0001500838,0.0048446236,0.0043754443],"genre_scores_gemma":[0.680533,0.0002831727,0.3082357,0.0011722071,0.00010925019,0.0002890573,0.0005298623,0.00028811165,0.008559695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955195,0.000096474025,0.00002468396,0.00011328166,0.00015278951,0.000060906015],"domain_scores_gemma":[0.9994764,0.00019794819,0.00007354439,0.00008417605,0.0001197038,0.000048298425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011597725,0.0006798225,0.00080432807,0.00043683403,0.00037395261,0.00069251633,0.0021670875,0.0014175794,0.0034634103],"category_scores_gemma":[0.002161709,0.00034316504,0.00067432225,0.00027510987,0.0008358852,0.0011213436,0.001919612,0.0017483234,0.0010462716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012932203,0.00009544688,0.00081911864,0.0000815952,0.00009867749,0.00013644622,0.000053743428,0.8567366,0.010032641,0.013756178,0.006107234,0.11195296],"study_design_scores_gemma":[0.00000787414,0.000029327433,0.000037888727,0.0000031543796,0.0000048479296,0.00001675423,0.0000020169055,0.9959407,0.0008290585,0.0024524773,0.0006717366,0.0000042252955],"about_ca_topic_score_codex":0.0024144435,"about_ca_topic_score_gemma":0.0026351195,"teacher_disagreement_score":0.0034634103,"about_ca_system_score_codex":0.0008819691,"about_ca_system_score_gemma":0.001113069,"threshold_uncertainty_score":0.0115863085},"labels":[],"label_agreement":null},{"id":"W3115696055","doi":"10.1109/isncc49221.2020.9297264","title":"Polymorphic Adversarial DDoS attack on IDS using GAN","year":2020,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Adversarial system; Denial-of-service attack; Intrusion detection system; Generative adversarial network; Adversarial machine learning; Artificial intelligence; Graphics; Computer security; Generative grammar; Machine learning; Deep learning; World Wide Web; Operating system; The Internet","score_opus":0.09998868787046503,"score_gpt":0.31452162719290533,"score_spread":0.2145329393224403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115696055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22415613,0.0007348198,0.75412905,0.0015067295,0.00026449692,0.00015649223,0.00029022913,0.0018241343,0.016937992],"genre_scores_gemma":[0.9871082,0.00013624558,0.010752401,0.00011414847,0.000017487879,0.000032410673,0.00005774489,0.000027254599,0.0017541533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948126,0.00018938856,0.000018224831,0.00010279422,0.00011475971,0.0000936152],"domain_scores_gemma":[0.9983759,0.001009129,0.0001653411,0.0001836003,0.0001864781,0.000079575286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011007714,0.00074209896,0.00075198355,0.00042962414,0.00032813224,0.0007337207,0.0007521667,0.0007481527,0.0015203732],"category_scores_gemma":[0.0026755726,0.00028818467,0.00062712707,0.00028684808,0.0011681658,0.0011873246,0.00095484767,0.0013959297,0.00025812993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058577818,0.000015067562,0.0006877262,0.000014008274,0.0000148682075,0.00007173484,0.000021520269,0.98428243,0.001114896,0.008268123,0.0006137999,0.0048373025],"study_design_scores_gemma":[0.0000026447642,0.000011752854,0.000069501504,0.0000015861436,0.0000026078515,0.000019367943,0.000002062634,0.9975115,0.00023823945,0.0020365177,0.000101540594,0.0000026868615],"about_ca_topic_score_codex":0.0021868974,"about_ca_topic_score_gemma":0.0014067923,"teacher_disagreement_score":0.0021868974,"about_ca_system_score_codex":0.0010147493,"about_ca_system_score_gemma":0.0005118106,"threshold_uncertainty_score":0.007362604},"labels":[],"label_agreement":null},{"id":"W3115877967","doi":"10.1109/msp.2020.2982820","title":"Deep Neural Network Perception Models and Robust Autonomous Driving Systems: Practical Solutions for Mitigation and Improvement","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Perception; Computer science; Human error; Safe driving; Risk analysis (engineering); Advanced driver assistance systems; Automotive engineering; Simulation; Transport engineering; Computer security; Artificial intelligence; Engineering; Business; Psychology","score_opus":0.04065619632948921,"score_gpt":0.27478584631754005,"score_spread":0.23412964998805083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115877967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007989878,0.005598645,0.9803335,0.0028407425,0.00018552042,0.000022327713,0.00009186309,0.00048298534,0.0024545677],"genre_scores_gemma":[0.77813613,0.008568833,0.20338917,0.0009278742,0.0006413075,0.00013717185,0.00037648817,0.0001811349,0.007641768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960536,0.000103273655,0.000024137213,0.00010549742,0.00011232176,0.000049325263],"domain_scores_gemma":[0.99893445,0.00056231814,0.00012620054,0.00010097238,0.0002348491,0.00004122459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013563604,0.0014391759,0.00074145925,0.00042397488,0.00028953384,0.0010259554,0.001375135,0.0019696448,0.0025184636],"category_scores_gemma":[0.004271253,0.0006429624,0.00045684102,0.00051949074,0.0010365833,0.0020124125,0.0017260129,0.0030332722,0.0005956219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010565597,0.00007374288,0.00091706915,0.00018523051,0.000086683845,0.000053456242,0.000078454956,0.72714293,0.0034906515,0.025547063,0.006636299,0.23568283],"study_design_scores_gemma":[0.000003832884,0.000022603766,0.00013741707,0.000013338347,0.000007347143,0.000009917408,0.000009671093,0.987553,0.0005263692,0.010599858,0.0011099606,0.0000066991047],"about_ca_topic_score_codex":0.006376546,"about_ca_topic_score_gemma":0.004334919,"teacher_disagreement_score":0.006376546,"about_ca_system_score_codex":0.0009407072,"about_ca_system_score_gemma":0.0009047501,"threshold_uncertainty_score":0.012678862},"labels":[],"label_agreement":null},{"id":"W3116279284","doi":"10.1088/2632-2153/abf834","title":"Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines <sup>†</sup>","year":2021,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Creative Destruction Lab; University of Toronto; Perimeter Institute","funders":"“la Caixa” Foundation","keywords":"Boltzmann machine; MNIST database; Computer science; Discriminative model; Probabilistic logic; Relevance (law); Complement (music); Quantum; Artificial intelligence; Machine learning; Artificial neural network; Deep neural networks; Adversarial system; Generative grammar; Theoretical computer science; Physics","score_opus":0.012054372130597568,"score_gpt":0.2660878600578742,"score_spread":0.25403348792727665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116279284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09792819,0.0010623157,0.87773734,0.0028909585,0.00048868835,0.000103748316,0.00035283156,0.0025870663,0.016848847],"genre_scores_gemma":[0.9155956,0.00035334256,0.07707943,0.00074300106,0.00013850348,0.0000746394,0.0003765111,0.0002938765,0.0053450847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862814,0.00046675373,0.000050059247,0.00016736473,0.00050658605,0.00018113745],"domain_scores_gemma":[0.9968875,0.0015230704,0.00025710242,0.00095837767,0.00023714454,0.00013678837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001856359,0.0005615087,0.00071373687,0.0003972845,0.00053816976,0.0010456115,0.0014421028,0.0015641944,0.004850296],"category_scores_gemma":[0.006632706,0.00026063807,0.0005857599,0.00038879245,0.0019739997,0.0020222254,0.002577326,0.002534986,0.0016141575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055529806,0.00017737293,0.0023596645,0.00021998194,0.00015160315,0.00015922608,0.00014119301,0.61312604,0.031831756,0.23034774,0.014560387,0.10636983],"study_design_scores_gemma":[0.000015181418,0.00006558752,0.0002560023,0.0000159527,0.000008756117,0.00006805086,0.00001748149,0.9281833,0.010975131,0.056813005,0.0035634246,0.00001796594],"about_ca_topic_score_codex":0.000510913,"about_ca_topic_score_gemma":0.00063990033,"teacher_disagreement_score":0.004850296,"about_ca_system_score_codex":0.0006981552,"about_ca_system_score_gemma":0.00060066633,"threshold_uncertainty_score":0.016225874},"labels":[],"label_agreement":null},{"id":"W3116666954","doi":"10.1109/spw50608.2020.00027","title":"On the Robustness of Cooperative Multi-Agent Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Canadian Institute for Advanced Research","keywords":"Reinforcement learning; Adversary; Robustness (evolution); Adversarial system; Computer science; Harm; Artificial intelligence; Computer security; Benchmark (surveying); Differentiable function; Mathematics; Psychology; Social psychology","score_opus":0.04048919975561101,"score_gpt":0.27154417538605996,"score_spread":0.23105497563044897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116666954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28330442,0.0014750378,0.7040144,0.0013610675,0.00014469853,0.00014301315,0.00014657421,0.001024135,0.00838667],"genre_scores_gemma":[0.98484266,0.00015445505,0.013983851,0.00011743497,0.000025938816,0.000050088915,0.000057624882,0.000048215676,0.0007198431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975466,0.0010408555,0.000115211646,0.00042666867,0.00050279306,0.00036785266],"domain_scores_gemma":[0.9771516,0.017418869,0.0019041067,0.0016096344,0.0011929091,0.0007229107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005973188,0.0013304993,0.001020918,0.0008346715,0.00059441733,0.00090657175,0.0012087829,0.001164062,0.0011932071],"category_scores_gemma":[0.03023584,0.00041198984,0.0006486656,0.00032502593,0.0021786285,0.0014846355,0.0023367596,0.0021513258,0.00026371688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011374911,0.00004373136,0.0011329739,0.000037689086,0.000049342107,0.000040393174,0.00003613562,0.9859962,0.0009403954,0.0045774034,0.00029917472,0.006732863],"study_design_scores_gemma":[0.000007694341,0.0000642851,0.0001585957,0.0000072509065,0.0000055692217,0.000010261728,0.000007181654,0.9956132,0.00038076006,0.0036438874,0.00009667506,0.0000046307614],"about_ca_topic_score_codex":0.0042056255,"about_ca_topic_score_gemma":0.0018484076,"teacher_disagreement_score":0.005973188,"about_ca_system_score_codex":0.00144027,"about_ca_system_score_gemma":0.0012093432,"threshold_uncertainty_score":0.031589627},"labels":[],"label_agreement":null},{"id":"W3119147336","doi":"10.1109/tcad.2021.3091436","title":"Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Detector; Computer science; MNIST database; Subnetwork; Artificial intelligence; Robustness (evolution); Artificial neural network; Adversarial system; Convolutional neural network; Deep learning; Inference; Computer engineering; Telecommunications","score_opus":0.019666066305374482,"score_gpt":0.21438165053981104,"score_spread":0.19471558423443655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119147336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034552738,0.00049927173,0.96144557,0.000250083,0.000052795374,0.000044828434,0.00006537732,0.0008477802,0.0022415617],"genre_scores_gemma":[0.85800225,0.00038457167,0.13837261,0.00025276328,0.000046793793,0.00008605325,0.00014761137,0.0001409989,0.0025664568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989813,0.00024074904,0.000047990638,0.00024103576,0.00037338186,0.00011555607],"domain_scores_gemma":[0.9982231,0.00097370567,0.00020635998,0.00029347604,0.00024526802,0.00005808994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012891982,0.0011924919,0.00090919586,0.000643382,0.00048244465,0.0008405809,0.0016976625,0.0010122934,0.0013128201],"category_scores_gemma":[0.006086328,0.00051977334,0.0005702323,0.0004460759,0.0014989967,0.0021733865,0.0021369704,0.0018355971,0.00040772898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014647604,0.0000593049,0.000698126,0.00007937703,0.000054319964,0.000093020724,0.00005737622,0.90064096,0.017083406,0.017724516,0.0012778384,0.062085263],"study_design_scores_gemma":[0.0000035888352,0.000032064636,0.000102470425,0.0000075256175,0.000007811834,0.000035637622,0.000005435516,0.9863306,0.0069870106,0.0061386554,0.00034184736,0.0000073235624],"about_ca_topic_score_codex":0.0018673696,"about_ca_topic_score_gemma":0.0024243998,"teacher_disagreement_score":0.0018673696,"about_ca_system_score_codex":0.0013720948,"about_ca_system_score_gemma":0.00089074095,"threshold_uncertainty_score":0.009955347},"labels":[],"label_agreement":null},{"id":"W3120441983","doi":"10.1109/ssci47803.2020.9308574","title":"Detecting Subject-Weapon Visual Relationships","year":2020,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Subject (documents); Artificial intelligence; Computer vision; World Wide Web","score_opus":0.04256632379751425,"score_gpt":0.27579387838801933,"score_spread":0.2332275545905051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120441983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7196877,0.004374561,0.2466951,0.0006654235,0.00044564318,0.00030892788,0.0037488323,0.003964003,0.02010993],"genre_scores_gemma":[0.94756335,0.00076776155,0.041098706,0.00018100631,0.00012180906,0.000043048854,0.0042464025,0.000072664385,0.0059052776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944013,0.00007441557,0.000021157284,0.00018714103,0.00018720796,0.00008986716],"domain_scores_gemma":[0.9994562,0.00012881446,0.00013394226,0.00011505752,0.00012351396,0.000042425218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005443204,0.00079239823,0.00047507658,0.0013997289,0.00023796427,0.00075126946,0.00077081966,0.0006829398,0.0017657123],"category_scores_gemma":[0.0016132809,0.0002037457,0.0004196103,0.0006258595,0.0003320796,0.00090593274,0.001081655,0.0006371685,0.00087846466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082984276,0.0006522275,0.05810267,0.00039024994,0.00030221473,0.00082796137,0.00027961037,0.05252967,0.098593414,0.0039038076,0.027029732,0.75655866],"study_design_scores_gemma":[0.000037350474,0.0005951694,0.09057834,0.000097667355,0.00015708785,0.0022337092,0.00041574409,0.7937667,0.07978906,0.005401786,0.026881311,0.00004617705],"about_ca_topic_score_codex":0.0031311589,"about_ca_topic_score_gemma":0.005426115,"teacher_disagreement_score":0.0031311589,"about_ca_system_score_codex":0.0003838774,"about_ca_system_score_gemma":0.00031807442,"threshold_uncertainty_score":0.0062258244},"labels":[],"label_agreement":null},{"id":"W3120768806","doi":"10.48550/arxiv.2101.05036","title":"Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Correctness; Probabilistic logic; Minimum bounding box; Variance (accounting); Entropy (arrow of time); Regression; Scoring rule; Data mining; Statistics; Algorithm; Mathematics","score_opus":0.05686327775697341,"score_gpt":0.24338534148535917,"score_spread":0.18652206372838576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120768806","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057221588,0.00051979505,0.937641,0.0005046511,0.00005102831,0.00007643655,0.00033458896,0.002207764,0.0014431971],"genre_scores_gemma":[0.81738186,0.000353037,0.17821732,0.0003918231,0.000085452804,0.00016498404,0.0013591949,0.0005813268,0.001464916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99419314,0.0021254043,0.0002563406,0.0009803207,0.002102948,0.0003418081],"domain_scores_gemma":[0.97892123,0.014989989,0.0014747523,0.0023168,0.0018746521,0.0004225514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013925945,0.0019335863,0.001675097,0.0020639503,0.0005657295,0.0024777243,0.002525303,0.0022242682,0.0017882686],"category_scores_gemma":[0.059370648,0.0010021448,0.00089245784,0.0010541561,0.0025274218,0.0052424483,0.003977924,0.0034935856,0.0005774381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002315581,0.00006619209,0.0067774435,0.00011833102,0.00013957343,0.00008022431,0.00007495413,0.894967,0.0039304164,0.01459971,0.0018697039,0.077144824],"study_design_scores_gemma":[0.0000047368767,0.00003215763,0.0004983446,0.000021610995,0.000008501046,0.000027057342,0.000010753505,0.9828476,0.0036970293,0.01258302,0.00025630122,0.000012810515],"about_ca_topic_score_codex":0.0030851082,"about_ca_topic_score_gemma":0.003674605,"teacher_disagreement_score":0.013925945,"about_ca_system_score_codex":0.002397285,"about_ca_system_score_gemma":0.0013252326,"threshold_uncertainty_score":0.073648274},"labels":[],"label_agreement":null},{"id":"W3122623456","doi":"10.1016/j.neunet.2020.12.009","title":"Words as a window: Using word embeddings to explore the learned representations of Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Neural Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Victoria","funders":"Western Canada Research Grid; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Window (computing); Hierarchy; Word (group theory); Artificial neural network; Machine learning; Deep learning; Natural language processing","score_opus":0.052324885988923674,"score_gpt":0.332143349806977,"score_spread":0.27981846381805336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122623456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16035388,0.0012652535,0.8344195,0.00044494277,0.00014726445,0.000056378172,0.00039044404,0.0014906595,0.0014316215],"genre_scores_gemma":[0.8515296,0.00078478333,0.1421682,0.00024323883,0.000100003774,0.00010639257,0.0009911805,0.00040053998,0.0036761018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997732,0.00007805206,0.00001279674,0.00007376027,0.000034269146,0.000027935617],"domain_scores_gemma":[0.9990252,0.00060987053,0.000084211904,0.0001575377,0.00007433263,0.000048853053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067111023,0.0008989041,0.0007015968,0.00068801705,0.00020200227,0.00075107906,0.00083820266,0.0009068366,0.0019041942],"category_scores_gemma":[0.0034540978,0.00045498786,0.00053520175,0.00079851074,0.000659509,0.00347774,0.0015931793,0.0014867455,0.00049496756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012179657,0.00030453413,0.0046446496,0.00031606073,0.0001968969,0.00028321697,0.0005343838,0.40887177,0.037691735,0.035062026,0.008079269,0.50279754],"study_design_scores_gemma":[0.0000149977,0.00005245256,0.00024453044,0.000012749839,0.000019088278,0.000020433377,0.000039955365,0.97513264,0.0023967666,0.021311104,0.0007451231,0.0000101232845],"about_ca_topic_score_codex":0.001959411,"about_ca_topic_score_gemma":0.0025148499,"teacher_disagreement_score":0.001959411,"about_ca_system_score_codex":0.00031085493,"about_ca_system_score_gemma":0.00038675003,"threshold_uncertainty_score":0.006370127},"labels":[],"label_agreement":null},{"id":"W3122725470","doi":"10.1049/ell2.12070","title":"Maximising robustness and diversity for improving the deep neural network safety","year":2021,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robustness (evolution); Artificial neural network; Computer science; Diversity (politics); Artificial intelligence; Reliability engineering; Engineering; Sociology; Biology","score_opus":0.009057066319669875,"score_gpt":0.21476840856096785,"score_spread":0.20571134224129797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122725470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05959306,0.0004974808,0.93594134,0.0002912659,0.000051623538,0.000030514235,0.000033824817,0.00060338003,0.0029575115],"genre_scores_gemma":[0.9324335,0.00020071185,0.06497426,0.00013597807,0.000049192375,0.000031869273,0.000045270062,0.00009522023,0.0020340148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990109,0.0002766553,0.000049846203,0.00018168101,0.00033372748,0.00014717018],"domain_scores_gemma":[0.9971692,0.0015013515,0.000365818,0.0005519296,0.0002654283,0.0001463062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017474315,0.0011875982,0.0008910635,0.0005984845,0.00036328068,0.00085680024,0.0011802505,0.0015577466,0.0021117327],"category_scores_gemma":[0.006224772,0.00038977398,0.0006764173,0.00025827566,0.0014252573,0.0017346952,0.0029110003,0.0020100754,0.0005665408],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043632303,0.000095295436,0.0011949689,0.00012681042,0.00012163298,0.00024480233,0.000107335494,0.79228157,0.07911482,0.032450862,0.001249453,0.092576124],"study_design_scores_gemma":[0.00000975337,0.00011791764,0.00015490361,0.000011907419,0.000012587444,0.00010076512,0.000009861171,0.97575456,0.014910338,0.008281929,0.0006235987,0.000011872178],"about_ca_topic_score_codex":0.00032349047,"about_ca_topic_score_gemma":0.00028401008,"teacher_disagreement_score":0.0021117327,"about_ca_system_score_codex":0.0006104992,"about_ca_system_score_gemma":0.0004339166,"threshold_uncertainty_score":0.009241402},"labels":[],"label_agreement":null},{"id":"W3123086775","doi":"10.1007/s10664-021-09982-4","title":"Can Offline Testing of Deep Neural Networks Replace Their Online Testing?","year":2021,"lang":"en","type":"preprint","venue":"Empirical Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"H2020 European Research Council; Canada Research Chairs; Ministry of Education; National Research Foundation of Korea; Fonds National de la Recherche Luxembourg; National Research Foundation; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Computer science; Online and offline; Test strategy; Manual testing; Orthogonal array testing; White-box testing; Context (archaeology); Black-box testing; Exploit; Model-based testing; Machine learning; Artificial intelligence; Test case; Computer security; Operating system; Software","score_opus":0.03600581497936919,"score_gpt":0.27307237304567505,"score_spread":0.23706655806630586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123086775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41725865,0.0018078111,0.55886406,0.0053563616,0.00056393276,0.0002164974,0.00047823938,0.0042174244,0.011236999],"genre_scores_gemma":[0.958581,0.00013075596,0.039564747,0.0004852441,0.00005112372,0.00008724066,0.00016384744,0.0001994102,0.00073673716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98641044,0.008255851,0.0006476193,0.0016727254,0.002268869,0.00074447755],"domain_scores_gemma":[0.86898315,0.097686395,0.009100891,0.017312737,0.005687712,0.0012290532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014047326,0.0016807185,0.0008862258,0.0009884975,0.0003751558,0.0014636257,0.0034358697,0.0022614594,0.0035593451],"category_scores_gemma":[0.11362766,0.00060932036,0.00056471507,0.00067682937,0.0027961896,0.0061310246,0.0021647236,0.003090594,0.0009798696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019216975,0.0013249088,0.072148085,0.00062324095,0.00026857923,0.0007425373,0.00039085574,0.42325607,0.015367592,0.029810693,0.008691807,0.44545385],"study_design_scores_gemma":[0.000055087596,0.00040445584,0.0041400087,0.0001214575,0.000031836164,0.00017731995,0.000091246984,0.9542134,0.0113047995,0.027800536,0.0016261331,0.000033802364],"about_ca_topic_score_codex":0.0031166319,"about_ca_topic_score_gemma":0.0028387008,"teacher_disagreement_score":0.014047326,"about_ca_system_score_codex":0.0014539729,"about_ca_system_score_gemma":0.0017373138,"threshold_uncertainty_score":0.074290216},"labels":[],"label_agreement":null},{"id":"W3123387852","doi":"","title":"One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Vertex (graph theory); Robustness (evolution); Graph; Artificial intelligence; Machine learning; Data mining; Theoretical computer science","score_opus":0.06869823887299034,"score_gpt":0.2869022972394296,"score_spread":0.21820405836643927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123387852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14597012,0.00044231032,0.84708077,0.00089156226,0.00012545177,0.000065247965,0.00010067786,0.00062434137,0.004699489],"genre_scores_gemma":[0.9749094,0.00017541256,0.023030922,0.00012883304,0.000017173414,0.000034468747,0.00006859114,0.000044357326,0.0015908112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993587,0.00019163717,0.000027198394,0.00014377895,0.00014370163,0.00013506679],"domain_scores_gemma":[0.9977817,0.0012980938,0.00028853543,0.00024337308,0.00024966276,0.00013862365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009923219,0.00091623905,0.0006956533,0.0006056703,0.00060065946,0.0007064898,0.0010382759,0.0013732924,0.0012152692],"category_scores_gemma":[0.005957182,0.00031652473,0.00068366097,0.00036366688,0.0016752596,0.0017167781,0.0018957385,0.00176418,0.00018825333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009155134,0.000017525565,0.00086284545,0.000022625283,0.000027058884,0.00012859357,0.000048474667,0.9626955,0.0024804666,0.019238958,0.00068286416,0.013703521],"study_design_scores_gemma":[0.0000029625664,0.000018703473,0.00007160884,0.0000037033003,0.000004433555,0.000016424687,0.0000063072926,0.9919441,0.0006994155,0.007071345,0.00015747448,0.0000035079365],"about_ca_topic_score_codex":0.0050025554,"about_ca_topic_score_gemma":0.0032322647,"teacher_disagreement_score":0.0050025554,"about_ca_system_score_codex":0.0013114255,"about_ca_system_score_gemma":0.00065211754,"threshold_uncertainty_score":0.009946883},"labels":[],"label_agreement":null},{"id":"W3123621629","doi":"","title":"Generative Adversarial Neural Architecture Search with Importance Sampling","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Discriminator; Architecture; Artificial intelligence; Machine learning; Sampling (signal processing); Generator (circuit theory); Focus (optics)","score_opus":0.024089310124237425,"score_gpt":0.2792792047021891,"score_spread":0.25518989457795166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123621629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057984274,0.0006888574,0.9353103,0.0005034634,0.00008131666,0.0001343301,0.00009734083,0.0010843017,0.004115865],"genre_scores_gemma":[0.8753387,0.0002423865,0.119554594,0.00042021376,0.00006702767,0.00023766363,0.00024456027,0.00018171422,0.0037130907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914324,0.00036626688,0.00003899346,0.00015107832,0.00019263432,0.00010789144],"domain_scores_gemma":[0.99604577,0.002775414,0.0002478135,0.00048847403,0.0002915318,0.00015100084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024084582,0.0011693125,0.0011860302,0.00061842013,0.0003966283,0.0007909367,0.0017514352,0.0012781267,0.002422069],"category_scores_gemma":[0.008206789,0.00053681,0.00068835117,0.00048459266,0.0015363108,0.001275033,0.0018725441,0.002132633,0.0004972123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009156992,0.00004025841,0.0009957297,0.00005212449,0.000041269155,0.0000613793,0.00003114494,0.9553949,0.0014109374,0.015050364,0.0011257672,0.025704632],"study_design_scores_gemma":[0.000009515686,0.00002356085,0.00005085616,0.0000045060206,0.0000047453605,0.0000140377715,0.0000032655312,0.99326855,0.0003880867,0.006054477,0.00017529578,0.0000031123802],"about_ca_topic_score_codex":0.0020069364,"about_ca_topic_score_gemma":0.0034764246,"teacher_disagreement_score":0.002422069,"about_ca_system_score_codex":0.0010052392,"about_ca_system_score_gemma":0.0011826318,"threshold_uncertainty_score":0.012737334},"labels":[],"label_agreement":null},{"id":"W3123662205","doi":"10.48550/arxiv.2004.01832","title":"SOAR: Second-Order Adversarial Regularization","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Soar; Adversarial system; Robustness (evolution); Deep neural networks; Computer science; Bounded function; Taylor series; Regularization (linguistics); Upper and lower bounds; Mathematical optimization; Artificial intelligence; Algorithm; Artificial neural network; Applied mathematics; Mathematics","score_opus":0.05397763036073673,"score_gpt":0.19358903616669326,"score_spread":0.13961140580595655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123662205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004644957,0.00022291827,0.9910883,0.00027639107,0.000055767898,0.000035908764,0.000066460496,0.00066259474,0.0029466788],"genre_scores_gemma":[0.6477857,0.00080938655,0.3341264,0.0009510628,0.0003206996,0.0003373549,0.00059955183,0.0008546689,0.014215151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987754,0.00043606988,0.00004226871,0.00020065595,0.00044263294,0.00010287457],"domain_scores_gemma":[0.9980317,0.0010462308,0.00020098439,0.00043108154,0.00020891517,0.00008111942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022476932,0.0014853241,0.0009675527,0.00068632414,0.00041452408,0.00084586383,0.001519185,0.00136485,0.003170027],"category_scores_gemma":[0.0048622396,0.00040943746,0.0008630431,0.00049730093,0.0016334956,0.0013460334,0.00246594,0.003454679,0.001052873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106023756,0.000071279544,0.00050988805,0.0001082393,0.00009690348,0.00011593913,0.000058126763,0.8337241,0.01144407,0.08372739,0.0069823293,0.063055776],"study_design_scores_gemma":[0.0000044513044,0.000025901894,0.00006593548,0.000007220318,0.0000047219055,0.00002980005,0.000002648532,0.9841999,0.002229486,0.012189019,0.0012339298,0.000007046121],"about_ca_topic_score_codex":0.001152621,"about_ca_topic_score_gemma":0.0014588458,"teacher_disagreement_score":0.003170027,"about_ca_system_score_codex":0.0007963068,"about_ca_system_score_gemma":0.00086854066,"threshold_uncertainty_score":0.0118870735},"labels":[],"label_agreement":null},{"id":"W3125423558","doi":"10.1109/icip42928.2021.9506016","title":"Towards Universal Physical Attacks On Cascaded Camera-Lidar 3d Object Detection Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Point cloud; Adversarial system; Artificial intelligence; Rendering (computer graphics); Robustness (evolution); Computer vision; RGB color model; Modal; Deep learning; Object detection; Segmentation","score_opus":0.019434725265085724,"score_gpt":0.2706573068201649,"score_spread":0.2512225815550792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125423558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11940082,0.00037053064,0.87354606,0.00072243286,0.000102679056,0.00008903894,0.00012154314,0.0014282429,0.004218614],"genre_scores_gemma":[0.95598805,0.00009552496,0.041641444,0.00023417184,0.000025530002,0.000044216973,0.00007996114,0.0000655503,0.0018255896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99889874,0.00027134575,0.000038673126,0.0002770276,0.00033405298,0.00018012422],"domain_scores_gemma":[0.9981048,0.0009371699,0.00025118442,0.00041302206,0.00019316442,0.00010063455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018194581,0.0012437511,0.0008129311,0.0005080124,0.0004992787,0.0007149956,0.0019182768,0.0016983608,0.0015957914],"category_scores_gemma":[0.005240872,0.00062059856,0.0010874439,0.00023173059,0.0021393653,0.0020897395,0.0038495427,0.0026727759,0.0003070845],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012776883,0.000028985523,0.0007811603,0.000031409196,0.000048010283,0.000116339055,0.00004799847,0.9642035,0.00538099,0.013352906,0.00062251627,0.015258363],"study_design_scores_gemma":[0.0000025913591,0.000019422503,0.00006253776,0.0000032835574,0.0000032987894,0.000016347869,0.0000026532414,0.9951338,0.0011597092,0.0034804186,0.000112501424,0.000003445483],"about_ca_topic_score_codex":0.003550663,"about_ca_topic_score_gemma":0.0032712873,"teacher_disagreement_score":0.003550663,"about_ca_system_score_codex":0.0016510339,"about_ca_system_score_gemma":0.0007781231,"threshold_uncertainty_score":0.011979163},"labels":[],"label_agreement":null},{"id":"W3135151133","doi":"10.1609/aaai.v36i8.20817","title":"Consistency Regularization for Adversarial Robustness","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Overfitting; Robustness (evolution); Regularization (linguistics); Computer science; Adversarial system; Artificial intelligence; Early stopping; Machine learning; Deep neural networks; Regularization perspectives on support vector machines; Deep learning; Artificial neural network; Mathematics; Inverse problem","score_opus":0.05603997000771983,"score_gpt":0.2877849246909347,"score_spread":0.23174495468321488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135151133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045012715,0.0003482067,0.99065876,0.00044065688,0.00008611169,0.000061040264,0.00008458534,0.0006831855,0.0031361387],"genre_scores_gemma":[0.58075875,0.0011139554,0.40292436,0.0016826409,0.0005134198,0.00072952185,0.0008724886,0.0014676822,0.009937112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965875,0.0013579223,0.00016796935,0.0006052271,0.0010190951,0.00026229676],"domain_scores_gemma":[0.99176276,0.005104348,0.00065510726,0.0015489281,0.0006989573,0.00022989411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005240268,0.0019254558,0.0013758714,0.0009912212,0.000837607,0.0014880508,0.0022677341,0.002211723,0.0047057024],"category_scores_gemma":[0.02077631,0.0006505979,0.001436339,0.00074746046,0.003060271,0.0024600355,0.0042441534,0.0064907013,0.0013974108],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019872231,0.00010739226,0.0017010812,0.00029817375,0.00017252818,0.00018186461,0.00013562648,0.7518003,0.011587883,0.14037389,0.010021536,0.083420984],"study_design_scores_gemma":[0.000015183784,0.000058926224,0.00025986653,0.000047676014,0.00001557254,0.00008947705,0.000012627008,0.9382761,0.003842465,0.053856086,0.0035021545,0.000023872166],"about_ca_topic_score_codex":0.0013429322,"about_ca_topic_score_gemma":0.0011593562,"teacher_disagreement_score":0.005240268,"about_ca_system_score_codex":0.0013937299,"about_ca_system_score_gemma":0.0013548422,"threshold_uncertainty_score":0.027713478},"labels":[],"label_agreement":null},{"id":"W3135261467","doi":"10.48550/arxiv.2103.03098","title":"Accounting for Variance in Machine Learning Benchmarks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Canadian Institute for Advanced Research; McGill University; École de Technologie Supérieure; Université de Montréal","funders":"","keywords":"Variance (accounting); Computer science; Artificial intelligence; Accounting; Machine learning; Economics","score_opus":0.04317978687297177,"score_gpt":0.20125338507045085,"score_spread":0.15807359819747907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135261467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42158595,0.007272026,0.5444962,0.0038389228,0.001180364,0.00043567392,0.0022377116,0.006287106,0.012666078],"genre_scores_gemma":[0.93397486,0.0003655686,0.060822338,0.0004575944,0.00017462278,0.0002789078,0.0018521256,0.0008410539,0.0012330731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9766148,0.013050656,0.0012797051,0.0030049977,0.004960595,0.0010892279],"domain_scores_gemma":[0.91789305,0.053824767,0.003743696,0.019980663,0.0037885658,0.000769164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02161669,0.0015937877,0.0015870051,0.0016649322,0.0009586849,0.002707478,0.002574245,0.0019227196,0.0017225205],"category_scores_gemma":[0.13092108,0.0005597568,0.0011780963,0.002013631,0.0024528939,0.0035763192,0.002991475,0.0033063816,0.0006225149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010739065,0.00050930545,0.033198133,0.0007959215,0.0008632099,0.00029354336,0.0002779115,0.7854847,0.007548311,0.04325171,0.015357258,0.11134607],"study_design_scores_gemma":[0.00009325987,0.0005331562,0.007673598,0.00015256865,0.00012033993,0.00022872948,0.00010898548,0.90126336,0.014043586,0.07158127,0.004133468,0.0000676754],"about_ca_topic_score_codex":0.0017383363,"about_ca_topic_score_gemma":0.0021716396,"teacher_disagreement_score":0.02161669,"about_ca_system_score_codex":0.0014763225,"about_ca_system_score_gemma":0.0015948231,"threshold_uncertainty_score":0.11432129},"labels":[],"label_agreement":null},{"id":"W3135367144","doi":"10.1162/neco_a_01468","title":"TARA: Training and Representation Alteration for AI Fairness and Domain Generalization","year":2022,"lang":"en","type":"preprint","venue":"Neural Computation","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Debiasing; Computer science; Representation (politics); Machine learning; Artificial intelligence; Baseline (sea); Generalization; Set (abstract data type); Domain (mathematical analysis); Training set; Independence (probability theory); Pareto principle; Mathematics; Statistics; Psychology","score_opus":0.04785894605328452,"score_gpt":0.3429036926849287,"score_spread":0.2950447466316442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135367144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009956496,0.0001111749,0.98645455,0.00021577645,0.000054277323,0.00008094543,0.00004520777,0.0013361805,0.0017455623],"genre_scores_gemma":[0.48258978,0.00017902245,0.50888795,0.00071624626,0.00016064022,0.000404827,0.00035800858,0.00067057536,0.0060329866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961165,0.0014101188,0.00019648964,0.0009205619,0.0010396244,0.00031668498],"domain_scores_gemma":[0.9917459,0.0031620034,0.00065982994,0.0034260533,0.0006992928,0.00030690973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054781833,0.0012653592,0.0009194389,0.0008946817,0.0007698235,0.0016475564,0.0030186481,0.0015502498,0.0037716364],"category_scores_gemma":[0.019619757,0.00045228697,0.0011212486,0.0006397884,0.0023932264,0.0028595822,0.0048578987,0.0035782957,0.0013551668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054686924,0.00033619467,0.0037924333,0.00019310879,0.00017086283,0.0001802045,0.00050801266,0.39916733,0.03754736,0.06563068,0.0067903996,0.4851365],"study_design_scores_gemma":[0.000036650123,0.00015149314,0.0004794042,0.000026307765,0.000025757969,0.000122068064,0.00004858645,0.94096637,0.01758306,0.036174208,0.0043593384,0.000026751426],"about_ca_topic_score_codex":0.0013934459,"about_ca_topic_score_gemma":0.0017642967,"teacher_disagreement_score":0.0054781833,"about_ca_system_score_codex":0.0011359449,"about_ca_system_score_gemma":0.0019403627,"threshold_uncertainty_score":0.028971791},"labels":[],"label_agreement":null},{"id":"W3135854322","doi":"10.48550/arxiv.2103.05633","title":"Proof-of-Learning: Definitions and Practice","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Stochastic gradient descent; Artificial intelligence; Proof-of-work system; Proof of concept; Machine learning; Variance (accounting); Mechanism (biology); Convergence (economics); Theoretical computer science; Computer security; Artificial neural network","score_opus":0.10666418834915203,"score_gpt":0.22532728098161506,"score_spread":0.11866309263246302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135854322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027259325,0.01017544,0.9435584,0.007601454,0.0003756302,0.00026227577,0.00022273793,0.00073082076,0.03434727],"genre_scores_gemma":[0.23934954,0.017725844,0.722577,0.005838688,0.002465758,0.002344684,0.000706173,0.0012033178,0.0077890116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9630978,0.017375598,0.0031657289,0.007041672,0.007895712,0.001423377],"domain_scores_gemma":[0.8967951,0.07709991,0.0034756172,0.016373076,0.0050509316,0.0012053426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032473113,0.003110581,0.0027499278,0.00465365,0.0039383206,0.017465692,0.0064847018,0.00920893,0.008995481],"category_scores_gemma":[0.08747001,0.0033335134,0.00295562,0.007037513,0.040271893,0.030287128,0.011736584,0.020264277,0.00492909],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024815747,0.00003700742,0.00025629683,0.0003698052,0.000021218828,0.000101017584,0.00046405688,0.0024107362,0.00018766033,0.975387,0.0028292444,0.017911252],"study_design_scores_gemma":[0.000029304614,0.00002875596,0.00007374096,0.0003972785,0.000011737097,0.00024795748,0.000114087255,0.006571348,0.0005092665,0.970289,0.021694124,0.000033289805],"about_ca_topic_score_codex":0.0019872522,"about_ca_topic_score_gemma":0.0005964976,"teacher_disagreement_score":0.032473113,"about_ca_system_score_codex":0.0068399766,"about_ca_system_score_gemma":0.0040710443,"threshold_uncertainty_score":0.1717363},"labels":[],"label_agreement":null},{"id":"W3136737068","doi":"10.1109/iros51168.2021.9636638","title":"Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Point cloud; Computer science; Artificial intelligence; Computer vision; Adversarial system; Object detection; Benchmark (surveying); RGB color model; Lidar; Key (lock); Domain (mathematical analysis); Point (geometry); Image (mathematics); Object (grammar); Vulnerability (computing); Pattern recognition (psychology); Computer security; Remote sensing; Mathematics; Geography","score_opus":0.06681095259077346,"score_gpt":0.31419538868519464,"score_spread":0.24738443609442118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136737068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.191846,0.000782249,0.7971965,0.0010695125,0.00015708801,0.0000947431,0.00024138809,0.001322279,0.007290267],"genre_scores_gemma":[0.97515565,0.00016627072,0.022533534,0.00017725203,0.00002760933,0.000030004534,0.00012830431,0.0000588345,0.0017224923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985474,0.00047397887,0.000043205044,0.00027259128,0.00045919543,0.00020351817],"domain_scores_gemma":[0.99728644,0.0017799824,0.00026922414,0.00039331612,0.00017122853,0.00009981559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014725036,0.001163167,0.00079616904,0.00050609367,0.00048673348,0.00063705735,0.0010610062,0.0012627376,0.0014503483],"category_scores_gemma":[0.0059976825,0.00045923216,0.001038772,0.0003190022,0.0017037912,0.0015305541,0.002747903,0.0020359368,0.00030527558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012641095,0.000023679742,0.0007645617,0.000024435705,0.000050745362,0.00009448684,0.000041075535,0.97298753,0.0028661182,0.009589991,0.00088063604,0.012550344],"study_design_scores_gemma":[0.0000024890953,0.00002218274,0.00011512057,0.000004351418,0.000004054888,0.000027819593,0.000004840798,0.9952715,0.0011964134,0.0031537833,0.0001923347,0.000005164997],"about_ca_topic_score_codex":0.004045431,"about_ca_topic_score_gemma":0.0031325924,"teacher_disagreement_score":0.004045431,"about_ca_system_score_codex":0.0012454683,"about_ca_system_score_gemma":0.0005863718,"threshold_uncertainty_score":0.009036541},"labels":[],"label_agreement":null},{"id":"W3138128528","doi":"10.1109/tifs.2022.3175603","title":"Multidiscriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; End-to-end principle; Adversarial system; Computer security; Computer network; Artificial intelligence","score_opus":0.013017597229911864,"score_gpt":0.24394094372140002,"score_spread":0.23092334649148816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138128528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023855753,0.00039215022,0.97175986,0.00028784788,0.000061270934,0.000041600684,0.000052145766,0.0012313892,0.0023179897],"genre_scores_gemma":[0.86063814,0.00024263731,0.13306142,0.00035964514,0.000054557404,0.00009310171,0.00017766799,0.00015130964,0.0052215196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927455,0.00024066474,0.000028707007,0.00012479546,0.00024142656,0.000089819616],"domain_scores_gemma":[0.99880946,0.000678013,0.00009452694,0.00021199773,0.000147724,0.000058236583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014281023,0.0010887231,0.00080758904,0.00041761604,0.00035752324,0.00058181345,0.0010151066,0.0013255039,0.0015790113],"category_scores_gemma":[0.0030130881,0.00030541647,0.0005102304,0.00021322681,0.0009271168,0.0011172062,0.0019490552,0.0021170217,0.00065135205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031942717,0.00006721689,0.000724689,0.00008276954,0.000079370824,0.00020449977,0.00011040059,0.81096846,0.023044037,0.029826725,0.0030611798,0.13151132],"study_design_scores_gemma":[0.0000040767495,0.000033838962,0.00006223706,0.0000051075012,0.0000043213404,0.000056689973,0.0000055049004,0.9912958,0.004215426,0.0038198503,0.0004908105,0.000006235996],"about_ca_topic_score_codex":0.00061995175,"about_ca_topic_score_gemma":0.0007075894,"teacher_disagreement_score":0.0015790113,"about_ca_system_score_codex":0.0006653516,"about_ca_system_score_gemma":0.0006137043,"threshold_uncertainty_score":0.007552564},"labels":[],"label_agreement":null},{"id":"W3141811040","doi":"10.1109/tvt.2021.3069426","title":"An Adversarial Attack Based on Incremental Learning Techniques for Unmanned in 6G Scenes","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Software deployment; Computer science; Artificial intelligence; Machine learning; Forgetting; Deep learning; Artificial neural network; Deep neural networks; Incremental learning; Pascal (unit); Computer security","score_opus":0.014490792182113047,"score_gpt":0.289374839424724,"score_spread":0.27488404724261095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141811040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08614953,0.000708881,0.9040579,0.0008064967,0.0002757445,0.000108739565,0.00016494391,0.0017761135,0.005951687],"genre_scores_gemma":[0.9319555,0.000288775,0.06370554,0.00042894055,0.000061770195,0.00006978146,0.0002547779,0.000088048306,0.0031468077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993315,0.00015250033,0.000021418504,0.0001258324,0.00023280972,0.00013600057],"domain_scores_gemma":[0.99931633,0.00032234116,0.00006679666,0.00015373864,0.00009226109,0.0000485291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071499933,0.0010631962,0.0005918784,0.00033388459,0.00040267713,0.00045398972,0.0009152501,0.0007587633,0.0013976883],"category_scores_gemma":[0.0021459206,0.00021100353,0.00064227125,0.00023605142,0.0009177317,0.0012975612,0.001374563,0.00158721,0.00026687604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004786564,0.00011676129,0.0018322242,0.00008199208,0.0001126222,0.00072009256,0.00014448474,0.81827354,0.02348485,0.01842086,0.0076842993,0.12864973],"study_design_scores_gemma":[0.000008112145,0.00007472001,0.00030680283,0.0000061575674,0.000009836339,0.0001385818,0.00001347711,0.9893,0.0048940443,0.0040952447,0.0011406041,0.0000123961145],"about_ca_topic_score_codex":0.002236498,"about_ca_topic_score_gemma":0.0021891554,"teacher_disagreement_score":0.002236498,"about_ca_system_score_codex":0.00052413,"about_ca_system_score_gemma":0.0004482763,"threshold_uncertainty_score":0.004675746},"labels":[],"label_agreement":null},{"id":"W3146780562","doi":"10.48550/arxiv.2104.03863","title":"A single gradient step finds adversarial examples on random two-layers neural networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Dimension (graph theory); Gradient descent; Artificial neural network; Term (time); Fraction (chemistry); Mathematics; Function (biology); Computer science; Combinatorics; Algorithm; Topology (electrical circuits); Discrete mathematics; Artificial intelligence; Physics","score_opus":0.05902119718817905,"score_gpt":0.20376040303005738,"score_spread":0.14473920584187833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146780562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074474275,0.00045430663,0.91800356,0.0012731493,0.00007877405,0.000105036626,0.00013371272,0.0010065751,0.004470586],"genre_scores_gemma":[0.79937047,0.00031667732,0.19264399,0.00063721364,0.00013074557,0.00020235094,0.0002713565,0.0003676707,0.006059579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836713,0.0007028855,0.00007375185,0.00031616096,0.00036106954,0.00017900082],"domain_scores_gemma":[0.9911878,0.006597075,0.00044194612,0.00094970106,0.00050352025,0.00032001786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003758351,0.0019329957,0.0018887682,0.0008311535,0.00062793604,0.0010565419,0.0018555933,0.0024696528,0.0022120946],"category_scores_gemma":[0.020187346,0.0011245287,0.0010940117,0.00046439646,0.0037143517,0.0035852424,0.004627209,0.0039794436,0.0006580537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005179164,0.00009411032,0.0011985366,0.00017752498,0.00015353283,0.00025743563,0.00012803655,0.8331483,0.0068246643,0.113608085,0.0033293476,0.040562406],"study_design_scores_gemma":[0.000017867967,0.000045048506,0.0000972878,0.000013528182,0.00000777816,0.000036104197,0.0000074554264,0.95207226,0.00190239,0.04550135,0.00028705128,0.00001179616],"about_ca_topic_score_codex":0.0011294918,"about_ca_topic_score_gemma":0.0013318247,"teacher_disagreement_score":0.003758351,"about_ca_system_score_codex":0.0012202878,"about_ca_system_score_gemma":0.00077714084,"threshold_uncertainty_score":0.019876301},"labels":[],"label_agreement":null},{"id":"W3150116240","doi":"10.1109/lsp.2021.3106239","title":"Cyclic Defense GAN Against Speech Adversarial Attacks","year":2021,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Spectrogram; Generative grammar; SIGNAL (programming language); Key (lock); Signal processing","score_opus":0.015164131035845817,"score_gpt":0.25572130532070914,"score_spread":0.2405571742848633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150116240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01540875,0.00021946599,0.978678,0.00024292323,0.00008056528,0.00003840761,0.000048476715,0.00085710076,0.0044262484],"genre_scores_gemma":[0.871858,0.00024298967,0.12070958,0.0004959788,0.00009902176,0.000105938605,0.0001999419,0.00017612349,0.006112382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992181,0.00025253213,0.000023455972,0.00015264556,0.00026314714,0.00009014234],"domain_scores_gemma":[0.99877614,0.0006332458,0.00010321742,0.00029261602,0.00014094243,0.000053786684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012190458,0.0009773872,0.0005582032,0.00039453915,0.00029075623,0.00052267674,0.001128639,0.0010295425,0.0020288553],"category_scores_gemma":[0.0030546219,0.00024342495,0.000469839,0.00020457576,0.0009845922,0.00092844985,0.0017364176,0.0019127249,0.0006715114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020928559,0.000067769644,0.00089679594,0.00008306344,0.00009167151,0.00022626773,0.00011971451,0.7743879,0.04502685,0.06682069,0.0044967546,0.107573256],"study_design_scores_gemma":[0.0000049143578,0.000044438297,0.00009230204,0.000005552004,0.0000059444797,0.00007789887,0.0000065635686,0.9863809,0.0046104724,0.0073035597,0.0014594614,0.000008079004],"about_ca_topic_score_codex":0.0005220857,"about_ca_topic_score_gemma":0.00064257556,"teacher_disagreement_score":0.0020288553,"about_ca_system_score_codex":0.0004594099,"about_ca_system_score_gemma":0.00047495164,"threshold_uncertainty_score":0.006787181},"labels":[],"label_agreement":null},{"id":"W3152676947","doi":"","title":"Dataset Inference: Ownership Resolution in Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversary; Computer science; Inference; Artificial intelligence; Machine learning; Key (lock); Set (abstract data type); Process (computing); Focus (optics); Statistical model; Decision boundary; Computer security","score_opus":0.09053814841093495,"score_gpt":0.22877082816631233,"score_spread":0.1382326797553774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152676947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01696149,0.0012267813,0.9712191,0.004099017,0.00013249162,0.00012771887,0.00059614633,0.0035429741,0.0020943307],"genre_scores_gemma":[0.6720545,0.0009896,0.31858343,0.0020422162,0.0004831865,0.00045098152,0.0022711114,0.0005181806,0.0026067763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97778535,0.010796349,0.0012734487,0.004236441,0.0051446855,0.0007638288],"domain_scores_gemma":[0.910605,0.04951808,0.0043076533,0.032053173,0.0026670843,0.0008488835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021288082,0.0013866316,0.0021681655,0.0022874104,0.0018504154,0.004142007,0.0073562996,0.004207359,0.002705083],"category_scores_gemma":[0.088366024,0.0012316785,0.0021497193,0.0024494133,0.00613975,0.014339267,0.011412318,0.008220665,0.0010061446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012157725,0.0003424033,0.021143315,0.0006834434,0.0005125957,0.0006778205,0.001274662,0.18691123,0.004226377,0.2906286,0.024380391,0.46800345],"study_design_scores_gemma":[0.0000673918,0.0000912654,0.00096716476,0.00012063209,0.000052397063,0.00029087212,0.00012690718,0.6527293,0.006197548,0.33194402,0.007362157,0.00005033069],"about_ca_topic_score_codex":0.0020431974,"about_ca_topic_score_gemma":0.0015663435,"teacher_disagreement_score":0.021288082,"about_ca_system_score_codex":0.002217591,"about_ca_system_score_gemma":0.0026782101,"threshold_uncertainty_score":0.11258346},"labels":[],"label_agreement":null},{"id":"W3153236199","doi":"10.1109/tnnls.2021.3072166","title":"Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Simple (philosophy); Robustness (evolution); Turing; Artificial neural network; Scalability; Relation (database); Focus (optics); Theoretical computer science; Programming language; Data mining; Database","score_opus":0.012021901891088588,"score_gpt":0.2230785195788173,"score_spread":0.2110566176877287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153236199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085290246,0.00036982377,0.9009099,0.0017188786,0.00012740583,0.00013250095,0.00018537794,0.0015889563,0.009676996],"genre_scores_gemma":[0.9044485,0.0001974442,0.09093926,0.000544064,0.0000569557,0.00009451313,0.00018113038,0.00014054401,0.003397692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794704,0.0007471465,0.00011320887,0.00035346678,0.0006122074,0.00022688234],"domain_scores_gemma":[0.99275523,0.004290117,0.00052395574,0.001991759,0.00030021035,0.0001387348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002848907,0.00089610595,0.00074411905,0.0005699885,0.0007707837,0.0014681875,0.0017800684,0.0017213458,0.0033333313],"category_scores_gemma":[0.0138218785,0.000500468,0.0010853523,0.0003613297,0.002733087,0.0032096468,0.004432151,0.0037877995,0.0006272541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023480688,0.00008337684,0.0013564855,0.00010659607,0.00013448483,0.00026947854,0.00022550985,0.7454719,0.008537997,0.19186316,0.0037111032,0.048005164],"study_design_scores_gemma":[0.000011452452,0.000033901153,0.000104108185,0.000014570765,0.0000119650385,0.000059099963,0.000016774544,0.9198494,0.00252936,0.076208465,0.00115098,0.000009867746],"about_ca_topic_score_codex":0.001906494,"about_ca_topic_score_gemma":0.00212914,"teacher_disagreement_score":0.0033333313,"about_ca_system_score_codex":0.0012587287,"about_ca_system_score_gemma":0.00083400006,"threshold_uncertainty_score":0.015066624},"labels":[],"label_agreement":null},{"id":"W3156452696","doi":"10.1109/infocom42981.2021.9488730","title":"First-Order Efficient General-Purpose Clean-Label Data Poisoning","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Generalizability theory; Transferability; Overhead (engineering); Machine learning; Header; Data mining; Artificial intelligence; Feature (linguistics); Set (abstract data type); Order (exchange); Data set; Computer network","score_opus":0.03964793452346984,"score_gpt":0.2994501845072867,"score_spread":0.2598022499838169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156452696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015804054,0.00012697563,0.9809832,0.00040865815,0.0000364474,0.00008029536,0.00006957272,0.0012128736,0.0012778528],"genre_scores_gemma":[0.7306273,0.0002536445,0.26069722,0.00077375985,0.0000859529,0.00024123242,0.00041621883,0.0004111595,0.0064935186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768233,0.00054017216,0.00012941119,0.00039715078,0.0009279084,0.0003229803],"domain_scores_gemma":[0.9946344,0.0022378375,0.0005305285,0.0018442784,0.0005640462,0.00018894309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031792345,0.0015877238,0.001417998,0.00061873905,0.00072353176,0.001355354,0.0022336184,0.00211937,0.0022329362],"category_scores_gemma":[0.012697325,0.000751614,0.0012694799,0.00065783825,0.0026415598,0.003675838,0.0050294446,0.004348266,0.0008981413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005746096,0.00024566057,0.0026527597,0.00022578624,0.00011390119,0.0003657943,0.0002473303,0.7714824,0.020994877,0.08827472,0.006567644,0.10825451],"study_design_scores_gemma":[0.000013020465,0.000046949077,0.00009192104,0.000006341965,0.0000072411563,0.00006413202,0.0000092643795,0.9748779,0.006170355,0.018135846,0.00056800485,0.00000895897],"about_ca_topic_score_codex":0.0015838764,"about_ca_topic_score_gemma":0.0019343173,"teacher_disagreement_score":0.0031792345,"about_ca_system_score_codex":0.0016271825,"about_ca_system_score_gemma":0.0019050853,"threshold_uncertainty_score":0.016813636},"labels":[],"label_agreement":null},{"id":"W3158885076","doi":"","title":"A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets.","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nash equilibrium; Minimax; Artificial neural network; Minimax theorem; Stochastic game; Computer science; Mathematical economics; Reinforcement learning; Game theory; Regular polygon; Mathematical optimization; Class (philosophy); Artificial intelligence; Mathematics","score_opus":0.13272251734304122,"score_gpt":0.3522322219739912,"score_spread":0.21950970463094996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158885076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0088224765,0.0017008372,0.94342357,0.0057999515,0.0003992537,0.00012408069,0.00033283327,0.00025162753,0.0391455],"genre_scores_gemma":[0.66578126,0.005135547,0.27244335,0.005712948,0.001275977,0.0015057822,0.00054333254,0.00085004803,0.046751752],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983089,0.0007524201,0.000067702305,0.0003973024,0.00029361772,0.00018012346],"domain_scores_gemma":[0.99180603,0.0064268373,0.00045133574,0.00051289616,0.00048418765,0.00031880365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046225996,0.0021386743,0.0017712648,0.00097331416,0.001266527,0.0031190873,0.0022961916,0.0027937058,0.013197698],"category_scores_gemma":[0.021867957,0.00077212567,0.0018498721,0.0009071917,0.004976314,0.009314742,0.0037497268,0.0070886244,0.001500913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005802869,0.000038243077,0.00027548333,0.00020541961,0.00006627672,0.00007133254,0.00014177596,0.054161754,0.00057782367,0.92191017,0.010183955,0.012309748],"study_design_scores_gemma":[0.000021623211,0.000043257085,0.000119650285,0.00009419633,0.000018662518,0.000049872862,0.000026159669,0.18371749,0.00031304712,0.8112245,0.0043454356,0.000026201467],"about_ca_topic_score_codex":0.0021403306,"about_ca_topic_score_gemma":0.001831674,"teacher_disagreement_score":0.013197698,"about_ca_system_score_codex":0.002609964,"about_ca_system_score_gemma":0.002099438,"threshold_uncertainty_score":0.04415065},"labels":[],"label_agreement":null},{"id":"W3159603021","doi":"","title":"Entangled Watermarks as a Defense against Model Extraction","year":2021,"lang":"en","type":"article","venue":"USENIX Security Symposium","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Task (project management); Overfitting; Digital watermarking; Adversary; MNIST database; Outlier; Inference; Artificial intelligence; Crowdsourcing; Computer security; Machine learning; Data mining; Deep learning; Image (mathematics)","score_opus":0.008388954721593844,"score_gpt":0.2574032386230892,"score_spread":0.24901428390149538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159603021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07343132,0.00053372234,0.91834486,0.0014123905,0.00011893446,0.00010059232,0.00020287582,0.0019747005,0.003880659],"genre_scores_gemma":[0.8797332,0.00036487193,0.11571248,0.0004325699,0.00011147221,0.000108656146,0.00030488125,0.00023824867,0.002993535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995307,0.0015927628,0.00028100316,0.0008372728,0.0015399561,0.00044195398],"domain_scores_gemma":[0.9737549,0.009875948,0.001958403,0.0132970195,0.0007496634,0.0003640837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054538148,0.0015290296,0.0012189553,0.0013114279,0.00087535894,0.0023874887,0.0018707027,0.0025049103,0.0024587498],"category_scores_gemma":[0.027043076,0.0010255538,0.001143351,0.001145353,0.003980209,0.008027429,0.00750038,0.0046441816,0.0009183178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084316917,0.00033805353,0.0052887783,0.00024876327,0.00027865957,0.0005756026,0.00062748237,0.473057,0.049305864,0.24300008,0.005692915,0.22074366],"study_design_scores_gemma":[0.00003083656,0.00020738623,0.0005336323,0.00004416455,0.00003690639,0.00033345626,0.0000585126,0.879963,0.023179114,0.09194209,0.0036303573,0.000040542633],"about_ca_topic_score_codex":0.0005064455,"about_ca_topic_score_gemma":0.0005065314,"teacher_disagreement_score":0.0054538148,"about_ca_system_score_codex":0.00092739647,"about_ca_system_score_gemma":0.0008593832,"threshold_uncertainty_score":0.028842866},"labels":[],"label_agreement":null},{"id":"W3159729842","doi":"10.1109/icas49788.2021.9551151","title":"Quality Assurance Challenges For Machine Learning Software Applications During Software Development Life Cycle Phases","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Systems development life cycle; Quality assurance; Software quality assurance; Computer science; Software quality analyst; Software engineering; Software quality; Software quality control; Software; Software development process; Quality (philosophy); Software development; Engineering management; Process management; Data science; Engineering; Operations management","score_opus":0.0452561642815391,"score_gpt":0.3155992123112202,"score_spread":0.27034304802968107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159729842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20430823,0.011020553,0.75769174,0.016235331,0.00024108065,0.00031532632,0.00027735156,0.0014114085,0.008499054],"genre_scores_gemma":[0.8966688,0.0028606127,0.097885706,0.0008004559,0.00014083913,0.00015739321,0.00030591432,0.00021781746,0.00096240896],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9692215,0.0131043,0.002598786,0.0022922975,0.011775091,0.0010080916],"domain_scores_gemma":[0.81534,0.11934437,0.020881062,0.014045713,0.028777046,0.0016119183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035304286,0.0006115833,0.0007444953,0.0021966174,0.0010493237,0.004873456,0.0017036799,0.0016983335,0.0009755539],"category_scores_gemma":[0.16468953,0.0005885841,0.0007690053,0.001963707,0.0021670226,0.004689533,0.003057267,0.002910913,0.00033418936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044746613,0.00031518834,0.055500787,0.0019169746,0.00024448303,0.00067896536,0.0037050513,0.21838523,0.016661327,0.10741091,0.0059253606,0.5888082],"study_design_scores_gemma":[0.000062662904,0.0006378847,0.021412201,0.0013418637,0.00016757842,0.0010721214,0.0022536672,0.6972165,0.030116625,0.21300839,0.032569006,0.00014150843],"about_ca_topic_score_codex":0.0030611951,"about_ca_topic_score_gemma":0.0022188956,"teacher_disagreement_score":0.035304286,"about_ca_system_score_codex":0.0027075228,"about_ca_system_score_gemma":0.0033005534,"threshold_uncertainty_score":0.18670917},"labels":[],"label_agreement":null},{"id":"W3160165418","doi":"10.1109/icse43902.2021.00044","title":"Self-Checking Deep Neural Networks in Deployment","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Satellite of Excellence in Trustworthy Software Systems, National University of Singapore; National Research Foundation Singapore; National University of Singapore; National Research Foundation","keywords":"Software deployment; Computer science; Artificial neural network; Artificial intelligence; Software engineering","score_opus":0.010850642651352103,"score_gpt":0.24849453508354244,"score_spread":0.23764389243219033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160165418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29209033,0.0019158411,0.56370854,0.003321935,0.00092104974,0.0003413493,0.0005492684,0.12740502,0.009746643],"genre_scores_gemma":[0.87528986,0.00028159254,0.11592233,0.0013371417,0.00006566041,0.000114330345,0.00069888774,0.003381572,0.0029086475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928229,0.0025332544,0.00049556233,0.0016238684,0.0020255002,0.0004989382],"domain_scores_gemma":[0.9674531,0.014941353,0.0022730941,0.0114830155,0.0032352486,0.0006141441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010043878,0.0014415049,0.0006217065,0.0008211213,0.0005786317,0.002054193,0.0029973725,0.001677261,0.0029829564],"category_scores_gemma":[0.049385555,0.0013642794,0.0006858069,0.00038736535,0.002233392,0.005935018,0.0031258215,0.002942618,0.0015660289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027247067,0.0005554747,0.047206134,0.0008241185,0.0004804932,0.0021768217,0.0017672507,0.27337724,0.07110507,0.03735009,0.07411307,0.48831955],"study_design_scores_gemma":[0.00016705034,0.00047845644,0.004502699,0.0002174438,0.00009100117,0.00072741223,0.00024037363,0.87803155,0.06974445,0.026330419,0.019341785,0.00012740544],"about_ca_topic_score_codex":0.003742093,"about_ca_topic_score_gemma":0.003634199,"teacher_disagreement_score":0.010043878,"about_ca_system_score_codex":0.001474614,"about_ca_system_score_gemma":0.0014676579,"threshold_uncertainty_score":0.053117692},"labels":[],"label_agreement":null},{"id":"W3162883552","doi":"10.1016/j.neucom.2023.02.058","title":"Bounding information leakage in machine learning","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; École de Technologie Supérieure","funders":"Agence Nationale de la Recherche; European Commission","keywords":"Computer science; Inference; Bounding overwatch; Artificial intelligence; Information leakage; Adversary; Machine learning; Data mining; Theoretical computer science; Computer security","score_opus":0.011956857053634124,"score_gpt":0.24444435834814326,"score_spread":0.23248750129450915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162883552","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008462739,0.0021256665,0.9832659,0.0017426624,0.00015236874,0.000023088196,0.00006542794,0.00027996156,0.0038821953],"genre_scores_gemma":[0.88539773,0.0035487914,0.10182966,0.00095205277,0.0009721904,0.00015405878,0.00018475282,0.00034339857,0.0066173286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954984,0.0022387595,0.00015450563,0.000628179,0.0011923608,0.0002878729],"domain_scores_gemma":[0.97152555,0.023820542,0.0012425566,0.0019093615,0.001144919,0.00035707187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006358392,0.0017700504,0.002061205,0.0015760618,0.0007057875,0.0031484577,0.001863572,0.0029251294,0.0021333627],"category_scores_gemma":[0.04176705,0.00076140853,0.00088580366,0.0014205012,0.0050779935,0.005702586,0.0055651884,0.005138731,0.0005307748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019809633,0.000060920072,0.000567641,0.00032322505,0.00012844345,0.000120106466,0.00010534672,0.5824767,0.0028106393,0.36173242,0.0038369782,0.047639564],"study_design_scores_gemma":[0.0000051550237,0.000023324152,0.00010375778,0.00002913276,0.000012871745,0.000030302697,0.0000075211588,0.80708164,0.00101847,0.19109468,0.0005819264,0.000011186088],"about_ca_topic_score_codex":0.0008492085,"about_ca_topic_score_gemma":0.00040939706,"teacher_disagreement_score":0.006358392,"about_ca_system_score_codex":0.002137713,"about_ca_system_score_gemma":0.0010549488,"threshold_uncertainty_score":0.033626735},"labels":[],"label_agreement":null},{"id":"W3163539156","doi":"10.1109/istc49272.2021.9594170","title":"Variants on Block Design Based Gradient Codes for Adversarial Stragglers","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Coding (social sciences); Redundancy (engineering); Probabilistic logic; Algorithm; Code (set theory); Mathematics; Artificial intelligence; Statistics","score_opus":0.04363415106037342,"score_gpt":0.28796214812479803,"score_spread":0.24432799706442462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163539156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009968828,0.00022688038,0.98673636,0.0001300378,0.00004686605,0.00006026208,0.000069628055,0.00022132626,0.0025397819],"genre_scores_gemma":[0.6101934,0.0006371343,0.38112733,0.00034116843,0.0000960454,0.0004344369,0.00031477064,0.00022395547,0.006631834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817216,0.0006770301,0.000090344656,0.00026246865,0.00061850337,0.00017947213],"domain_scores_gemma":[0.9959428,0.0020097613,0.00044939073,0.0008005902,0.00062061235,0.00017693946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002076889,0.0010116522,0.00077607954,0.0007890956,0.00047751033,0.0009996588,0.0011932901,0.0010787213,0.0027583595],"category_scores_gemma":[0.008752675,0.0004112698,0.00046474944,0.0008091615,0.0017911905,0.0016908837,0.0020653356,0.0017873232,0.001119245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036063592,0.00006771,0.00080541574,0.00015559397,0.00004279667,0.00011599265,0.00015105659,0.49665093,0.0126027735,0.378914,0.002973926,0.10715913],"study_design_scores_gemma":[0.00003775569,0.00018357072,0.0001309241,0.00004083886,0.000011364945,0.00011605364,0.000015805848,0.9008768,0.007097106,0.087319106,0.004137329,0.000033326236],"about_ca_topic_score_codex":0.00078372663,"about_ca_topic_score_gemma":0.00072721584,"teacher_disagreement_score":0.0027583595,"about_ca_system_score_codex":0.0007978532,"about_ca_system_score_gemma":0.0011892183,"threshold_uncertainty_score":0.010983825},"labels":[],"label_agreement":null},{"id":"W3163682331","doi":"10.13033/ijahp.v13i1.766","title":"DECISION MAKING IN DYNAMIC ENVIRONMENTS AN APPLICATION OF MACHINE LEARNING TO THE ANALYTICAL HIERARCHY PROCESS","year":2021,"lang":"en","type":"article","venue":"International Journal of the Analytic Hierarchy Process","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Analytic hierarchy process; Interdependence; Adaptability; Process (computing); Hierarchy; Dynamic decision-making; Reinforcement learning; Machine learning; Artificial intelligence; Operations research; Data mining","score_opus":0.009131523521617443,"score_gpt":0.3282281510338472,"score_spread":0.3190966275122298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163682331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041969363,0.00017575403,0.99338746,0.00043702204,0.000028437145,0.00006920465,0.000017486665,0.000039576822,0.0016480153],"genre_scores_gemma":[0.34411868,0.00066650857,0.6534636,0.00017965645,0.00011193679,0.0003319696,0.00006097383,0.00003597223,0.0010305917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932615,0.0041510956,0.00032389897,0.0006645012,0.0013744333,0.00022459013],"domain_scores_gemma":[0.9897815,0.007852105,0.00089250854,0.0004077181,0.00085610134,0.00021006893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009411722,0.0011033135,0.0014323927,0.0017420647,0.001219447,0.0026687498,0.0018262159,0.0011927248,0.0021532318],"category_scores_gemma":[0.015437498,0.0005541383,0.0013144711,0.0015935147,0.0038372504,0.0026216903,0.002385932,0.0028937112,0.00022127182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041079482,0.00008284579,0.0011002958,0.00038499827,0.00017477227,0.00012719865,0.0005789107,0.65286225,0.0012163315,0.29288664,0.00090243353,0.0496423],"study_design_scores_gemma":[0.00001642636,0.0000474462,0.00015803805,0.00005018386,0.000016111979,0.000021149874,0.00007710001,0.7427447,0.00043958402,0.25493208,0.0014754302,0.000021823991],"about_ca_topic_score_codex":0.0036765242,"about_ca_topic_score_gemma":0.00313545,"teacher_disagreement_score":0.009411722,"about_ca_system_score_codex":0.00242062,"about_ca_system_score_gemma":0.0032257314,"threshold_uncertainty_score":0.049774528},"labels":[],"label_agreement":null},{"id":"W3164255502","doi":"10.1109/tr.2021.3074750","title":"Supporting Deep Neural Network Safety Analysis and Retraining Through Heatmap-Based Unsupervised Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Retraining; Context (archaeology); Deep neural networks; Artificial intelligence; Cluster analysis; Machine learning; Artificial neural network; Automotive industry; Set (abstract data type); Engineering","score_opus":0.012635468399278697,"score_gpt":0.2768781151755608,"score_spread":0.2642426467762821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164255502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11300107,0.00024785878,0.8750759,0.00040162107,0.00007683566,0.0001035108,0.00019258047,0.009460643,0.0014400375],"genre_scores_gemma":[0.8378022,0.000077980025,0.15995474,0.00022997486,0.000021714097,0.00010854171,0.00033718807,0.00030644092,0.0011612637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992079,0.00020425284,0.000051275434,0.00023222595,0.0002229622,0.00008137862],"domain_scores_gemma":[0.99534035,0.0023450935,0.0005148703,0.0007582117,0.0009140469,0.00012751883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019043882,0.0015128591,0.0005056001,0.0008831275,0.00043781011,0.00063757336,0.0021238017,0.0009733861,0.0013958326],"category_scores_gemma":[0.011568973,0.000473701,0.00060773,0.00033316537,0.0009474276,0.0014908477,0.0014313523,0.0019072682,0.00031163322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001335207,0.00010708942,0.0044039492,0.00007179797,0.000053954605,0.00019386507,0.00015701483,0.8672855,0.009083925,0.0021567892,0.0014945036,0.11485819],"study_design_scores_gemma":[0.0000033072786,0.00002190593,0.0002253085,0.000004450001,0.0000040143714,0.0000149223,0.000007676575,0.9937757,0.004248976,0.0015205482,0.00016887432,0.000004324473],"about_ca_topic_score_codex":0.0086259395,"about_ca_topic_score_gemma":0.011054292,"teacher_disagreement_score":0.0086259395,"about_ca_system_score_codex":0.0014282641,"about_ca_system_score_gemma":0.0015778925,"threshold_uncertainty_score":0.017151415},"labels":[],"label_agreement":null},{"id":"W3164414416","doi":"10.1109/isbi48211.2021.9433801","title":"Information Flow Through U-Nets","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Information flow; Mutual information; Image (mathematics); Segmentation; Layer (electronics); Flow (mathematics); Artificial neural network; Image segmentation; Artificial intelligence; Order (exchange); Data mining; Theoretical computer science","score_opus":0.010482966876142126,"score_gpt":0.24763462056884075,"score_spread":0.23715165369269864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164414416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024013799,0.0003542674,0.9662402,0.00048262908,0.00006418622,0.00005630301,0.00010316379,0.00039658212,0.008288827],"genre_scores_gemma":[0.8001356,0.0008840301,0.18634316,0.0006210347,0.00012656124,0.00030493623,0.00028107883,0.00029109043,0.0110125085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984251,0.00058029185,0.00010094457,0.00034852742,0.00036289485,0.00018222013],"domain_scores_gemma":[0.9958633,0.0026758674,0.00034482277,0.00049325154,0.00043780406,0.00018484415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025754783,0.0008621279,0.00072900363,0.001112432,0.0010978355,0.002154197,0.001328362,0.0016185936,0.005154222],"category_scores_gemma":[0.011283711,0.0007118559,0.00085634686,0.000708756,0.0022829932,0.0046018115,0.002899654,0.0020222333,0.0007521524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002703326,0.0000462406,0.00073921913,0.00011616983,0.000054272587,0.00017053352,0.0001875241,0.34336552,0.005954291,0.58636796,0.0024644563,0.06026341],"study_design_scores_gemma":[0.000013852045,0.0000550789,0.00012433156,0.00003496927,0.000018849689,0.00005227692,0.000022051456,0.71426773,0.0035436929,0.27960068,0.0022493268,0.000017217624],"about_ca_topic_score_codex":0.0017843125,"about_ca_topic_score_gemma":0.0010619775,"teacher_disagreement_score":0.005154222,"about_ca_system_score_codex":0.0017898661,"about_ca_system_score_gemma":0.0011863727,"threshold_uncertainty_score":0.01724261},"labels":[],"label_agreement":null},{"id":"W3164510030","doi":"10.1109/access.2021.3083421","title":"Security Hardening of Botnet Detectors Using Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Engineering and Physical Sciences Research Council; Northumbria University","keywords":"Botnet; Computer science; Adversarial system; Artificial intelligence; Machine learning; Malware; Emulation; Test set; Classifier (UML); Oversampling; Data mining; Computer security; Computer network; The Internet; Bandwidth (computing); World Wide Web","score_opus":0.0323627462343424,"score_gpt":0.3187290371346199,"score_spread":0.28636629090027754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164510030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19602488,0.0006866813,0.7928541,0.00075525936,0.0001379899,0.00016755817,0.00021527497,0.005322962,0.003835251],"genre_scores_gemma":[0.9315053,0.00014854352,0.066122934,0.0003183019,0.000046449128,0.00006185888,0.0003355461,0.00016521396,0.0012958669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988298,0.00042257246,0.0000516405,0.00025745624,0.0002918926,0.00014664646],"domain_scores_gemma":[0.9951292,0.0030538498,0.00045002552,0.00080902374,0.00043115724,0.00012655056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028467188,0.0012275886,0.0011280423,0.001034052,0.0004003345,0.0008485367,0.0011512208,0.0010462048,0.001093082],"category_scores_gemma":[0.008770441,0.00049840735,0.00094947807,0.00039307316,0.0012157063,0.0016524759,0.0017684249,0.0021622602,0.0004219772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103132996,0.0000960356,0.0029180497,0.00006057449,0.000077150435,0.00008217697,0.000057068268,0.92418927,0.0069226786,0.004368407,0.001893594,0.059231825],"study_design_scores_gemma":[0.000003314954,0.000023702289,0.00019762147,0.0000049195087,0.000004588881,0.000024371655,0.0000040159057,0.9956642,0.00232613,0.0015589978,0.00018352192,0.000004529605],"about_ca_topic_score_codex":0.0019084356,"about_ca_topic_score_gemma":0.001943998,"teacher_disagreement_score":0.0028467188,"about_ca_system_score_codex":0.001226,"about_ca_system_score_gemma":0.000753177,"threshold_uncertainty_score":0.01505506},"labels":[],"label_agreement":null},{"id":"W3164791059","doi":"10.1109/mim.2021.9436102","title":"Machine Learning in Measurement Part 2: Uncertainty Quantification","year":2021,"lang":"en","type":"article","venue":"IEEE Instrumentation & Measurement Magazine","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Usability; Computer science; Measurement uncertainty; Uncertainty quantification; Software deployment; Artificial intelligence; Risk analysis (engineering); Machine learning; Human–computer interaction; Software engineering; Mathematics; Statistics","score_opus":0.06298068963506236,"score_gpt":0.2769282678517717,"score_spread":0.21394757821670934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164791059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00198962,0.06891373,0.84065694,0.016063077,0.0075695016,0.00018952679,0.00083307846,0.0005303005,0.063254245],"genre_scores_gemma":[0.21300866,0.1866351,0.39837918,0.022534018,0.06069162,0.0016297901,0.0028820615,0.0021130794,0.11212653],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963631,0.0012293805,0.00021163444,0.00072178425,0.0012936288,0.00018050888],"domain_scores_gemma":[0.9938619,0.0041237604,0.00040939424,0.0006797093,0.0007850424,0.0001403014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003410068,0.0018129271,0.0012973085,0.0021774056,0.0009868466,0.0042318148,0.0014724475,0.0036059734,0.017383644],"category_scores_gemma":[0.010516991,0.0009336238,0.0013499163,0.003031627,0.0048980024,0.0058396673,0.002875332,0.0069868667,0.006709543],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042074458,0.000058209844,0.0007681146,0.00093996467,0.00007538914,0.00022706919,0.00030623106,0.015280948,0.0017332012,0.7309477,0.08283391,0.16678716],"study_design_scores_gemma":[0.000008751827,0.000089415706,0.001223996,0.0008571445,0.000041391457,0.00060730113,0.00007809477,0.024924401,0.002057068,0.63869387,0.33133042,0.00008828721],"about_ca_topic_score_codex":0.0012542097,"about_ca_topic_score_gemma":0.0005719839,"teacher_disagreement_score":0.017383644,"about_ca_system_score_codex":0.0023890191,"about_ca_system_score_gemma":0.0015122347,"threshold_uncertainty_score":0.058154047},"labels":[],"label_agreement":null},{"id":"W3165663175","doi":"10.55016/ojs/sppp.v12i1.68098","title":"NORAD : Remaining Relevant","year":2019,"lang":"en","type":"article","venue":"The School of Public Policy Publications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Direktoratet for Utviklingssamarbeid; Government of Canada","keywords":"Geography; Business","score_opus":0.02799825043978594,"score_gpt":0.3012415412819413,"score_spread":0.2732432908421554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165663175","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013299496,0.008381231,0.0018905884,0.1365916,0.16765895,0.0010978533,0.025005732,0.0026050252,0.65543914],"genre_scores_gemma":[0.017052043,0.009653193,0.0015504323,0.053180847,0.028652707,0.0013579924,0.016662827,0.0022229853,0.869667],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99432963,0.0009475511,0.000586158,0.00091567164,0.002069048,0.0011519841],"domain_scores_gemma":[0.9790835,0.0018302476,0.00088448374,0.0026466667,0.0107895415,0.004765563],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005510928,0.0008673288,0.0015704071,0.0021525228,0.0026163866,0.0130613465,0.003070701,0.0053516887,0.55689687],"category_scores_gemma":[0.050553046,0.0004324484,0.00091498677,0.0020169637,0.0012730785,0.0039947326,0.0048239287,0.0042345542,0.40986136],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003861936,0.000015401694,0.00025551618,0.00037230185,0.0000067036885,0.00017551491,0.000072724455,0.000016619788,0.0000737139,0.004014731,0.95954573,0.035412356],"study_design_scores_gemma":[0.000010070111,0.0000056655444,0.00015315693,0.0002449241,0.0000028613126,0.00008350967,0.00009232805,0.000015572308,0.000030883995,0.00077725964,0.9985788,0.0000048513884],"about_ca_topic_score_codex":0.0031052448,"about_ca_topic_score_gemma":0.0038619563,"teacher_disagreement_score":0.55689687,"about_ca_system_score_codex":0.003170973,"about_ca_system_score_gemma":0.01680828,"threshold_uncertainty_score":0.6320329},"labels":[],"label_agreement":null},{"id":"W3165873519","doi":"10.1145/3466132.3467860","title":"Quantum-safe Trust for Vehicles","year":2021,"lang":"en","type":"article","venue":"Queue","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer security; Automotive industry; Nothing; Entertainment; Computer science; Internet privacy; Business; Engineering; Law; Political science","score_opus":0.020402990468839942,"score_gpt":0.2808047395620381,"score_spread":0.2604017490931982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165873519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04441475,0.0033995241,0.8707174,0.011477394,0.0007354069,0.00011296311,0.0004969975,0.0007810111,0.06786454],"genre_scores_gemma":[0.9501963,0.0014008733,0.03177296,0.00089601986,0.00033084612,0.000113539354,0.00022158153,0.00014033804,0.014927436],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976611,0.0006884057,0.000091121954,0.00046007827,0.0006917977,0.00040743768],"domain_scores_gemma":[0.99400514,0.0030698862,0.0004969759,0.0011665632,0.0008847404,0.00037661218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001875323,0.0006442788,0.0008501318,0.00068039965,0.001534357,0.0027721345,0.0010732572,0.0022017069,0.0075691203],"category_scores_gemma":[0.013573471,0.00044377527,0.00075023284,0.00058648945,0.0046702134,0.0061747157,0.0040054773,0.0035543412,0.0013874044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005696363,0.000010296339,0.0001744246,0.00006303964,0.0000149316165,0.00007336435,0.00013921017,0.016000882,0.0005817448,0.9712657,0.0031682942,0.008451145],"study_design_scores_gemma":[0.000010609444,0.000017489514,0.000065595596,0.000021477435,0.0000052263536,0.00003239982,0.00003136407,0.062459886,0.00025981094,0.9335596,0.0035238534,0.000012693398],"about_ca_topic_score_codex":0.0039808573,"about_ca_topic_score_gemma":0.002459662,"teacher_disagreement_score":0.0075691203,"about_ca_system_score_codex":0.0031243716,"about_ca_system_score_gemma":0.0017686842,"threshold_uncertainty_score":0.025321305},"labels":[],"label_agreement":null},{"id":"W3168454669","doi":"10.1007/s10922-021-09608-6","title":"A Novel Lightweight Defense Method Against Adversarial Patches-Based Attacks on Automated Vehicle Make and Model Recognition Systems","year":2021,"lang":"en","type":"article","venue":"Journal of Network and Systems Management","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adversarial system; Robustness (evolution); Overhead (engineering); Artificial intelligence; Computer security; Cyber-physical system; Convolutional neural network; Focus (optics); Deep learning; Situation awareness; Machine learning","score_opus":0.029133663416774862,"score_gpt":0.27578963752991376,"score_spread":0.2466559741131389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168454669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03662241,0.00023421897,0.9585343,0.00022011987,0.00012660919,0.00006659624,0.000036779024,0.0017721116,0.0023867995],"genre_scores_gemma":[0.8650755,0.00014992045,0.12815937,0.00022053509,0.000118826545,0.00007416308,0.00014296985,0.00012400498,0.005934603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909854,0.00014246699,0.000033187443,0.00015562751,0.0004163988,0.0001538188],"domain_scores_gemma":[0.99900913,0.0003119878,0.00010669462,0.00035311095,0.00016083496,0.000058165628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059955183,0.00067725976,0.00085557986,0.0005335448,0.00046244508,0.0006311057,0.0009607542,0.0010704176,0.002005994],"category_scores_gemma":[0.0020671643,0.00028949103,0.0006061271,0.00030699317,0.00056528347,0.001205403,0.0019499804,0.0013518031,0.0007637831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073200517,0.00024470908,0.0019888459,0.0001394244,0.00020030334,0.00040596625,0.00012832537,0.33941764,0.11282946,0.03859369,0.008880163,0.4964395],"study_design_scores_gemma":[0.0000113785345,0.000081113125,0.00027724195,0.0000039299694,0.000013540104,0.00012078538,0.00000975753,0.9848764,0.008925819,0.004367547,0.001303005,0.000009428811],"about_ca_topic_score_codex":0.000695837,"about_ca_topic_score_gemma":0.0007901061,"teacher_disagreement_score":0.002005994,"about_ca_system_score_codex":0.00039507454,"about_ca_system_score_gemma":0.00064417016,"threshold_uncertainty_score":0.006710708},"labels":[],"label_agreement":null},{"id":"W3168455774","doi":"10.1007/978-3-030-62144-5_4","title":"Extraction of Complex DNN Models: Real Threat or Boogeyman?","year":2020,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Granularity; Adversary; Confidentiality; Artificial intelligence; Business model; Information extraction; Extraction (chemistry); Data mining; Machine learning; Computer security; Programming language","score_opus":0.11592522219046456,"score_gpt":0.34389868589094147,"score_spread":0.2279734637004769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168455774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005208586,0.001220867,0.9852299,0.0016151542,0.0002911591,0.000034395915,0.00021732668,0.00092187617,0.005260766],"genre_scores_gemma":[0.26396883,0.006154788,0.68884957,0.0024860217,0.0010638338,0.00013898815,0.0026712893,0.0014412255,0.03322547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900204,0.000260844,0.00006522376,0.00026487242,0.00033984642,0.00006707288],"domain_scores_gemma":[0.9963397,0.002020604,0.00021520132,0.00096134876,0.00036377288,0.00009936976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002704742,0.0013934943,0.0011315941,0.0006988308,0.0004144949,0.0027686437,0.0015592107,0.0019121744,0.0069871573],"category_scores_gemma":[0.012706439,0.00092783524,0.0008936376,0.0008429632,0.0012461708,0.0062097935,0.0025185654,0.0041555846,0.0048572537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024431042,0.00006744632,0.0014932131,0.0003570533,0.00017492962,0.0003925926,0.00018561851,0.12192422,0.0110621,0.13020381,0.02930691,0.70458776],"study_design_scores_gemma":[0.00000937014,0.000039564227,0.00042138257,0.00012843993,0.000031525276,0.00039988034,0.00004561643,0.7716388,0.008687221,0.19543165,0.023135688,0.000030917174],"about_ca_topic_score_codex":0.0010838308,"about_ca_topic_score_gemma":0.0017569136,"teacher_disagreement_score":0.0069871573,"about_ca_system_score_codex":0.0010572376,"about_ca_system_score_gemma":0.00067580066,"threshold_uncertainty_score":0.023374379},"labels":[],"label_agreement":null},{"id":"W3168614584","doi":"10.1145/3447548.3467432","title":"Multi-view Correlation based Black-box Adversarial Attack for 3D Object Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Institute for Advanced Research","funders":"","keywords":"Computer science; Artificial intelligence; Point cloud; Computer vision; Object detection; Lidar; Adversarial system; Deep learning; Focus (optics); Segmentation; Object (grammar); Image segmentation; Remote sensing; Geography","score_opus":0.02781512169500735,"score_gpt":0.2947118951256256,"score_spread":0.2668967734306183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168614584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030565094,0.00021028156,0.9662429,0.00037185263,0.00004736755,0.00004272695,0.00005461089,0.0004376876,0.0020274073],"genre_scores_gemma":[0.9322201,0.00022681187,0.06364407,0.0003072952,0.00003715637,0.00010249071,0.00014634163,0.00007514882,0.0032405087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881434,0.00037142454,0.000041325635,0.000263336,0.0003515011,0.0001580704],"domain_scores_gemma":[0.9978719,0.001373159,0.00022277751,0.00026833094,0.00017173622,0.00009203009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012961846,0.0011254535,0.0009284384,0.0004470999,0.0003430821,0.000572171,0.0013009574,0.0013573277,0.0016217135],"category_scores_gemma":[0.004061493,0.0004568423,0.0011525863,0.00035245894,0.0019167938,0.0015382423,0.002625672,0.0025717865,0.0002921559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018510052,0.0000361635,0.0009133673,0.00004840074,0.00006420812,0.00021408567,0.00007410806,0.93801,0.006858488,0.025251875,0.0012522306,0.027092017],"study_design_scores_gemma":[0.0000027012204,0.000015990126,0.000070000984,0.0000028710333,0.0000039391457,0.000026034908,0.0000028542763,0.99573576,0.0010158203,0.0029842767,0.00013560703,0.000004148158],"about_ca_topic_score_codex":0.00164891,"about_ca_topic_score_gemma":0.0014070624,"teacher_disagreement_score":0.00164891,"about_ca_system_score_codex":0.0010968037,"about_ca_system_score_gemma":0.0006071572,"threshold_uncertainty_score":0.007957876},"labels":[],"label_agreement":null},{"id":"W3173019690","doi":"10.1109/tpami.2022.3174724","title":"Adversarial Robustness Via Fisher-Rao Regularization","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"Grand Équipement National De Calcul Intensif; European Commission; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Softmax function; Robustness (evolution); Computer science; Artificial intelligence; Regularization (linguistics); Geodesic; Categorical variable; Entropy (arrow of time); Gaussian; Binary number; Pattern recognition (psychology); Algorithm; Mathematics; Machine learning; Artificial neural network","score_opus":0.014402546151968823,"score_gpt":0.24770109857305256,"score_spread":0.23329855242108374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173019690","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010952588,0.00028427914,0.9851842,0.00028054413,0.000036390436,0.000025317982,0.00006498567,0.00034412643,0.0028276702],"genre_scores_gemma":[0.8186525,0.00077741157,0.17111519,0.00045441583,0.00015871896,0.00021682665,0.0004710361,0.0003282573,0.00782565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813205,0.0006760454,0.00007579828,0.00028766834,0.0006667263,0.00016172967],"domain_scores_gemma":[0.9962846,0.0024013112,0.00039032398,0.0005048765,0.00030735254,0.000111509406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003074896,0.0014823582,0.0011289939,0.001034934,0.00045167323,0.0012543944,0.0013249612,0.001409861,0.001920884],"category_scores_gemma":[0.009731623,0.0003843945,0.0008994593,0.0006587276,0.0021456145,0.0017910285,0.0026234551,0.0024926884,0.00075616396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096609445,0.000034767098,0.0005681189,0.00006625113,0.0000608678,0.000087806664,0.000045308618,0.8960019,0.0057314695,0.063503645,0.0022252905,0.0315779],"study_design_scores_gemma":[0.0000029295907,0.000028890347,0.00012365585,0.0000097751135,0.0000044321687,0.000040915227,0.000005016479,0.9780212,0.0016108472,0.019608099,0.00053379935,0.000010452758],"about_ca_topic_score_codex":0.001019277,"about_ca_topic_score_gemma":0.00077606295,"teacher_disagreement_score":0.003074896,"about_ca_system_score_codex":0.0011121819,"about_ca_system_score_gemma":0.000851721,"threshold_uncertainty_score":0.016261816},"labels":[],"label_agreement":null},{"id":"W3173829726","doi":"10.1109/tsc.2021.3090365","title":"Defending Adversarial Attacks via Semantic Feature Manipulation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Autoencoder; Transferability; Pattern recognition (psychology); Machine learning; Intuition; Feature (linguistics); Deep learning","score_opus":0.013977471200933194,"score_gpt":0.26141477683655084,"score_spread":0.24743730563561764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173829726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067992955,0.00044405984,0.92630565,0.00049428,0.000067032684,0.00009523804,0.00009351888,0.0011320362,0.003375194],"genre_scores_gemma":[0.9072346,0.00022095902,0.09018546,0.00028582744,0.000055676886,0.00010134569,0.00015772236,0.00008451066,0.0016738785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982444,0.00058034394,0.000092710005,0.00033701494,0.0005710677,0.00017448899],"domain_scores_gemma":[0.9954289,0.002259452,0.00052413676,0.0014170817,0.00027769187,0.000092700226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016799459,0.0012282851,0.000980898,0.00059976475,0.000450932,0.0009179749,0.0011323057,0.0013108316,0.0010283098],"category_scores_gemma":[0.008141681,0.0003635225,0.00078966876,0.00035326055,0.001875283,0.0020008495,0.0028117076,0.002347305,0.00039987298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038533885,0.00019753803,0.004214474,0.00023565086,0.00020358845,0.00042319295,0.00021553713,0.6166204,0.06811118,0.08924492,0.0040173894,0.21613078],"study_design_scores_gemma":[0.000011171207,0.000115506824,0.00045789717,0.000020345025,0.000016494927,0.00025464033,0.00002218486,0.95955354,0.0156062385,0.022556428,0.0013670035,0.000018497416],"about_ca_topic_score_codex":0.000297691,"about_ca_topic_score_gemma":0.00037100204,"teacher_disagreement_score":0.0016799459,"about_ca_system_score_codex":0.0005330629,"about_ca_system_score_gemma":0.0004916633,"threshold_uncertainty_score":0.00888449},"labels":[],"label_agreement":null},{"id":"W3174355057","doi":"10.1609/aaai.v35i11.17163","title":"DIBS: Diversity Inducing Information Bottleneck in Model Ensembles","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Vector Institute; University of Toronto","funders":"","keywords":"MNIST database; Computer science; Generalization; Machine learning; Artificial intelligence; Benchmark (surveying); Information bottleneck method; Bayesian probability; Bottleneck; Artificial neural network; Data mining; Mutual information; Mathematics; Geography","score_opus":0.07016165741512634,"score_gpt":0.2809434166131086,"score_spread":0.21078175919798228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174355057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02910574,0.00075263437,0.9653918,0.00052757957,0.00009353756,0.00008114448,0.00018142918,0.002274457,0.0015917354],"genre_scores_gemma":[0.7382812,0.00063723215,0.25271118,0.0010980258,0.00026924053,0.00043852496,0.0014698247,0.00082935323,0.0042654434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772125,0.0008835623,0.00011837095,0.0004424275,0.00061224523,0.0002220931],"domain_scores_gemma":[0.9939354,0.0033782583,0.00044876136,0.0012130963,0.00063978316,0.0003847288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005081934,0.0020391392,0.0025142233,0.00097465457,0.00095608295,0.0016141697,0.003206262,0.0019656331,0.0024057557],"category_scores_gemma":[0.014512838,0.0010742132,0.0014752608,0.00095779024,0.001416423,0.0035692998,0.00510316,0.0047805645,0.0009273253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000255062,0.00014780762,0.0016131242,0.0001244685,0.00017056105,0.00015857548,0.00013448556,0.86168337,0.003496365,0.017751874,0.0055272933,0.108936995],"study_design_scores_gemma":[0.000009197842,0.000028655824,0.00006466373,0.000009477721,0.000008708682,0.00002205299,0.000006111578,0.99014574,0.0010492953,0.008291015,0.00035920116,0.000005894837],"about_ca_topic_score_codex":0.0041053337,"about_ca_topic_score_gemma":0.0046106465,"teacher_disagreement_score":0.005081934,"about_ca_system_score_codex":0.0016373292,"about_ca_system_score_gemma":0.0020544415,"threshold_uncertainty_score":0.026876152},"labels":[],"label_agreement":null},{"id":"W3174532363","doi":"10.1609/aaai.v35i13.17371","title":"Amnesiac Machine Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Inference; General Data Protection Regulation; Computer security; Artificial intelligence; European union; Inversion (geology); Training set; Machine learning; Data Protection Act 1998; Business","score_opus":0.010708430898537895,"score_gpt":0.2383034027856965,"score_spread":0.2275949718871586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174532363","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0889845,0.0013193282,0.88537264,0.0042306706,0.00020359925,0.00015005267,0.000355551,0.0033638182,0.016019883],"genre_scores_gemma":[0.9150393,0.00069523905,0.074195,0.00082329457,0.00012983021,0.00012672308,0.0003167846,0.00016510414,0.0085087195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973742,0.00082151085,0.00018376978,0.0005531292,0.000844094,0.00022334888],"domain_scores_gemma":[0.987495,0.0047813924,0.0014021585,0.005362487,0.0007236185,0.00023541054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031073035,0.0008036179,0.0007078902,0.00067678647,0.0004797182,0.0012991973,0.001562513,0.0011131587,0.0036202746],"category_scores_gemma":[0.022234378,0.0003143076,0.0006023521,0.0003905363,0.0028932875,0.0031736041,0.00286815,0.0026647535,0.0009974365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056733284,0.00031825947,0.008999926,0.00043499208,0.00040633473,0.0007562779,0.0005821442,0.23132974,0.018147541,0.2523772,0.023580499,0.4624997],"study_design_scores_gemma":[0.000042934636,0.00021674886,0.0024364016,0.0001105104,0.00007977214,0.0010190414,0.00010551364,0.7115682,0.018147929,0.2512094,0.015006656,0.000056967965],"about_ca_topic_score_codex":0.0012041637,"about_ca_topic_score_gemma":0.0010058666,"teacher_disagreement_score":0.0036202746,"about_ca_system_score_codex":0.000953133,"about_ca_system_score_gemma":0.0010632804,"threshold_uncertainty_score":0.016433239},"labels":[],"label_agreement":null},{"id":"W3174820339","doi":"10.1609/aaai.v35i2.16211","title":"Towards Universal Physical Attacks on Single Object Tracking","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer vision; BitTorrent tracker; Computer science; Camouflage; Video tracking; Feature (linguistics); Pattern recognition (psychology); Object (grammar); Eye tracking","score_opus":0.08011184715313713,"score_gpt":0.3143710254128679,"score_spread":0.23425917825973075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174820339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119309545,0.0005208173,0.87281704,0.0005252657,0.00009097819,0.00006942681,0.000084098225,0.0013801825,0.0052026906],"genre_scores_gemma":[0.9321299,0.00026118103,0.06564746,0.0002104282,0.00003950904,0.000042845488,0.00010242169,0.00009329675,0.0014729734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833363,0.00051119865,0.000081642924,0.00033199074,0.00055793294,0.00018367065],"domain_scores_gemma":[0.99599504,0.0019148861,0.00055851525,0.001152784,0.0002330706,0.00014567073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019822782,0.0008381791,0.000793987,0.0004996803,0.00037729176,0.00072739576,0.00076737173,0.0011489694,0.0012832828],"category_scores_gemma":[0.009029739,0.00036571288,0.0005764999,0.00030410377,0.0020116887,0.001917698,0.0029015983,0.0016643718,0.0003852098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048652274,0.00010372684,0.0021800133,0.000164748,0.00009857386,0.0004174533,0.0002071296,0.77338773,0.07476114,0.049789973,0.002823432,0.09557957],"study_design_scores_gemma":[0.00001988362,0.00014829142,0.00055620156,0.000018256365,0.000011084594,0.0002386475,0.000024742545,0.96903807,0.016175989,0.012154018,0.0015965553,0.000018240738],"about_ca_topic_score_codex":0.0006168272,"about_ca_topic_score_gemma":0.00035893742,"teacher_disagreement_score":0.0019822782,"about_ca_system_score_codex":0.00068371807,"about_ca_system_score_gemma":0.00042973313,"threshold_uncertainty_score":0.010483384},"labels":[],"label_agreement":null},{"id":"W3175734114","doi":"10.1109/icde51399.2021.00202","title":"Stealthy Targeted Data Poisoning Attack on Knowledge Graphs","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of British Columbia","funders":"","keywords":"Intuition; Computer science; Embedding; Reinforcement learning; Adversarial system; Artificial intelligence; Machine learning; Benchmark (surveying); Computer security","score_opus":0.0819910612534251,"score_gpt":0.3568086033172135,"score_spread":0.2748175420637884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175734114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41747808,0.0026640024,0.56226325,0.0018587423,0.00015000948,0.00030032988,0.0014317002,0.0062892977,0.007564617],"genre_scores_gemma":[0.9426538,0.00038276488,0.053621374,0.00030736288,0.000033283788,0.00005521139,0.0010253185,0.000089956695,0.0018310503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896467,0.00029371787,0.00006446577,0.00029995854,0.00026287275,0.00011442539],"domain_scores_gemma":[0.995154,0.0027090677,0.000579341,0.0011135298,0.00028000076,0.0001640093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013103918,0.0011065381,0.0010329718,0.00082118396,0.00044012396,0.0008560103,0.0013819747,0.0014394041,0.0009153251],"category_scores_gemma":[0.0074489717,0.00030629593,0.00066379097,0.00061613036,0.0013283436,0.0026372257,0.0016357539,0.0015219925,0.0003473218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047174538,0.00018060236,0.004280351,0.00025303444,0.00017962312,0.00041741048,0.00015317704,0.8262038,0.009314735,0.011448047,0.006029154,0.14106834],"study_design_scores_gemma":[0.00002314467,0.00011717214,0.00062063674,0.000020244013,0.000028952783,0.00020401302,0.00003751599,0.9795125,0.0051946403,0.012933768,0.0012960284,0.000011370222],"about_ca_topic_score_codex":0.0023989151,"about_ca_topic_score_gemma":0.002694364,"teacher_disagreement_score":0.0023989151,"about_ca_system_score_codex":0.001051408,"about_ca_system_score_gemma":0.00076431385,"threshold_uncertainty_score":0.00762856},"labels":[],"label_agreement":null},{"id":"W3176400240","doi":"10.48550/arxiv.2106.14406","title":"Poisoning the Search Space in Neural Architecture Search","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Architecture; Space (punctuation); Computer science; Artificial intelligence; Machine learning; Geography; Operating system","score_opus":0.06289176457676696,"score_gpt":0.21614636387600422,"score_spread":0.15325459929923727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176400240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27659282,0.0009938206,0.71172917,0.0013522843,0.00009428176,0.00012930874,0.00011181597,0.0019089128,0.007087552],"genre_scores_gemma":[0.90372336,0.0002146585,0.093797,0.00031081325,0.000025770652,0.00009189041,0.00008491264,0.00014848413,0.0016030438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985807,0.0006462399,0.00008105622,0.0002184231,0.00033085776,0.0001428157],"domain_scores_gemma":[0.9949909,0.0028219833,0.0005078269,0.0012893763,0.00027594832,0.00011403817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002868949,0.0007298281,0.0006864605,0.0005538408,0.0004837418,0.00089112925,0.0011561948,0.0013168612,0.0017771968],"category_scores_gemma":[0.010970478,0.000482412,0.0006799877,0.00046973754,0.002321279,0.0023449408,0.0020536636,0.0023672194,0.00043136333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032194992,0.00009554406,0.0029147468,0.00010732202,0.00012700772,0.0001992867,0.00017985606,0.8726676,0.012745273,0.042808767,0.0022906251,0.065542035],"study_design_scores_gemma":[0.00004111553,0.00017553277,0.00026383935,0.000022358901,0.000026043834,0.00011754766,0.00003913746,0.95450467,0.007942898,0.035415027,0.0014401108,0.000011645346],"about_ca_topic_score_codex":0.00065945653,"about_ca_topic_score_gemma":0.0011760338,"teacher_disagreement_score":0.002868949,"about_ca_system_score_codex":0.0008370573,"about_ca_system_score_gemma":0.0009359568,"threshold_uncertainty_score":0.01517266},"labels":[],"label_agreement":null},{"id":"W3176690543","doi":"10.1609/aaai.v35i18.17945","title":"SecDD: Efficient and Secure Method for Remotely Training Neural Networks (Student Abstract)","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overfitting; Leverage (statistics); Computer science; Deep neural networks; Training (meteorology); Artificial neural network; Adversarial system; Machine learning; Artificial intelligence; Vulnerability (computing); Deep learning; Computer network; Distributed computing; Computer security","score_opus":0.07787970669094256,"score_gpt":0.34495881990182703,"score_spread":0.26707911321088446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176690543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004788397,0.00009141875,0.9911288,0.00028233655,0.00009124741,0.00003606746,0.000069796486,0.0023399093,0.0011720182],"genre_scores_gemma":[0.28904185,0.00019577733,0.69882643,0.00048696875,0.00018120625,0.00031481948,0.0005820701,0.00093320553,0.009437607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989027,0.00032909267,0.00007380337,0.00020035794,0.00040843134,0.00008556443],"domain_scores_gemma":[0.9980963,0.00066896185,0.000101553145,0.0008012775,0.0002601633,0.00007159951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019034214,0.0007693098,0.00077355845,0.00048266473,0.0004867314,0.0009454213,0.001449593,0.0012693978,0.008446374],"category_scores_gemma":[0.0062990827,0.000528911,0.0007254038,0.0003652526,0.0013788687,0.0017971514,0.0033198253,0.0028028109,0.002914917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060680014,0.00014802419,0.0016033776,0.00019800062,0.000114125,0.0003808726,0.00015528743,0.3202751,0.026924089,0.09223756,0.024927229,0.53242946],"study_design_scores_gemma":[0.00004357203,0.000057972476,0.00009171865,0.000014993979,0.0000068330387,0.00011956733,0.000012031833,0.95270514,0.011373823,0.031387918,0.004174295,0.0000120983605],"about_ca_topic_score_codex":0.0006845062,"about_ca_topic_score_gemma":0.0013745577,"teacher_disagreement_score":0.008446374,"about_ca_system_score_codex":0.0006742837,"about_ca_system_score_gemma":0.0011134545,"threshold_uncertainty_score":0.02825588},"labels":[],"label_agreement":null},{"id":"W3176864543","doi":"10.65109/rgis8995","title":"Transferable Environment Poisoning: Training-time Attack on Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement; Training (meteorology); Reinforcement learning; Computer science; Computer security; Psychology; Artificial intelligence; Social psychology; Geography; Meteorology","score_opus":0.03446290798627397,"score_gpt":0.25970961281123434,"score_spread":0.22524670482496037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176864543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053154353,0.00013498256,0.9416817,0.000398912,0.000044804558,0.000089032554,0.000026215694,0.00085696566,0.003612934],"genre_scores_gemma":[0.954455,0.00008294314,0.043501437,0.00016591989,0.00002165338,0.00007979348,0.000022324311,0.000059163038,0.0016117105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873275,0.000524169,0.000049913462,0.00022404078,0.00028079364,0.00018842894],"domain_scores_gemma":[0.9962739,0.0022547254,0.00041538532,0.0007072377,0.00017488716,0.0001738369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017084855,0.0008245305,0.0006683102,0.00032640062,0.00036046442,0.0007084072,0.0010108964,0.0011552332,0.0015879755],"category_scores_gemma":[0.007616469,0.00023567538,0.0005677841,0.00020452739,0.00200574,0.0014899657,0.0022316289,0.0021272486,0.00025563154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001824884,0.00010910314,0.001195438,0.000056033085,0.00007060882,0.0002595702,0.00013340649,0.90807086,0.009294529,0.04806574,0.0008597875,0.031702347],"study_design_scores_gemma":[0.000012747183,0.000063082465,0.000093272974,0.00000513919,0.000004708409,0.00003599548,0.000007957972,0.98213196,0.0019809555,0.015332995,0.0003259405,0.0000052799464],"about_ca_topic_score_codex":0.00093669555,"about_ca_topic_score_gemma":0.0006286719,"teacher_disagreement_score":0.0017084855,"about_ca_system_score_codex":0.0007682688,"about_ca_system_score_gemma":0.00079480157,"threshold_uncertainty_score":0.009035468},"labels":[],"label_agreement":null},{"id":"W3178326529","doi":"10.1145/3460319.3464809","title":"AdvDoor: adversarial backdoor attack of deep learning system","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backdoor; Adversarial system; Computer science; Computer security; Deep learning; Artificial intelligence; Machine learning","score_opus":0.01220603919960346,"score_gpt":0.25369748207739706,"score_spread":0.24149144287779362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178326529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12121341,0.0007416521,0.8684154,0.00075881457,0.00022572887,0.00016200895,0.00023772838,0.0042964793,0.003948745],"genre_scores_gemma":[0.9618772,0.0001733953,0.035665922,0.00027540833,0.000022812532,0.000054509295,0.00013744693,0.000068668865,0.0017246336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989304,0.0002762054,0.000055205164,0.0002071186,0.00031069003,0.00022027442],"domain_scores_gemma":[0.9985476,0.00065694976,0.00014967575,0.00040385363,0.00016300862,0.0000789849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011440052,0.0009878249,0.0006439829,0.0004830801,0.00038161283,0.0006662415,0.00095536135,0.00090831565,0.0016191684],"category_scores_gemma":[0.004270904,0.00027917474,0.00067277777,0.0002487824,0.0011651847,0.0016008851,0.0022075514,0.0017908639,0.00026384412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076443015,0.0002237964,0.0068100537,0.00026049718,0.00030243286,0.00072306255,0.00018618902,0.729364,0.03586063,0.032170456,0.008040176,0.1852942],"study_design_scores_gemma":[0.000011783791,0.000094312716,0.00027615746,0.000009866093,0.000013992755,0.00012833589,0.000009750294,0.9847056,0.008972804,0.0050085457,0.0007570778,0.000011772894],"about_ca_topic_score_codex":0.001550576,"about_ca_topic_score_gemma":0.0012023144,"teacher_disagreement_score":0.0016191684,"about_ca_system_score_codex":0.0006611903,"about_ca_system_score_gemma":0.00078011135,"threshold_uncertainty_score":0.0060501695},"labels":[],"label_agreement":null},{"id":"W3179442871","doi":"10.1109/cvpr46437.2021.00978","title":"AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":166,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Adversarial system; Lidar; Autonomy; Advanced driver assistance systems; Artificial intelligence","score_opus":0.013068569054236685,"score_gpt":0.2829613151278228,"score_spread":0.2698927460735861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179442871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14739226,0.00025556516,0.83784735,0.00056785054,0.00015191331,0.00033679503,0.001474981,0.0057625184,0.0062107877],"genre_scores_gemma":[0.88684696,0.000109199966,0.10833576,0.000171449,0.000021858557,0.00021381764,0.0020167415,0.00035093707,0.0019332083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956733,0.00016694688,0.000015994308,0.0000862785,0.00011670843,0.000046766276],"domain_scores_gemma":[0.99867415,0.00077215163,0.00009365672,0.00025270702,0.00012764831,0.000079696896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010631998,0.0008829386,0.00033808124,0.000479867,0.0003449941,0.00046214147,0.0013532348,0.00088092854,0.002749244],"category_scores_gemma":[0.0034617616,0.00039234987,0.000682051,0.00017971877,0.0009092721,0.00084158714,0.0018981529,0.0011076734,0.00048016012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006655152,0.000032530064,0.0012371005,0.000038433624,0.000025037372,0.00008238724,0.000055292403,0.9814156,0.0015321497,0.004207413,0.0017917481,0.009515739],"study_design_scores_gemma":[0.000007695422,0.000021160062,0.00010296832,0.000004208431,0.0000021818942,0.000023713561,0.000010447971,0.994361,0.0013159668,0.0032871494,0.0008593678,0.000004105121],"about_ca_topic_score_codex":0.0027701345,"about_ca_topic_score_gemma":0.004169476,"teacher_disagreement_score":0.0027701345,"about_ca_system_score_codex":0.0006217379,"about_ca_system_score_gemma":0.00070211757,"threshold_uncertainty_score":0.009197116},"labels":[],"label_agreement":null},{"id":"W3180051575","doi":"10.1109/access.2021.3108545","title":"Taxonomy of Saliency Metrics for Channel Pruning","year":2021,"lang":"en","type":"preprint","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"Science Foundation Ireland","keywords":"Pruning; Computer science; Metric (unit); Artificial intelligence; Convolutional neural network; Machine learning; Taxonomy (biology); Reduction (mathematics); Context (archaeology); Computation; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.10394718325737422,"score_gpt":0.34521017342544164,"score_spread":0.2412629901680674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180051575","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046555426,0.0069380347,0.9335137,0.0008451583,0.00023475332,0.00045716655,0.00032879531,0.0012440699,0.009882877],"genre_scores_gemma":[0.53964555,0.0029671162,0.45331913,0.0002609265,0.0003086007,0.0006191602,0.00055121235,0.00033003837,0.0019982313],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99601066,0.0009349548,0.00051722804,0.00043800834,0.0019061866,0.00019301189],"domain_scores_gemma":[0.9888459,0.005455285,0.0016111365,0.0011701855,0.0024693434,0.0004481681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042113564,0.0019802651,0.0009795752,0.0051583014,0.00090806006,0.0019279381,0.0014885602,0.0014512484,0.0016349077],"category_scores_gemma":[0.024658913,0.00037083743,0.0007284439,0.002621659,0.0019666005,0.0048795766,0.0020294315,0.0016116842,0.00032171246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004668451,0.0002104422,0.00716413,0.0012691545,0.00024854395,0.0002957909,0.0006382178,0.13766532,0.02694613,0.16734986,0.011064436,0.64668113],"study_design_scores_gemma":[0.00007259736,0.0010572821,0.007870594,0.00046050313,0.00024646465,0.001158259,0.00030942968,0.7406496,0.030515222,0.19668427,0.020791158,0.0001846558],"about_ca_topic_score_codex":0.0012215211,"about_ca_topic_score_gemma":0.0016563989,"teacher_disagreement_score":0.0051583014,"about_ca_system_score_codex":0.001938063,"about_ca_system_score_gemma":0.0010523164,"threshold_uncertainty_score":0.02227199},"labels":[],"label_agreement":null},{"id":"W3180119292","doi":"10.1007/s10664-021-09982-4","title":"Can Offline Testing of Deep Neural Networks Replace Their Online Testing?: A Case Study of Automated Driving Systems.","year":2021,"lang":"en","type":"article","venue":"PubMed","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"H2020 European Research Council; Canada Research Chairs; Ministry of Education; National Research Foundation of Korea; Fonds National de la Recherche Luxembourg; National Research Foundation; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Computer science; Test strategy; Online and offline; Manual testing; Orthogonal array testing; Context (archaeology); White-box testing; Model-based testing; Non-regression testing; Machine learning; Software performance testing; Black-box testing; Artificial intelligence; Test case; Operating system; Software","score_opus":0.03895238907324856,"score_gpt":0.2590227423652258,"score_spread":0.22007035329197727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180119292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94086,0.00042497853,0.052026995,0.001261599,0.000104924664,0.00014804216,0.0002221912,0.0007765537,0.00417475],"genre_scores_gemma":[0.99166065,0.00003890419,0.0075885397,0.00011010776,0.000007400174,0.000024280234,0.0000677822,0.000033423454,0.0004688467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976421,0.0012865112,0.00008915776,0.00031533057,0.00043921536,0.00022764299],"domain_scores_gemma":[0.98226756,0.012556468,0.0012103679,0.0020718914,0.0014571954,0.00043645545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035102656,0.0006996022,0.00027594314,0.0004299882,0.00033112738,0.0005427483,0.0013724699,0.0013471461,0.0015586866],"category_scores_gemma":[0.02229806,0.00020324027,0.0002759447,0.00028626667,0.0011071682,0.0016589653,0.00069865334,0.0011855316,0.00039718603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032495179,0.0030748043,0.123373985,0.0008549986,0.00034240668,0.0066524297,0.0016316214,0.43008173,0.040215943,0.013408661,0.012175305,0.36493868],"study_design_scores_gemma":[0.00016803308,0.0016633386,0.019184198,0.000116693525,0.000074887685,0.0012985364,0.0007585196,0.9192986,0.03834958,0.013101232,0.005929957,0.000056483143],"about_ca_topic_score_codex":0.004765221,"about_ca_topic_score_gemma":0.006336198,"teacher_disagreement_score":0.004765221,"about_ca_system_score_codex":0.00091201527,"about_ca_system_score_gemma":0.000715153,"threshold_uncertainty_score":0.018564284},"labels":[],"label_agreement":null},{"id":"W3181970103","doi":"10.1109/cvprw53098.2021.00371","title":"A Watermarking-Based Framework for Protecting Deep Image Classifiers Against Adversarial Attacks","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digital watermarking; Adversarial system; Artificial intelligence; Computer science; Watermark; Deep learning; Encoder; Image (mathematics); Robustness (evolution); Classifier (UML); Pattern recognition (psychology); Adversary; Contextual image classification; Computer vision; Computer security","score_opus":0.01895271064321068,"score_gpt":0.2922332760152485,"score_spread":0.2732805653720378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181970103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017142307,0.0005804562,0.9791166,0.00027363404,0.00007798086,0.000055053926,0.000069935864,0.0013664243,0.0013176337],"genre_scores_gemma":[0.6138914,0.00084932504,0.37981445,0.00040873996,0.0001696933,0.00014889226,0.00028806456,0.00013532587,0.0042941086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991844,0.00015368887,0.00005533301,0.00015898224,0.00033855715,0.00010894828],"domain_scores_gemma":[0.9983191,0.00047006624,0.00028395499,0.0005587479,0.00029359967,0.00007453597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014107663,0.0010685129,0.00072574336,0.0009336139,0.00039305797,0.0008250878,0.0012970405,0.0017070108,0.0014033915],"category_scores_gemma":[0.004261653,0.00039188954,0.0007224304,0.0004796871,0.0014097119,0.002468759,0.0018985646,0.0019809392,0.0007402936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042756498,0.0002355685,0.0018135711,0.0002612738,0.00015767521,0.00040482663,0.00014394178,0.37659344,0.1402668,0.056395948,0.0056596915,0.41763964],"study_design_scores_gemma":[0.000015953572,0.00018685828,0.00028592764,0.00002792105,0.000032130927,0.000270911,0.000011833403,0.935849,0.046773683,0.013050472,0.0034693114,0.000025926884],"about_ca_topic_score_codex":0.0006240788,"about_ca_topic_score_gemma":0.00069154124,"teacher_disagreement_score":0.0017070108,"about_ca_system_score_codex":0.0006126066,"about_ca_system_score_gemma":0.0008243069,"threshold_uncertainty_score":0.007460952},"labels":[],"label_agreement":null},{"id":"W3183870986","doi":"10.1109/jiot.2021.3099164","title":"Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Research Grants Council, University Grants Committee","keywords":"Adversarial system; Deep learning; Object detection; Robustness (evolution); Active safety; Perspective (graphical); Vehicle dynamics; Deep neural networks","score_opus":0.024976327450216766,"score_gpt":0.337601712418554,"score_spread":0.31262538496833725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183870986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9018856,0.0012989411,0.09102195,0.0004425105,0.00020010005,0.0002025364,0.00040626765,0.00090851664,0.0036335604],"genre_scores_gemma":[0.9925362,0.00014778225,0.0065718344,0.00006554134,0.000013820552,0.00003492053,0.00025386538,0.000021366614,0.00035461533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982311,0.0005171244,0.00009380231,0.00031852705,0.0006098378,0.0002296681],"domain_scores_gemma":[0.99425113,0.0033888763,0.0007617224,0.000599993,0.0006571137,0.0003411606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024409494,0.000997025,0.0006241124,0.0008264045,0.00031045213,0.00051327376,0.00084366027,0.0010479984,0.00073713594],"category_scores_gemma":[0.0092121465,0.00021281104,0.00049575634,0.00028361363,0.0013112463,0.0012798865,0.0013835792,0.0010403948,0.00020077087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064888294,0.00023864332,0.007892633,0.00015076158,0.000115572184,0.00013100254,0.000055595585,0.95252967,0.0073770606,0.0024574213,0.0011118576,0.027290858],"study_design_scores_gemma":[0.000017489321,0.00042273253,0.0021568069,0.000014238148,0.000021574298,0.00006421753,0.000026068632,0.98930055,0.006157084,0.0015004366,0.00030529726,0.000013477297],"about_ca_topic_score_codex":0.0032882267,"about_ca_topic_score_gemma":0.0021698156,"teacher_disagreement_score":0.0032882267,"about_ca_system_score_codex":0.0013930419,"about_ca_system_score_gemma":0.0007128844,"threshold_uncertainty_score":0.012909114},"labels":[],"label_agreement":null},{"id":"W3184952526","doi":"10.1007/978-1-4842-7092-9_6","title":"Residual Network GANs","year":2021,"lang":"en","type":"book-chapter","venue":"Apress eBooks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Bible College","funders":"","keywords":"Adversarial system; Generator (circuit theory); Residual; Generative grammar; Computer science; Key (lock); Generative adversarial network; Artificial intelligence; Computer security; Algorithm; Deep learning; Physics; Power (physics)","score_opus":0.020893649371232007,"score_gpt":0.2342891720675915,"score_spread":0.2133955226963595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184952526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015366985,0.006450853,0.85213417,0.0012638114,0.000914423,0.00006589392,0.000814803,0.0032866606,0.13353278],"genre_scores_gemma":[0.14727914,0.021864252,0.43313962,0.002070982,0.0013485586,0.00036879184,0.005487789,0.003795503,0.3846454],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997197,0.00005302113,0.000009349827,0.0000700705,0.00012654209,0.000021335189],"domain_scores_gemma":[0.99974424,0.00012040805,0.000012303883,0.000054355558,0.00005146192,0.000017250726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049082533,0.0010898401,0.00052090216,0.0005409319,0.00025184455,0.0012988064,0.001163336,0.0009637127,0.024135267],"category_scores_gemma":[0.0014802163,0.0004422029,0.0005983388,0.00071834703,0.00075669854,0.00172254,0.0012173586,0.0029580717,0.015297833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040400453,0.00003489735,0.00017754218,0.00028615075,0.000053848602,0.00010269225,0.00007160602,0.105207704,0.003748518,0.42142734,0.12607339,0.3427759],"study_design_scores_gemma":[0.000012191212,0.000033865766,0.00022308517,0.00016188457,0.000020492904,0.00029738803,0.0000185718,0.25800598,0.0038402253,0.35821855,0.3791322,0.00003557387],"about_ca_topic_score_codex":0.0008814596,"about_ca_topic_score_gemma":0.0016697611,"teacher_disagreement_score":0.024135267,"about_ca_system_score_codex":0.00060075335,"about_ca_system_score_gemma":0.00039000576,"threshold_uncertainty_score":0.08074051},"labels":[],"label_agreement":null},{"id":"W3185323353","doi":"10.1109/deeptest52559.2021.00010","title":"TF-DM: Tool for Studying ML Model Resilience to Data Faults","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dependability; Computer science; Reliability (semiconductor); Resilience (materials science); Reliability engineering; Data modeling; Focus (optics); Fault (geology); Quality (philosophy); Data mining; Software engineering; Engineering","score_opus":0.07931967226435244,"score_gpt":0.35007559044317765,"score_spread":0.2707559181788252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185323353","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18735038,0.0015458438,0.5273985,0.0010946458,0.00048425124,0.0006404357,0.053752244,0.2202774,0.007456238],"genre_scores_gemma":[0.6044383,0.0006184564,0.32615888,0.0006723096,0.00009969417,0.0012544566,0.05689422,0.007012529,0.0028511367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983601,0.0003878611,0.00016564861,0.00035865372,0.00059511664,0.00013258905],"domain_scores_gemma":[0.9900573,0.0068043424,0.0007059096,0.0015921122,0.0006463838,0.00019389442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002581583,0.0019514479,0.0006764586,0.0028696219,0.00061802944,0.0012569819,0.0022241604,0.0016852925,0.006565344],"category_scores_gemma":[0.020307304,0.00057652,0.0013743456,0.0014203442,0.0009672858,0.0020785707,0.0018918116,0.0021734494,0.0016961504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009324351,0.0006210442,0.031266596,0.0020685832,0.00059887127,0.0006737945,0.00045516016,0.65569705,0.014392874,0.018175136,0.13016513,0.14495333],"study_design_scores_gemma":[0.000079087215,0.00014143181,0.0027217302,0.00005668016,0.000034841098,0.00026371435,0.000062712,0.96203595,0.011813166,0.011587227,0.011162514,0.00004101551],"about_ca_topic_score_codex":0.005790721,"about_ca_topic_score_gemma":0.0060707484,"teacher_disagreement_score":0.006565344,"about_ca_system_score_codex":0.000980528,"about_ca_system_score_gemma":0.0014761378,"threshold_uncertainty_score":0.021963239},"labels":[],"label_agreement":null},{"id":"W3185425515","doi":"10.1007/s10515-022-00337-x","title":"How to certify machine learning based safety-critical systems? A systematic literature review","year":2022,"lang":"en","type":"article","venue":"Automated Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Certification; Computer science; Life-critical system; Context (archaeology); Domain (mathematical analysis); Systematic review; Robustness (evolution); Engineering management; Artificial intelligence; Software; Engineering; Management; Political science","score_opus":0.007356704105584707,"score_gpt":0.23390435369138968,"score_spread":0.22654764958580498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185425515","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009985692,0.9930508,0.0037943725,0.0008396968,0.00016996848,0.00012602205,0.00014707875,0.000023756558,0.0008496828],"genre_scores_gemma":[0.025737746,0.9673058,0.004993605,0.0010367292,0.00023770916,0.00018993548,0.00030175262,0.000022133432,0.00017460805],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99221146,0.0026389316,0.002244035,0.00074066647,0.002012988,0.00015188107],"domain_scores_gemma":[0.89953905,0.08725156,0.0064387466,0.0019713002,0.004421565,0.00037784516],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013468898,0.0011465449,0.0025490555,0.0073453747,0.0005537595,0.0031594322,0.0021216695,0.002564458,0.0041253516],"category_scores_gemma":[0.10239517,0.0007066278,0.0025989213,0.0036756264,0.0017164758,0.0051224795,0.0016859407,0.0017863936,0.00057918427],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000230457,0.00009572051,0.0022901634,0.38300812,0.0022284223,0.00022467416,0.00035898224,0.0031333917,0.00053947105,0.010957544,0.0088154385,0.5881176],"study_design_scores_gemma":[0.0002140584,0.0006364651,0.004944678,0.75756204,0.01437773,0.001675831,0.0010326601,0.0037478812,0.0019013527,0.022759464,0.19099393,0.000153925],"about_ca_topic_score_codex":0.0022873408,"about_ca_topic_score_gemma":0.004327551,"teacher_disagreement_score":0.9865311,"about_ca_system_score_codex":0.0017496779,"about_ca_system_score_gemma":0.010782253,"threshold_uncertainty_score":0.07123119},"labels":[],"label_agreement":null},{"id":"W3185468338","doi":"10.48550/arxiv.2107.13491","title":"Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Metamorphic rock; Test (biology); Maximum likelihood; Computer science; Artificial intelligence; Econometrics; Geology; Machine learning; Statistics; Geochemistry; Paleontology; Mathematics","score_opus":0.09383745150203482,"score_gpt":0.22704431383882048,"score_spread":0.13320686233678566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185468338","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5297983,0.00031750658,0.46478224,0.00066909374,0.00003106558,0.00019241759,0.00050721463,0.0005634396,0.0031387294],"genre_scores_gemma":[0.98429966,0.00008684632,0.013669413,0.00007046664,0.000013740125,0.00016073052,0.00038469408,0.00006756319,0.0012468958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980051,0.0007438796,0.00011922612,0.0004465029,0.00044222936,0.00024313663],"domain_scores_gemma":[0.972369,0.020502131,0.0030694546,0.0020799055,0.0014901396,0.00048936997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005837968,0.0010155351,0.00070701074,0.0014497096,0.00036422894,0.0015418063,0.0022045947,0.0013906673,0.0018137643],"category_scores_gemma":[0.03715551,0.00074962754,0.0008986438,0.0005544548,0.0021273294,0.0026620456,0.0017070271,0.0025105653,0.00025621912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027173152,0.000060619994,0.010458729,0.000053765987,0.000045454653,0.00015452025,0.00016948958,0.9595609,0.0015082763,0.01714727,0.00041241173,0.010156916],"study_design_scores_gemma":[0.0000043322293,0.000018015407,0.00085310166,0.0000086781665,0.0000042003544,0.000034211276,0.00001006274,0.9928853,0.0007373853,0.0053715263,0.00006714962,0.0000060077855],"about_ca_topic_score_codex":0.0034182635,"about_ca_topic_score_gemma":0.0027655396,"teacher_disagreement_score":0.005837968,"about_ca_system_score_codex":0.002118626,"about_ca_system_score_gemma":0.0005773913,"threshold_uncertainty_score":0.03087449},"labels":[],"label_agreement":null},{"id":"W3188010467","doi":"10.1109/tr.2021.3096332","title":"<i>DeepRepair:</i> Style-Guided Repairing for Deep Neural Networks in the Real-World Operational Environment","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program; Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Style (visual arts); Artificial intelligence; Reliability engineering; Engineering; Geography","score_opus":0.01966179932126976,"score_gpt":0.2733146995977378,"score_spread":0.25365290027646803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188010467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006999844,0.00046796852,0.98085475,0.0005116509,0.0002520796,0.000077113145,0.00030118003,0.0062544807,0.0042809397],"genre_scores_gemma":[0.17203423,0.00092922617,0.79226625,0.0014377128,0.00041221894,0.0002707185,0.0028142515,0.0015800437,0.028255204],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993548,0.000105865496,0.00005135996,0.00018571806,0.00022817345,0.000074150084],"domain_scores_gemma":[0.9990809,0.00011414583,0.00009660242,0.00037973755,0.00026720267,0.00006133915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001099542,0.0016903115,0.0006224574,0.00050184375,0.0004879308,0.0012936625,0.003604519,0.0019675957,0.0052527534],"category_scores_gemma":[0.002391524,0.00046301034,0.0009091707,0.00067370525,0.00095725886,0.002552275,0.0019650625,0.002504584,0.0031216906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024491566,0.00015002767,0.0015991112,0.00023147716,0.00013740554,0.00034695058,0.00018151461,0.18011527,0.02889341,0.024661068,0.055461325,0.7079775],"study_design_scores_gemma":[0.000013707885,0.00007517468,0.00039825472,0.000025006506,0.000022616437,0.00011106146,0.000020583213,0.9369419,0.031823914,0.0095986305,0.020935422,0.00003368698],"about_ca_topic_score_codex":0.005704225,"about_ca_topic_score_gemma":0.011006147,"teacher_disagreement_score":0.005704225,"about_ca_system_score_codex":0.0010888645,"about_ca_system_score_gemma":0.0010644722,"threshold_uncertainty_score":0.017572224},"labels":[],"label_agreement":null},{"id":"W3189688144","doi":"10.1109/ius52206.2021.9593490","title":"Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Inference; Rendering (computer graphics); Deep learning; Machine learning; Pattern recognition (psychology); Breast ultrasound; Adversarial system; Deep neural networks; Artificial neural network; Breast imaging; Breast cancer; Mammography; Cancer; Medicine","score_opus":0.024608460389167534,"score_gpt":0.3221601307774401,"score_spread":0.29755167038827257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189688144","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85022455,0.0013674316,0.13318568,0.0058977744,0.00019273946,0.00031302194,0.000492455,0.0006560468,0.0076703667],"genre_scores_gemma":[0.98417896,0.0002284133,0.013579491,0.0003047496,0.000061880935,0.000033989934,0.00014426412,0.000054448734,0.0014139086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982231,0.0008638224,0.00006972415,0.00022608126,0.0004126572,0.00020449415],"domain_scores_gemma":[0.9834449,0.0134721855,0.00080620276,0.0013393604,0.0005379592,0.0003992868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00328487,0.0006701209,0.0005722994,0.0007736481,0.00079798215,0.00096411165,0.0008877356,0.002743486,0.001218979],"category_scores_gemma":[0.015116133,0.00019018361,0.0006528943,0.00058938825,0.0019590696,0.0012045922,0.001024222,0.002033334,0.0002667463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021508245,0.0012297434,0.07781662,0.00054269476,0.00043718304,0.029858893,0.0026237776,0.6690455,0.016025882,0.04546648,0.019744758,0.13505761],"study_design_scores_gemma":[0.00007755542,0.0002872038,0.0079767695,0.000052226736,0.000044997065,0.0043852236,0.0004351946,0.95333695,0.0071905176,0.021973087,0.00419488,0.000045462486],"about_ca_topic_score_codex":0.002783906,"about_ca_topic_score_gemma":0.0030798481,"teacher_disagreement_score":0.00328487,"about_ca_system_score_codex":0.0010109454,"about_ca_system_score_gemma":0.00037202344,"threshold_uncertainty_score":0.01737225},"labels":[],"label_agreement":null},{"id":"W3189865072","doi":"","title":"Turning Your Strength against You: Detecting and Mitigating Robust and Universal Adversarial Patch Attack.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Image (mathematics); Artificial intelligence; Inpainting; Deep neural networks; Consistency (knowledge bases); Ambiguity; Pixel; Pattern recognition (psychology); Deep learning; Computer security; Machine learning; Computer vision","score_opus":0.05495538597630591,"score_gpt":0.20867108203378046,"score_spread":0.15371569605747454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189865072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31145415,0.0027588683,0.6732765,0.0010866419,0.00047943863,0.00022982928,0.00017476955,0.0044367835,0.0061030183],"genre_scores_gemma":[0.9367625,0.00039558654,0.059334874,0.00047088924,0.00006891251,0.000047327158,0.00019394679,0.000115142444,0.00261088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924123,0.00013787369,0.000035402645,0.00019585957,0.00025834635,0.00013129816],"domain_scores_gemma":[0.9980332,0.00078705983,0.00032252254,0.000465871,0.00026077774,0.00013054747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009450867,0.0012057849,0.0007539436,0.0005723971,0.00045995784,0.00063785777,0.0011367324,0.0013662265,0.0007734144],"category_scores_gemma":[0.0044734864,0.0003999557,0.00067461026,0.00021776897,0.00130749,0.0019861213,0.0016250259,0.0019069588,0.0003224688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010248856,0.000322767,0.011489194,0.00034271728,0.00041392024,0.0012912478,0.00042635002,0.45686844,0.11695228,0.011678709,0.0118373865,0.38735214],"study_design_scores_gemma":[0.000015771753,0.00023589129,0.0012941547,0.000019276982,0.000044452958,0.00032430698,0.000045965327,0.96215034,0.031210598,0.0030366036,0.0016014609,0.000021205004],"about_ca_topic_score_codex":0.0024361962,"about_ca_topic_score_gemma":0.0030156863,"teacher_disagreement_score":0.0024361962,"about_ca_system_score_codex":0.00068448373,"about_ca_system_score_gemma":0.00056105107,"threshold_uncertainty_score":0.004998207},"labels":[],"label_agreement":null},{"id":"W3192134083","doi":"10.1155/2021/4280328","title":"A Novel Defensive Strategy for Facial Manipulation Detection Combining Bilateral Filtering and Joint Adversarial Training","year":2021,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Adversarial system; Computer science; Joint (building); Preprocessor; Artificial intelligence; Face (sociological concept); Vulnerability (computing); Detector; Computer vision; Machine learning; Computer security","score_opus":0.05252463860637685,"score_gpt":0.2707353112620546,"score_spread":0.21821067265567773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192134083","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01535926,0.00017237058,0.9823414,0.00018718842,0.00005224514,0.000047348414,0.000021766326,0.0004603155,0.0013581017],"genre_scores_gemma":[0.74201375,0.0003521017,0.25130963,0.00046182386,0.000089223875,0.00013129164,0.00013939981,0.00008984515,0.0054128114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991535,0.00014434934,0.00003742632,0.00021321044,0.00031065484,0.00014090884],"domain_scores_gemma":[0.9991997,0.00030581685,0.0001196685,0.00016378675,0.00014842974,0.00006258331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011082671,0.0013395426,0.0009878903,0.000715334,0.0004430465,0.0005731584,0.0013570738,0.0013005072,0.0014982683],"category_scores_gemma":[0.0025526187,0.00042881997,0.00095788925,0.00033226548,0.0009898461,0.0013151391,0.0016906689,0.0014089288,0.0005155975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026861942,0.00019849357,0.0032522846,0.00012636009,0.00018415449,0.00036977712,0.00019115847,0.32903972,0.11990267,0.018820135,0.0042851903,0.5233614],"study_design_scores_gemma":[0.00000677295,0.00010095435,0.00048763264,0.000007644443,0.000024378704,0.00019597798,0.000014146362,0.9833468,0.012045113,0.0028357678,0.0009166585,0.00001815137],"about_ca_topic_score_codex":0.001668034,"about_ca_topic_score_gemma":0.0016397218,"teacher_disagreement_score":0.001668034,"about_ca_system_score_codex":0.0004975778,"about_ca_system_score_gemma":0.0007253181,"threshold_uncertainty_score":0.005861163},"labels":[],"label_agreement":null},{"id":"W3192689066","doi":"10.21203/rs.3.rs-763355/v1","title":"How Adversarial attacks affect Deep Neural Networks Detecting COVID-19?","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Adversarial system; Coronavirus disease 2019 (COVID-19); Affect (linguistics); Computer science; Artificial intelligence; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Deep neural networks; Artificial neural network; Computer security; Psychology; Virology; Communication; Medicine","score_opus":0.07542373474166618,"score_gpt":0.4039140561979412,"score_spread":0.328490321456275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192689066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40951884,0.0014781293,0.5625394,0.0069356007,0.0010476785,0.000112378904,0.0006415942,0.0028100717,0.014916239],"genre_scores_gemma":[0.97678137,0.00019958608,0.018688915,0.0005078735,0.000120860714,0.00001832687,0.00034734566,0.0001892211,0.0031464407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810684,0.00054411887,0.000065761524,0.00045576683,0.00050173665,0.0003257088],"domain_scores_gemma":[0.99286544,0.003924146,0.00069585943,0.0013949033,0.0007722998,0.00034738382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029288253,0.0009860162,0.0008190294,0.00082698785,0.00080270256,0.0015901482,0.001030062,0.002349632,0.0035865582],"category_scores_gemma":[0.028443022,0.00051364134,0.0005766241,0.00046547988,0.0016826246,0.0033499794,0.002142253,0.0031095378,0.00091587775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013679919,0.000352302,0.017634312,0.00021972718,0.0003697802,0.00065173703,0.00020602893,0.6641889,0.030101486,0.0756055,0.024918752,0.18438344],"study_design_scores_gemma":[0.00000972343,0.000056306973,0.0013318274,0.000019771733,0.00001723137,0.0001522662,0.00003847232,0.970006,0.008985086,0.018394196,0.00097436894,0.000014675067],"about_ca_topic_score_codex":0.0020808715,"about_ca_topic_score_gemma":0.0019035293,"teacher_disagreement_score":0.0035865582,"about_ca_system_score_codex":0.00095945067,"about_ca_system_score_gemma":0.0007336427,"threshold_uncertainty_score":0.01548928},"labels":[],"label_agreement":null},{"id":"W3192770481","doi":"10.1016/j.dcan.2021.07.009","title":"Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects","year":2021,"lang":"en","type":"article","venue":"Digital Communications and Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Status quo; Computer science; Computer security; Risk analysis (engineering); Artificial intelligence; Business","score_opus":0.017321134183116274,"score_gpt":0.26727047517853614,"score_spread":0.24994934099541988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192770481","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013457396,0.7258242,0.18597533,0.02686633,0.0016324221,0.00011473897,0.00007400351,0.00038817583,0.0456674],"genre_scores_gemma":[0.35102594,0.5808246,0.048660167,0.005320738,0.0056048566,0.00021190547,0.00016562238,0.000095880016,0.008090383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99800175,0.0007613851,0.00011267221,0.00028270847,0.000662368,0.000179226],"domain_scores_gemma":[0.992717,0.005351293,0.00047355623,0.000653183,0.00063928176,0.00016558374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004233563,0.0010193061,0.0011449313,0.0018752589,0.00086140115,0.003615242,0.0019072753,0.0037835664,0.002205346],"category_scores_gemma":[0.007865011,0.00049654423,0.0007819567,0.001346572,0.0039713285,0.009204112,0.002353,0.003921881,0.00080309925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001326344,0.00017357882,0.0012324116,0.0016758391,0.000089443136,0.00021140683,0.00023420385,0.026844,0.0011267529,0.48405963,0.011773584,0.47244644],"study_design_scores_gemma":[0.00005044171,0.0004913169,0.0011611639,0.0031306804,0.00012213236,0.0013224877,0.0006533793,0.10513172,0.0041070976,0.64703816,0.23666112,0.00013019123],"about_ca_topic_score_codex":0.00033385406,"about_ca_topic_score_gemma":0.00020531676,"teacher_disagreement_score":0.004233563,"about_ca_system_score_codex":0.001183972,"about_ca_system_score_gemma":0.000706777,"threshold_uncertainty_score":0.022389531},"labels":[],"label_agreement":null},{"id":"W3192857103","doi":"10.24963/ijcai.2021/343","title":"Robust Regularization with Adversarial Labelling of Perturbed Samples","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Beijing Advanced Innovation Center for Big Data and Brain Computing; Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Adversarial system; Regularization (linguistics); Computer science; Deep neural networks; Robustness (evolution); Mathematical optimization; Artificial intelligence; Upper and lower bounds; Artificial neural network; Machine learning; Algorithm; Mathematics","score_opus":0.025289900179979525,"score_gpt":0.22345920891026563,"score_spread":0.1981693087302861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192857103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010142014,0.00017862172,0.9879616,0.00026392358,0.000028470196,0.000022242395,0.000041797026,0.00023877757,0.0011224778],"genre_scores_gemma":[0.7478174,0.00042178063,0.24532628,0.0005494062,0.00012604232,0.00018899281,0.00041005504,0.00025109935,0.004908945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986578,0.0005331035,0.00005025456,0.00033603676,0.00031747503,0.000105302264],"domain_scores_gemma":[0.9973008,0.0015878169,0.00030269052,0.0004726762,0.0002347033,0.000101407095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025059674,0.001467057,0.0010945685,0.0005444058,0.0004861743,0.00090772175,0.0019009345,0.0016214015,0.0012742074],"category_scores_gemma":[0.007371866,0.0005439423,0.0008429539,0.00050053524,0.0025487232,0.001937316,0.0025455311,0.0028912192,0.0003975078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008858716,0.000026282989,0.00046800438,0.000063749154,0.000039716913,0.00007764308,0.000059017628,0.9381457,0.004827543,0.0334451,0.0014592843,0.021299383],"study_design_scores_gemma":[0.0000035898386,0.00001603063,0.000051974726,0.0000057976217,0.0000028484649,0.000019278907,0.000003876192,0.9863608,0.0010006498,0.012194421,0.00033569537,0.0000050407466],"about_ca_topic_score_codex":0.0020460822,"about_ca_topic_score_gemma":0.0020490473,"teacher_disagreement_score":0.0025059674,"about_ca_system_score_codex":0.0012584254,"about_ca_system_score_gemma":0.0008580536,"threshold_uncertainty_score":0.013253033},"labels":[],"label_agreement":null},{"id":"W3193670971","doi":"10.14722/autosec.2021.23014","title":"Demo Paper: Impact of Stealthy Attacks on Autonomous Robotic Vehicle Missions","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer security; Remotely operated underwater vehicle; Mobile robot; Robot; Aeronautics; Embedded system; Real-time computing; Engineering; Artificial intelligence","score_opus":0.021060777487804917,"score_gpt":0.31672296790362836,"score_spread":0.2956621904158234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193670971","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3215507,0.0099099055,0.33674157,0.046554107,0.025311954,0.0006112471,0.009410569,0.006415953,0.2434941],"genre_scores_gemma":[0.9483664,0.0013928001,0.0143520115,0.0022318938,0.0012428712,0.000074113406,0.0021139632,0.00031437955,0.02991149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993748,0.00016052125,0.000014366112,0.000081659506,0.00026788478,0.00010066183],"domain_scores_gemma":[0.9979005,0.0011380907,0.0000964034,0.00027453294,0.00035472726,0.00023579078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012804131,0.00070462795,0.0005164258,0.0005207902,0.00046464227,0.0008194615,0.0006553652,0.0013278642,0.008245143],"category_scores_gemma":[0.004318256,0.00016022802,0.00033225186,0.0004177576,0.0007403666,0.0011867976,0.0013513801,0.0012053186,0.0013652003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030360748,0.00021155083,0.0029822553,0.000533483,0.00031024317,0.0010508241,0.000097204706,0.55389494,0.01862596,0.03208984,0.24916185,0.13800573],"study_design_scores_gemma":[0.000302528,0.00074359705,0.0046540033,0.00013521544,0.000096196076,0.000761179,0.00019212783,0.8472921,0.028675241,0.04916972,0.06788499,0.00009319065],"about_ca_topic_score_codex":0.0021891377,"about_ca_topic_score_gemma":0.0030417154,"teacher_disagreement_score":0.008245143,"about_ca_system_score_codex":0.0007192279,"about_ca_system_score_gemma":0.00043461204,"threshold_uncertainty_score":0.027582765},"labels":[],"label_agreement":null},{"id":"W3195393125","doi":"10.1007/s11263-022-01720-7","title":"SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness","year":2022,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; York University; Toronto Metropolitan University; Vector Institute","funders":"","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Pattern recognition (psychology); Categorical variable; Pooling; Segmentation; Convolutional neural network; Boosting (machine learning); Adversarial system; Cluster analysis; Object detection; Salient; Machine learning","score_opus":0.012390749501443669,"score_gpt":0.3148305969806831,"score_spread":0.30243984747923947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195393125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028400624,0.00016887348,0.990564,0.00010674166,0.00006458963,0.000072876916,0.00018266038,0.0053797937,0.0006203701],"genre_scores_gemma":[0.13539702,0.00025918553,0.8503991,0.0004805613,0.00016559643,0.0003892714,0.0020249276,0.0027383165,0.0081459535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997872,0.00045896345,0.00009799684,0.00067802495,0.0006822078,0.0002107277],"domain_scores_gemma":[0.9981279,0.00086599455,0.00012543311,0.0005111105,0.00025146615,0.000118233445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002781701,0.0030162008,0.0026013772,0.0021650584,0.0010854645,0.0024714966,0.0038849672,0.0045351842,0.010047122],"category_scores_gemma":[0.006880502,0.0015942977,0.0024269256,0.0018098466,0.001914326,0.0036803896,0.008202261,0.00513069,0.005676272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010173597,0.00027524054,0.0007297493,0.00027158816,0.00041980259,0.00031776997,0.00017271073,0.32980418,0.029121289,0.030470708,0.014576369,0.59282315],"study_design_scores_gemma":[0.000014647231,0.000047052792,0.000092122085,0.000011801345,0.000018003364,0.000075027194,0.00001585947,0.97072697,0.009912302,0.016838757,0.0022273175,0.000020104882],"about_ca_topic_score_codex":0.0029622796,"about_ca_topic_score_gemma":0.0049918406,"teacher_disagreement_score":0.010047122,"about_ca_system_score_codex":0.0010528459,"about_ca_system_score_gemma":0.0014856905,"threshold_uncertainty_score":0.03361094},"labels":[],"label_agreement":null},{"id":"W3195903354","doi":"10.47611/jsrhs.v10i2.1612","title":"Impact of Model Architecture Against Adversarial Example's Effectivity","year":2021,"lang":"en","type":"article","venue":"Journal of Student Research","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Milton District Hospital","funders":"","keywords":"Adversarial system; Architecture; Computer science; Artificial intelligence; Cloning (programming); Machine learning; Adversarial machine learning; Programming language","score_opus":0.12119182819399531,"score_gpt":0.4543848082551557,"score_spread":0.3331929800611604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195903354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91023844,0.00045464956,0.08292008,0.0005209911,0.00011089897,0.00021631944,0.0001314056,0.0006227419,0.0047844457],"genre_scores_gemma":[0.9932482,0.00008349705,0.0060764463,0.00005985549,0.0000058349146,0.000040344283,0.00006528177,0.00003665382,0.00038385094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964307,0.0016285118,0.00022243941,0.00050958444,0.0007889601,0.0004198415],"domain_scores_gemma":[0.9667252,0.022835476,0.0020370847,0.0058472785,0.001964821,0.0005902279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054597612,0.0010322208,0.00060849916,0.00042132076,0.0004535133,0.0012922474,0.00093820546,0.0011236795,0.0023001584],"category_scores_gemma":[0.041090474,0.00042598165,0.00055603427,0.00017582186,0.001778814,0.0024272224,0.0019699184,0.001985543,0.00044228553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015329083,0.00041456413,0.0182108,0.0004126735,0.000249093,0.00027631046,0.00035636514,0.879383,0.03931718,0.008518883,0.0007685754,0.05055955],"study_design_scores_gemma":[0.00008409217,0.0026712106,0.010839936,0.0001664344,0.00022590697,0.00046115968,0.0003310963,0.90544254,0.065535575,0.011757122,0.0023994858,0.00008550852],"about_ca_topic_score_codex":0.0011526583,"about_ca_topic_score_gemma":0.0009184977,"teacher_disagreement_score":0.0054597612,"about_ca_system_score_codex":0.00076479727,"about_ca_system_score_gemma":0.00079039944,"threshold_uncertainty_score":0.028874278},"labels":[],"label_agreement":null},{"id":"W3196468539","doi":"","title":"Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision.","year":2021,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Convolutional neural network; Robustness (evolution); Classifier (UML); Deep learning; Implementation; Artificial intelligence; Fault tolerance; Floating point; Embedded system; Computer hardware; Real-time computing; Computer engineering; Distributed computing; Algorithm; Software engineering","score_opus":0.11602436251585312,"score_gpt":0.34684251110278225,"score_spread":0.23081814858692912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196468539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07548168,0.0007094046,0.9120107,0.0026275676,0.0001275682,0.00008069392,0.00010371251,0.0008798785,0.00797879],"genre_scores_gemma":[0.9397677,0.00031872222,0.05688891,0.0004340533,0.00009321075,0.00008179057,0.00011860552,0.00013670772,0.0021602958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979856,0.0006773721,0.000084436135,0.00025248682,0.000799248,0.00020085611],"domain_scores_gemma":[0.990524,0.0056776507,0.00096631545,0.001481524,0.0010976702,0.0002528277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037988506,0.00087000505,0.0004777145,0.0006851661,0.0006406833,0.0013352602,0.0016984087,0.0024763956,0.0023534768],"category_scores_gemma":[0.020505557,0.00038064053,0.000541156,0.00025197282,0.003575388,0.0034112628,0.0030350275,0.0036178124,0.00042881726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006892115,0.00016308189,0.004187291,0.00031640855,0.000070350536,0.0007485059,0.00038239933,0.6554035,0.027578104,0.22196014,0.005273099,0.083227985],"study_design_scores_gemma":[0.00001735054,0.0001335243,0.0003056253,0.00004107454,0.000014607164,0.00016517605,0.00004697503,0.8920352,0.017820463,0.087972865,0.0014345621,0.000012485521],"about_ca_topic_score_codex":0.0010534574,"about_ca_topic_score_gemma":0.0010816931,"teacher_disagreement_score":0.0037988506,"about_ca_system_score_codex":0.0013768977,"about_ca_system_score_gemma":0.0011195238,"threshold_uncertainty_score":0.02009052},"labels":[],"label_agreement":null},{"id":"W3197018685","doi":"","title":"Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; McGill University","funders":"","keywords":"Reinforcement learning; Adversarial system; Artificial intelligence; Reinforcement; Computer science; Deep learning; Inference; Machine learning; Vulnerability (computing); Computer security; Psychology; Social psychology","score_opus":0.08241447342643901,"score_gpt":0.2403000220203051,"score_spread":0.15788554859386608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197018685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42740765,0.0008098293,0.55719703,0.005270959,0.00022214116,0.00015100058,0.00038966615,0.0012525914,0.0072992],"genre_scores_gemma":[0.9768723,0.00009381372,0.021330804,0.0003687032,0.000024026467,0.000046747267,0.00011441396,0.000050621977,0.0010984589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953041,0.0024624649,0.00014856017,0.0007484343,0.0009688458,0.00036755324],"domain_scores_gemma":[0.9666249,0.025246799,0.0022184143,0.004157695,0.0010250695,0.00072707864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065155383,0.00060056185,0.00065462146,0.00038567753,0.0008054061,0.0010938909,0.0011684546,0.0017462366,0.0014645667],"category_scores_gemma":[0.045649618,0.00038969005,0.0007155232,0.00033487155,0.0027374171,0.003460367,0.0025023406,0.0042406344,0.00031171404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022843631,0.0005883544,0.018795641,0.00025970855,0.00037027954,0.00066969363,0.0007834098,0.735513,0.012361458,0.09593848,0.008483546,0.12395216],"study_design_scores_gemma":[0.00004746731,0.00011898882,0.0010976561,0.000023908682,0.000019166186,0.00007841681,0.000044668617,0.9476273,0.0041907397,0.046065785,0.0006676071,0.000018311574],"about_ca_topic_score_codex":0.0023449163,"about_ca_topic_score_gemma":0.0021479465,"teacher_disagreement_score":0.0065155383,"about_ca_system_score_codex":0.001475818,"about_ca_system_score_gemma":0.0013216541,"threshold_uncertainty_score":0.034457862},"labels":[],"label_agreement":null},{"id":"W3197661641","doi":"10.1007/s11042-021-11394-x","title":"Protecting image privacy through adversarial perturbation","year":2021,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Adversarial system; Computer security; Image (mathematics); Privacy protection; Artificial intelligence; Computer vision; Theoretical computer science; Human–computer interaction","score_opus":0.023518482825712415,"score_gpt":0.28157887179221125,"score_spread":0.25806038896649885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197661641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014252856,0.00045436868,0.97864807,0.0010433019,0.000090032336,0.000033984797,0.00010258078,0.0003937194,0.004981078],"genre_scores_gemma":[0.88842225,0.0009411547,0.103081875,0.0006747537,0.00026103412,0.00008148204,0.00019464361,0.00015736901,0.0061854925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969091,0.0010852395,0.00008925382,0.0005165653,0.0011797651,0.00022012535],"domain_scores_gemma":[0.992214,0.0043900423,0.00050153193,0.0024765013,0.00031701097,0.00010092817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002340815,0.00085354736,0.0008989418,0.0008442481,0.00061249506,0.001942121,0.0012547654,0.0020292937,0.0019557935],"category_scores_gemma":[0.015043393,0.00045788038,0.0005961245,0.00089225586,0.0031166123,0.0038625102,0.0034271164,0.0033657455,0.00068219204],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004368191,0.00013338072,0.001154714,0.000190525,0.00015266555,0.0005131739,0.00022411179,0.45414624,0.031469874,0.37725666,0.0068894043,0.12743229],"study_design_scores_gemma":[0.000011430714,0.00004312697,0.0002854718,0.000026730228,0.000018017989,0.00025694654,0.00003754043,0.8009207,0.011659924,0.18405563,0.0026645667,0.000019963905],"about_ca_topic_score_codex":0.0005498501,"about_ca_topic_score_gemma":0.00034142422,"teacher_disagreement_score":0.002340815,"about_ca_system_score_codex":0.00084970455,"about_ca_system_score_gemma":0.00059199065,"threshold_uncertainty_score":0.012379527},"labels":[],"label_agreement":null},{"id":"W3197880668","doi":"10.1109/access.2021.3110239","title":"Use Procedural Noise to Achieve Backdoor Attack","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Shaanxi Province; Canadian Institute for Advanced Research","keywords":"Backdoor; Robustness (evolution); Computer science; Computer security; Noise (video); Artificial intelligence","score_opus":0.07036943486635322,"score_gpt":0.3503355920491337,"score_spread":0.2799661571827805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197880668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058273897,0.00046751022,0.9343485,0.00026373804,0.00011090456,0.0001280117,0.00009269516,0.002099652,0.0042150924],"genre_scores_gemma":[0.9142873,0.0003106657,0.08181327,0.00030743706,0.000052408195,0.000111580994,0.00025439117,0.00021914649,0.0026437428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796534,0.00052953354,0.00009972782,0.00040077936,0.00068486226,0.0003197514],"domain_scores_gemma":[0.99720615,0.00108808,0.0003792714,0.0009211172,0.000258076,0.00014731284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013457175,0.0012341769,0.0009064901,0.00086558756,0.0006569204,0.00113958,0.0010217626,0.0013121457,0.0018050729],"category_scores_gemma":[0.0061766645,0.0002978116,0.0011678064,0.00041644528,0.0017877758,0.0024708172,0.0031115662,0.001694947,0.00068132044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000762052,0.0002617746,0.006557594,0.00031461832,0.0002952909,0.00087076897,0.00040338392,0.6083138,0.07659818,0.084539816,0.0060237534,0.21505907],"study_design_scores_gemma":[0.000030950287,0.00029676862,0.0008036891,0.000037066882,0.000050300652,0.0005393789,0.000059791404,0.9424775,0.028146798,0.023597104,0.0039101685,0.000050464547],"about_ca_topic_score_codex":0.00074631063,"about_ca_topic_score_gemma":0.00064360927,"teacher_disagreement_score":0.0018050729,"about_ca_system_score_codex":0.0005678185,"about_ca_system_score_gemma":0.000631408,"threshold_uncertainty_score":0.0071169734},"labels":[],"label_agreement":null},{"id":"W3199057025","doi":"10.48550/arxiv.2109.04608","title":"Spatially Focused Attack against Spatiotemporal Graph Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Artificial neural network; Artificial intelligence; Theoretical computer science","score_opus":0.061677927918367625,"score_gpt":0.2055105099633899,"score_spread":0.14383258204502228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199057025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120953746,0.00028890246,0.8726518,0.00081079005,0.00010773816,0.000066415196,0.000083670995,0.0005894813,0.004447473],"genre_scores_gemma":[0.97573495,0.0001591394,0.022397086,0.00020189506,0.000020978263,0.000039274943,0.00006294364,0.000042318774,0.0013414256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924904,0.0002517488,0.000036944824,0.00015979985,0.00018173212,0.00012068896],"domain_scores_gemma":[0.99747014,0.0013953608,0.0003908196,0.0003592572,0.0002595405,0.00012482532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010450333,0.0010218244,0.0006338723,0.00053915475,0.0005161377,0.0005466723,0.00097617175,0.001123974,0.0011413683],"category_scores_gemma":[0.005919576,0.00030861452,0.0007436834,0.00031417067,0.0014884447,0.0016258368,0.0020342234,0.0016710724,0.00019809391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089086505,0.000021788926,0.00087013317,0.000031898828,0.000048617687,0.00015103785,0.000060693223,0.94627845,0.00760911,0.02716064,0.0010182956,0.016660186],"study_design_scores_gemma":[0.0000037724742,0.000027961501,0.0001138354,0.0000045175934,0.000006890359,0.00003059298,0.000010690751,0.9884328,0.0014942455,0.009605729,0.00026397724,0.000004912441],"about_ca_topic_score_codex":0.0027637056,"about_ca_topic_score_gemma":0.002215111,"teacher_disagreement_score":0.0027637056,"about_ca_system_score_codex":0.001031253,"about_ca_system_score_gemma":0.0005743242,"threshold_uncertainty_score":0.0074822903},"labels":[],"label_agreement":null},{"id":"W3199726524","doi":"","title":"Poisoning the Search Space in Neural Architecture Search","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Exploit; Architecture; Artificial intelligence; Machine learning; Artificial neural network; Search algorithm; Domain (mathematical analysis); Data mining; Computer security; Algorithm; Mathematics","score_opus":0.03840090311261272,"score_gpt":0.3208602456421639,"score_spread":0.2824593425295512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199726524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29363894,0.0009753487,0.6949496,0.0011476738,0.00009639396,0.00014042476,0.00009924396,0.002013919,0.006938367],"genre_scores_gemma":[0.8999754,0.00019514022,0.09764245,0.0002880525,0.000022022683,0.000091120935,0.00007506777,0.00014525134,0.00156552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986092,0.00060943316,0.000087996,0.00020950504,0.00033440904,0.00014951061],"domain_scores_gemma":[0.9951676,0.0025803116,0.00051707885,0.0013209407,0.00029754813,0.000116472525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028824513,0.0007230658,0.0006913346,0.00055326516,0.0004734236,0.0008727238,0.0011650793,0.0012624045,0.0017643883],"category_scores_gemma":[0.010800318,0.0004768696,0.00065402314,0.00045258668,0.0021275238,0.0023424234,0.0019863758,0.0022108802,0.00042343524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036205296,0.00011304416,0.0033598382,0.00011765341,0.00013527434,0.00023794336,0.0002118519,0.8567016,0.0154172005,0.04263506,0.0023230652,0.078385316],"study_design_scores_gemma":[0.00004410784,0.00020752406,0.000291479,0.000022979397,0.000028440056,0.00014525212,0.00004639222,0.9567513,0.009174563,0.031685088,0.0015900804,0.0000127553785],"about_ca_topic_score_codex":0.0006502204,"about_ca_topic_score_gemma":0.0012173309,"teacher_disagreement_score":0.0028824513,"about_ca_system_score_codex":0.0007846779,"about_ca_system_score_gemma":0.0009465444,"threshold_uncertainty_score":0.015244067},"labels":[],"label_agreement":null},{"id":"W3200109625","doi":"10.1007/978-3-031-04673-5_13","title":"Robustness Analysis of Deep Learning Frameworks on Mobile Platforms","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Robustness (evolution); Computer science; Deep learning; Mobile device; Artificial intelligence; Deep neural networks; Machine learning; Artificial neural network; Computer engineering","score_opus":0.010323369697730228,"score_gpt":0.2548251061674014,"score_spread":0.24450173646967116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200109625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042511947,0.0009236419,0.9473472,0.00067310286,0.00008131204,0.000043520293,0.00013971003,0.0005409671,0.0077385944],"genre_scores_gemma":[0.93691367,0.00082165026,0.051419698,0.00019328984,0.00011762595,0.00011137015,0.00023281053,0.00034020212,0.00984972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898654,0.00031478074,0.000031271633,0.00017146941,0.0002931495,0.00020270734],"domain_scores_gemma":[0.99527836,0.0033664303,0.00031104655,0.00040971776,0.00047517172,0.00015919826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023279751,0.0011627737,0.00094911904,0.0007558355,0.00043998004,0.0014095213,0.0015815978,0.001485286,0.0045942976],"category_scores_gemma":[0.011329136,0.00061867334,0.00088383124,0.00053942204,0.0016693202,0.0021941816,0.002691261,0.0023943365,0.00045353145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010021204,0.000025024712,0.00028876937,0.000063855936,0.000040205658,0.00005010724,0.000030055155,0.9088111,0.0021401406,0.06735873,0.0015214204,0.019570436],"study_design_scores_gemma":[0.0000018666852,0.000014256295,0.00007081909,0.00000668756,0.0000041619232,0.0000069642824,0.000004647515,0.9839229,0.00042695797,0.0153556755,0.0001817636,0.0000032476278],"about_ca_topic_score_codex":0.0028883435,"about_ca_topic_score_gemma":0.0016528049,"teacher_disagreement_score":0.0045942976,"about_ca_system_score_codex":0.0017982903,"about_ca_system_score_gemma":0.0008613532,"threshold_uncertainty_score":0.015369475},"labels":[],"label_agreement":null},{"id":"W3200761006","doi":"10.1109/ijcnn52387.2021.9533410","title":"Non-divergent Imitation for Verification of Complex Learned Controllers","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Oracle; Computer science; Maximization; Context (archaeology); Distillation; Metric (unit); Fidelity; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics; Engineering","score_opus":0.05511895398908459,"score_gpt":0.3151126056946477,"score_spread":0.2599936517055631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200761006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03975857,0.00004992469,0.95743173,0.00033002152,0.000018680113,0.000065491964,0.0000598758,0.00088759523,0.0013980643],"genre_scores_gemma":[0.90303564,0.00004080218,0.095197625,0.00018254452,0.0000144183805,0.000085363245,0.00008303585,0.00012715474,0.0012333965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654216,0.001009912,0.00016839427,0.0009694317,0.00092798367,0.00038210137],"domain_scores_gemma":[0.97314554,0.019975046,0.001870566,0.0035617792,0.0009086769,0.00053848204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004852614,0.0011129376,0.0009736704,0.00049910456,0.00074493705,0.0012942823,0.002453412,0.0021456603,0.0036942041],"category_scores_gemma":[0.03787331,0.0006114787,0.0009962992,0.00033461626,0.005391917,0.0044608014,0.005009531,0.004425091,0.0004646072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043308444,0.00011487048,0.0019854722,0.00018867102,0.000076076234,0.00031939187,0.00028146693,0.8042322,0.010724858,0.13635075,0.00058809255,0.044705067],"study_design_scores_gemma":[0.00001907047,0.000064320564,0.00008147539,0.000012586553,0.0000056643594,0.000035503006,0.000013758443,0.9467515,0.005522704,0.047287855,0.0001936725,0.00001206583],"about_ca_topic_score_codex":0.0020169655,"about_ca_topic_score_gemma":0.0021663567,"teacher_disagreement_score":0.004852614,"about_ca_system_score_codex":0.0017064811,"about_ca_system_score_gemma":0.0023002997,"threshold_uncertainty_score":0.025663376},"labels":[],"label_agreement":null},{"id":"W3203863013","doi":"","title":"Physical Security of Deep Learning on Edge Devices: Comprehensive Evaluation of Fault Injection Attack Vectors","year":2019,"lang":"en","type":"preprint","venue":"IACR Cryptology ePrint Archive","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Softmax function; Computer science; Deep learning; Fault (geology); Enhanced Data Rates for GSM Evolution; Computer security; Artificial intelligence; Countermeasure; Artificial neural network; Fault injection; Attack surface; Deep neural networks; Edge device; Reliability (semiconductor); Machine learning; Software; Power (physics); Cloud computing","score_opus":0.03799752205158395,"score_gpt":0.3391170141051966,"score_spread":0.30111949205361266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203863013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9430218,0.00071000797,0.052503828,0.00034410396,0.00008798617,0.000057286787,0.00015864523,0.00060238596,0.0025138264],"genre_scores_gemma":[0.9963965,0.0000756895,0.0031249134,0.000035913494,0.0000032334788,0.000008017756,0.00005614118,0.000012073993,0.00028757044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992906,0.00024136115,0.000051342315,0.00008164313,0.00022190914,0.00011311722],"domain_scores_gemma":[0.99618703,0.0025039143,0.00040730054,0.0004099249,0.00036628428,0.00012558176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013114961,0.0007048166,0.00040475107,0.0005713876,0.00017208386,0.00039794552,0.00051110587,0.000667869,0.0010415491],"category_scores_gemma":[0.0047568134,0.00013102492,0.00031080956,0.00030371087,0.0007131939,0.0008596654,0.0006371648,0.00067045377,0.00012563715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008253159,0.00026158753,0.004243277,0.00017164148,0.000101431055,0.00020165433,0.000035026464,0.94473135,0.010267251,0.002076758,0.0009628045,0.03612185],"study_design_scores_gemma":[0.000015148623,0.000599126,0.0010962471,0.000019515452,0.000025603254,0.00006489907,0.000021160722,0.97948533,0.017540606,0.0009005026,0.00022391768,0.000007933001],"about_ca_topic_score_codex":0.0011002593,"about_ca_topic_score_gemma":0.00075040443,"teacher_disagreement_score":0.0013114961,"about_ca_system_score_codex":0.00071999425,"about_ca_system_score_gemma":0.00025228364,"threshold_uncertainty_score":0.006935954},"labels":[],"label_agreement":null},{"id":"W3206880386","doi":"10.1145/3474085.3475591","title":"DAWN","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Digital watermarking; Adversary; Watermark; Embedding; Artificial intelligence; Adversarial system; Machine learning; Insider threat; Set (abstract data type); Computer security; Surrogate model; Data mining; Image (mathematics); Insider; Law","score_opus":0.0088661727294761,"score_gpt":0.24205187760416955,"score_spread":0.23318570487469345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206880386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0155785,0.0018000337,0.6386022,0.0040524076,0.0018061787,0.00060345465,0.010680742,0.11101126,0.21586521],"genre_scores_gemma":[0.28381002,0.0028264325,0.38201192,0.0034391545,0.00057434203,0.00089379825,0.038092196,0.012358884,0.27599326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882716,0.00017144106,0.000062138104,0.00033487074,0.00049524923,0.000109148474],"domain_scores_gemma":[0.9981006,0.0004143601,0.00011359445,0.0009412341,0.0003024575,0.00012779127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014713788,0.0008792748,0.0005361026,0.00075599103,0.0005635542,0.0020923724,0.002236999,0.001449306,0.07537915],"category_scores_gemma":[0.0052550016,0.00052287756,0.0006797921,0.00050096295,0.00069895905,0.0034247376,0.0034266294,0.0022599988,0.037529893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065649324,0.00027537614,0.0030738057,0.00058036693,0.00012377482,0.0003310403,0.00018674598,0.040555805,0.015454379,0.13186961,0.29941788,0.50747466],"study_design_scores_gemma":[0.00010115791,0.00015929193,0.0008087714,0.00009832642,0.000026176493,0.0004317151,0.000054197124,0.22441536,0.02052381,0.076174386,0.6771393,0.00006755201],"about_ca_topic_score_codex":0.0020097329,"about_ca_topic_score_gemma":0.0032242385,"teacher_disagreement_score":0.07537915,"about_ca_system_score_codex":0.0009685949,"about_ca_system_score_gemma":0.0015494794,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3209063479","doi":"10.1109/ijcnn55064.2022.9892343","title":"On the Effectiveness of Interpretable Feedforward Neural Network","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Artificial intelligence; Feedforward neural network; Artificial neural network; Feed forward; Machine learning; Class (philosophy); Time delay neural network; Linear classifier; Support vector machine; Engineering","score_opus":0.019670531961297057,"score_gpt":0.2527584031892731,"score_spread":0.23308787122797606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209063479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17060001,0.005092672,0.8070649,0.0031587393,0.00028137377,0.00006861008,0.00023773288,0.00086471986,0.0126312515],"genre_scores_gemma":[0.95509654,0.0016216977,0.040539507,0.000357882,0.00018934441,0.000051611216,0.00022241287,0.000094800904,0.0018261046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983157,0.0007834474,0.00009182978,0.0002943787,0.00040846624,0.000106230815],"domain_scores_gemma":[0.983937,0.012982395,0.0009645553,0.0012002544,0.000774514,0.00014133021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046689413,0.0015284256,0.00066637044,0.00082900684,0.0003671175,0.0010706359,0.0009496929,0.0017048913,0.0018086787],"category_scores_gemma":[0.025622254,0.00034872245,0.00056078105,0.00033267058,0.0023898385,0.0027382805,0.0013655208,0.0023576657,0.00028139347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051425584,0.00010050388,0.0041023814,0.0002555934,0.00013243583,0.00034480917,0.00021941247,0.7942859,0.00878558,0.07244889,0.0021074496,0.11670274],"study_design_scores_gemma":[0.000014309016,0.00015095607,0.0009595467,0.000064988155,0.000027106444,0.000090734495,0.000027645341,0.9513721,0.003881986,0.04263116,0.0007598218,0.000019689594],"about_ca_topic_score_codex":0.0012027978,"about_ca_topic_score_gemma":0.00091304514,"teacher_disagreement_score":0.0046689413,"about_ca_system_score_codex":0.00092799845,"about_ca_system_score_gemma":0.00037508513,"threshold_uncertainty_score":0.024691999},"labels":[],"label_agreement":null},{"id":"W3209066245","doi":"10.48550/arxiv.2110.15907","title":"Learning to Be Cautious","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Counterfactual thinking; Regret; Computer science; Construct (python library); Task (project management); Reinforcement learning; Counterfactual conditional; Function (biology); Artificial intelligence; Key (lock); Machine learning; Subroutine; Field (mathematics); Psychology; Computer security; Social psychology; Mathematics","score_opus":0.058760604200396895,"score_gpt":0.20376213160878498,"score_spread":0.1450015274083881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209066245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078032404,0.00019567745,0.9073392,0.0016521863,0.000089993635,0.00018091097,0.00011867883,0.0016456109,0.010745288],"genre_scores_gemma":[0.8649889,0.0001122042,0.1284608,0.0005605333,0.000038309587,0.00019186296,0.00015545153,0.0002636194,0.0052282964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996624,0.0011111787,0.00017349646,0.0010153325,0.0007007127,0.00037534288],"domain_scores_gemma":[0.9899154,0.0051440285,0.0016244635,0.0019581367,0.0007739659,0.0005839489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004015494,0.0011012182,0.0009060826,0.00044313585,0.0008737522,0.0013840134,0.0019078463,0.0019427818,0.0038213385],"category_scores_gemma":[0.024541978,0.00062838796,0.0007089653,0.00025092994,0.0032401308,0.0025711493,0.0024074283,0.003390995,0.0010033102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049379806,0.00033117385,0.008406685,0.00026093883,0.00020265415,0.00042402706,0.00088726095,0.75154656,0.012435369,0.10050525,0.0073853983,0.1171209],"study_design_scores_gemma":[0.000042780564,0.00011446182,0.00082455366,0.000056309225,0.000024108149,0.0001259653,0.00006827167,0.9079426,0.0039955718,0.08396941,0.002796665,0.000039359318],"about_ca_topic_score_codex":0.00286755,"about_ca_topic_score_gemma":0.00326671,"teacher_disagreement_score":0.004015494,"about_ca_system_score_codex":0.0012237803,"about_ca_system_score_gemma":0.003058968,"threshold_uncertainty_score":0.021236181},"labels":[],"label_agreement":null},{"id":"W3211367579","doi":"","title":"Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models","year":2021,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Jacobian matrix and determinant; Artificial intelligence; Classifier (UML); Artificial neural network; Deep neural networks; Selection (genetic algorithm); Machine learning; Pattern recognition (psychology); Mathematics; Applied mathematics","score_opus":0.015990527970888624,"score_gpt":0.2491866928081204,"score_spread":0.23319616483723177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211367579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064884275,0.00005973675,0.9922265,0.00009289298,0.000028176448,0.000017861103,0.000026104097,0.00037509634,0.000685177],"genre_scores_gemma":[0.6005198,0.00022269007,0.38696826,0.00019759507,0.00014376156,0.0001584756,0.0003976412,0.00052544306,0.010866319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966776,0.00009215027,0.000015141278,0.000077642566,0.00010517831,0.00004212438],"domain_scores_gemma":[0.9992242,0.00034463708,0.00006276907,0.0001569883,0.00016868781,0.00004271499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009506992,0.0006167132,0.0007703963,0.00055469427,0.00040300196,0.0007589184,0.0013632976,0.001183358,0.0046173194],"category_scores_gemma":[0.003246141,0.00042820172,0.0007585948,0.0005368328,0.00068142207,0.0016198123,0.0014161963,0.00190876,0.0010039624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014094856,0.000093987306,0.00063048425,0.00007481939,0.00006196484,0.00016428642,0.00014001424,0.678479,0.011446139,0.088463485,0.005935605,0.2143693],"study_design_scores_gemma":[0.0000015971779,0.000006527753,0.000029052171,0.0000014957665,0.0000018894128,0.0000054447396,0.0000022876663,0.99256116,0.0004911521,0.006713442,0.00018338893,0.0000025217926],"about_ca_topic_score_codex":0.00289449,"about_ca_topic_score_gemma":0.0040500406,"teacher_disagreement_score":0.0046173194,"about_ca_system_score_codex":0.000559596,"about_ca_system_score_gemma":0.0006799949,"threshold_uncertainty_score":0.015446544},"labels":[],"label_agreement":null},{"id":"W3212787291","doi":"10.1109/qrs54544.2021.00037","title":"On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Adversarial system; Probabilistic logic; Reinforcement learning; Computer science; Artificial intelligence; Computer security; Multi-agent system; Autonomous agent; Grid; Machine learning; Mathematics","score_opus":0.06773412524589584,"score_gpt":0.41051923656668443,"score_spread":0.3427851113207886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212787291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016733414,0.00025918082,0.9807196,0.00042797273,0.000028354427,0.000089950314,0.000037378082,0.00034837765,0.0013557252],"genre_scores_gemma":[0.6192753,0.0006417008,0.37820643,0.00026578706,0.00008968855,0.00042056185,0.00015739298,0.00021389345,0.0007292076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98241043,0.011149671,0.0008155123,0.001219123,0.003677322,0.0007280483],"domain_scores_gemma":[0.7167638,0.25812137,0.009750491,0.00799838,0.006019915,0.0013460586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024737876,0.0018899264,0.0011406074,0.0023225027,0.0010527009,0.0028816208,0.0019298034,0.0019918869,0.0023700893],"category_scores_gemma":[0.13489774,0.000672757,0.0015501162,0.00090998545,0.005616733,0.0042417,0.0029376165,0.003175537,0.00024429918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012828594,0.00010596007,0.0036671087,0.00021069321,0.00007547016,0.00004307006,0.00019371633,0.8959075,0.0011905472,0.067933485,0.00023878107,0.030305317],"study_design_scores_gemma":[0.00001836989,0.000078674646,0.00016898081,0.000052145326,0.000010829484,0.000018083676,0.000021584705,0.9593529,0.0007525616,0.03924681,0.00026784567,0.000011264169],"about_ca_topic_score_codex":0.0042536943,"about_ca_topic_score_gemma":0.0021880225,"teacher_disagreement_score":0.024737876,"about_ca_system_score_codex":0.0032425898,"about_ca_system_score_gemma":0.0045526377,"threshold_uncertainty_score":0.13082796},"labels":[],"label_agreement":null},{"id":"W3213792049","doi":"","title":"Adversarial Attack Generation Empowered by Min-Max Optimization","year":2021,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Minimax; Computer science; Robustness (evolution); Mathematical optimization; Robust optimization; Simplex; Optimization problem; Set (abstract data type); Theoretical computer science; Artificial intelligence; Algorithm; Mathematics","score_opus":0.020611067852627677,"score_gpt":0.2633538074082902,"score_spread":0.2427427395556625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213792049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050521656,0.00017528806,0.9916442,0.00019583518,0.000022929307,0.000035596004,0.000036955476,0.00035734047,0.0024796645],"genre_scores_gemma":[0.66619444,0.00060673215,0.32408595,0.0006166743,0.00014013934,0.000367028,0.00034849474,0.0005431973,0.007097289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984409,0.00065481314,0.000065766595,0.00030773063,0.00036036657,0.00017037422],"domain_scores_gemma":[0.997378,0.0016432513,0.00023693312,0.00042464683,0.00019944883,0.00011775362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031105664,0.0018253286,0.0013449398,0.00070197275,0.00054255896,0.0014014835,0.0016531494,0.0014384441,0.0033919944],"category_scores_gemma":[0.007330571,0.00066609384,0.0009584224,0.00058517756,0.002259171,0.00241281,0.0039583296,0.0035828755,0.001041829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072649804,0.000038520124,0.00033297576,0.00007215611,0.000050714065,0.00006629619,0.000051171126,0.91330355,0.0029398254,0.046098202,0.002139953,0.03483406],"study_design_scores_gemma":[0.000005295598,0.0000274619,0.000043834214,0.000010885735,0.0000055232385,0.000031120722,0.000006766213,0.97401583,0.0013877962,0.023772487,0.0006864742,0.0000065109025],"about_ca_topic_score_codex":0.0006471759,"about_ca_topic_score_gemma":0.0007624261,"teacher_disagreement_score":0.0033919944,"about_ca_system_score_codex":0.0010416107,"about_ca_system_score_gemma":0.0010667996,"threshold_uncertainty_score":0.016450465},"labels":[],"label_agreement":null},{"id":"W3213820586","doi":"","title":"How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding.","year":2021,"lang":"en","type":"article","venue":"Empirical Methods in Natural Language Processing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Queen's University","funders":"","keywords":"Robustness (evolution); Computer science; Adversarial system; Distillation; Artificial intelligence; Machine learning; Benchmark (surveying); Domain knowledge; Natural language; Artificial neural network; Theoretical computer science","score_opus":0.10184368763546528,"score_gpt":0.4440969145619636,"score_spread":0.3422532269264983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213820586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23399444,0.031462923,0.6979976,0.008438332,0.0005060991,0.0004465457,0.0014180003,0.0021717472,0.023564298],"genre_scores_gemma":[0.85418296,0.0035619556,0.13575122,0.0011238353,0.00015720868,0.00013391716,0.0017778953,0.00034365573,0.0029674391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99219984,0.0036664489,0.0004425801,0.0019340379,0.0015175233,0.0002395153],"domain_scores_gemma":[0.96384037,0.028590195,0.0011810428,0.0047176676,0.0012896039,0.00038111332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012739208,0.0014752103,0.0011162859,0.0018034556,0.0010223258,0.0021328714,0.0017630913,0.002141556,0.0031240496],"category_scores_gemma":[0.056393493,0.00044081823,0.0011165773,0.0014402652,0.0029978415,0.0066098426,0.0020558888,0.0040385034,0.0011270392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007186966,0.0005284288,0.01774077,0.0012521197,0.00085227285,0.00017188094,0.00035004906,0.3827002,0.0033726888,0.053738754,0.021370945,0.51720315],"study_design_scores_gemma":[0.00006294808,0.00026986827,0.003425262,0.00026835967,0.00011306097,0.0004560672,0.0003088182,0.8818595,0.006287686,0.095632836,0.011251107,0.000064516054],"about_ca_topic_score_codex":0.0029153454,"about_ca_topic_score_gemma":0.003289579,"teacher_disagreement_score":0.012739208,"about_ca_system_score_codex":0.001286654,"about_ca_system_score_gemma":0.0012457281,"threshold_uncertainty_score":0.0673722},"labels":[],"label_agreement":null},{"id":"W3214544808","doi":"10.48550/arxiv.2002.08313","title":"NNoculation: Catching BadNets in the Wild","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Backdoor; Software deployment; Computer science; Adversarial system; Field (mathematics); Computer security; Artificial intelligence; Contrast (vision); Suite; Machine learning; Mathematics; Software engineering","score_opus":0.09004250246331236,"score_gpt":0.2089939376827504,"score_spread":0.11895143521943805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214544808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15026864,0.0014772088,0.83169484,0.0010489115,0.00040095215,0.00024391213,0.00018597647,0.0067595607,0.007920028],"genre_scores_gemma":[0.9283081,0.00028053063,0.067751825,0.0006757753,0.000052226533,0.00011282922,0.00020371082,0.00028409663,0.0023309153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854904,0.00039489608,0.00005858734,0.00034849823,0.00039801947,0.0002509052],"domain_scores_gemma":[0.9968232,0.0011984543,0.0003598436,0.0012592572,0.00023356423,0.00012563656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015771923,0.0014372981,0.0008462673,0.00058780285,0.0005123033,0.00081390503,0.001748008,0.0017982647,0.0013441286],"category_scores_gemma":[0.0060740146,0.0005216467,0.000717081,0.00019467481,0.002503979,0.0030320552,0.0035056183,0.0023361514,0.0005551073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007771586,0.00025770295,0.0067272973,0.00049198506,0.00032649562,0.00087858393,0.0004478567,0.57570064,0.10121614,0.055920843,0.011864464,0.24539083],"study_design_scores_gemma":[0.000031564126,0.0003926268,0.0010723608,0.00008143659,0.000056001358,0.0006281531,0.00007958776,0.9233012,0.04641661,0.021954337,0.005930797,0.0000554011],"about_ca_topic_score_codex":0.0009889934,"about_ca_topic_score_gemma":0.0012702879,"teacher_disagreement_score":0.0017982647,"about_ca_system_score_codex":0.0007533783,"about_ca_system_score_gemma":0.00066307874,"threshold_uncertainty_score":0.008341134},"labels":[],"label_agreement":null},{"id":"W3215557120","doi":"10.1109/dcoss52077.2021.00063","title":"A Reverse Turing Like Test for Quad-copters","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Turing test; Drone; Computer science; Turing; Artificial intelligence; Operator (biology); Test (biology); Machine learning; Programming language","score_opus":0.014732979721342337,"score_gpt":0.26369789779698977,"score_spread":0.24896491807564744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215557120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49301398,0.00081441255,0.46088374,0.0059778634,0.0009747452,0.0002778948,0.0017354311,0.0028013077,0.033520773],"genre_scores_gemma":[0.9685609,0.000062406885,0.025793886,0.00070941506,0.00010997043,0.00009081465,0.001060862,0.00013593867,0.0034758723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938332,0.0025578889,0.00041524036,0.0013411405,0.0013881214,0.00046436948],"domain_scores_gemma":[0.9596163,0.027179124,0.0025454226,0.0069303173,0.002589715,0.0011391002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005778701,0.00079333514,0.001166928,0.0011589393,0.0010302877,0.0015553368,0.001393203,0.0023550594,0.0052775326],"category_scores_gemma":[0.059675343,0.00028825775,0.0011130366,0.0005950579,0.0038217579,0.0035154936,0.0027141424,0.0020705725,0.0016643306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031164133,0.0006227147,0.078190364,0.0007839278,0.0006618316,0.00227444,0.0014198119,0.33689642,0.021337802,0.17683832,0.088084385,0.28977352],"study_design_scores_gemma":[0.00006876563,0.0004242198,0.0071292026,0.000084117324,0.000037353795,0.00083558296,0.00033954644,0.8522854,0.013391072,0.11756448,0.007757138,0.000083052146],"about_ca_topic_score_codex":0.001291219,"about_ca_topic_score_gemma":0.000902386,"teacher_disagreement_score":0.005778701,"about_ca_system_score_codex":0.0009853077,"about_ca_system_score_gemma":0.0008046765,"threshold_uncertainty_score":0.03056103},"labels":[],"label_agreement":null},{"id":"W3217650841","doi":"10.1007/978-3-031-17143-7_19","title":"Real-Time Adversarial Perturbations Against Deep Reinforcement Learning Policies: Attacks and Defenses","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Computer science; Reinforcement learning; Artificial intelligence; Computer security","score_opus":0.012097065327787046,"score_gpt":0.24990895340961897,"score_spread":0.23781188808183193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217650841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033603493,0.0019739151,0.94732004,0.0014239012,0.00035285574,0.000087222266,0.00006872464,0.0010951539,0.014074625],"genre_scores_gemma":[0.92746955,0.0013883776,0.060893953,0.00045936712,0.00020636017,0.00010924071,0.00008875432,0.00013431994,0.009250124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988096,0.00038670137,0.00004125381,0.00018040142,0.00040365037,0.00017852886],"domain_scores_gemma":[0.9962374,0.0025198446,0.00029207135,0.00057547615,0.0002551513,0.00012014187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018827209,0.0011314399,0.0008429194,0.00042927905,0.00040445873,0.001040098,0.0010227058,0.0017668919,0.0019057301],"category_scores_gemma":[0.0076636802,0.00042437657,0.00057169946,0.00046265955,0.0016238751,0.001797812,0.0025183258,0.0036316726,0.000520824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003781078,0.000104044026,0.00047908796,0.00010346478,0.00008549724,0.00011086916,0.000072743336,0.7299228,0.011271191,0.13174689,0.0062617366,0.11946362],"study_design_scores_gemma":[0.0000079451065,0.00006021032,0.000103244,0.00001468714,0.0000066763955,0.000059907154,0.000010454766,0.9571596,0.0023469895,0.03923658,0.0009854641,0.000008298448],"about_ca_topic_score_codex":0.00062045926,"about_ca_topic_score_gemma":0.00043406224,"teacher_disagreement_score":0.0019057301,"about_ca_system_score_codex":0.000945474,"about_ca_system_score_gemma":0.00065730244,"threshold_uncertainty_score":0.009956896},"labels":[],"label_agreement":null},{"id":"W4200549712","doi":"10.3389/fnbot.2021.808369","title":"Editorial: Intelligence and Safety for Humanoid Robots: Design, Control, and Applications","year":2021,"lang":"en","type":"editorial","venue":"Frontiers in Neurorobotics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Southern University of Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Humanoid robot; Robot; Control (management); Front (military); Computer security; Human–computer interaction; Artificial intelligence; Engineering","score_opus":0.011816544942878733,"score_gpt":0.26066082569477683,"score_spread":0.2488442807518981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200549712","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024029003,0.0059803966,0.0003058907,0.020411436,0.9693207,0.000022532142,0.00010149057,0.00009201881,0.0037414571],"genre_scores_gemma":[0.00048019816,0.005551072,0.00017398392,0.010667808,0.96129817,0.000031582076,0.00006885514,0.00006773071,0.021660572],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970868,0.00034723867,0.00030216016,0.00037822212,0.0016893431,0.00019624768],"domain_scores_gemma":[0.9884162,0.004245339,0.0007748646,0.00028235224,0.004554167,0.0017270657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004170791,0.0037923704,0.0037239105,0.003276355,0.0026224598,0.006287384,0.0026045067,0.011268118,0.03302679],"category_scores_gemma":[0.015212126,0.0009944997,0.0020862254,0.0011370054,0.0020618003,0.0038071624,0.0013998009,0.012261124,0.029061452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024091736,0.0000051973598,0.0000067461506,0.00012396662,0.0000074898385,0.000042227828,0.0000033015106,0.000025388617,0.000039851915,0.0002644586,0.99519134,0.004265968],"study_design_scores_gemma":[0.0000442804,0.000021970554,0.0000972427,0.00024977862,0.000027049899,0.00016178895,0.000011799534,0.00014238393,0.000115879586,0.0013361957,0.99777645,0.000015119624],"about_ca_topic_score_codex":0.0009996402,"about_ca_topic_score_gemma":0.0030908627,"teacher_disagreement_score":0.03302679,"about_ca_system_score_codex":0.0019578468,"about_ca_system_score_gemma":0.00214325,"threshold_uncertainty_score":0.11048561},"labels":[],"label_agreement":null},{"id":"W4205678875","doi":"10.1109/ase51524.2021.9678871","title":"DeepMemory: Model-based Memorization Analysis of Deep Neural Language Models","year":2021,"lang":"en","type":"article","venue":"2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Concordia University; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Perplexity; Memorization; Language model; Artificial intelligence; Artificial neural network; Machine learning; Data modeling; Robustness (evolution); Natural language processing; Database","score_opus":0.020935447441932053,"score_gpt":0.2813157815033691,"score_spread":0.26038033406143707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205678875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20848402,0.0010134439,0.7779106,0.0007086614,0.00010130114,0.00017391088,0.0010163136,0.009314076,0.0012777541],"genre_scores_gemma":[0.89419085,0.00036892245,0.10111942,0.0002658123,0.00005876254,0.00019988247,0.0016009192,0.00022467648,0.0019708467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991678,0.0002087232,0.00008302704,0.00023533133,0.00020942763,0.00009579878],"domain_scores_gemma":[0.9957151,0.0021625066,0.0006785806,0.0007986831,0.0005353322,0.00010976852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001729058,0.0010691636,0.00070929923,0.001229561,0.00034480175,0.001033433,0.0015021855,0.0006577314,0.0012719557],"category_scores_gemma":[0.0103772115,0.00036437984,0.0009969985,0.0005662029,0.0005183704,0.0029391858,0.0013717723,0.0020283987,0.00037617196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087098195,0.00040050593,0.019712674,0.00040555483,0.00037450643,0.00052693335,0.00052640995,0.42339557,0.023396594,0.009290509,0.007040185,0.5140596],"study_design_scores_gemma":[0.000011650767,0.00009126831,0.0008535247,0.00001108236,0.000028148796,0.000049829192,0.000034713175,0.9850172,0.007703545,0.005690846,0.0004949721,0.000013279771],"about_ca_topic_score_codex":0.0039941156,"about_ca_topic_score_gemma":0.0057098013,"teacher_disagreement_score":0.0039941156,"about_ca_system_score_codex":0.0010909879,"about_ca_system_score_gemma":0.0010715092,"threshold_uncertainty_score":0.009144247},"labels":[],"label_agreement":null},{"id":"W4206060246","doi":"10.1109/access.2021.3133334","title":"You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Adversarial system; Pruning; Artificial intelligence; Machine learning; Deep neural networks; Artificial neural network; FLOPS; Deep learning; Sample (material); Pattern recognition (psychology)","score_opus":0.0766063510594319,"score_gpt":0.30660380868449816,"score_spread":0.22999745762506624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206060246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10064207,0.0007133575,0.89343524,0.000711989,0.00008169399,0.00009547116,0.00010712962,0.001763042,0.0024500045],"genre_scores_gemma":[0.80024904,0.00034483674,0.19596772,0.0005752754,0.00005628983,0.000087794804,0.00027572605,0.00018182333,0.0022616251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986297,0.00041868255,0.00004584651,0.00027185178,0.00046995498,0.00016402961],"domain_scores_gemma":[0.9964161,0.0016866805,0.00044562286,0.00097258657,0.0003438307,0.00013513184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001847171,0.0011356057,0.00097089924,0.00090162194,0.00049067585,0.0007912016,0.0015282044,0.0014898713,0.0009884533],"category_scores_gemma":[0.008204512,0.00052522175,0.0008687837,0.0003920726,0.0011365338,0.0025897769,0.0017593331,0.0026213971,0.00040555757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047272237,0.00020555922,0.0073498576,0.000113899034,0.00025544688,0.000500876,0.00025514438,0.6759916,0.029986994,0.028064886,0.0072451443,0.24955791],"study_design_scores_gemma":[0.000007840667,0.000052846404,0.0003504031,0.00001254469,0.000017973569,0.00012137685,0.000011916762,0.98825246,0.004669251,0.005825609,0.00066778733,0.00000997263],"about_ca_topic_score_codex":0.0025147626,"about_ca_topic_score_gemma":0.0033176013,"teacher_disagreement_score":0.0025147626,"about_ca_system_score_codex":0.00064420543,"about_ca_system_score_gemma":0.00087608857,"threshold_uncertainty_score":0.009768903},"labels":[],"label_agreement":null},{"id":"W4206243656","doi":"10.1186/s13635-021-00125-2","title":"Secure machine learning against adversarial samples at test time","year":2022,"lang":"en","type":"article","venue":"EURASIP Journal on Information Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Air Force; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Adversarial system; Artificial intelligence; Machine learning; Deep neural networks; Test set; Robustness (evolution); Scalability; Classifier (UML); Gradient descent; Deep learning; Adversarial machine learning; Attack model; Artificial neural network; Computer security","score_opus":0.008340035973071574,"score_gpt":0.2230166545099525,"score_spread":0.21467661853688094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206243656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4855162,0.0006742545,0.49912527,0.0010949622,0.00025702454,0.00017320806,0.0003314413,0.0077557466,0.005071935],"genre_scores_gemma":[0.9744975,0.000044060973,0.024159787,0.0001384525,0.000016084276,0.0000649422,0.00018695946,0.000085329266,0.00080695184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760383,0.000673074,0.00014748324,0.00052601384,0.000749286,0.0003002713],"domain_scores_gemma":[0.99257964,0.0031099268,0.0006038973,0.002629838,0.0008089999,0.0002677553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022656918,0.001065529,0.0007426176,0.00045098853,0.0005112655,0.000709576,0.0012627232,0.0009451835,0.0017061861],"category_scores_gemma":[0.011159852,0.000255781,0.00061848346,0.0002929466,0.0011307697,0.001654959,0.0016941895,0.0017527547,0.0007688679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001564783,0.00029851194,0.0077043977,0.00009977855,0.00014187628,0.0005995109,0.00014138396,0.81710505,0.033908036,0.013351833,0.004938589,0.12014617],"study_design_scores_gemma":[0.000010920689,0.00008424779,0.00027917972,0.0000064700785,0.000006473115,0.0000510711,0.000010693044,0.982852,0.013548992,0.00278843,0.0003551913,0.00000637003],"about_ca_topic_score_codex":0.0013330303,"about_ca_topic_score_gemma":0.000977335,"teacher_disagreement_score":0.0022656918,"about_ca_system_score_codex":0.0010647983,"about_ca_system_score_gemma":0.00090754277,"threshold_uncertainty_score":0.011982262},"labels":[],"label_agreement":null},{"id":"W4206437641","doi":"10.1109/access.2022.3141077","title":"A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Army Research Office; New York University Abu Dhabi","keywords":"Computer science; Adversarial system; Detector; Feature (linguistics); Line (geometry); Artificial intelligence; Pattern recognition (psychology); Mathematics; Telecommunications","score_opus":0.0219248319604238,"score_gpt":0.3122909309673224,"score_spread":0.29036609900689864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206437641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020688806,0.00024678628,0.97590566,0.00011090617,0.000071539034,0.000078182784,0.00008021144,0.0020135732,0.00080432696],"genre_scores_gemma":[0.57562065,0.0002173452,0.4198039,0.0003125513,0.000062872925,0.00015449409,0.0003931106,0.00023513398,0.0031999787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998326,0.0002478385,0.000090381945,0.00038320283,0.00076957745,0.00018303207],"domain_scores_gemma":[0.9973054,0.0007705633,0.00049068907,0.0007883794,0.00052019075,0.00012484635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011484737,0.0012116977,0.0013683165,0.0012484388,0.00049912266,0.000843393,0.002332345,0.0012955607,0.001514159],"category_scores_gemma":[0.004858118,0.0004277705,0.0009628054,0.0006561205,0.0010029242,0.0021091187,0.0020167334,0.0020898727,0.0009881684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006144138,0.00037675418,0.005214301,0.000217922,0.00020734656,0.00051765976,0.00023904782,0.13594337,0.09686057,0.011339551,0.0073977616,0.7410713],"study_design_scores_gemma":[0.000021919368,0.0002523557,0.0012076152,0.000018193174,0.000038631453,0.00073214766,0.000026782585,0.9381534,0.052482903,0.0037359619,0.003283498,0.000046596157],"about_ca_topic_score_codex":0.0008265049,"about_ca_topic_score_gemma":0.00089788844,"teacher_disagreement_score":0.002332345,"about_ca_system_score_codex":0.0005665101,"about_ca_system_score_gemma":0.0008265533,"threshold_uncertainty_score":0.006073773},"labels":[],"label_agreement":null},{"id":"W4210408749","doi":"10.46713/jdst.004.01","title":"Military Dataset Processing Approaches or Trauma Risk Mitigation in Machine Learning Practitioners","year":2022,"lang":"en","type":"article","venue":"Journal of Defence & Security Technologies","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Offensive; Computer science; Preprocessor; Artificial intelligence; Machine learning; Work (physics); Motion (physics); Data science; Engineering; Operations research","score_opus":0.027131001614369068,"score_gpt":0.26745563320690635,"score_spread":0.24032463159253728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210408749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028263781,0.0007570998,0.9463326,0.009264519,0.00027801233,0.0005152242,0.00092760345,0.002010779,0.011650388],"genre_scores_gemma":[0.33491156,0.0010767672,0.65332264,0.0020695284,0.00035719082,0.00079635996,0.002117933,0.00051723066,0.0048307227],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931098,0.0035078903,0.00054898596,0.0010935032,0.0014674535,0.00027243284],"domain_scores_gemma":[0.96870685,0.012321845,0.0024979105,0.011787333,0.004241362,0.00044475275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017089041,0.00086866564,0.00052752043,0.0016577739,0.0008914881,0.003949781,0.0023480332,0.001672587,0.007407956],"category_scores_gemma":[0.04801241,0.00050100137,0.0007345498,0.0015432825,0.0016475228,0.005556258,0.0030970133,0.0024749588,0.0025088596],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006606353,0.0005749774,0.02345882,0.0011812857,0.00029374423,0.0004771729,0.0027532221,0.053943828,0.038812716,0.15987507,0.037048675,0.6809198],"study_design_scores_gemma":[0.00020176436,0.0011107771,0.021021878,0.0010348355,0.00025870072,0.0012327376,0.0037288838,0.30435666,0.102792904,0.35306928,0.21095946,0.00023217156],"about_ca_topic_score_codex":0.00078102626,"about_ca_topic_score_gemma":0.0011652458,"teacher_disagreement_score":0.017089041,"about_ca_system_score_codex":0.0010003052,"about_ca_system_score_gemma":0.0014817112,"threshold_uncertainty_score":0.090376556},"labels":[],"label_agreement":null},{"id":"W4210455774","doi":"10.1145/3490489","title":"NPC: <u>N</u> euron <u>P</u> ath <u>C</u> overage via Characterizing Decision Logic of Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; JST-Mirai Program; National Satellite of Excellence in Trustworthy Software Systems, National University of Singapore; National Research Foundation; Bộ Giáo dục và Ðào tạo; Ministry of Education - Singapore; National Research Foundation Singapore; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Path (computing); Machine learning; Artificial neural network; Graph; Deep neural networks; Decision tree; Mirroring; Theoretical computer science","score_opus":0.039775669112155874,"score_gpt":0.2872848317002451,"score_spread":0.24750916258808922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210455774","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1438845,0.00035517899,0.8338144,0.001359008,0.000107520005,0.00019565236,0.0009630001,0.0019549709,0.01736581],"genre_scores_gemma":[0.8934185,0.000321288,0.09914385,0.0003842722,0.00005123688,0.00022044599,0.0008509186,0.00026525723,0.005344263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988047,0.00022451808,0.000056356515,0.00034051793,0.00038156717,0.00019232971],"domain_scores_gemma":[0.99758554,0.0013562379,0.00023740384,0.00017990047,0.00055225904,0.00008869036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008836144,0.0005439384,0.00030671497,0.0010314785,0.00045551668,0.0015150417,0.0006761031,0.0007424562,0.0044388534],"category_scores_gemma":[0.006607119,0.00024203383,0.0006829772,0.00054262805,0.0015467855,0.0019020756,0.0010843591,0.0012460081,0.0005135263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049490086,0.00017729598,0.011840859,0.0004433319,0.00010315845,0.0013237328,0.00046947118,0.30113497,0.023277601,0.35572568,0.008406529,0.2966025],"study_design_scores_gemma":[0.000012473661,0.00004477341,0.0009185545,0.0000421397,0.000023716673,0.00013548059,0.000060929822,0.7820353,0.008500022,0.20464961,0.0035591202,0.000017871478],"about_ca_topic_score_codex":0.008501739,"about_ca_topic_score_gemma":0.008574104,"teacher_disagreement_score":0.008501739,"about_ca_system_score_codex":0.0015008107,"about_ca_system_score_gemma":0.0012280148,"threshold_uncertainty_score":0.016904533},"labels":[],"label_agreement":null},{"id":"W4210598630","doi":"10.3233/jcs-210094","title":"Adversarial examples for network intrusion detection systems","year":2022,"lang":"en","type":"article","venue":"Journal of Computer Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Adversarial system; Exploit; Adversary; Intrusion detection system; Artificial intelligence; Robustness (evolution); Machine learning; Pattern recognition (psychology); Computer security","score_opus":0.01233739287635993,"score_gpt":0.2411539419570545,"score_spread":0.22881654908069457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210598630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036289096,0.0006195264,0.95674217,0.00055027514,0.00008913819,0.00014907181,0.00009886422,0.0012106047,0.004251325],"genre_scores_gemma":[0.838695,0.00038267463,0.15683998,0.00022440935,0.000072105286,0.00018595532,0.00016928744,0.000109071625,0.0033215801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981975,0.0007077851,0.00008674292,0.00027924663,0.00061673374,0.000111971414],"domain_scores_gemma":[0.9933785,0.004685306,0.00046439195,0.00090781046,0.00042264402,0.00014130687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020074875,0.00068836886,0.0006890601,0.00061891275,0.00040852925,0.001062932,0.0011531617,0.001130324,0.0032018805],"category_scores_gemma":[0.01163483,0.00038198553,0.00055853266,0.00031026482,0.0015441763,0.0019504683,0.0024737264,0.002144002,0.0005260146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017911142,0.000050826067,0.0007777115,0.000104922285,0.000034520366,0.000077484976,0.00007657889,0.8937285,0.0044845613,0.034170166,0.0015861873,0.064729504],"study_design_scores_gemma":[0.0000073800575,0.00004073368,0.00010569584,0.000010087891,0.0000041983785,0.00004011666,0.000009802784,0.9849289,0.0019589504,0.011761474,0.0011253217,0.0000072400276],"about_ca_topic_score_codex":0.00086864,"about_ca_topic_score_gemma":0.00075647427,"teacher_disagreement_score":0.0032018805,"about_ca_system_score_codex":0.0008889299,"about_ca_system_score_gemma":0.00046950116,"threshold_uncertainty_score":0.010711312},"labels":[],"label_agreement":null},{"id":"W4212903494","doi":"10.5220/0010871000003116","title":"Soft Adversarial Training Can Retain Natural Accuracy","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 14th International Conference on Agents and Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Robustness (evolution); Software deployment; Artificial intelligence; Machine learning; Artificial neural network; Deep neural networks; Training (meteorology); Computer security; Software engineering","score_opus":0.09054413138897463,"score_gpt":0.31931994658703605,"score_spread":0.22877581519806142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212903494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03324252,0.00025547665,0.96016705,0.00060258433,0.00006358625,0.000048673737,0.00005632141,0.00052658253,0.005037245],"genre_scores_gemma":[0.903934,0.00030379818,0.09152243,0.00034182364,0.000072795876,0.00009390518,0.00013574467,0.00016801474,0.0034273993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979759,0.00074338616,0.0000968063,0.00037896316,0.00053716206,0.00026782556],"domain_scores_gemma":[0.9870595,0.008857005,0.00086573296,0.0022785547,0.0006426333,0.00029657717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040600123,0.001285486,0.00090447307,0.0005583716,0.00055682956,0.0011658802,0.001210476,0.001347857,0.0019399788],"category_scores_gemma":[0.018740961,0.00040693505,0.00068692194,0.00033072953,0.0028548925,0.0024612409,0.0030649316,0.0034363936,0.0005406667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016681211,0.000079976744,0.0013455575,0.00009711116,0.000044764245,0.00010939638,0.00010646448,0.8817989,0.007884958,0.06435579,0.0013663677,0.042643953],"study_design_scores_gemma":[0.0000067994483,0.00007330489,0.00022904795,0.000022255615,0.0000075123485,0.000047357407,0.0000119905335,0.96354985,0.0029166262,0.032302193,0.0008237905,0.000009323003],"about_ca_topic_score_codex":0.0010334629,"about_ca_topic_score_gemma":0.0010210644,"teacher_disagreement_score":0.0040600123,"about_ca_system_score_codex":0.0009683799,"about_ca_system_score_gemma":0.0009269366,"threshold_uncertainty_score":0.02147168},"labels":[],"label_agreement":null},{"id":"W4221129115","doi":"10.1002/hbm.25784","title":"Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation","year":2022,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; Toronto Rehabilitation Institute; Ontario Brain Institute; Université de Montréal; Heart and Stroke Foundation; York University; Montreal Heart Institute; Toronto Western Hospital; University Health Network; Ottawa Hospital; Thunder Bay Regional Research Institute; University of Ottawa; Sunnybrook Health Science Centre; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Western University; University of Toronto","funders":"Faculty of Health Sciences, Queen's University; London Health Sciences Foundation; Government of Ontario; St. Michael's Hospital Foundation; University Health Network; Temerty Family Foundation; Health Sciences Centre Foundation; Ontario Brain Institute; University of Ottawa; Queen's University; Canadian Institutes of Health Research; Centre for Addiction and Mental Health Foundation; McMaster University","keywords":"Hyperintensity; Segmentation; Artificial intelligence; Adversarial system; White matter; Bayesian probability; Pattern recognition (psychology); Computer science; Psychology; Magnetic resonance imaging; Medicine; Radiology","score_opus":0.0137712265731811,"score_gpt":0.25203649864947275,"score_spread":0.23826527207629164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221129115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029849546,0.0006077723,0.9663413,0.00047143947,0.000054555858,0.00006729069,0.00018111279,0.00079252024,0.0016344561],"genre_scores_gemma":[0.8153161,0.00051001343,0.17761114,0.0004529488,0.00009007381,0.00026803638,0.0005901028,0.00022826706,0.004933264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991154,0.00035694707,0.000047845355,0.00021118738,0.00016378591,0.000104798884],"domain_scores_gemma":[0.9966102,0.0023961987,0.00031182595,0.00016184538,0.00040916845,0.000110694586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002851749,0.0013703012,0.0011975984,0.0010068561,0.0004884795,0.001083179,0.0017161939,0.0019535786,0.0018225828],"category_scores_gemma":[0.008186032,0.0010377434,0.0011671147,0.00050232303,0.001287544,0.0011985387,0.0019593132,0.002842549,0.00041990782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000549984,0.000013734543,0.00037646163,0.000020422764,0.000025889132,0.00003081467,0.000025721882,0.98026204,0.00052484,0.0032201977,0.0003224132,0.015122565],"study_design_scores_gemma":[0.0000015058035,0.000004913857,0.0000410301,0.0000037219268,0.0000020963248,0.000004753136,0.0000011709853,0.99824333,0.00017590664,0.0014466478,0.00007248155,0.00000243514],"about_ca_topic_score_codex":0.018571634,"about_ca_topic_score_gemma":0.014634829,"teacher_disagreement_score":0.018571634,"about_ca_system_score_codex":0.0021999446,"about_ca_system_score_gemma":0.0015517371,"threshold_uncertainty_score":0.036927104},"labels":[],"label_agreement":null},{"id":"W4225322540","doi":"10.3390/s22093445","title":"A Universal Detection Method for Adversarial Examples and Fake Images","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Adversarial system; Computer science; Artificial intelligence; Computer vision; Computer security","score_opus":0.01314637095750225,"score_gpt":0.2676303055536461,"score_spread":0.2544839345961438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225322540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0127322,0.00031182752,0.9841151,0.00022340272,0.00008578946,0.00008223043,0.000065962,0.0014704376,0.0009131366],"genre_scores_gemma":[0.5540267,0.00052733463,0.43926865,0.00065090274,0.00019173262,0.00020468346,0.0004205944,0.00021284241,0.004496546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978542,0.00031875525,0.00011251618,0.0007543092,0.0006744944,0.00028572057],"domain_scores_gemma":[0.9978362,0.0005423958,0.00030773992,0.00071998313,0.00045128842,0.00014247015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002140358,0.0014718545,0.0020674486,0.0016098145,0.00064934196,0.0009883826,0.0021161574,0.0020377093,0.0015199301],"category_scores_gemma":[0.005818602,0.00059907435,0.0013418985,0.0007739895,0.0020454212,0.002627417,0.0031839982,0.0024998805,0.000660904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039451872,0.00027415782,0.0052419463,0.00027717874,0.00031363918,0.00059956836,0.00024568953,0.16247234,0.043035064,0.040111944,0.013105596,0.73392844],"study_design_scores_gemma":[0.000014644863,0.00006585749,0.00068521104,0.00002057823,0.00003542423,0.00059591385,0.000016516711,0.9663335,0.022017134,0.008173041,0.0020062898,0.000035829325],"about_ca_topic_score_codex":0.0016081678,"about_ca_topic_score_gemma":0.001695649,"teacher_disagreement_score":0.002140358,"about_ca_system_score_codex":0.00097533554,"about_ca_system_score_gemma":0.0013995004,"threshold_uncertainty_score":0.0113194585},"labels":[],"label_agreement":null},{"id":"W4225885311","doi":"10.23919/date54114.2022.9774635","title":"FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions","year":2022,"lang":"en","type":"preprint","venue":"2022 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Fault tolerance; Inference; Redundancy (engineering); Resilience (materials science); Activation function; Deep neural networks; Artificial neural network; Latency (audio); Word error rate; Artificial intelligence; Distributed computing","score_opus":0.04523905429616132,"score_gpt":0.2870918710591243,"score_spread":0.241852816762963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225885311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032625098,0.0007992922,0.9571821,0.00030805796,0.00014264634,0.00007258546,0.00014789354,0.0065406677,0.0021816671],"genre_scores_gemma":[0.785164,0.00044133957,0.2057365,0.0006556978,0.00009235799,0.00018429835,0.0005879767,0.00058289437,0.00655493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943846,0.00009249536,0.000030520005,0.0001374604,0.00022166909,0.00007938998],"domain_scores_gemma":[0.99909985,0.0003343695,0.0001262188,0.00021411855,0.00016951634,0.000056004872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012476083,0.0019389514,0.0007600518,0.00051639037,0.00033993917,0.00084172306,0.0024632553,0.0013751286,0.0025007473],"category_scores_gemma":[0.0040524034,0.0005375486,0.0006610281,0.00031295547,0.0010321165,0.001933799,0.0021816378,0.0025185056,0.0009297051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002213361,0.000096695134,0.0006775365,0.00009870491,0.00008782539,0.00012436794,0.000051944287,0.87133473,0.01135817,0.005160402,0.003302876,0.10748534],"study_design_scores_gemma":[0.000009568693,0.00005479634,0.0000767386,0.000007856489,0.000009243841,0.000024705452,0.0000040759974,0.99287647,0.0041244985,0.0022274628,0.00057820504,0.0000063695725],"about_ca_topic_score_codex":0.0032375448,"about_ca_topic_score_gemma":0.004813911,"teacher_disagreement_score":0.0032375448,"about_ca_system_score_codex":0.00088129914,"about_ca_system_score_gemma":0.0010450433,"threshold_uncertainty_score":0.00836581},"labels":[],"label_agreement":null},{"id":"W4225986943","doi":"10.1145/3502726","title":"On the Robustness of Metric Learning: An Adversarial Perspective","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Robustness (evolution); Pairwise comparison; Computer science; Metric (unit); Adversarial system; Artificial intelligence; Machine learning","score_opus":0.04692128029731266,"score_gpt":0.309908607807081,"score_spread":0.26298732750976833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225986943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016197387,0.0018098165,0.97773963,0.0010898353,0.00009837188,0.000048300444,0.000075489166,0.00024295271,0.0026981903],"genre_scores_gemma":[0.84618974,0.0036811016,0.14535701,0.00075116206,0.0005203259,0.0002490075,0.00037934544,0.00032906752,0.0025433693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99106795,0.0038805353,0.00054438936,0.001660833,0.0022897187,0.00055657333],"domain_scores_gemma":[0.94816285,0.039645437,0.0036427695,0.0052188453,0.0024557456,0.00087437895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008724856,0.0028673292,0.0018399268,0.0025226718,0.0010174714,0.0025402808,0.0023949267,0.0030511154,0.0015051443],"category_scores_gemma":[0.06184284,0.0008873204,0.0018856513,0.00169526,0.0058805165,0.0074311118,0.0054895505,0.006412732,0.00047158124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016596234,0.00006000792,0.002101506,0.00020237306,0.00019115544,0.0001921633,0.0001684388,0.8275742,0.0033899096,0.12821996,0.0012549276,0.03647931],"study_design_scores_gemma":[0.000007899924,0.00012770835,0.00031283998,0.000047481073,0.000024443118,0.000121478755,0.000026293155,0.9165372,0.0019447431,0.079937354,0.0008845998,0.000027965758],"about_ca_topic_score_codex":0.0017328515,"about_ca_topic_score_gemma":0.00072330737,"teacher_disagreement_score":0.008724856,"about_ca_system_score_codex":0.002261467,"about_ca_system_score_gemma":0.0012186301,"threshold_uncertainty_score":0.046141982},"labels":[],"label_agreement":null},{"id":"W4226014058","doi":"10.1109/qrs-c55045.2021.00081","title":"DeepGuard: A DeepBillboard Attack Detection Technique against Connected and Autonomous Vehicles","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Adversarial system; Automation; Artificial intelligence; Generalization; Artificial neural network; Reliability (semiconductor); Computer security; Machine learning; Convolutional neural network; Engineering","score_opus":0.04040820855967763,"score_gpt":0.32198734835978304,"score_spread":0.2815791398001054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226014058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14252765,0.0009910563,0.83427995,0.00093517505,0.00030873757,0.00036485534,0.00037896066,0.012859216,0.00735442],"genre_scores_gemma":[0.88641316,0.00027638275,0.106589206,0.00048190705,0.00004964953,0.00009434638,0.0007364418,0.00015460068,0.005204251],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990796,0.00014768985,0.00003969654,0.00016619063,0.00041136166,0.0001554679],"domain_scores_gemma":[0.9990546,0.0002764717,0.00016632982,0.00023508127,0.00018749856,0.00008000908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010092066,0.0011959649,0.00074350124,0.00081024214,0.00041672267,0.00075632834,0.0017724895,0.0012037371,0.0013821549],"category_scores_gemma":[0.0028344265,0.00042991914,0.00070388295,0.00029664187,0.0012616147,0.0021664237,0.0027561935,0.0023717722,0.00042699353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071790954,0.0003928852,0.0054325787,0.00021053066,0.00032096,0.0006539106,0.00021721021,0.44533893,0.035054993,0.015372575,0.020626092,0.4756615],"study_design_scores_gemma":[0.000021758351,0.00017657113,0.0003605333,0.000012549178,0.000014778481,0.00011777607,0.000017685705,0.9819094,0.012132127,0.0033370177,0.0018866686,0.000013231096],"about_ca_topic_score_codex":0.0039451164,"about_ca_topic_score_gemma":0.00427332,"teacher_disagreement_score":0.0039451164,"about_ca_system_score_codex":0.000969551,"about_ca_system_score_gemma":0.0011435051,"threshold_uncertainty_score":0.007844269},"labels":[],"label_agreement":null},{"id":"W4226140795","doi":"10.1007/978-3-031-01333-1_18","title":"AGS: Attribution Guided Sharpening as a Defense Against Adversarial Attacks","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"MNIST database; Sharpening; Computer science; Adversarial system; Oracle; Deep neural networks; Artificial intelligence; Deep learning; Classifier (UML); Artificial neural network; Benchmark (surveying); Machine learning; Handwriting; Software engineering","score_opus":0.02268317233576097,"score_gpt":0.27692089995432223,"score_spread":0.2542377276185613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226140795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021259928,0.0008508414,0.9558185,0.0007055352,0.00048337338,0.00010444784,0.00012821416,0.0058264635,0.014822807],"genre_scores_gemma":[0.79052436,0.00083471835,0.17484,0.0010203355,0.00048084612,0.00013782714,0.00033286566,0.0009876366,0.030841382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885225,0.0002218979,0.000038891238,0.00023616066,0.00046842854,0.00018228557],"domain_scores_gemma":[0.99764246,0.0009772839,0.00022425147,0.0008143362,0.00021656905,0.0001249728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013848626,0.0011931203,0.0009405491,0.00086884777,0.00052137853,0.001618331,0.0018069467,0.0021756669,0.008368495],"category_scores_gemma":[0.0044804104,0.0004881018,0.00069145253,0.0006243022,0.0022784001,0.002486791,0.0042135348,0.004178008,0.0027676392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010569915,0.00023997392,0.0007133625,0.00029234806,0.000114720846,0.00032526287,0.00021034603,0.2845096,0.06464784,0.21944728,0.028844511,0.39959785],"study_design_scores_gemma":[0.000053960033,0.00027691436,0.0003540063,0.000037234535,0.000032389653,0.00025866774,0.000045707235,0.77401006,0.020804727,0.19371755,0.010365036,0.00004382086],"about_ca_topic_score_codex":0.00030883792,"about_ca_topic_score_gemma":0.00027654838,"teacher_disagreement_score":0.008368495,"about_ca_system_score_codex":0.0005643083,"about_ca_system_score_gemma":0.00057614234,"threshold_uncertainty_score":0.027995408},"labels":[],"label_agreement":null},{"id":"W4226150786","doi":"10.1609/aaai.v36i2.20010","title":"Adversarial Attack for Asynchronous Event-Based Data","year":2022,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Adversarial system; Asynchronous communication; Computer science; Artificial intelligence; Event (particle physics); Robustness (evolution); Deep learning; Set (abstract data type); Machine learning; Deep neural networks; Data mining","score_opus":0.17045644340709656,"score_gpt":0.37420678574071875,"score_spread":0.2037503423336222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226150786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049657244,0.00026801397,0.94499284,0.00064451847,0.00012235972,0.00010004212,0.00026112745,0.0010854908,0.002868426],"genre_scores_gemma":[0.9340363,0.00017164912,0.061789364,0.000414495,0.000060480073,0.00014005827,0.0005086504,0.00012948528,0.0027496198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835545,0.0005006627,0.000096059055,0.00038144446,0.0004848838,0.00018156353],"domain_scores_gemma":[0.9948096,0.0033906875,0.00044365195,0.0009177589,0.0003052661,0.00013300093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023835544,0.0009849867,0.0008193643,0.0004887797,0.0005191264,0.0007654298,0.0014191563,0.0012944809,0.0020411965],"category_scores_gemma":[0.009216587,0.00040579017,0.00088882184,0.00033316694,0.0015317723,0.0020571493,0.0028871435,0.003009575,0.00041390874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028464865,0.000057097954,0.001325693,0.000060513306,0.000059631013,0.00019874635,0.00007356842,0.93297344,0.0048803743,0.023411537,0.0030011323,0.033673596],"study_design_scores_gemma":[0.000008801999,0.000026365044,0.0001276733,0.000005929055,0.0000041637772,0.000037676855,0.0000070336187,0.98665816,0.0021871633,0.01041726,0.00051396666,0.0000058155138],"about_ca_topic_score_codex":0.00137401,"about_ca_topic_score_gemma":0.0011692417,"teacher_disagreement_score":0.0023835544,"about_ca_system_score_codex":0.0010795308,"about_ca_system_score_gemma":0.0006676252,"threshold_uncertainty_score":0.0126056075},"labels":[],"label_agreement":null},{"id":"W4226207655","doi":"10.1109/qrs54544.2021.00118","title":"Understanding the Resilience of Neural Network Ensembles against Faulty Training Data","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Resilience (materials science); Machine learning; Artificial intelligence; Artificial neural network; Training set; Ensemble learning; Entropy (arrow of time); Training (meteorology); Ensemble forecasting; Data mining","score_opus":0.21383400830963548,"score_gpt":0.3701135125969131,"score_spread":0.15627950428727763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226207655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7650633,0.0008904764,0.22843662,0.0014953224,0.00012335765,0.00006528535,0.00022843614,0.0006576907,0.003039623],"genre_scores_gemma":[0.9931665,0.00012758796,0.006163506,0.00008485196,0.000024032975,0.000022895974,0.00009099858,0.000027568609,0.0002919816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897075,0.00032210094,0.00006913585,0.00020514421,0.00024710284,0.00018567998],"domain_scores_gemma":[0.9868471,0.008194512,0.0014686632,0.0019337821,0.0011537942,0.00040208382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038745422,0.0007348652,0.00059284386,0.0010132655,0.0005416111,0.0009133366,0.0008083643,0.0011357128,0.0008754389],"category_scores_gemma":[0.031365916,0.00037758247,0.00045926255,0.00040111263,0.0012116025,0.0026729577,0.001504632,0.0014578681,0.00019933608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010355347,0.00003245485,0.00836568,0.000039089377,0.000063856,0.000079451056,0.00009855825,0.96977353,0.004004749,0.002623571,0.000332222,0.01448332],"study_design_scores_gemma":[0.0000042268057,0.00009639929,0.002738444,0.000020442061,0.000022601116,0.000054776996,0.000062126855,0.98455703,0.0039505563,0.008213253,0.0002667853,0.000013530847],"about_ca_topic_score_codex":0.0031126754,"about_ca_topic_score_gemma":0.0020215567,"teacher_disagreement_score":0.0038745422,"about_ca_system_score_codex":0.0009369771,"about_ca_system_score_gemma":0.0005655292,"threshold_uncertainty_score":0.020490766},"labels":[],"label_agreement":null},{"id":"W4226290207","doi":"10.1145/3527848","title":"A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations","year":2022,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Accountability; Focus (optics); Work (physics); Management science; Data science; Risk analysis (engineering); Political science; Law","score_opus":0.12538502868306525,"score_gpt":0.38632274532314775,"score_spread":0.2609377166400825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226290207","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036619084,0.69506925,0.24140371,0.015503041,0.00083895936,0.00019950741,0.00026369584,0.0002513002,0.042808626],"genre_scores_gemma":[0.14669183,0.70588326,0.1293489,0.0039507025,0.003571706,0.00045052756,0.0005965078,0.00016732498,0.009339253],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99507254,0.0025896744,0.00029389563,0.00059462845,0.0012934196,0.00015579056],"domain_scores_gemma":[0.96409786,0.031201366,0.00087384303,0.0018645832,0.0017249165,0.00023750195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007879614,0.001134007,0.0011732957,0.0032592148,0.0007704312,0.0034695365,0.0027094523,0.0029723337,0.007071663],"category_scores_gemma":[0.029350754,0.0006863922,0.0011675697,0.0043830774,0.00345905,0.0064677075,0.0020456528,0.003637737,0.0015522128],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054744927,0.00011108067,0.0012887937,0.0034284946,0.00013002088,0.0001194048,0.0004783235,0.011061311,0.00021152206,0.45147663,0.014073882,0.51756585],"study_design_scores_gemma":[0.000039997092,0.00012212353,0.0017640279,0.005542986,0.00012550365,0.0007858167,0.0004982611,0.030288452,0.00074394024,0.60937023,0.35061023,0.000108305816],"about_ca_topic_score_codex":0.0023750735,"about_ca_topic_score_gemma":0.0021089502,"teacher_disagreement_score":0.007879614,"about_ca_system_score_codex":0.0024085096,"about_ca_system_score_gemma":0.0028370654,"threshold_uncertainty_score":0.041671872},"labels":[],"label_agreement":null},{"id":"W4230549531","doi":"10.1037/cbs0000270.supp","title":"Supplemental Material for Mock Juror Decision-Making in a Self-Defence Trial Involving Police Use of Force","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Social psychology; Applied psychology; Criminology","score_opus":0.07873180526727346,"score_gpt":0.29309208705651885,"score_spread":0.21436028178924538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230549531","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025666257,0.00056270673,0.041805778,0.0041217767,0.0021816688,0.0023859243,0.8474042,0.0064463904,0.06942541],"genre_scores_gemma":[0.23319815,0.0018260124,0.14572583,0.00508506,0.0014618278,0.008501722,0.45112,0.0041002394,0.1489812],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99966,0.00007781491,0.0000377428,0.000050160652,0.00011447152,0.000059707898],"domain_scores_gemma":[0.99084973,0.0072091096,0.00028895584,0.00029784723,0.0010845945,0.00026984347],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00056600024,0.0007327135,0.00071096845,0.0011313349,0.0008138271,0.00076457317,0.0013898589,0.0021264157,0.74538016],"category_scores_gemma":[0.012024769,0.00041334407,0.0006292507,0.00079087034,0.00018710304,0.0006082063,0.00072062237,0.0007318994,0.078559585],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082149025,0.0007500634,0.003102671,0.0015194877,0.000088802466,0.00064319157,0.00022355022,0.005677648,0.0013159611,0.0042460766,0.9200156,0.061595473],"study_design_scores_gemma":[0.0029440739,0.0016246189,0.09758309,0.004324946,0.00025236583,0.00459068,0.0015142182,0.10877244,0.0045352457,0.10529594,0.66812485,0.00043743057],"about_ca_topic_score_codex":0.014360769,"about_ca_topic_score_gemma":0.03496295,"teacher_disagreement_score":0.25461984,"about_ca_system_score_codex":0.00069779035,"about_ca_system_score_gemma":0.0016707869,"threshold_uncertainty_score":0.36318427},"labels":[],"label_agreement":null},{"id":"W4241449150","doi":"10.31219/osf.io/9ad4u","title":"The Ethics of Emotion in AI Systems","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Western University","funders":"","keywords":"Computer science; Ask price; Proxy (statistics); Cognitive science; Artificial intelligence; Data science; Psychology; Machine learning","score_opus":0.04994146105648705,"score_gpt":0.3323305545443182,"score_spread":0.28238909348783114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241449150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038870446,0.003840244,0.7181428,0.08013702,0.000777172,0.00012213514,0.00029580123,0.00034960397,0.15746474],"genre_scores_gemma":[0.9338954,0.0014252858,0.051446088,0.0042626904,0.0005870681,0.00026426255,0.00013144586,0.00017536322,0.0078125335],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98554856,0.009633753,0.0005432476,0.0012118538,0.002619691,0.00044282034],"domain_scores_gemma":[0.9818413,0.01075408,0.001201197,0.0039640204,0.0017148798,0.0005243944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010968632,0.00055251684,0.0006178142,0.000858881,0.0018325339,0.0069414745,0.001233432,0.003084346,0.002589416],"category_scores_gemma":[0.030503504,0.00044398595,0.0004912915,0.00071431114,0.018766405,0.007962236,0.0038830028,0.004594664,0.00082013884],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001738502,0.0000065319978,0.0004467166,0.000033787892,0.000015828622,0.000029438328,0.00049933954,0.003663733,0.00027774918,0.9880945,0.0012842858,0.00563072],"study_design_scores_gemma":[0.000008734512,0.000010957498,0.0002527407,0.000024832128,0.0000046376526,0.000038459537,0.00016641218,0.01060468,0.00024575813,0.97902054,0.009607993,0.000014195509],"about_ca_topic_score_codex":0.0014667265,"about_ca_topic_score_gemma":0.0005078292,"teacher_disagreement_score":0.010968632,"about_ca_system_score_codex":0.0022664734,"about_ca_system_score_gemma":0.0012721047,"threshold_uncertainty_score":0.058008313},"labels":[],"label_agreement":null},{"id":"W4241602076","doi":"10.22215/etd/2020-14257","title":"Evaluating Adversarial Learning on Different Types of Deep Learning-based Intrusion Detection Systems using min-max optimization","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Adversarial system; Robustness (evolution); Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Machine learning; Artificial neural network; Deep neural networks; Intrusion detection system; Intrusion; Benchmark (surveying)","score_opus":0.026232270733617103,"score_gpt":0.3097000790148169,"score_spread":0.2834678082811998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241602076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5918885,0.0030019484,0.38967237,0.0015942269,0.0004159906,0.00030748584,0.00041960026,0.0014844403,0.011215408],"genre_scores_gemma":[0.97050416,0.00037289748,0.026297107,0.00016687172,0.000051304345,0.00008393039,0.0003261843,0.00007327397,0.0021243047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980799,0.0008234927,0.00011326887,0.0003217045,0.00043870803,0.00022294701],"domain_scores_gemma":[0.9911851,0.006810288,0.0004729143,0.0005223728,0.00076342956,0.00024588886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054031317,0.00181131,0.0010699175,0.0008305783,0.00040054845,0.0010723746,0.0012092262,0.0013410584,0.0018182452],"category_scores_gemma":[0.012055517,0.0004033008,0.0009397276,0.0005019375,0.0012606262,0.0016599385,0.0015050691,0.0018674886,0.00029025823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030757044,0.00010609096,0.000798016,0.000062672516,0.00008944138,0.000017922699,0.000010207071,0.97931904,0.00085597107,0.0017780403,0.0005626746,0.01609219],"study_design_scores_gemma":[0.000005425412,0.00008437907,0.00015017211,0.0000035423486,0.000009008711,0.0000048017005,0.00000257106,0.99828076,0.0009025033,0.00051102455,0.00004289438,0.000002835325],"about_ca_topic_score_codex":0.003396117,"about_ca_topic_score_gemma":0.0024376437,"teacher_disagreement_score":0.0054031317,"about_ca_system_score_codex":0.0019594138,"about_ca_system_score_gemma":0.0009569862,"threshold_uncertainty_score":0.028574824},"labels":[],"label_agreement":null},{"id":"W4242053016","doi":"10.1109/iccad.2017.8203770","title":"Fault injection attack on deep neural network","year":2017,"lang":"en","type":"article","venue":"2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":181,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Guangdong Academy of Sciences; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Fault injection; Artificial neural network; Computer science; Fault (geology); Set (abstract data type); Gradient descent; Deep neural networks; Artificial intelligence; Descent (aeronautics); Class (philosophy); Control theory (sociology); Engineering","score_opus":0.16177474869558003,"score_gpt":0.3634640765171886,"score_spread":0.20168932782160856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242053016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30247676,0.00086176046,0.69076914,0.0005163073,0.00013011493,0.000055081102,0.00009396966,0.0019798772,0.0031170517],"genre_scores_gemma":[0.9849554,0.000092372706,0.014356676,0.00007754305,0.000009029509,0.000015110195,0.0000309739,0.000022454553,0.00044041855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989831,0.00026857955,0.00006754024,0.00017593428,0.00035271572,0.00015210896],"domain_scores_gemma":[0.9973182,0.0015643564,0.00038889662,0.00037209396,0.00028554694,0.00007083491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094805215,0.00081621093,0.0006297313,0.00058976474,0.0002613617,0.00039747672,0.00061498286,0.0007850859,0.00058953295],"category_scores_gemma":[0.0053124093,0.00022525285,0.00047608503,0.00029427122,0.0008823868,0.0010361272,0.00095484086,0.00093455554,0.00010628719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004953602,0.000085488144,0.0040855524,0.000108732995,0.00010555611,0.00054503395,0.00011584804,0.86109036,0.031600684,0.010862811,0.0012429884,0.08966164],"study_design_scores_gemma":[0.0000048747997,0.000056696976,0.00030889543,0.000006655148,0.0000094340985,0.00006853612,0.000005534639,0.9885123,0.008169606,0.0026864894,0.00016563384,0.000005339826],"about_ca_topic_score_codex":0.0012560891,"about_ca_topic_score_gemma":0.0008146829,"teacher_disagreement_score":0.0012560891,"about_ca_system_score_codex":0.0008087649,"about_ca_system_score_gemma":0.00033516958,"threshold_uncertainty_score":0.0058680177},"labels":[],"label_agreement":null},{"id":"W4280506080","doi":"10.1007/s10489-022-03495-3","title":"Advanced defensive distillation with ensemble voting and noisy logits","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Vector Institute","funders":"","keywords":"Computer science; Adversarial system; Robustness (evolution); Voting; Artificial intelligence; Artificial neural network; Vulnerability (computing); Machine learning; Logit; Distillation; Data mining; Computer security","score_opus":0.01004385080624384,"score_gpt":0.2261156491019867,"score_spread":0.21607179829574286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280506080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021110099,0.0003386324,0.9745874,0.00041563017,0.00012803671,0.000029855617,0.000073974734,0.0004957076,0.002820582],"genre_scores_gemma":[0.7792491,0.00021875263,0.20970483,0.0003076178,0.00017682111,0.00011288568,0.0002823646,0.00019289042,0.009754793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983261,0.0006953754,0.00007953475,0.00027997728,0.0003965819,0.0002224871],"domain_scores_gemma":[0.9977557,0.0012071944,0.0001503143,0.00043129417,0.00035196904,0.00010351751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029610728,0.0012370298,0.0017828065,0.0009606828,0.0009435788,0.0016948816,0.0021667979,0.0019081525,0.0046327296],"category_scores_gemma":[0.0077233817,0.00072067184,0.0011231679,0.0010068499,0.0016655655,0.0035858285,0.004437629,0.0033655935,0.00093640096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028956207,0.00012161725,0.00068063743,0.00008223277,0.0001235016,0.0000878517,0.000076785305,0.7914965,0.0031842745,0.095449835,0.0027832866,0.10562384],"study_design_scores_gemma":[0.0000055810033,0.000017727752,0.00004367813,0.0000055558485,0.000007209713,0.000013660198,0.0000037058808,0.98002714,0.0007224722,0.018884476,0.00026193986,0.0000068835798],"about_ca_topic_score_codex":0.0016448864,"about_ca_topic_score_gemma":0.002578935,"teacher_disagreement_score":0.0046327296,"about_ca_system_score_codex":0.00074025017,"about_ca_system_score_gemma":0.0011156024,"threshold_uncertainty_score":0.01565981},"labels":[],"label_agreement":null},{"id":"W4280530301","doi":"10.1126/science.adc8720","title":"The bias hunter","year":2022,"lang":"en","type":"article","venue":"Science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Outrage; Political science; Law","score_opus":0.02072917719532785,"score_gpt":0.2684817712262224,"score_spread":0.24775259403089456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280530301","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026383944,0.018970719,0.284423,0.2646375,0.015668185,0.00025404146,0.0008061822,0.0036720366,0.38518438],"genre_scores_gemma":[0.5232649,0.010713406,0.1498707,0.07147907,0.0079287,0.00031717538,0.00066934247,0.0022630005,0.23349379],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928209,0.0021980891,0.00019931555,0.0013074931,0.0028825658,0.0005915871],"domain_scores_gemma":[0.9802388,0.0077174827,0.0011048096,0.0055966554,0.0036795354,0.0016627171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011265909,0.0010957391,0.0009881628,0.0028575365,0.0047741868,0.0076608197,0.0018927122,0.0062222383,0.018412098],"category_scores_gemma":[0.037016205,0.0006355546,0.0007614539,0.000827249,0.011906705,0.011300246,0.008949801,0.00959383,0.01159139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022188746,0.00006165495,0.0024206701,0.00019337944,0.00008222146,0.00048598574,0.0013077098,0.0021780236,0.00409331,0.598243,0.19549617,0.19521591],"study_design_scores_gemma":[0.00004168778,0.0001183564,0.00075420225,0.00062600593,0.000031166837,0.0022688548,0.00069665414,0.00772298,0.0082004005,0.3959947,0.58341944,0.00012557913],"about_ca_topic_score_codex":0.0011254916,"about_ca_topic_score_gemma":0.0016833642,"teacher_disagreement_score":0.018412098,"about_ca_system_score_codex":0.0018881345,"about_ca_system_score_gemma":0.0024246743,"threshold_uncertainty_score":0.061594546},"labels":[],"label_agreement":null},{"id":"W4280562623","doi":"10.1145/3529318","title":"Testing Feedforward Neural Networks Training Programs","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Debugging; Hyperparameter; Artificial neural network; Machine learning; Artificial intelligence; Software; Deep neural networks; Training (meteorology); Deep learning; Test data; Software engineering; Programming language","score_opus":0.1199166601035608,"score_gpt":0.30557276794227944,"score_spread":0.18565610783871866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280562623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6682219,0.00036797847,0.3031165,0.0007074854,0.00018762308,0.00019782846,0.0011772593,0.020868171,0.00515529],"genre_scores_gemma":[0.90135956,0.00009857047,0.094984815,0.0002094924,0.000013796848,0.00016954212,0.0012246176,0.00050738786,0.0014322796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998387,0.00048323075,0.00013360898,0.00043455279,0.0003903679,0.00017123122],"domain_scores_gemma":[0.9854833,0.010276351,0.00074858614,0.0016238784,0.0016988401,0.0001690707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002495747,0.0011182879,0.0003660788,0.0005617309,0.00028893407,0.00055189076,0.0018660235,0.0009599824,0.0041976073],"category_scores_gemma":[0.02015126,0.00044473936,0.00054490566,0.00033346756,0.0010180789,0.0015373228,0.0007865357,0.001041575,0.0006368725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006553139,0.00041776817,0.018162066,0.0005268664,0.00010984268,0.0003860383,0.00025588242,0.7647144,0.020340586,0.0056269094,0.004996429,0.18380794],"study_design_scores_gemma":[0.000028993749,0.0001202529,0.0007253119,0.000031493102,0.0000118668495,0.0000315047,0.000024091418,0.977572,0.018792417,0.0020251751,0.00062881777,0.000008080959],"about_ca_topic_score_codex":0.0052579236,"about_ca_topic_score_gemma":0.0061716703,"teacher_disagreement_score":0.0052579236,"about_ca_system_score_codex":0.0013739655,"about_ca_system_score_gemma":0.0012741789,"threshold_uncertainty_score":0.0140423775},"labels":[],"label_agreement":null},{"id":"W4281390966","doi":"10.1145/3488932.3517402","title":"InfoCensor","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mutual information; Computer science; Artificial intelligence; Machine learning; Inference; Interaction information; Deep learning; Adversary; Adversarial system; Bounded function; Softmax function; Mathematics; Computer security","score_opus":0.02784295292011382,"score_gpt":0.2723720666151164,"score_spread":0.2445291136950026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281390966","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006071171,0.0047901813,0.33510637,0.013089297,0.0077934675,0.00096846255,0.065973334,0.22516184,0.34104583],"genre_scores_gemma":[0.17312329,0.008919221,0.1441618,0.010316235,0.004091891,0.002142585,0.16295129,0.04250858,0.4517851],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997393,0.00048144758,0.00016573169,0.0004653733,0.0011507779,0.00034356798],"domain_scores_gemma":[0.993298,0.0012369733,0.00034015242,0.0036902446,0.0009774567,0.0004571153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029127696,0.0016713254,0.0014633065,0.0023954576,0.0013463259,0.0049105403,0.004323754,0.002673254,0.28289676],"category_scores_gemma":[0.018001873,0.00090927124,0.0018152333,0.002209735,0.0013799814,0.0067352727,0.0059879627,0.004087173,0.17434023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005273387,0.00010521076,0.0007883128,0.00064042176,0.00011170645,0.00028236155,0.00010314933,0.0033258835,0.0020553085,0.048900627,0.6979102,0.2452495],"study_design_scores_gemma":[0.00014541886,0.00009381088,0.00043516973,0.00024207936,0.000050826424,0.00045812107,0.000048146878,0.030205242,0.010932387,0.06763923,0.88967246,0.0000771922],"about_ca_topic_score_codex":0.0021933143,"about_ca_topic_score_gemma":0.0031349459,"teacher_disagreement_score":0.28289676,"about_ca_system_score_codex":0.0013588573,"about_ca_system_score_gemma":0.0033039562,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4281400028","doi":"10.1145/3533028.3533305","title":"How I stopped worrying about training data bugs and started complaining","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Amazon Web Services; Google; National Science Foundation","keywords":"Debugging; Computer science; Downstream (manufacturing); Complaint; Inference; Training (meteorology); Training set; Set (abstract data type); Quality (philosophy); Data quality; Data integrity; Data set; Data science; Machine learning; Artificial intelligence; Software engineering; Computer security; Engineering; Programming language; Operations management","score_opus":0.09801001456079682,"score_gpt":0.283168824638234,"score_spread":0.18515881007743717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281400028","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033446755,0.006257821,0.37972367,0.50783145,0.018832467,0.0005467113,0.0016793185,0.014512086,0.03716972],"genre_scores_gemma":[0.35676518,0.00519811,0.30071706,0.23451202,0.0065819863,0.0008138162,0.0019980434,0.009876973,0.08353682],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.985615,0.006209565,0.000629183,0.0030182225,0.0034925556,0.0010354349],"domain_scores_gemma":[0.94015896,0.024326494,0.004912641,0.012236855,0.014111706,0.0042533753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02575825,0.0015660648,0.001040308,0.0013702961,0.0037811405,0.008187251,0.0038149795,0.007886472,0.017864494],"category_scores_gemma":[0.17438008,0.0013491471,0.0014813443,0.0010441417,0.006486671,0.0157392,0.0039802394,0.012982974,0.015915038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095376757,0.00047316024,0.017246569,0.0007064335,0.00034271443,0.0018116389,0.01184585,0.006949018,0.0070605837,0.04978564,0.5399017,0.3629229],"study_design_scores_gemma":[0.00025169313,0.0009101211,0.0067312717,0.0023691228,0.00030468582,0.00721374,0.009928205,0.032435235,0.01825448,0.20874605,0.71185935,0.0009959984],"about_ca_topic_score_codex":0.004025058,"about_ca_topic_score_gemma":0.0036148452,"teacher_disagreement_score":0.02575825,"about_ca_system_score_codex":0.001904565,"about_ca_system_score_gemma":0.0030170225,"threshold_uncertainty_score":0.13622427},"labels":[],"label_agreement":null},{"id":"W4282827306","doi":"10.1109/icse-companion55297.2022.9793750","title":"HUDD: A tool to debug DNNs for safety analysis","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg; European Commission","keywords":"Debugging; Computer science; Cluster analysis; Retraining; Root cause; Deep neural networks; Root (linguistics); Artificial neural network; Artificial intelligence; Image (mathematics); Domain (mathematical analysis); Pattern recognition (psychology); Machine learning; Reliability engineering; Mathematics","score_opus":0.02880629283998286,"score_gpt":0.27895533068758605,"score_spread":0.2501490378476032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282827306","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007838158,0.00046935774,0.6931722,0.00038323138,0.00024931086,0.00019440455,0.004222516,0.29038826,0.0030825944],"genre_scores_gemma":[0.19362181,0.00078264606,0.74847215,0.0010037267,0.000072332725,0.0010511966,0.011339064,0.034068216,0.009588868],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923503,0.00018307753,0.00009278519,0.00019272882,0.00023105831,0.00006532669],"domain_scores_gemma":[0.9964294,0.0025196944,0.00022095977,0.00045559744,0.00028283807,0.000091581474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022712152,0.0022883106,0.0006030736,0.0018661584,0.0003964871,0.0013651906,0.0035301703,0.0013895888,0.022797992],"category_scores_gemma":[0.010991399,0.0011655549,0.0010790203,0.0004890171,0.0008719927,0.002336514,0.002736424,0.002569835,0.004161365],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011530304,0.00031671434,0.011772726,0.0024527449,0.00055441464,0.0020149595,0.0010095896,0.23830897,0.021555979,0.023301717,0.18561721,0.51194197],"study_design_scores_gemma":[0.00024543726,0.00014676624,0.0012728922,0.00024740756,0.00005807671,0.00047440248,0.00012807126,0.8648391,0.035852082,0.03245644,0.06417672,0.00010260309],"about_ca_topic_score_codex":0.0032048968,"about_ca_topic_score_gemma":0.0062910523,"teacher_disagreement_score":0.022797992,"about_ca_system_score_codex":0.0009942558,"about_ca_system_score_gemma":0.0012798529,"threshold_uncertainty_score":0.076266885},"labels":[],"label_agreement":null},{"id":"W4283065923","doi":"","title":"A testing approach for dependable Machine Learning systems","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Computer science; System testing; Reliability engineering; Machine learning; Artificial intelligence; Software engineering; Engineering","score_opus":0.0259846505532992,"score_gpt":0.24425796822071638,"score_spread":0.21827331766741717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283065923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007829461,0.00016827766,0.98939323,0.00032980554,0.000038015107,0.000039068924,0.000063507294,0.00038370505,0.0017549766],"genre_scores_gemma":[0.74441534,0.00034606192,0.24480617,0.00038715833,0.00030905908,0.00028672718,0.00056705705,0.00052776217,0.008354605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99642557,0.0014858954,0.00019264992,0.0006423232,0.0009888017,0.00026484363],"domain_scores_gemma":[0.98037153,0.014956929,0.00056996365,0.0017690706,0.0019425725,0.00038981476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045101545,0.0016361682,0.0015677871,0.0016171625,0.00066037284,0.0016823715,0.0033368673,0.0020102502,0.0062122247],"category_scores_gemma":[0.021657566,0.00062349765,0.0015627653,0.0010013907,0.0025110107,0.0033298589,0.0034605858,0.0031652567,0.00070753274],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004375829,0.00013960777,0.0023576075,0.00038863314,0.00019317589,0.00062469183,0.00026616102,0.5736135,0.009804692,0.2335852,0.0042805555,0.1743086],"study_design_scores_gemma":[0.0000077919185,0.000047958652,0.000115752046,0.000013613539,0.000015561212,0.000042517833,0.000011953015,0.92463464,0.0011683131,0.07337458,0.00056005165,0.0000072640582],"about_ca_topic_score_codex":0.001819471,"about_ca_topic_score_gemma":0.0012794799,"teacher_disagreement_score":0.0062122247,"about_ca_system_score_codex":0.0012110209,"about_ca_system_score_gemma":0.00090220425,"threshold_uncertainty_score":0.02385223},"labels":[],"label_agreement":null},{"id":"W4283071535","doi":"10.36227/techrxiv.20085902.v1","title":"Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Robustness (evolution); Computer security; Cover (algebra); Field (mathematics); Artificial intelligence; Deep learning; Data science; Machine learning; Engineering","score_opus":0.024039486634767498,"score_gpt":0.3164262645323233,"score_spread":0.2923867778975558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283071535","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0126571115,0.104555994,0.8567319,0.0027333896,0.00070437853,0.00023111425,0.00012681517,0.0008761377,0.021383211],"genre_scores_gemma":[0.63790697,0.13691704,0.20273921,0.0028308486,0.0026851746,0.0005384734,0.00059981295,0.0005485982,0.015233798],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997538,0.00064566795,0.00020530705,0.00042550632,0.00097723,0.00020837496],"domain_scores_gemma":[0.9930391,0.0050648605,0.0004259912,0.00091988395,0.00040073204,0.00014947794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033110327,0.0019079489,0.002081741,0.0017993298,0.00070450664,0.0022780632,0.0021059588,0.003149259,0.002547051],"category_scores_gemma":[0.009736481,0.00088674563,0.0017595968,0.001434221,0.002864906,0.0043887557,0.003523745,0.004347271,0.0010941369],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025989738,0.00027110908,0.002811363,0.0022205357,0.00043633426,0.0003319763,0.0003646646,0.18965918,0.0065769902,0.17014492,0.017696371,0.6092267],"study_design_scores_gemma":[0.000060430404,0.0006519554,0.0024546299,0.0010797591,0.00022466498,0.0017415114,0.00021984479,0.7199239,0.008910545,0.19652106,0.06808212,0.00012958888],"about_ca_topic_score_codex":0.0007711811,"about_ca_topic_score_gemma":0.0005414098,"teacher_disagreement_score":0.0033110327,"about_ca_system_score_codex":0.0009901484,"about_ca_system_score_gemma":0.0007715689,"threshold_uncertainty_score":0.017510653},"labels":[],"label_agreement":null},{"id":"W4283215813","doi":"10.2514/6.2022-4027","title":"Attack and Defense on Aircraft Trajectory Prediction Algorithms","year":2022,"lang":"en","type":"article","venue":"AIAA AVIATION 2022 Forum","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Adversarial system; Robustness (evolution); Trajectory; Collision; Air traffic control; Computer science; Collision avoidance; Algorithm; Air traffic management; Artificial intelligence; Computer security; Engineering; Aerospace engineering","score_opus":0.013668187771175752,"score_gpt":0.24809928662091457,"score_spread":0.2344310988497388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283215813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17063668,0.0025312463,0.8102118,0.0017393053,0.00043772356,0.00034151576,0.0008430606,0.0054664575,0.007792135],"genre_scores_gemma":[0.787218,0.0007466159,0.20368554,0.00038396587,0.00015528174,0.00022083418,0.002341557,0.00020176907,0.005046434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987565,0.00032755017,0.000093723946,0.00034818423,0.00031202997,0.00016193413],"domain_scores_gemma":[0.99555606,0.0027079298,0.0003334181,0.0005722484,0.00068868493,0.00014172037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024130577,0.0012735473,0.0008541242,0.0010452693,0.0006698431,0.0011757525,0.0012869812,0.0015218947,0.0023084988],"category_scores_gemma":[0.0105851,0.00038182878,0.0007420958,0.000903007,0.0007886273,0.0016874117,0.0015343883,0.0026837,0.0009794463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032552666,0.000119269425,0.0038644993,0.000064387496,0.00005858373,0.00007911289,0.0000611487,0.80439883,0.001388292,0.0039446354,0.004518057,0.18117762],"study_design_scores_gemma":[0.000007091733,0.000031807685,0.00024825596,0.0000079567535,0.0000037492143,0.000017717155,0.0000099084045,0.9977176,0.0006000214,0.00092624326,0.00042679434,0.0000029124433],"about_ca_topic_score_codex":0.0116449455,"about_ca_topic_score_gemma":0.0062531116,"teacher_disagreement_score":0.0116449455,"about_ca_system_score_codex":0.0012736722,"about_ca_system_score_gemma":0.0017506994,"threshold_uncertainty_score":0.023154378},"labels":[],"label_agreement":null},{"id":"W4283697320","doi":"10.1109/isqed54688.2022.9806152","title":"Stealthy Attack on Algorithmic-Protected DNNs via Smart Bit Flipping","year":2022,"lang":"en","type":"article","venue":"2022 23rd International Symposium on Quality Electronic Design (ISQED)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; Government of Canada; CW+","keywords":"Computer science; Robustness (evolution); Deep neural networks; Vulnerability (computing); Adversarial system; Threat model; Computer security; Artificial intelligence; Artificial neural network","score_opus":0.04025858625416238,"score_gpt":0.32173591005360186,"score_spread":0.28147732379943946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283697320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1620298,0.00055205583,0.82850647,0.00059534976,0.00019516794,0.00009348992,0.000115156334,0.0014558721,0.0064566056],"genre_scores_gemma":[0.9504205,0.00015634256,0.04742732,0.00026171768,0.000023538354,0.000055854427,0.000056737088,0.000060549894,0.0015374221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990325,0.00023274851,0.000073029034,0.00020089258,0.0003188548,0.00014210492],"domain_scores_gemma":[0.99796677,0.0008926731,0.0002889114,0.00056687935,0.00022589303,0.000058868183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010955178,0.0009054651,0.0005349212,0.0005267117,0.00040335135,0.00058264507,0.0011356961,0.001068747,0.0015606381],"category_scores_gemma":[0.005206411,0.00030692763,0.0005370232,0.00025158608,0.0013567151,0.0014043475,0.0015906964,0.0014402337,0.0003399359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053075165,0.00007625808,0.0026022997,0.00020456787,0.00016871339,0.0005612116,0.00020516182,0.72030467,0.0843191,0.059335176,0.00244282,0.1292493],"study_design_scores_gemma":[0.000021528189,0.00013598854,0.00029326472,0.000028974126,0.00003158207,0.00016644305,0.000017617609,0.93895614,0.036264155,0.02232418,0.001742044,0.000018020957],"about_ca_topic_score_codex":0.00062597194,"about_ca_topic_score_gemma":0.0007424173,"teacher_disagreement_score":0.0015606381,"about_ca_system_score_codex":0.00069751905,"about_ca_system_score_gemma":0.0005034918,"threshold_uncertainty_score":0.0057937503},"labels":[],"label_agreement":null},{"id":"W4283710515","doi":"10.1002/int.22947","title":"A data variability index: Quantifying complexity of models and analyzing adversarial data","year":2022,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Data mining; Transformation (genetics); Algorithm; Lipschitz continuity; Piecewise; Mathematical optimization; Mathematics","score_opus":0.24637303769046864,"score_gpt":0.3822583595158197,"score_spread":0.13588532182535107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283710515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034282584,0.0004967412,0.9626166,0.00063071586,0.000055999983,0.00008565485,0.00020450508,0.00017406479,0.0014531768],"genre_scores_gemma":[0.7744575,0.0008867413,0.22181976,0.00033016596,0.000251137,0.00032136418,0.0007125184,0.0001870067,0.0010337242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99520147,0.0015444681,0.00044103645,0.0009396457,0.0016167298,0.00025646892],"domain_scores_gemma":[0.9379745,0.049551565,0.004290401,0.0058819796,0.0016412091,0.00066033984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0092747975,0.001562115,0.0014080052,0.002487949,0.0008001717,0.0029198397,0.0018860991,0.0025318915,0.001110241],"category_scores_gemma":[0.051085755,0.0006679237,0.0016277746,0.0013905235,0.0040260046,0.006010779,0.0040427404,0.0045360606,0.00017714687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020392048,0.0000978006,0.0085276775,0.00026772651,0.0002213348,0.00036553468,0.0003078106,0.8555317,0.0065815165,0.091249555,0.0007477716,0.035897642],"study_design_scores_gemma":[0.0000056853046,0.00016505939,0.0017288626,0.000062787585,0.000025827561,0.00021543761,0.00006791942,0.9208914,0.0028427786,0.073125206,0.0008168352,0.000052100764],"about_ca_topic_score_codex":0.0009651143,"about_ca_topic_score_gemma":0.0006105138,"teacher_disagreement_score":0.0092747975,"about_ca_system_score_codex":0.0017830839,"about_ca_system_score_gemma":0.0010110857,"threshold_uncertainty_score":0.04905039},"labels":[],"label_agreement":null},{"id":"W4283728566","doi":"10.1007/s11432-021-3457-7","title":"Certified defense against patch attacks via mask-guided randomized smoothing","year":2022,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Certification; Computer science; Certificate; Smoothing; Artificial intelligence; Gaussian; Computer security; Machine learning; Computer vision; Algorithm","score_opus":0.01983673073073933,"score_gpt":0.2840578002274118,"score_spread":0.26422106949667246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283728566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10495403,0.00038853125,0.88160866,0.0010611682,0.00024651038,0.00010523284,0.000191854,0.0028063068,0.008637589],"genre_scores_gemma":[0.94517535,0.00011938929,0.050719578,0.00028305853,0.00010388235,0.000066453445,0.00013613053,0.000118404416,0.0032777954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983138,0.0003549854,0.00005516977,0.00038628615,0.0005772038,0.00031260515],"domain_scores_gemma":[0.99518365,0.0021761414,0.00042678375,0.0015810438,0.00040811644,0.00022438657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001469783,0.00093762856,0.0012685879,0.00058410683,0.0005659218,0.001035915,0.001171197,0.002169512,0.0030919649],"category_scores_gemma":[0.007914417,0.00047564885,0.00068075804,0.00041031055,0.0015737714,0.0017034042,0.003240308,0.0022931,0.00095688447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016332269,0.0002327085,0.002802972,0.00027486315,0.00023545788,0.0005113217,0.00024020768,0.48331308,0.1169954,0.22727469,0.014363322,0.15212275],"study_design_scores_gemma":[0.00006223921,0.00013837992,0.0005208711,0.000015525902,0.000025433777,0.0001806094,0.000022903558,0.9362873,0.010917156,0.05020306,0.001599385,0.000027079823],"about_ca_topic_score_codex":0.0004828177,"about_ca_topic_score_gemma":0.000447179,"teacher_disagreement_score":0.0030919649,"about_ca_system_score_codex":0.000593917,"about_ca_system_score_gemma":0.0010707485,"threshold_uncertainty_score":0.010343611},"labels":[],"label_agreement":null},{"id":"W4283741227","doi":"10.1109/i2mtc48687.2022.9806449","title":"A Novel Method to Estimate Measurement Error in AI-Assisted Measurements","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Observational error; Artificial intelligence; Algorithm; Statistics; Mathematics","score_opus":0.13028622222322997,"score_gpt":0.3819306952879926,"score_spread":0.2516444730647627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283741227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004803067,0.00017322662,0.99302727,0.00015127825,0.000091128066,0.000047460522,0.00018989133,0.0009006794,0.00061596127],"genre_scores_gemma":[0.36827946,0.00042589198,0.62427485,0.00047116127,0.00041706025,0.000454581,0.0019308593,0.0004684995,0.0032775903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951367,0.0010591375,0.00032778384,0.0015932056,0.0016568124,0.00022647483],"domain_scores_gemma":[0.98977625,0.0041437475,0.0016027524,0.0023800137,0.0018971269,0.00020002075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034896785,0.0020998758,0.0015269694,0.0017960148,0.0007245996,0.0018782495,0.003250456,0.0019384706,0.0015937111],"category_scores_gemma":[0.022019748,0.0006244268,0.0011852082,0.0017628126,0.0013879641,0.002707336,0.0037080683,0.0041754814,0.0010917266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003646996,0.00031945403,0.010201313,0.0004243115,0.00036017204,0.0002563695,0.00034574108,0.5266031,0.023398146,0.030881925,0.009621447,0.3972233],"study_design_scores_gemma":[0.00001535352,0.00007742822,0.0017036137,0.00003315743,0.000024740846,0.00013228688,0.00002510825,0.9749976,0.00869302,0.0107801,0.0034802584,0.00003740144],"about_ca_topic_score_codex":0.0028651063,"about_ca_topic_score_gemma":0.0026210868,"teacher_disagreement_score":0.0034896785,"about_ca_system_score_codex":0.0010642983,"about_ca_system_score_gemma":0.001519912,"threshold_uncertainty_score":0.018455386},"labels":[],"label_agreement":null},{"id":"W4283794091","doi":"10.1609/aaai.v36i1.19938","title":"PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University; Université de Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Regularization (linguistics); Robustness (evolution); Pattern recognition (psychology); Feature vector; Generalization; Block (permutation group theory); Deep neural networks; Deep learning; Machine learning; Mathematics","score_opus":0.05613656257909424,"score_gpt":0.2852592275984625,"score_spread":0.22912266501936826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283794091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013005402,0.00022517194,0.98369795,0.00013913248,0.000030900155,0.00006710418,0.00009103445,0.0018543702,0.0008890732],"genre_scores_gemma":[0.3632815,0.00037770116,0.6278802,0.00039116514,0.0000955737,0.00042159122,0.0008223343,0.000787145,0.0059427307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995541,0.00013361574,0.000020846315,0.00008515965,0.0001519976,0.000054228003],"domain_scores_gemma":[0.999356,0.00019663297,0.00007793501,0.0002166588,0.000111567024,0.0000413346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001210649,0.00098548,0.0008202153,0.00055045576,0.00042470338,0.00040565748,0.0014223495,0.0010573772,0.0019128259],"category_scores_gemma":[0.0025147195,0.00042690808,0.0007514596,0.0005359713,0.0007742115,0.0010152531,0.001471205,0.0022180723,0.0006364389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035127538,0.00028470246,0.002205917,0.00021054495,0.0002741685,0.0001801334,0.00019342951,0.5061443,0.0636306,0.035482336,0.015333447,0.37570912],"study_design_scores_gemma":[0.000014929776,0.0000618517,0.0001988197,0.000007995526,0.000012108479,0.00004360913,0.0000057192856,0.98569447,0.007993415,0.0039206324,0.0020378844,0.000008548451],"about_ca_topic_score_codex":0.0030547788,"about_ca_topic_score_gemma":0.0052571706,"teacher_disagreement_score":0.0030547788,"about_ca_system_score_codex":0.00057159783,"about_ca_system_score_gemma":0.0009104711,"threshold_uncertainty_score":0.006402552},"labels":[],"label_agreement":null},{"id":"W4283810295","doi":"10.1609/aaai.v36i2.20010","title":"Adversarial Attack for Asynchronous Event-Based Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Adversarial system; Asynchronous communication; Computer science; Artificial intelligence; Event (particle physics); Robustness (evolution); Machine learning; Set (abstract data type); Deep learning; Deep neural networks; Data mining","score_opus":0.05597670562048369,"score_gpt":0.33060190637949893,"score_spread":0.27462520075901525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283810295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061643414,0.00034270957,0.9321719,0.0006875662,0.0001595151,0.00011256755,0.00031596102,0.0012420366,0.0033243902],"genre_scores_gemma":[0.94613844,0.00017700902,0.04987159,0.0003964377,0.0000518485,0.00012652254,0.0005161446,0.000111136906,0.0026109845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985654,0.0004017852,0.00008731705,0.00034473996,0.0004273752,0.00017342842],"domain_scores_gemma":[0.99607724,0.0024733979,0.00035886024,0.0006984161,0.00027379885,0.000118320466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020330641,0.0009889374,0.0007930827,0.0004359307,0.0005118352,0.00068279303,0.0013530859,0.0011508146,0.0020359561],"category_scores_gemma":[0.0075494805,0.00036489277,0.0008535791,0.00029950668,0.0013912023,0.001912917,0.0026371027,0.002826513,0.00040992428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003189929,0.000064107066,0.0016262936,0.000068500674,0.000063846666,0.00023987443,0.00007439798,0.93024915,0.0056044934,0.020248782,0.00347354,0.03796812],"study_design_scores_gemma":[0.000009173504,0.000031754662,0.00016126395,0.0000066316243,0.0000048593292,0.000044957716,0.00000800305,0.9878215,0.0024935172,0.008823626,0.00058805017,0.000006658329],"about_ca_topic_score_codex":0.0015001134,"about_ca_topic_score_gemma":0.0014060691,"teacher_disagreement_score":0.0020359561,"about_ca_system_score_codex":0.0009985437,"about_ca_system_score_gemma":0.0006659438,"threshold_uncertainty_score":0.010752022},"labels":[],"label_agreement":null},{"id":"W4283812303","doi":"10.1609/aaai.v36i11.21658","title":"Towards One Shot Search Space Poisoning in Neural Architecture Search (Student Abstract)","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Exploit; Robustness (evolution); Architecture; Computer science; Space (punctuation); Artificial intelligence; Machine learning; Shot (pellet); Computer security; Artificial neural network; Data mining; Geography; Operating system","score_opus":0.12110724663107772,"score_gpt":0.3499893014507844,"score_spread":0.22888205481970667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283812303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6687947,0.00085139263,0.319613,0.0008163086,0.00014552669,0.00011189696,0.00011488463,0.0030298813,0.006522465],"genre_scores_gemma":[0.9649697,0.000067544315,0.03364418,0.00014288361,0.000014559402,0.0000253782,0.00005696812,0.00006570349,0.0010129767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991918,0.00032188784,0.00004171587,0.00012917127,0.00021535908,0.00010005628],"domain_scores_gemma":[0.9967866,0.001798826,0.0003207592,0.000683487,0.00029205458,0.0001182666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001928005,0.0005301494,0.00049333007,0.00042138947,0.00030278284,0.0006191709,0.00087436184,0.0010295009,0.0012905655],"category_scores_gemma":[0.007864308,0.00023701778,0.00036167572,0.00028985395,0.0014547242,0.0015673386,0.0012342257,0.0014429752,0.00029350963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078627886,0.000288232,0.003798633,0.00014469076,0.00016313694,0.0002640318,0.00015026916,0.8634263,0.02925575,0.015914531,0.003294189,0.08251399],"study_design_scores_gemma":[0.00003252168,0.00026969943,0.00030213143,0.000008999271,0.000012391204,0.00007121858,0.0000244346,0.98067445,0.012109122,0.006121407,0.0003657437,0.000007841179],"about_ca_topic_score_codex":0.0011809265,"about_ca_topic_score_gemma":0.0013716638,"teacher_disagreement_score":0.001928005,"about_ca_system_score_codex":0.00056520675,"about_ca_system_score_gemma":0.00060181326,"threshold_uncertainty_score":0.010196388},"labels":[],"label_agreement":null},{"id":"W4283824745","doi":"10.1007/978-3-031-16980-9_15","title":"Backdoor Attack is a Devil in Federated GAN-Based Medical Image Synthesis","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Backdoor; Computer science; Discriminator; Image (mathematics); Adversarial system; Training set; Deep learning; Artificial intelligence; Regularization (linguistics); Machine learning; Computer security","score_opus":0.018382007862446165,"score_gpt":0.2748480418714027,"score_spread":0.25646603400895657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283824745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009769531,0.00029239102,0.9863002,0.00021821212,0.00008570365,0.000028890727,0.000042464555,0.0005020767,0.0027605428],"genre_scores_gemma":[0.6294205,0.00062139996,0.35656014,0.0005031062,0.0001317929,0.00007982072,0.00019408473,0.00023588807,0.0122533385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993844,0.00019650695,0.00002500408,0.00012051197,0.0002122774,0.00006123895],"domain_scores_gemma":[0.99938035,0.00031800484,0.000042533808,0.00016589508,0.000065376335,0.000027746199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010616897,0.00060673745,0.0007401229,0.00027960094,0.00023126863,0.000805462,0.00075927324,0.0012789335,0.0021389765],"category_scores_gemma":[0.0021596805,0.00036540136,0.0006689015,0.0002601118,0.0008650863,0.001103845,0.0019012025,0.0019671277,0.0006310624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008705367,0.00014041238,0.0010668765,0.00027133588,0.00022865372,0.00067015813,0.00021471661,0.3907964,0.091012634,0.1129508,0.0065198354,0.39525765],"study_design_scores_gemma":[0.000011557284,0.000090056776,0.00017449557,0.000024851284,0.000020229632,0.00040572265,0.000011973082,0.96168786,0.016422916,0.018696595,0.0024381126,0.000015628522],"about_ca_topic_score_codex":0.0003338044,"about_ca_topic_score_gemma":0.000446678,"teacher_disagreement_score":0.0021389765,"about_ca_system_score_codex":0.000303217,"about_ca_system_score_gemma":0.00040095148,"threshold_uncertainty_score":0.007155597},"labels":[],"label_agreement":null},{"id":"W4284884156","doi":"10.2196/38440","title":"Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Backdoor; Computer science; Machine learning; Artificial intelligence; Data mining; Computer security","score_opus":0.05934919064266191,"score_gpt":0.3395018828589073,"score_spread":0.2801526922162454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284884156","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7808177,0.00086904736,0.2147414,0.00069232006,0.000070751725,0.00029400893,0.00033252523,0.0010803656,0.0011018902],"genre_scores_gemma":[0.944867,0.00017249538,0.05377506,0.000058329366,0.000011513216,0.00007283797,0.0004986583,0.000032435688,0.00051154115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9944858,0.0032577326,0.0003480055,0.0005536492,0.0010561075,0.00029876918],"domain_scores_gemma":[0.9406551,0.044788748,0.0027560527,0.006221175,0.005070339,0.0005086862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016671073,0.0010734424,0.0007164266,0.00069113105,0.00035853303,0.00071536604,0.0015263044,0.0015830118,0.00087391626],"category_scores_gemma":[0.04564837,0.000536599,0.0011795256,0.00044956733,0.000909292,0.0017106305,0.0020103736,0.0024049927,0.00026072704],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045233124,0.00091950316,0.027456153,0.00023765139,0.00036369206,0.00016679041,0.00023490122,0.8838461,0.0031008814,0.0033688876,0.0011353343,0.07871787],"study_design_scores_gemma":[0.000012881994,0.00016628961,0.001146544,0.000011907853,0.000011654194,0.000019706113,0.00001543191,0.9965364,0.0015944624,0.00036107656,0.000117902826,0.0000058277947],"about_ca_topic_score_codex":0.006737075,"about_ca_topic_score_gemma":0.0043249037,"teacher_disagreement_score":0.016671073,"about_ca_system_score_codex":0.0011487155,"about_ca_system_score_gemma":0.0012970861,"threshold_uncertainty_score":0.08816612},"labels":[],"label_agreement":null},{"id":"W4285090712","doi":"10.1007/s11042-021-11473-z","title":"Mitigating adversarial evasion attacks by deep active learning for medical image classification","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Computer science; Artificial intelligence; Machine learning; Sample (material); Deep learning; Artificial neural network; Centroid; Software deployment; Cluster analysis; Adversarial system; Task (project management); Evasion (ethics); Data mining","score_opus":0.017668348404246168,"score_gpt":0.29365427283252254,"score_spread":0.27598592442827635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285090712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13410446,0.0007312054,0.8581639,0.001173421,0.00019094837,0.000099964855,0.0000739423,0.002113381,0.003348793],"genre_scores_gemma":[0.96643984,0.000099238394,0.031850293,0.00027233423,0.000036392215,0.00003701406,0.000052205563,0.00005427184,0.0011583106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863917,0.0005527232,0.000063088206,0.00021330439,0.00039241477,0.00013926867],"domain_scores_gemma":[0.9959603,0.002476883,0.0003711584,0.00062683574,0.00043259584,0.00013216946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002757123,0.00084177725,0.000702936,0.0005990692,0.00035702783,0.0008039936,0.0012804741,0.0012141082,0.0011428404],"category_scores_gemma":[0.0068911975,0.0002944118,0.00068808557,0.00027374778,0.001311849,0.0013954391,0.0017730928,0.00208186,0.0003190168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034934763,0.00016543597,0.0018452504,0.000057437373,0.000079407706,0.00017808625,0.000095560266,0.8868005,0.0094913095,0.0069129714,0.0019911237,0.09203356],"study_design_scores_gemma":[0.000003274314,0.00003349733,0.000078649435,0.00000412602,0.000003175802,0.000014673127,0.000003910627,0.99585634,0.00245806,0.0013653819,0.0001759096,0.0000030465233],"about_ca_topic_score_codex":0.0017712151,"about_ca_topic_score_gemma":0.0012083235,"teacher_disagreement_score":0.002757123,"about_ca_system_score_codex":0.00080702745,"about_ca_system_score_gemma":0.00056619506,"threshold_uncertainty_score":0.014581203},"labels":[],"label_agreement":null},{"id":"W4285230246","doi":"10.1109/jsait.2022.3182943","title":"Soft BIBD and Product Gradient Codes","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Information Theory","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Coding (social sciences); Code (set theory); Algorithm; Mathematics; Artificial intelligence; Statistics","score_opus":0.007631716982751596,"score_gpt":0.23078160945333973,"score_spread":0.22314989247058814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285230246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01927253,0.00041804157,0.9716789,0.00032781088,0.00008576474,0.000066439716,0.00015206562,0.00030597846,0.0076924777],"genre_scores_gemma":[0.73646736,0.0006563241,0.25070053,0.0006093001,0.00015277312,0.00035325284,0.00031513214,0.00019271579,0.01055254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815506,0.0005961833,0.00008709931,0.00027448672,0.0006975248,0.00018959],"domain_scores_gemma":[0.9958852,0.001907646,0.0004912012,0.00084658456,0.0006687506,0.00020061046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019171222,0.0008323062,0.00075680594,0.0009198026,0.0005280217,0.0014100322,0.0010898467,0.001142338,0.004020916],"category_scores_gemma":[0.009384258,0.0003850464,0.00036777864,0.00080008205,0.0020782612,0.0020586643,0.0022530893,0.001674605,0.0014828157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003517854,0.000053609492,0.00071611925,0.00018059551,0.000029377567,0.00014144034,0.00013160601,0.22153306,0.009955701,0.65786797,0.0035655557,0.10547314],"study_design_scores_gemma":[0.000040773677,0.00015865813,0.00020861822,0.00005994961,0.000010418553,0.00018839359,0.000025737963,0.75854856,0.0069883484,0.2288258,0.004900201,0.00004461126],"about_ca_topic_score_codex":0.00061330554,"about_ca_topic_score_gemma":0.00054930925,"teacher_disagreement_score":0.004020916,"about_ca_system_score_codex":0.0007599298,"about_ca_system_score_gemma":0.0009928505,"threshold_uncertainty_score":0.013451338},"labels":[],"label_agreement":null},{"id":"W4285267503","doi":"10.1109/tifs.2022.3186791","title":"Guided Erasable Adversarial Attack (GEAA) Toward Shared Data Protection","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Laboratory of Pattern Recognition; National Natural Science Foundation of China","keywords":"Computer science; Adversarial system; Robustness (evolution); Computer security; Watermark; Implementation; Noise reduction; Deep learning; Data mining; Artificial intelligence; Embedding","score_opus":0.053612762451441566,"score_gpt":0.2779557731531623,"score_spread":0.22434301070172072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285267503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021128,0.00035698054,0.97322696,0.00043237407,0.00008157277,0.00006802711,0.000075474854,0.0006964189,0.003934073],"genre_scores_gemma":[0.80853164,0.00043518428,0.18403254,0.00053932925,0.00008738017,0.00014672455,0.0001912472,0.00018346989,0.005852452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979317,0.0006134142,0.00009780233,0.0003646512,0.0007534812,0.000238929],"domain_scores_gemma":[0.9964694,0.0015231314,0.00033452202,0.0012094494,0.00032945463,0.00013419717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018062983,0.0010976132,0.00082373846,0.00058595696,0.00057302864,0.0011055729,0.0013712922,0.0018475226,0.0026396208],"category_scores_gemma":[0.0058908784,0.0003577858,0.00085515185,0.00039178398,0.002115183,0.0024711792,0.004038814,0.0027610732,0.00081175874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005711027,0.0001401686,0.001907434,0.00019963627,0.00021201474,0.0006694194,0.00031870033,0.5942028,0.06270987,0.1616158,0.006721849,0.17073125],"study_design_scores_gemma":[0.00002323478,0.00011165687,0.0002931869,0.000035056608,0.000024804393,0.0003691277,0.00003610467,0.9261998,0.023976652,0.044064492,0.004835296,0.00003064663],"about_ca_topic_score_codex":0.0005013728,"about_ca_topic_score_gemma":0.000534201,"teacher_disagreement_score":0.0026396208,"about_ca_system_score_codex":0.00060058705,"about_ca_system_score_gemma":0.0007773819,"threshold_uncertainty_score":0.009552777},"labels":[],"label_agreement":null},{"id":"W4285298979","doi":"10.1007/978-3-031-01233-4_6","title":"Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Adversarial system; Transferability; Deep neural networks; Artificial intelligence; Robustness (evolution); Perception; Machine learning; Segmentation; Feature (linguistics); Deep learning; Pattern recognition (psychology)","score_opus":0.023516948799216307,"score_gpt":0.253109046255464,"score_spread":0.22959209745624767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285298979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052281242,0.0006474905,0.94018114,0.00027350092,0.00011741284,0.000074162446,0.000117457195,0.0022824109,0.0040251273],"genre_scores_gemma":[0.8664524,0.0003553562,0.12705147,0.000291202,0.0000768472,0.0000902051,0.0004709182,0.00045636253,0.0047553363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991216,0.00028541472,0.000037171405,0.00020587434,0.0002592725,0.000090703434],"domain_scores_gemma":[0.99804795,0.0011582769,0.00017573287,0.0003374287,0.00019584259,0.00008475897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001598636,0.0013371852,0.00072501093,0.0005972207,0.0002773733,0.00075656624,0.0012478016,0.0010257253,0.0022607967],"category_scores_gemma":[0.005267804,0.00034184146,0.00064363005,0.0003752733,0.0012112724,0.0013060109,0.0019923684,0.0019067443,0.0006216893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018338836,0.0000450557,0.00049650425,0.00006778559,0.000053386313,0.000092440016,0.00004049659,0.89638513,0.014314864,0.007853126,0.0019435044,0.078524366],"study_design_scores_gemma":[0.0000018365038,0.000023788427,0.000075174714,0.0000046323535,0.000002995782,0.000018072651,0.0000026672976,0.9934777,0.003911455,0.0022444124,0.00023442895,0.0000026699422],"about_ca_topic_score_codex":0.0016338488,"about_ca_topic_score_gemma":0.001116035,"teacher_disagreement_score":0.0022607967,"about_ca_system_score_codex":0.0011710486,"about_ca_system_score_gemma":0.00045787255,"threshold_uncertainty_score":0.008496642},"labels":[],"label_agreement":null},{"id":"W4285407588","doi":"10.1109/cwit55308.2022.9817678","title":"Modeling and Energy Analysis of Adversarial Perturbations in Deep Image Classification Security","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Prime (order theory); Information retrieval; Algorithm; Mathematics; Combinatorics","score_opus":0.012823989598319006,"score_gpt":0.25229959010978054,"score_spread":0.23947560051146152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285407588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11904541,0.00087885547,0.8727197,0.0014854566,0.00008681804,0.00004609816,0.00019821062,0.0005107358,0.00502878],"genre_scores_gemma":[0.9629386,0.00048146787,0.030305443,0.00019893172,0.000058165653,0.00007089342,0.00023535689,0.0001326314,0.0055785347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945384,0.00017056137,0.000020104775,0.000100826794,0.00016722572,0.00008731817],"domain_scores_gemma":[0.99798656,0.001350423,0.00020004786,0.00019473695,0.000187565,0.00008055561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017545501,0.0009385063,0.0008276693,0.00066019606,0.00036614807,0.0008455749,0.001110333,0.0011255183,0.00198783],"category_scores_gemma":[0.005907741,0.000564211,0.00063674717,0.00043264445,0.0016099242,0.0018259804,0.001625223,0.0019705964,0.00031951413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008303785,0.00001853016,0.00054107344,0.00002479382,0.000020299854,0.000058919,0.000028065462,0.9654781,0.0017687369,0.022083431,0.00082768133,0.009067327],"study_design_scores_gemma":[0.0000011396585,0.000006076709,0.00007891692,0.0000028319484,0.000001436394,0.000007284162,0.000002578924,0.9933391,0.0002702259,0.006206794,0.00008120566,0.000002378734],"about_ca_topic_score_codex":0.0026878896,"about_ca_topic_score_gemma":0.0022109426,"teacher_disagreement_score":0.0026878896,"about_ca_system_score_codex":0.0017372825,"about_ca_system_score_gemma":0.0008122888,"threshold_uncertainty_score":0.012604892},"labels":[],"label_agreement":null},{"id":"W4285411630","doi":"10.5220/0011307200003283","title":"Resilience of GANs against Adversarial Attacks","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Adversarial system; Resilience (materials science); Computer science; Computer security; Artificial intelligence; Physics","score_opus":0.009104756857500465,"score_gpt":0.24655119248992668,"score_spread":0.23744643563242623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285411630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11098618,0.0012586104,0.86747,0.0015069292,0.00032635924,0.00008175654,0.00038676165,0.0023816233,0.015601745],"genre_scores_gemma":[0.9744951,0.00043373276,0.020231076,0.00025318522,0.00009620355,0.00005386509,0.00025650047,0.00022226991,0.0039581014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989599,0.00033861183,0.000040999566,0.00020034917,0.00026992089,0.00019020996],"domain_scores_gemma":[0.9942192,0.0036907338,0.000404569,0.0010130208,0.00044323094,0.00022916844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019449809,0.0011759351,0.00090931373,0.00068294635,0.0003656911,0.0010098751,0.0011047496,0.0012787709,0.0026742101],"category_scores_gemma":[0.013958921,0.00051072234,0.0005732753,0.00036813735,0.0012791253,0.0017511969,0.0025429514,0.0023503979,0.0006281455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016834203,0.00003630614,0.00087407103,0.000072956456,0.0000755135,0.00014604455,0.000062640276,0.9157759,0.006469687,0.0507599,0.0029074373,0.02265107],"study_design_scores_gemma":[0.0000049740506,0.000030358897,0.00022469122,0.000013581845,0.000007918035,0.00005997451,0.000010823486,0.9730909,0.0012026046,0.024792105,0.00055513263,0.0000069876023],"about_ca_topic_score_codex":0.0012898404,"about_ca_topic_score_gemma":0.00085434393,"teacher_disagreement_score":0.0026742101,"about_ca_system_score_codex":0.0007788909,"about_ca_system_score_gemma":0.00058693794,"threshold_uncertainty_score":0.010286152},"labels":[],"label_agreement":null},{"id":"W4285601015","doi":"10.24963/ijcai.2022/433","title":"Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Office of Experimental Program to Stimulate Competitive Research; Ministry of Science and ICT, South Korea; Institute for Information and Communications Technology Promotion; National Science Foundation","keywords":"Computer science; Generality; Hallucinating; Robustness (evolution); Vulnerability (computing); Adversarial system; Computer security; Machine learning; Artificial intelligence","score_opus":0.07167116162927387,"score_gpt":0.3026082033544833,"score_spread":0.23093704172520943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285601015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13166356,0.0006489601,0.8591498,0.0010584955,0.00013903943,0.00006560371,0.00007936732,0.0010036746,0.006191437],"genre_scores_gemma":[0.98026705,0.00016042871,0.017657943,0.00017783583,0.00004327729,0.000031007534,0.00004237035,0.00005753718,0.0015625759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985701,0.0005709435,0.00005017372,0.00020720328,0.0003735155,0.00022803603],"domain_scores_gemma":[0.99442506,0.003542777,0.00056833826,0.0010038911,0.00028055743,0.00017933546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022901031,0.0010831893,0.0011148375,0.00037004566,0.0005804739,0.0009020443,0.0012165803,0.0013650176,0.0013328639],"category_scores_gemma":[0.009665108,0.00035448922,0.0007499955,0.0002604761,0.001872323,0.0031451061,0.002845463,0.002336576,0.0002833996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029969827,0.0000791814,0.0013343998,0.00007299766,0.0000995473,0.0003174883,0.0002079744,0.8845735,0.00990525,0.055376157,0.002904472,0.044829328],"study_design_scores_gemma":[0.000005955785,0.00004586888,0.00007526151,0.000005333576,0.0000047820404,0.00005738265,0.000012878673,0.98620254,0.0013115712,0.011996721,0.0002733832,0.000008258261],"about_ca_topic_score_codex":0.0014961504,"about_ca_topic_score_gemma":0.0013697473,"teacher_disagreement_score":0.0022901031,"about_ca_system_score_codex":0.00068266294,"about_ca_system_score_gemma":0.0006878974,"threshold_uncertainty_score":0.012111366},"labels":[],"label_agreement":null},{"id":"W4285606915","doi":"10.24963/ijcai.2022/246","title":"A Solver + Gradient Descent Training Algorithm for Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Maxima and minima; MNIST database; Solver; Computer science; Artificial neural network; Gradient descent; Algorithm; Convergence (economics); Stochastic gradient descent; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.07515808732222891,"score_gpt":0.28625197004294567,"score_spread":0.21109388272071677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285606915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020439385,0.00012606037,0.9927146,0.00011008747,0.00004652191,0.000054443277,0.000096214884,0.0027974227,0.0020107424],"genre_scores_gemma":[0.044322666,0.00009912399,0.9502601,0.00022510538,0.00003152178,0.00028290146,0.00041577907,0.0006571373,0.0037056394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959797,0.00010346878,0.000026862634,0.00008695778,0.00013800738,0.00004683269],"domain_scores_gemma":[0.99954945,0.00021735829,0.000037036338,0.00006182901,0.00010703878,0.00002729308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008229034,0.001806368,0.0008471891,0.00064409524,0.00038985687,0.0007987634,0.0015794775,0.0012946473,0.006802053],"category_scores_gemma":[0.0024134286,0.000808666,0.00066999195,0.00068478874,0.0006318809,0.00090351,0.0013811832,0.002013635,0.0027966537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011061908,0.00011060177,0.00079533475,0.00018870113,0.000101956844,0.00012053917,0.00007034596,0.6449279,0.00525123,0.029178364,0.015814764,0.3033296],"study_design_scores_gemma":[0.000025233747,0.00001930282,0.000032219126,0.000010331533,0.000004580836,0.000022968336,0.0000047284116,0.98954153,0.0011267498,0.0056919986,0.0035158668,0.0000043808795],"about_ca_topic_score_codex":0.004630017,"about_ca_topic_score_gemma":0.008323879,"teacher_disagreement_score":0.006802053,"about_ca_system_score_codex":0.00080698205,"about_ca_system_score_gemma":0.0016802208,"threshold_uncertainty_score":0.022755146},"labels":[],"label_agreement":null},{"id":"W4285787112","doi":"10.48550/arxiv.1909.02562","title":"TFCheck : A TensorFlow Library for Detecting Training Issues in Neural\\n Network Programs","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Implementation; Machine learning; Code (set theory); Artificial intelligence; Process (computing); Training set; Artificial neural network; Training (meteorology); Focus (optics); Software engineering; Programming language; Set (abstract data type)","score_opus":0.09169102174530505,"score_gpt":0.21956929549007262,"score_spread":0.12787827374476757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285787112","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01301546,0.00019271528,0.85226655,0.00033047074,0.000098446704,0.00019932407,0.001183358,0.13087831,0.0018354594],"genre_scores_gemma":[0.2804747,0.00038927363,0.6928725,0.0005957525,0.00011000856,0.0008196387,0.0047883964,0.015397048,0.00455278],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99685884,0.0008823129,0.0003646799,0.00059470447,0.0010282606,0.00027116193],"domain_scores_gemma":[0.98566496,0.008672519,0.0018327576,0.0023435247,0.0012165353,0.00026974306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005794582,0.0020084633,0.0007572367,0.0022910708,0.0010524336,0.0019266679,0.0029618994,0.0014151738,0.010231614],"category_scores_gemma":[0.030091345,0.0012365562,0.0015863562,0.0010256701,0.0024401613,0.0046839914,0.0026684704,0.0025996345,0.0022751505],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016402465,0.00039595418,0.019944116,0.0018000709,0.00035571653,0.0011121589,0.0011689586,0.25611472,0.032592334,0.08640822,0.08386649,0.514601],"study_design_scores_gemma":[0.000068418456,0.00015058026,0.0009189152,0.0001248808,0.00003540091,0.0002160141,0.000050543666,0.9064303,0.04008969,0.03881771,0.013033017,0.00006452136],"about_ca_topic_score_codex":0.0051112217,"about_ca_topic_score_gemma":0.0057000206,"teacher_disagreement_score":0.010231614,"about_ca_system_score_codex":0.0021325345,"about_ca_system_score_gemma":0.0039905626,"threshold_uncertainty_score":0.034228146},"labels":[],"label_agreement":null},{"id":"W4286256645","doi":"10.56553/popets-2022-0065","title":"Adversarial Images Against Super-Resolution Convolutional Neural Networks for Free","year":2022,"lang":"en","type":"article","venue":"Proceedings on Privacy Enhancing Technologies","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Convolutional neural network; Computer science; Adversarial system; Artificial intelligence; Classifier (UML); Deep learning; Pattern recognition (psychology); Black box; Machine learning","score_opus":0.013756476773220973,"score_gpt":0.24349974407465533,"score_spread":0.22974326730143435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286256645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16202499,0.00060848676,0.8245143,0.0011932104,0.00012778305,0.0000979926,0.00022098016,0.0017117811,0.009500495],"genre_scores_gemma":[0.9356409,0.00018958951,0.06001779,0.0003263829,0.00002933183,0.00005160175,0.0001572652,0.00012867256,0.0034585462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991141,0.00029011987,0.000023984769,0.00014297076,0.0003320558,0.0000967431],"domain_scores_gemma":[0.99683386,0.001896594,0.0003055598,0.00072228094,0.00017119556,0.00007048158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016416234,0.0006797629,0.00042919067,0.00031926416,0.00031332983,0.0006102127,0.00073077006,0.0008367546,0.0024967229],"category_scores_gemma":[0.0076071764,0.00027054801,0.00045235895,0.00020704043,0.0013717297,0.0015736034,0.0017035567,0.0015085549,0.0004972675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040023017,0.00006256994,0.0015892729,0.000105143794,0.00008437703,0.00037394674,0.00012852949,0.8691842,0.025522064,0.040527213,0.0035677282,0.058454726],"study_design_scores_gemma":[0.000008149069,0.000049327362,0.00029337403,0.000015835467,0.000008159584,0.0001065149,0.000013147598,0.9742698,0.013854297,0.010137054,0.0012355997,0.000008764263],"about_ca_topic_score_codex":0.00094769755,"about_ca_topic_score_gemma":0.00092517637,"teacher_disagreement_score":0.0024967229,"about_ca_system_score_codex":0.0007745904,"about_ca_system_score_gemma":0.00040385942,"threshold_uncertainty_score":0.008681774},"labels":[],"label_agreement":null},{"id":"W4286377411","doi":"10.1109/tii.2022.3192901","title":"Adversarial ELF Malware Detection Method Using Model Interpretation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Malware; Computer science; Adversarial machine learning; Executable; Artificial intelligence; Byte; Machine learning; Key (lock); Interpretation (philosophy); Anomaly detection; Data mining; Computer security","score_opus":0.05143939852851373,"score_gpt":0.3004563836992305,"score_spread":0.24901698517071677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286377411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022275794,0.0001302121,0.97427976,0.00018315381,0.000032344826,0.00004697933,0.000037516787,0.0017844435,0.0012298318],"genre_scores_gemma":[0.7769796,0.00021229558,0.2184407,0.00031242683,0.00006823974,0.00010298597,0.000265775,0.00021719435,0.0034007877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992667,0.00015041679,0.000032123466,0.00017304778,0.000280263,0.00009743932],"domain_scores_gemma":[0.9988324,0.00050678314,0.00017008022,0.00021923706,0.00022861708,0.000042897704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008810318,0.0011560104,0.00089080207,0.0011806058,0.00034104576,0.0007107865,0.0011111536,0.00093400147,0.0015140481],"category_scores_gemma":[0.0026862768,0.0003450385,0.0010239396,0.0003472862,0.00088066165,0.0012749715,0.0012080756,0.001864847,0.00046888733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016996232,0.00012174667,0.0025323965,0.00008134857,0.00009228125,0.0004264151,0.0001483251,0.7219633,0.017412128,0.01664793,0.0031027626,0.23730133],"study_design_scores_gemma":[0.0000019368024,0.0000141288,0.00008348558,0.0000024436843,0.0000040942477,0.000051042625,0.0000039525094,0.9943457,0.002294488,0.0029431197,0.000250892,0.000004711184],"about_ca_topic_score_codex":0.0013694812,"about_ca_topic_score_gemma":0.0011562394,"teacher_disagreement_score":0.0015140481,"about_ca_system_score_codex":0.00073628925,"about_ca_system_score_gemma":0.00067864597,"threshold_uncertainty_score":0.005342245},"labels":[],"label_agreement":null},{"id":"W4286485305","doi":"10.1145/3550271","title":"Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg; Université du Luxembourg","keywords":"Computer science; Cluster analysis; Retraining; Black box; Deep neural networks; Artificial intelligence; Machine learning; Artificial neural network; Feature (linguistics); Root (linguistics); Root cause; Pattern recognition (psychology); Reliability engineering","score_opus":0.037010912796732284,"score_gpt":0.30333438913844046,"score_spread":0.26632347634170817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286485305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06660454,0.0002599859,0.92854273,0.00015187626,0.00005835583,0.00008912724,0.00007449005,0.0026999256,0.0015189748],"genre_scores_gemma":[0.7867711,0.00019328215,0.20929885,0.00019753595,0.000024126457,0.000113818736,0.0002897043,0.00029079936,0.0028208836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947673,0.000073646544,0.0000344773,0.00016927828,0.00017127891,0.0000745608],"domain_scores_gemma":[0.99864024,0.0005035862,0.00021137278,0.00022952243,0.00036978113,0.000045493773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013532748,0.001648578,0.00062156457,0.0009567377,0.00041615064,0.00054150017,0.0016225685,0.000904542,0.0015091979],"category_scores_gemma":[0.004245228,0.00050747156,0.00068347243,0.00032304114,0.00088859326,0.0013136688,0.0011411133,0.0015391213,0.00044732122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019650765,0.00009052072,0.0023794896,0.00009470807,0.00007516816,0.0002667471,0.00014254836,0.74816304,0.025645562,0.0037386322,0.0013672699,0.21783985],"study_design_scores_gemma":[0.0000026163805,0.000032974076,0.00034778827,0.000007517243,0.000008073981,0.000027462576,0.00000872413,0.9868024,0.010728138,0.0017321537,0.0002964691,0.0000058285746],"about_ca_topic_score_codex":0.0056365184,"about_ca_topic_score_gemma":0.005879547,"teacher_disagreement_score":0.0056365184,"about_ca_system_score_codex":0.0013885642,"about_ca_system_score_gemma":0.0010806065,"threshold_uncertainty_score":0.011207402},"labels":[],"label_agreement":null},{"id":"W4287080893","doi":"10.48550/arxiv.2107.04863","title":"HOMRS: High Order Metamorphic Relations Selector for Deep Neural\\n Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Generalization; MNIST database; Set (abstract data type); Artificial intelligence; Artificial neural network; Scheme (mathematics); Machine learning; Order (exchange); Code (set theory); Path (computing); Exploit; Deep learning; Theoretical computer science; Programming language; Mathematics","score_opus":0.04421858953249278,"score_gpt":0.19225159889933205,"score_spread":0.14803300936683927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287080893","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08141959,0.00049242505,0.89857787,0.0005001286,0.000065614324,0.00020446705,0.0004424243,0.011941329,0.0063561965],"genre_scores_gemma":[0.77711356,0.00017896165,0.21481834,0.00036786214,0.000056219407,0.0002619593,0.0012511224,0.0007959501,0.005156111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985197,0.00053632725,0.000081523685,0.0002618986,0.0004699469,0.00013058458],"domain_scores_gemma":[0.99791807,0.0012292059,0.00023004824,0.00039526916,0.00015274441,0.00007467946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022932235,0.0011454882,0.00071317493,0.0009613727,0.00035834202,0.00075797376,0.0015986275,0.0010688672,0.0052160146],"category_scores_gemma":[0.0060408814,0.00048988586,0.0008320355,0.00038824708,0.0014926055,0.0016232776,0.0019564051,0.0022130127,0.0009873662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049128296,0.00019883958,0.002391171,0.00017415508,0.00010335455,0.00033850392,0.00010266649,0.65378904,0.01666027,0.036425892,0.008012228,0.2813127],"study_design_scores_gemma":[0.00001856593,0.00009483196,0.00015037536,0.000011021771,0.0000064804826,0.000034840778,0.000007914871,0.98492175,0.0050902464,0.00884874,0.0008093214,0.000006000513],"about_ca_topic_score_codex":0.0018349288,"about_ca_topic_score_gemma":0.0034351156,"teacher_disagreement_score":0.0052160146,"about_ca_system_score_codex":0.001067528,"about_ca_system_score_gemma":0.00080818986,"threshold_uncertainty_score":0.01744926},"labels":[],"label_agreement":null},{"id":"W4287117507","doi":"10.1109/tr.2022.3208239","title":"CoCoFuzzing: Testing Neural <u>Co</u>de Models With <u>Co</u>verage-Guided <u>Fuzzing</u>","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; York University","funders":"","keywords":"Generalizability theory; Robustness (evolution); Fuzz testing; Computer science; Artificial intelligence; Source code; Automatic summarization; Artificial neural network; Code (set theory); Machine learning; Natural language processing; Programming language; Mathematics; Software; Statistics","score_opus":0.04105776073760289,"score_gpt":0.28605850007625566,"score_spread":0.24500073933865277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287117507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6077134,0.00039974754,0.37578902,0.0009149671,0.00013415935,0.00021565268,0.0010612564,0.008846575,0.004925246],"genre_scores_gemma":[0.93586415,0.000039573646,0.061432905,0.00030724626,0.000012374693,0.000088713714,0.0009828233,0.00017253734,0.0010996686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850523,0.00032655464,0.00011009312,0.00050944666,0.00036036782,0.0001882734],"domain_scores_gemma":[0.9892033,0.0070599555,0.00075452693,0.0016419734,0.0010926073,0.00024770448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025435209,0.0013080294,0.0005307209,0.0008831136,0.00037744502,0.00086700893,0.0023457266,0.0016466202,0.0022610559],"category_scores_gemma":[0.01612915,0.000392872,0.0008387743,0.0003654459,0.0011702274,0.0018821626,0.0011871357,0.0014246446,0.00034212883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008028809,0.0003263137,0.024248213,0.00015883624,0.00020521384,0.0002672295,0.00013367012,0.8186872,0.011016691,0.0070617045,0.0042571286,0.13283488],"study_design_scores_gemma":[0.000010167796,0.000053859963,0.00038203035,0.0000055483583,0.0000075962,0.000021528715,0.000010683934,0.9924966,0.0058193007,0.0010215184,0.00016642886,0.0000047751173],"about_ca_topic_score_codex":0.01467129,"about_ca_topic_score_gemma":0.017207365,"teacher_disagreement_score":0.01467129,"about_ca_system_score_codex":0.0017522323,"about_ca_system_score_gemma":0.0017636755,"threshold_uncertainty_score":0.029171824},"labels":[],"label_agreement":null},{"id":"W4287265169","doi":"","title":"White Paper Machine Learning in Certified Systems","year":2021,"lang":"en","type":"report","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"White (mutation); Certification; Computer science; Artificial intelligence; Chemistry; Management; Economics","score_opus":0.021001499191982582,"score_gpt":0.2475724866179397,"score_spread":0.22657098742595713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287265169","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02326523,0.0131161595,0.41696948,0.108195364,0.014638987,0.00045554974,0.0019330899,0.004342363,0.41708386],"genre_scores_gemma":[0.498076,0.011771324,0.102480896,0.012433316,0.0075906594,0.00049133535,0.004045534,0.0021243102,0.36098665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943469,0.0017828743,0.00019794928,0.0010896211,0.0020140111,0.0005685879],"domain_scores_gemma":[0.98575425,0.0059316233,0.00061675947,0.0040654913,0.0026769254,0.00095496007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076457905,0.00083027047,0.00074636674,0.0011905958,0.0014455011,0.0057472405,0.0020198566,0.0041249637,0.05172432],"category_scores_gemma":[0.020704681,0.0004953402,0.0007120907,0.00117298,0.002628032,0.0073047937,0.0036309734,0.004292115,0.01525665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000159068,0.00011208805,0.0010907294,0.00031908692,0.00005440395,0.00028380434,0.0002089781,0.022742614,0.0013716861,0.5193005,0.25224936,0.2021077],"study_design_scores_gemma":[0.000063127016,0.00010622195,0.0008550523,0.00039215025,0.00002376048,0.00039044858,0.00016415399,0.07326479,0.0057362537,0.26444483,0.65451324,0.000045925317],"about_ca_topic_score_codex":0.0023758463,"about_ca_topic_score_gemma":0.0015687138,"teacher_disagreement_score":0.05172432,"about_ca_system_score_codex":0.0023491113,"about_ca_system_score_gemma":0.0032728796,"threshold_uncertainty_score":0.17303509},"labels":[],"label_agreement":null},{"id":"W4288048294","doi":"10.1109/dsn-w54100.2022.00020","title":"Towards Building Resilient Ensembles against Training Data Faults","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Construct (python library); Training (meteorology); Training set; Fault injection; Machine learning; Data modeling; Artificial intelligence; Distributed computing; Data mining; Software engineering; Computer network; Software","score_opus":0.06867818540571483,"score_gpt":0.3158087203122173,"score_spread":0.24713053490650247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288048294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016563071,0.00008447016,0.9806574,0.0001850104,0.000048484886,0.00004177817,0.00005768484,0.0013859011,0.000976272],"genre_scores_gemma":[0.5896145,0.00022717584,0.4050449,0.00068546395,0.00016706591,0.00027587774,0.0005157898,0.0006447846,0.002824383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799335,0.00057280145,0.00009741887,0.00037158106,0.00072934706,0.00023538874],"domain_scores_gemma":[0.99488586,0.0018931999,0.0003628944,0.0017116102,0.0009374728,0.00020884039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032317778,0.0014519148,0.0013101153,0.0010825739,0.0008117298,0.0013970954,0.001803701,0.0017791239,0.002298194],"category_scores_gemma":[0.012831339,0.0011056372,0.0011876922,0.00050981354,0.0014733964,0.002672246,0.0040430324,0.004087041,0.0018190979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007826465,0.00007260766,0.0012059158,0.000049965896,0.00011333702,0.00011026985,0.00014571009,0.9160963,0.017259095,0.012003779,0.0016611558,0.051203582],"study_design_scores_gemma":[0.000004548493,0.00005335545,0.00013774777,0.000011881459,0.000015943668,0.00004379993,0.000017853235,0.97366256,0.0077646533,0.017180027,0.0010947787,0.000012873116],"about_ca_topic_score_codex":0.00068196876,"about_ca_topic_score_gemma":0.00081963564,"teacher_disagreement_score":0.0032317778,"about_ca_system_score_codex":0.00066478906,"about_ca_system_score_gemma":0.00082922843,"threshold_uncertainty_score":0.017091513},"labels":[],"label_agreement":null},{"id":"W4288057808","doi":"10.1109/sp46214.2022.9833693","title":"SoK: How Robust is Image Classification Deep Neural Network Watermarking?","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium on Security and Privacy (SP)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Digital watermarking; Robustness (evolution); Watermark; Computer science; Artificial intelligence; Artificial neural network; Data mining; Deep neural networks; Set (abstract data type); Pattern recognition (psychology); Image (mathematics); Machine learning","score_opus":0.0187109019954699,"score_gpt":0.24713939262178664,"score_spread":0.22842849062631673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288057808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28123298,0.008348229,0.66968507,0.0075007165,0.0011972418,0.00031522597,0.0010989045,0.010132077,0.020489575],"genre_scores_gemma":[0.90599906,0.0013849004,0.08654941,0.0007491611,0.00018273531,0.00010994961,0.00073395675,0.00049977686,0.003791061],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99673456,0.00077459397,0.000254517,0.00066745846,0.0012699821,0.00029893577],"domain_scores_gemma":[0.99292254,0.0025294626,0.00079090573,0.00296881,0.00063548813,0.00015278488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048203776,0.0012273326,0.00095875753,0.0011272959,0.00072055304,0.002100991,0.0017080479,0.0025549985,0.002383341],"category_scores_gemma":[0.028550321,0.00035869714,0.0008223315,0.0006892249,0.002464201,0.006445234,0.0022471598,0.002452887,0.0009976579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015091515,0.00024304762,0.0065158866,0.0007678871,0.00058398524,0.00034073342,0.00028246417,0.3288765,0.0526871,0.052069236,0.011189086,0.5449349],"study_design_scores_gemma":[0.000070153794,0.00036899364,0.0015945762,0.00017761585,0.000121371275,0.000362468,0.00013986624,0.88630134,0.053772934,0.05077907,0.006237216,0.000074387746],"about_ca_topic_score_codex":0.0012587077,"about_ca_topic_score_gemma":0.0014369232,"teacher_disagreement_score":0.0048203776,"about_ca_system_score_codex":0.0012931555,"about_ca_system_score_gemma":0.0009451323,"threshold_uncertainty_score":0.025492907},"labels":[],"label_agreement":null},{"id":"W4288075268","doi":"10.18280/ts.390314","title":"A Deep Learning Powered System to Lie Detection While Online Study","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cheating; Computer science; Usability; Predictability; Deep learning; Artificial intelligence; Lying; Coronavirus disease 2019 (COVID-19); Online learning; Machine learning; Human–computer interaction; Multimedia; Psychology; Mathematics","score_opus":0.014865327811280572,"score_gpt":0.24487454179167278,"score_spread":0.2300092139803922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288075268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11737925,0.00049232243,0.8344612,0.0008572689,0.00030101064,0.00044739022,0.0013893195,0.037362684,0.007309643],"genre_scores_gemma":[0.72786015,0.00013732603,0.25858122,0.00088298344,0.00009801389,0.0003582437,0.0017427325,0.00039955103,0.0099397525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960846,0.00007468413,0.000016992197,0.00013558689,0.00011469389,0.00004964543],"domain_scores_gemma":[0.99944335,0.00016475549,0.000062909414,0.00011902788,0.00014040261,0.00006956016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006684338,0.0006163662,0.00039820114,0.00068650435,0.00029120094,0.00040023317,0.001403842,0.0007474757,0.004114192],"category_scores_gemma":[0.0021148499,0.0002802949,0.00026520097,0.0002976629,0.00025326718,0.0010043933,0.001377257,0.0010466769,0.0019521067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009872164,0.0016929704,0.009904681,0.00020798753,0.00020604783,0.0004946652,0.00023848594,0.050386537,0.06467913,0.004243166,0.028867964,0.8380912],"study_design_scores_gemma":[0.000040099334,0.00022526264,0.0022964303,0.00001964829,0.000021193891,0.00017515104,0.000023754126,0.96738493,0.021026475,0.0033225513,0.005437208,0.000027305394],"about_ca_topic_score_codex":0.0018102833,"about_ca_topic_score_gemma":0.0031982134,"teacher_disagreement_score":0.004114192,"about_ca_system_score_codex":0.00050840457,"about_ca_system_score_gemma":0.00046840886,"threshold_uncertainty_score":0.0137633085},"labels":[],"label_agreement":null},{"id":"W4288087940","doi":"10.48550/arxiv.1910.14107","title":"Investigating Resistance of Deep Learning-based IDS against Adversaries\\n using min-max Optimization","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Intrusion detection system; Deep learning; Robustness (evolution); Machine learning; Artificial neural network; Deep neural networks","score_opus":0.04455363439003174,"score_gpt":0.20295650276232022,"score_spread":0.15840286837228848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288087940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32552785,0.0014925334,0.66071266,0.0020504484,0.00013245092,0.0001363912,0.00019196913,0.0011886295,0.008567111],"genre_scores_gemma":[0.97899836,0.00023156924,0.019183958,0.00016280862,0.000021234458,0.000045082103,0.00007148378,0.000044183835,0.0012414309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984061,0.0006999509,0.00008192059,0.00027892404,0.0003154571,0.00021772736],"domain_scores_gemma":[0.99093854,0.006861673,0.0008138385,0.0007167881,0.0004393146,0.00022989919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039529,0.0012885875,0.0009079748,0.0007264564,0.0004006802,0.0008513039,0.00094366336,0.0012463601,0.0012323668],"category_scores_gemma":[0.0135238,0.00044005536,0.0006048913,0.00035756652,0.001722869,0.001846576,0.00175407,0.001877832,0.00021774447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019673888,0.000046442576,0.0012426432,0.000060603852,0.000063397376,0.000031625088,0.000029880588,0.97817105,0.0019704401,0.0070218323,0.0005315041,0.010633956],"study_design_scores_gemma":[0.0000027257365,0.000045022312,0.000114164686,0.0000049526075,0.0000047620542,0.00000836833,0.0000058259893,0.99652326,0.001082365,0.0021357483,0.00006989125,0.0000029000682],"about_ca_topic_score_codex":0.0016236732,"about_ca_topic_score_gemma":0.0009756688,"teacher_disagreement_score":0.0039529,"about_ca_system_score_codex":0.0017273148,"about_ca_system_score_gemma":0.0007096866,"threshold_uncertainty_score":0.020905197},"labels":[],"label_agreement":null},{"id":"W4288287132","doi":"10.48550/arxiv.1907.03038","title":"Faking and Discriminating the Navigation Data of a Micro Aerial Vehicle\\n Using Quantum Generative Adversarial Networks","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Discriminator; Computer science; Covert; Adversary; Point (geometry); Generator (circuit theory); Artificial intelligence; Quantum; Software; Computer engineering; Human–computer interaction; Computer security; Mathematics","score_opus":0.13293050084697128,"score_gpt":0.24946397879277105,"score_spread":0.11653347794579977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288287132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23882623,0.00026658885,0.7503879,0.0013839498,0.0000901861,0.00008181716,0.00011007655,0.0004566194,0.008396575],"genre_scores_gemma":[0.9744751,0.000086540516,0.023271233,0.00014771931,0.000016568576,0.000031586973,0.00005094499,0.000020283582,0.0019000587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931705,0.00026549748,0.000020175408,0.00012037221,0.00016885591,0.00010811603],"domain_scores_gemma":[0.9973001,0.0019008406,0.00030065802,0.0003094809,0.00012565473,0.00006323911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001123016,0.0005554566,0.0003913575,0.00028472283,0.00038399437,0.0007761221,0.0007476438,0.00096013147,0.0013548835],"category_scores_gemma":[0.004217374,0.0002075472,0.00043499214,0.0002377724,0.0020734447,0.0010115559,0.0015657516,0.0010903489,0.00020155919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023064064,0.00004495844,0.0014842182,0.000060375714,0.000045184057,0.00015793151,0.00009474342,0.91079295,0.0070062513,0.056921285,0.00081352144,0.022347823],"study_design_scores_gemma":[0.0000065603936,0.000034221906,0.000114140385,0.0000049233677,0.0000059519707,0.000023333263,0.000007903744,0.9862705,0.0025208616,0.010713114,0.00029191002,0.0000066251464],"about_ca_topic_score_codex":0.0015385081,"about_ca_topic_score_gemma":0.0013969976,"teacher_disagreement_score":0.0015385081,"about_ca_system_score_codex":0.00090718135,"about_ca_system_score_gemma":0.00061662117,"threshold_uncertainty_score":0.0065820813},"labels":[],"label_agreement":null},{"id":"W4288322434","doi":"10.48550/arxiv.1906.07745","title":"On the Robustness of the Backdoor-based Watermarking in Deep Neural\\n Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backdoor; Digital watermarking; Watermark; Robustness (evolution); Computer science; Deep learning; Black box; Artificial neural network; Artificial intelligence; Deep neural networks; Set (abstract data type); White box; Computer security; Data mining; Machine learning; Embedding; Image (mathematics)","score_opus":0.04076673861838839,"score_gpt":0.18264888267642793,"score_spread":0.14188214405803953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288322434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28978577,0.0019540458,0.69780356,0.001352751,0.00017214906,0.00005730949,0.0001823673,0.0016006434,0.007091472],"genre_scores_gemma":[0.9667036,0.0006220373,0.029607711,0.00013406048,0.00006456051,0.000030040666,0.00012876435,0.00011506303,0.0025942319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844813,0.00042160085,0.00009740743,0.00030760813,0.0004979245,0.00022743767],"domain_scores_gemma":[0.99074847,0.0060501085,0.0009798727,0.0015157147,0.00052585406,0.00017994846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025959886,0.0012570089,0.000761324,0.0008319764,0.0005077762,0.001277809,0.0012367065,0.0017095658,0.0015830819],"category_scores_gemma":[0.019068249,0.0006029351,0.0009273651,0.000478541,0.0024985133,0.0036521952,0.0029979781,0.002569411,0.0003839863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080991496,0.00010441008,0.0018007965,0.00017201266,0.00015843964,0.00022483384,0.00013943859,0.85003734,0.02278772,0.050983887,0.0010588861,0.07172227],"study_design_scores_gemma":[0.000005620517,0.00005041186,0.000111638794,0.000012765068,0.00001111895,0.000025178988,0.000007084924,0.9862037,0.006006224,0.0073884856,0.00016930148,0.0000085688025],"about_ca_topic_score_codex":0.0017876116,"about_ca_topic_score_gemma":0.0011873088,"teacher_disagreement_score":0.0025959886,"about_ca_system_score_codex":0.0013679817,"about_ca_system_score_gemma":0.00067589566,"threshold_uncertainty_score":0.013729095},"labels":[],"label_agreement":null},{"id":"W4288391571","doi":"10.1109/tse.2022.3194640","title":"FalsifAI: Falsification of AI-Enabled Hybrid Control Systems Guided by Time-Aware Coverage Criteria","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Correctness; Robustness (evolution); Artificial neural network; Notation; Context (archaeology); Hybrid system; Semantics (computer science); Artificial intelligence; Model checking; Theoretical computer science; Programming language; Machine learning","score_opus":0.007528728385960206,"score_gpt":0.225826501456591,"score_spread":0.2182977730706308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288391571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051563036,0.00029336946,0.9412859,0.0004476713,0.00007161962,0.000116484276,0.00018334536,0.0014353058,0.004603367],"genre_scores_gemma":[0.90059584,0.00016731686,0.096763074,0.0002096693,0.000047060814,0.00018378143,0.00027669646,0.00023270534,0.0015238712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99705184,0.00075209816,0.00018931383,0.00055337616,0.0010151777,0.00043816253],"domain_scores_gemma":[0.9888131,0.008006147,0.0009461608,0.0008778189,0.0010519265,0.00030480538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031500298,0.0013547093,0.0009052843,0.0012095106,0.0006931254,0.0021001077,0.0015694025,0.0014079623,0.002868589],"category_scores_gemma":[0.01687434,0.0004162077,0.0015078865,0.00041263996,0.0032570176,0.0019553467,0.0026180795,0.00153952,0.00027263063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042096118,0.000086822794,0.0048733703,0.00033675547,0.0001483546,0.0009361655,0.0005429609,0.78415513,0.0155335525,0.15188627,0.0015379753,0.039541688],"study_design_scores_gemma":[0.000019978017,0.00007529673,0.00024370934,0.000042201886,0.000019559926,0.000092935166,0.000045165092,0.94757956,0.005262566,0.045688737,0.00091135164,0.000018856488],"about_ca_topic_score_codex":0.0036730082,"about_ca_topic_score_gemma":0.0022805203,"teacher_disagreement_score":0.0036730082,"about_ca_system_score_codex":0.0015598461,"about_ca_system_score_gemma":0.0015561514,"threshold_uncertainty_score":0.01665914},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W4289443952","doi":"10.1016/j.displa.2022.102277","title":"Improving adversarial robustness of traffic sign image recognition networks","year":2022,"lang":"en","type":"article","venue":"Displays","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Softmax function; Convolutional neural network; Computer science; Robustness (evolution); Adversarial system; Artificial intelligence; Traffic sign recognition; Deep neural networks; MNIST database; Classifier (UML); Pattern recognition (psychology); Deep learning; Machine learning; Sign (mathematics); Traffic sign; Mathematics","score_opus":0.010438175969457184,"score_gpt":0.22586773927189782,"score_spread":0.21542956330244065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289443952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049958877,0.00020436785,0.9451637,0.00039880798,0.00007850326,0.000027346277,0.00006884951,0.00093342055,0.0031660872],"genre_scores_gemma":[0.9303655,0.00021676288,0.06390994,0.0002008114,0.00009028752,0.00002872736,0.00016234821,0.00019255385,0.004833056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992138,0.00023480569,0.000026741145,0.0001603528,0.00025300353,0.00011138001],"domain_scores_gemma":[0.99804014,0.00097480323,0.00020661141,0.00038482106,0.00031267808,0.000080884645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013106014,0.00097227335,0.0008605435,0.0006575581,0.00033418555,0.00090162584,0.0012080052,0.0012832707,0.0026718802],"category_scores_gemma":[0.006652047,0.00042238066,0.00055477675,0.00042536703,0.0009969538,0.0017200427,0.0018965048,0.001802327,0.0007516748],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019732676,0.000051333205,0.00046711188,0.000036835147,0.000046775,0.000081482736,0.000033593013,0.89160085,0.021816436,0.01295923,0.001671953,0.07103707],"study_design_scores_gemma":[0.0000014030309,0.000014784622,0.00008925558,0.000002399487,0.0000037533234,0.000013251926,0.0000022987626,0.9947188,0.0030524302,0.0019246938,0.00017354076,0.0000033487845],"about_ca_topic_score_codex":0.001974458,"about_ca_topic_score_gemma":0.0013115016,"teacher_disagreement_score":0.0026718802,"about_ca_system_score_codex":0.00076202187,"about_ca_system_score_gemma":0.00040701305,"threshold_uncertainty_score":0.0089383125},"labels":[],"label_agreement":null},{"id":"W4289519099","doi":"10.2196/36427","title":"Uncertainty Estimation in Medical Image Classification: Systematic Review","year":2022,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Estimation; Artificial intelligence; Data mining; Data science; Engineering","score_opus":0.04360340881777357,"score_gpt":0.384361274943199,"score_spread":0.3407578661254254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289519099","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012451157,0.998662,0.0005237771,0.0003204134,0.000088287394,0.000044102224,0.00004903433,0.000007147348,0.00018081548],"genre_scores_gemma":[0.004015246,0.9940726,0.0011446838,0.00034957394,0.00016325976,0.00011563664,0.00006601973,0.0000064004844,0.00006650004],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9928886,0.0025948985,0.0019456384,0.00066705927,0.0017750837,0.00012871595],"domain_scores_gemma":[0.92407215,0.06439188,0.0056497892,0.0009992872,0.0046106605,0.0002762097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011690927,0.0014362439,0.004228924,0.009064588,0.000495492,0.002428154,0.0020193213,0.0019311474,0.003395155],"category_scores_gemma":[0.07236048,0.00075674395,0.004208384,0.0062331874,0.001270029,0.0025790194,0.0014704759,0.0014189597,0.00052665145],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015709437,0.000030149577,0.0006325031,0.57602465,0.0037271522,0.00007837565,0.00015085936,0.0007722235,0.00018991496,0.0014348029,0.0064862585,0.41031596],"study_design_scores_gemma":[0.00012526802,0.00037953054,0.0031658027,0.84700644,0.022032602,0.00083082577,0.0002588887,0.00096698274,0.00075248996,0.004356515,0.120023236,0.000101361584],"about_ca_topic_score_codex":0.003602225,"about_ca_topic_score_gemma":0.006324866,"teacher_disagreement_score":0.011690927,"about_ca_system_score_codex":0.0020337193,"about_ca_system_score_gemma":0.0073933,"threshold_uncertainty_score":0.061828256},"labels":[],"label_agreement":null},{"id":"W4289978463","doi":"10.1007/s10664-022-10172-z","title":"Correction to: Can Offline Testing of Deep Neural Networks Replace Their Online Testing?","year":2022,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial neural network; Deep neural networks; Artificial intelligence; Online and offline; Machine learning; Information retrieval; Data mining; Data science; Operating system","score_opus":0.024391967307452477,"score_gpt":0.2589459812873127,"score_spread":0.23455401397986025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289978463","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00052625174,0.0005233037,0.0035534343,0.10065914,0.88661945,0.000034348483,0.0032583307,0.0020851975,0.0027406556],"genre_scores_gemma":[0.089931294,0.004460049,0.023055026,0.18497351,0.48522928,0.0005118367,0.008337678,0.005008125,0.19849318],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941571,0.00091582356,0.0010672018,0.0011713612,0.0020232764,0.0006652762],"domain_scores_gemma":[0.8946709,0.038600516,0.004846582,0.009979479,0.047378436,0.0045241746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005334527,0.0019196512,0.0028126917,0.0028609354,0.0030230195,0.0041291867,0.004664567,0.011214642,0.12843302],"category_scores_gemma":[0.17192732,0.0013147853,0.0016478859,0.0027470042,0.00304559,0.0032965043,0.002784527,0.011861451,0.059607904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007078413,0.000012344352,0.0001966074,0.00009852449,0.000029576606,0.00032410506,0.00005038982,0.00015110655,0.0001181132,0.0022518148,0.98635733,0.01033933],"study_design_scores_gemma":[0.0002519807,0.00007787706,0.0029617096,0.00050375477,0.0000919624,0.0016264677,0.0002473451,0.0035100107,0.0019512244,0.019457111,0.9691386,0.00018192639],"about_ca_topic_score_codex":0.008099953,"about_ca_topic_score_gemma":0.01021409,"teacher_disagreement_score":0.12843302,"about_ca_system_score_codex":0.00348728,"about_ca_system_score_gemma":0.0053290147,"threshold_uncertainty_score":0.42965126},"labels":[],"label_agreement":null},{"id":"W4291414590","doi":"10.3390/a15080283","title":"Adversarial Training Methods for Deep Learning: A Systematic Review","year":2022,"lang":"en","type":"review","venue":"Algorithms","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Artificial intelligence; Overfitting; Machine learning; Deep learning; Artificial neural network","score_opus":0.10578144414399059,"score_gpt":0.43023792526573557,"score_spread":0.32445648112174497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291414590","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012891678,0.99828416,0.0007150015,0.00026368684,0.00008476302,0.000039721148,0.000048978447,0.0000098604905,0.0004248225],"genre_scores_gemma":[0.0017068088,0.9966897,0.0010438489,0.00024051906,0.000058445803,0.00008046129,0.000048049937,0.000005344781,0.00012688589],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9979353,0.00067587156,0.00056828937,0.0002145161,0.0005398167,0.000066236265],"domain_scores_gemma":[0.981699,0.015539361,0.001181563,0.00029857247,0.0011595153,0.00012205922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004137965,0.001312587,0.0023616944,0.0051571075,0.00046232142,0.0018631802,0.0014801268,0.0018303083,0.0050602984],"category_scores_gemma":[0.023081591,0.000692096,0.0029964934,0.004394101,0.000741584,0.0021663834,0.0012111339,0.0015167191,0.00085135136],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010411574,0.00006167007,0.00037547178,0.38676026,0.0012002442,0.00010466069,0.00014047821,0.0016922023,0.0003732808,0.0055098245,0.011897274,0.5917804],"study_design_scores_gemma":[0.00010884883,0.00039144105,0.0019304786,0.5244196,0.0069907135,0.0009807136,0.00024328618,0.001239374,0.0009225964,0.010191056,0.45248252,0.00009942437],"about_ca_topic_score_codex":0.0028396796,"about_ca_topic_score_gemma":0.0055795205,"teacher_disagreement_score":0.0051571075,"about_ca_system_score_codex":0.0014913268,"about_ca_system_score_gemma":0.006027637,"threshold_uncertainty_score":0.021883905},"labels":[],"label_agreement":null},{"id":"W4292259045","doi":"10.3390/a15080291","title":"Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Computer science; Artificial intelligence; Categorical variable; Naive Bayes classifier; Machine learning; Preprocessor; Artificial neural network; Random forest; Decision tree; Support vector machine","score_opus":0.05529092021416768,"score_gpt":0.3014220145942617,"score_spread":0.24613109438009403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292259045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6055413,0.0024190908,0.34235847,0.0022177286,0.0005459224,0.00085123983,0.015326658,0.008221039,0.022518441],"genre_scores_gemma":[0.89003366,0.0004951881,0.095153116,0.00022535668,0.0001354584,0.00024000122,0.009238636,0.000093699266,0.0043848692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989742,0.0003281031,0.00006830326,0.00021575636,0.00031235671,0.00010119437],"domain_scores_gemma":[0.99635905,0.0019726835,0.0005425736,0.0006067071,0.00042988863,0.0000891476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015988826,0.0009998627,0.0003619401,0.0017852343,0.00042339612,0.0011062298,0.00058300945,0.0009596559,0.0016644447],"category_scores_gemma":[0.007216368,0.00016364889,0.0006093665,0.00077225815,0.00046777862,0.0013725739,0.001293734,0.0014753221,0.00080015685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009610624,0.00071137346,0.09383761,0.0013675321,0.000499273,0.001960988,0.0021527866,0.061937567,0.030055158,0.02040493,0.04083213,0.7452796],"study_design_scores_gemma":[0.000042873897,0.00038178847,0.054245833,0.00019035095,0.00013545087,0.000999442,0.001085198,0.85554916,0.04225993,0.019954689,0.025032438,0.00012291376],"about_ca_topic_score_codex":0.0019226256,"about_ca_topic_score_gemma":0.0032762245,"teacher_disagreement_score":0.0019226256,"about_ca_system_score_codex":0.00060666376,"about_ca_system_score_gemma":0.0003962721,"threshold_uncertainty_score":0.008455813},"labels":[],"label_agreement":null},{"id":"W4292971553","doi":"10.1109/tdsc.2022.3200421","title":"Self-Checking Deep Neural Networks for Anomalies and Adversaries in Deployment","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Bộ Giáo dục và Ðào tạo; National Research Foundation; Ministry of Education - Singapore; National Research Foundation Singapore; National University of Singapore; Cisco Systems","keywords":"Computer science; Adversarial system; Software deployment; Artificial neural network; Artificial intelligence; Deep neural networks; False alarm; Constant false alarm rate; Machine learning; Domain (mathematical analysis); Margin (machine learning); Software; Data mining; Software engineering","score_opus":0.010293780803748166,"score_gpt":0.23527321206014246,"score_spread":0.2249794312563943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292971553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4342671,0.00095147244,0.5095533,0.0019992953,0.00050253846,0.00016211027,0.00052632747,0.047073215,0.0049645784],"genre_scores_gemma":[0.94454247,0.00013261495,0.05228514,0.00056399556,0.000024018262,0.000058431353,0.00033492694,0.0008403645,0.0012181264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958169,0.0012428539,0.00029390186,0.0011644759,0.0011172658,0.0003645604],"domain_scores_gemma":[0.9791607,0.009525004,0.0021871855,0.006749243,0.0019707107,0.0004071273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048722816,0.001501446,0.0006029796,0.00074401335,0.0005205341,0.0014769153,0.002954406,0.0011472938,0.0015347572],"category_scores_gemma":[0.030007914,0.00086334907,0.00079149386,0.00035278636,0.0020356665,0.004197213,0.0021106508,0.0029392443,0.00060549745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010438767,0.00026282732,0.05222048,0.0003927036,0.0003008437,0.0013228807,0.0006380698,0.695904,0.04195595,0.0142212305,0.014047042,0.17769004],"study_design_scores_gemma":[0.000025396526,0.00013455008,0.0015746851,0.000041725616,0.000035775312,0.00018438195,0.000055608616,0.9633063,0.025199624,0.0072103515,0.0022020803,0.00002961615],"about_ca_topic_score_codex":0.005301762,"about_ca_topic_score_gemma":0.007389988,"teacher_disagreement_score":0.005301762,"about_ca_system_score_codex":0.0017150929,"about_ca_system_score_gemma":0.002043956,"threshold_uncertainty_score":0.025767386},"labels":[],"label_agreement":null},{"id":"W4293093392","doi":"10.1109/pst55820.2022.9851981","title":"Careful What You Wish For: on the Extraction of Adversarially Trained Models","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Synopsys","keywords":"Computer science; Adversarial system; Robustness (evolution); Artificial intelligence; Machine learning; Leverage (statistics); Adversarial machine learning; Adversary; Attack model; Computer security","score_opus":0.05422428654648595,"score_gpt":0.30858862036181944,"score_spread":0.2543643338153335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293093392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12366954,0.0029361155,0.724766,0.08399844,0.0016729209,0.00031767646,0.0013552565,0.0071418574,0.05414215],"genre_scores_gemma":[0.78649163,0.0016856437,0.15118419,0.020676155,0.00062373705,0.00018719448,0.001524301,0.0022009758,0.0354261],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941789,0.0025060324,0.00022826619,0.000764452,0.0020043212,0.00031801278],"domain_scores_gemma":[0.980602,0.009112172,0.0011624685,0.0069989692,0.0016579186,0.00046642128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063185757,0.0009975794,0.00073180493,0.00048895675,0.0012027863,0.0028709974,0.0010734607,0.0024071855,0.0072983857],"category_scores_gemma":[0.04386682,0.00065989565,0.0008932179,0.00048927264,0.0025991313,0.0073928353,0.0032592071,0.005688857,0.005356219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020338434,0.00037689044,0.032131005,0.00061224384,0.000713284,0.0025834558,0.0031875966,0.10668061,0.036734283,0.15197425,0.1923635,0.4706091],"study_design_scores_gemma":[0.00011029943,0.00037822127,0.00645125,0.000681917,0.00017016486,0.0038467143,0.0015148025,0.49813196,0.05047902,0.27164218,0.16629007,0.0003033962],"about_ca_topic_score_codex":0.0017955936,"about_ca_topic_score_gemma":0.0024074342,"teacher_disagreement_score":0.0072983857,"about_ca_system_score_codex":0.00086210325,"about_ca_system_score_gemma":0.0009038597,"threshold_uncertainty_score":0.03341621},"labels":[],"label_agreement":null},{"id":"W4293243601","doi":"10.1109/tcad.2022.3197986","title":"Bits-Ensemble: Toward Light-Weight Robust Deep Ensemble by Bits-Sharing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Robustness (evolution); Decoding methods; Ensemble forecasting; Security token; Algorithm; Artificial intelligence","score_opus":0.0333859136588336,"score_gpt":0.23257715355050643,"score_spread":0.1991912398916728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293243601","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016185058,0.00024666163,0.9800694,0.00019759237,0.000067093555,0.00004834402,0.0001271056,0.0016753756,0.0013834325],"genre_scores_gemma":[0.53421503,0.00045226212,0.4562004,0.00071328174,0.00017484555,0.00029854837,0.0010305166,0.0006128348,0.006302261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984363,0.00038437144,0.00008935578,0.0003363259,0.00056954287,0.00018414206],"domain_scores_gemma":[0.9976629,0.000568908,0.00018495033,0.00095594756,0.0004904485,0.0001368607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00194563,0.0015166698,0.0016310588,0.0006953312,0.00088078243,0.001386977,0.002707917,0.0013304657,0.0035981575],"category_scores_gemma":[0.0061265454,0.0005346294,0.0007963599,0.0009474729,0.0012262873,0.0050451965,0.0048321127,0.0025733404,0.0013853519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052846374,0.0001424024,0.0011514806,0.000090782996,0.00015263625,0.0001300658,0.0002067585,0.5786473,0.01663985,0.03939481,0.009347879,0.35356754],"study_design_scores_gemma":[0.000012551528,0.00005160472,0.00007446821,0.000008230363,0.000013496989,0.000031132357,0.000016595597,0.97479635,0.0059021963,0.017563157,0.0015143463,0.000015831492],"about_ca_topic_score_codex":0.0024189649,"about_ca_topic_score_gemma":0.003172054,"teacher_disagreement_score":0.0035981575,"about_ca_system_score_codex":0.0009336549,"about_ca_system_score_gemma":0.0013899972,"threshold_uncertainty_score":0.012037039},"labels":[],"label_agreement":null},{"id":"W4294831628","doi":"10.1007/978-3-031-14862-0_22","title":"A Safety Assurable Human-Inspired Perception Architecture","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interpretability; Cognitive architecture; Architecture; Sketch; Computer science; Artificial intelligence; Perception; Process (computing); Task (project management); Cognitive science; Vulnerability (computing); Cognition; Engineering; Systems engineering; Psychology; Computer security","score_opus":0.015045063438191673,"score_gpt":0.2582018612503229,"score_spread":0.24315679781213123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294831628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015032124,0.0002113058,0.9705067,0.00030018893,0.0001483664,0.000038242346,0.000050614773,0.0017335264,0.011979008],"genre_scores_gemma":[0.67470956,0.0003015977,0.30152214,0.0004505522,0.000073372124,0.00008547675,0.00012386015,0.00021968319,0.022513779],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998485,0.000017941205,0.000004451954,0.000048510232,0.000056226934,0.000024363508],"domain_scores_gemma":[0.9998723,0.000025170544,0.000010495506,0.000035503268,0.000035878562,0.000020571204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002401629,0.00041384794,0.00028707404,0.0001651993,0.00030536146,0.00063238543,0.0013053791,0.00090501615,0.006621014],"category_scores_gemma":[0.00046944633,0.00022055565,0.00042400073,0.00012615936,0.0005719403,0.0007721389,0.001296954,0.00092709775,0.0013944475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034848272,0.00019102891,0.00046372705,0.0001722215,0.00009492751,0.00024245513,0.00018031831,0.2955206,0.21851464,0.14295483,0.010271561,0.3310452],"study_design_scores_gemma":[0.000016550104,0.00023931803,0.00036307555,0.00001971352,0.000027260912,0.0001504709,0.00002151651,0.90825975,0.02680699,0.05271765,0.011351784,0.00002581649],"about_ca_topic_score_codex":0.0010335594,"about_ca_topic_score_gemma":0.0011460765,"teacher_disagreement_score":0.006621014,"about_ca_system_score_codex":0.0004646161,"about_ca_system_score_gemma":0.00050310243,"threshold_uncertainty_score":0.022149444},"labels":[],"label_agreement":null},{"id":"W4294974580","doi":"10.1609/aaai.v35i18.17900","title":"Gradient-Based Localization and Spatial Attention for Confidence Measure in Fine-Grained Recognition using Deep Neural Networks","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Measure (data warehouse); Artificial intelligence; Machine learning; Deep learning; Deep neural networks; Sampling (signal processing); Artificial neural network; Identification (biology); Segmentation; Retraining; Data mining; Pattern recognition (psychology); Computer vision","score_opus":0.07838287262522826,"score_gpt":0.2953814345206495,"score_spread":0.21699856189542127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294974580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022126578,0.00036429972,0.9758093,0.00028849085,0.000027554266,0.000024196266,0.000052558382,0.00063079235,0.0006762782],"genre_scores_gemma":[0.8310186,0.00034113208,0.16643861,0.00023461545,0.00013609965,0.00012716939,0.00023305645,0.00026610907,0.0012045096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750346,0.000753002,0.00016580538,0.0005900066,0.00077459664,0.00021315391],"domain_scores_gemma":[0.98453414,0.010216622,0.0015496806,0.0017787808,0.0013927254,0.0005279801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057639894,0.0009832334,0.001286806,0.0026231133,0.0006159475,0.0019581143,0.0025828248,0.0020597617,0.002035307],"category_scores_gemma":[0.033417158,0.0005716801,0.0007932304,0.0013922421,0.0028866413,0.0047938814,0.0045207,0.003085418,0.00039786624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040199637,0.00013763859,0.0038596862,0.00017196444,0.00008931493,0.00009541569,0.00022246044,0.7049349,0.006069214,0.09441179,0.00229328,0.1873124],"study_design_scores_gemma":[0.0000047762996,0.000024463317,0.0002666384,0.00001117585,0.000004602324,0.000016364469,0.0000054254397,0.97283465,0.0015951807,0.025032423,0.00019285448,0.000011470298],"about_ca_topic_score_codex":0.0022999966,"about_ca_topic_score_gemma":0.0017895475,"teacher_disagreement_score":0.0057639894,"about_ca_system_score_codex":0.0019230401,"about_ca_system_score_gemma":0.0010098501,"threshold_uncertainty_score":0.030483246},"labels":[],"label_agreement":null},{"id":"W4295190147","doi":"10.1016/j.compeleceng.2022.108356","title":"Explainable Artificial Intelligence for Cybersecurity","year":2022,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer security; Computer science; Artificial intelligence; Engineering","score_opus":0.01497874834486855,"score_gpt":0.2381330839261718,"score_spread":0.22315433558130326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295190147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04088378,0.0066821184,0.89659446,0.02012464,0.0006917163,0.00005344336,0.0003923815,0.00051428116,0.034063112],"genre_scores_gemma":[0.92731977,0.0033476872,0.058953386,0.0009757412,0.0007310058,0.00008107586,0.00026775213,0.00011621579,0.008207358],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99954057,0.00020714315,0.000020976695,0.000080149744,0.00011903049,0.000032219537],"domain_scores_gemma":[0.9972838,0.0017087281,0.00020376428,0.000582607,0.00016410123,0.00005699903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011613301,0.0004806909,0.00053866603,0.0006150045,0.00042286227,0.0013784933,0.0007709813,0.001579218,0.004715495],"category_scores_gemma":[0.0047615273,0.00027932943,0.0006047967,0.00034357485,0.0028291498,0.0032788883,0.0015581597,0.0026116585,0.000387492],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011086124,0.000012427219,0.00033511617,0.00009166757,0.000035956746,0.000053254465,0.000075195145,0.049259264,0.00052705966,0.9382031,0.0019199145,0.009475975],"study_design_scores_gemma":[0.0000030819335,0.000006988938,0.00012351807,0.000016171047,0.000006084726,0.000022575246,0.000013242502,0.072761916,0.00020007305,0.9234436,0.0033977192,0.0000051407487],"about_ca_topic_score_codex":0.0008333474,"about_ca_topic_score_gemma":0.00068443356,"teacher_disagreement_score":0.004715495,"about_ca_system_score_codex":0.0009831486,"about_ca_system_score_gemma":0.0004929918,"threshold_uncertainty_score":0.015774906},"labels":[],"label_agreement":null},{"id":"W4295249789","doi":"10.1007/s00371-022-02660-6","title":"A survey on adversarial attacks and defenses for object detection and their applications in autonomous vehicles","year":2022,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Adversarial system; Robustness (evolution); Computer science; Object detection; Artificial intelligence; Data science; Deep learning; Computer security; Machine learning; Risk analysis (engineering); Pattern recognition (psychology)","score_opus":0.021812780316253163,"score_gpt":0.28810720711830695,"score_spread":0.2662944268020538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295249789","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005616421,0.18993644,0.7916207,0.0014803226,0.00058108475,0.0001130159,0.00013521846,0.0003388195,0.010177986],"genre_scores_gemma":[0.36704782,0.3666434,0.2444501,0.0022435393,0.0045413394,0.00040171255,0.00075127295,0.00027119814,0.013649554],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997375,0.0007049252,0.00020059104,0.0004648506,0.0010951654,0.00015947141],"domain_scores_gemma":[0.9954145,0.0033883145,0.0003115448,0.00045657458,0.00035622687,0.0000728877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025812935,0.001472937,0.00215046,0.0021909613,0.00064954435,0.0025942791,0.0020120027,0.0027982262,0.002127857],"category_scores_gemma":[0.007201775,0.0007954028,0.0015556144,0.0029500872,0.0017612124,0.0035386712,0.0024650523,0.0028342325,0.000891156],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014749379,0.00017457156,0.0015353889,0.0024040875,0.0003059846,0.00015210045,0.00014172385,0.1850656,0.0046486864,0.16367766,0.011500415,0.6302463],"study_design_scores_gemma":[0.000028288576,0.00046742527,0.0019672085,0.00077231706,0.00018051077,0.0010520198,0.00015001622,0.7021551,0.006368026,0.20864415,0.07810665,0.0001082709],"about_ca_topic_score_codex":0.0009356519,"about_ca_topic_score_gemma":0.0005883418,"teacher_disagreement_score":0.0027982262,"about_ca_system_score_codex":0.0010131472,"about_ca_system_score_gemma":0.00086791354,"threshold_uncertainty_score":0.013651371},"labels":[],"label_agreement":null},{"id":"W4296592009","doi":"10.1145/3563210","title":"Arachne: Search-Based Repair of Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Debiasing; Deep neural networks; Convolutional neural network; Retraining; Artificial neural network; Artificial intelligence; Machine learning","score_opus":0.04776573394678502,"score_gpt":0.2979497318344487,"score_spread":0.25018399788766366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296592009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099513814,0.0007705787,0.88189,0.00051065546,0.00020281946,0.00018135803,0.0002532061,0.013163443,0.0035141397],"genre_scores_gemma":[0.7189787,0.0002866495,0.27126163,0.00053146906,0.000054493838,0.00026312814,0.000677752,0.0014523802,0.0064937514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990558,0.00021024059,0.00008193794,0.00023779547,0.00029494017,0.00011933479],"domain_scores_gemma":[0.9963242,0.0017780953,0.00044440513,0.0009415817,0.00041843642,0.00009326523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017833071,0.001133157,0.00078043033,0.00073734706,0.00043021532,0.0006413773,0.0025205344,0.0012888697,0.003468681],"category_scores_gemma":[0.0102388365,0.00057328015,0.00083705847,0.00031619053,0.0014484215,0.0018427147,0.0021085166,0.0018094874,0.00076011405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047269557,0.00017678374,0.0041367644,0.00037363247,0.00015605755,0.0005285449,0.00029944352,0.6809931,0.023708126,0.020468285,0.0072728945,0.26141366],"study_design_scores_gemma":[0.000024085153,0.00011462354,0.00019332903,0.000023704557,0.000019524143,0.00009104164,0.000032091662,0.977948,0.009424293,0.010153447,0.0019654902,0.000010357107],"about_ca_topic_score_codex":0.0029697304,"about_ca_topic_score_gemma":0.004262932,"teacher_disagreement_score":0.003468681,"about_ca_system_score_codex":0.000909056,"about_ca_system_score_gemma":0.0010114902,"threshold_uncertainty_score":0.011603951},"labels":[],"label_agreement":null},{"id":"W4297233070","doi":"10.48550/arxiv.1805.12302","title":"Adversarial Attacks on Face Detectors using Neural Net based Constrained\\n Optimization","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Adversarial system; Generator (circuit theory); Robustness (evolution); Artificial intelligence; Detector; Face (sociological concept); Scalability; Artificial neural network; Machine learning; JPEG; Pattern recognition (psychology); Data compression","score_opus":0.08441488832756999,"score_gpt":0.22491477608862065,"score_spread":0.14049988776105066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297233070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06687428,0.00029823036,0.92317724,0.00078974594,0.00008217393,0.00008374625,0.000103776576,0.0017489668,0.006841878],"genre_scores_gemma":[0.85375994,0.00019451941,0.13738273,0.00063712255,0.000056029498,0.000115442555,0.00021995029,0.00021075731,0.007423414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910885,0.00026439846,0.000029293984,0.00021134375,0.0002568096,0.00012934051],"domain_scores_gemma":[0.9986681,0.0007893794,0.00013699834,0.0002378676,0.00011162003,0.000055978107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014472234,0.0012483157,0.00082073297,0.00053073437,0.000422153,0.00072956935,0.0011695682,0.0013516163,0.0024732384],"category_scores_gemma":[0.0043411413,0.00045775512,0.00070755504,0.00026874276,0.0019064967,0.0015312348,0.00199734,0.0022236006,0.00057136494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015301273,0.00006997515,0.00061830075,0.00003744992,0.00005728366,0.00010888622,0.000036646474,0.922848,0.009095292,0.02124287,0.0025339392,0.043198265],"study_design_scores_gemma":[0.000004280566,0.000017244529,0.000056326007,0.0000027592798,0.0000021530689,0.000014976378,0.0000022790139,0.9941797,0.0018639774,0.0036417304,0.00021115498,0.0000034530326],"about_ca_topic_score_codex":0.003090391,"about_ca_topic_score_gemma":0.003396648,"teacher_disagreement_score":0.003090391,"about_ca_system_score_codex":0.0014875719,"about_ca_system_score_gemma":0.00085098116,"threshold_uncertainty_score":0.01079309},"labels":[],"label_agreement":null},{"id":"W4297691576","doi":"10.1007/s10994-022-06212-w","title":"Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio","year":2022,"lang":"en","type":"article","venue":"Machine Learning","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Robustness (evolution); Artificial neural network; Solver; Algorithm; Linear programming; Integer programming; Scalability; Machine learning; Artificial intelligence; Programming language; Database","score_opus":0.01773640680379212,"score_gpt":0.26762500283110385,"score_spread":0.24988859602731173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297691576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06955975,0.00022971242,0.91342634,0.0006140986,0.00008252763,0.00022025446,0.00015994263,0.007352462,0.0083548995],"genre_scores_gemma":[0.6006495,0.00008608156,0.39516008,0.00028962013,0.000032083302,0.00026359048,0.00032555786,0.00077121926,0.002422312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671197,0.0014660337,0.00015745872,0.00050279626,0.00081304956,0.0003487371],"domain_scores_gemma":[0.99092567,0.0065111048,0.0007037709,0.0010540071,0.00064290187,0.00016249773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033316824,0.0016621066,0.0009375687,0.0008711362,0.00050217874,0.0016834881,0.0019541355,0.0016750236,0.007662088],"category_scores_gemma":[0.017631227,0.00093417766,0.0011786828,0.00049951696,0.0016500085,0.0021987758,0.0024599524,0.0027291286,0.0012265384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024037443,0.00011576919,0.001050822,0.00010080788,0.000043567325,0.00013669659,0.000043414482,0.9251359,0.004716179,0.013273802,0.0019303663,0.053212203],"study_design_scores_gemma":[0.00001844592,0.000025258649,0.00003667612,0.000008109679,0.0000050600183,0.000013018416,0.000005765659,0.99365354,0.0023257444,0.0036339639,0.00027076245,0.0000036022159],"about_ca_topic_score_codex":0.0024995082,"about_ca_topic_score_gemma":0.003125545,"teacher_disagreement_score":0.007662088,"about_ca_system_score_codex":0.0013823168,"about_ca_system_score_gemma":0.002354878,"threshold_uncertainty_score":0.025632262},"labels":[],"label_agreement":null},{"id":"W4297825308","doi":"10.3390/jsan11030045","title":"Adversarial Attacks on Heterogeneous Multi-Agent Deep Reinforcement Learning System with Time-Delayed Data Transmission","year":2022,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Reinforcement learning; Robustness (evolution); Transmission (telecommunications); Adversarial system; Cluster (spacecraft); Data transmission; Artificial intelligence; Distributed computing; Computer network; Telecommunications","score_opus":0.016452183972789487,"score_gpt":0.245728690659178,"score_spread":0.2292765066863885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297825308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24363324,0.00030564828,0.7513167,0.00054650404,0.00008829318,0.000057629666,0.000051408457,0.0002595791,0.0037410168],"genre_scores_gemma":[0.9961117,0.000035020534,0.0031853262,0.000031882333,0.000006823051,0.000013035608,0.000007055123,0.0000045234847,0.00060478726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991411,0.0002425435,0.00003843723,0.00017978602,0.00019614348,0.0002019329],"domain_scores_gemma":[0.9981608,0.0009644055,0.0003916004,0.00014020773,0.00021423784,0.00012888487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013335636,0.0007147602,0.0007456168,0.00023451564,0.00044058886,0.00058994995,0.0008422915,0.0007438928,0.0006995528],"category_scores_gemma":[0.0032465777,0.00024418088,0.00041940494,0.00020971587,0.0011510152,0.00080410123,0.001274634,0.0010725816,0.00007256057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008805063,0.000018711551,0.00062157534,0.00001740483,0.000028117802,0.00012264602,0.0000404828,0.9859136,0.0015813974,0.007277661,0.00018236876,0.00410819],"study_design_scores_gemma":[0.0000037408565,0.00001986846,0.00007746879,9.0537276e-7,0.0000031388774,0.000008580497,0.000003915403,0.9985447,0.00019751505,0.0010972541,0.00004063061,0.000002313898],"about_ca_topic_score_codex":0.0046822936,"about_ca_topic_score_gemma":0.0023165573,"teacher_disagreement_score":0.0046822936,"about_ca_system_score_codex":0.0009911172,"about_ca_system_score_gemma":0.00067371194,"threshold_uncertainty_score":0.009310126},"labels":[],"label_agreement":null},{"id":"W4298179218","doi":"10.48550/arxiv.2205.15419","title":"Fool SHAP with Stealthily Biased Sampling","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Context (archaeology); Feature (linguistics); Audit; Computer science; Focus (optics); Sampling (signal processing); Econometrics; Mathematics; Economics; Physics; Accounting","score_opus":0.12278263874737137,"score_gpt":0.2208810434139566,"score_spread":0.09809840466658522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298179218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15478636,0.00038240547,0.83406085,0.003173397,0.00012239699,0.00022386345,0.00029935976,0.0013191768,0.005632139],"genre_scores_gemma":[0.95221853,0.00008906311,0.04535807,0.0006063954,0.000068023684,0.00010998823,0.00010127132,0.00009041218,0.0013583411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9922673,0.0045095463,0.0002488989,0.00090859726,0.0015413146,0.00052442023],"domain_scores_gemma":[0.9586375,0.027640196,0.003116918,0.009133099,0.00090388325,0.000568461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009919962,0.0010735983,0.0011859919,0.0010202349,0.00092862593,0.0017784348,0.0018290691,0.002496887,0.0024028658],"category_scores_gemma":[0.056946725,0.0005555223,0.0010243971,0.0006826714,0.0031405464,0.0030276622,0.0038748167,0.0039900225,0.00040144505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017536734,0.0002775344,0.016622327,0.00032493848,0.00046448174,0.0007457629,0.0010809272,0.4349653,0.013239359,0.38898078,0.007495208,0.13404968],"study_design_scores_gemma":[0.0000855138,0.00010771191,0.00076626585,0.00004087511,0.000034295455,0.00014480627,0.0000643257,0.71152246,0.0047212164,0.28103867,0.0014440918,0.0000297495],"about_ca_topic_score_codex":0.0008189416,"about_ca_topic_score_gemma":0.0008615034,"teacher_disagreement_score":0.009919962,"about_ca_system_score_codex":0.0015306781,"about_ca_system_score_gemma":0.0016238133,"threshold_uncertainty_score":0.0524624},"labels":[],"label_agreement":null},{"id":"W4301181330","doi":"10.1007/s10009-022-00684-w","title":"Analysis of recurrent neural networks via property-directed verification of surrogate models","year":2022,"lang":"en","type":"article","venue":"International Journal on Software Tools for Technology Transfer","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Campus France; Universität zu Lübeck; Deutsche Forschungsgemeinschaft; Deutscher Akademischer Austauschdienst","keywords":"Recurrent neural network; Property (philosophy); Computer science; Robustness (evolution); Automaton; Counterexample; Artificial intelligence; Model checking; Artificial neural network; Algorithm; Theoretical computer science; Machine learning; Mathematics","score_opus":0.026409466744672108,"score_gpt":0.2762155736869909,"score_spread":0.2498061069423188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301181330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06368891,0.00005560926,0.9337915,0.00015948093,0.000025778267,0.000056217883,0.00010299258,0.0008900533,0.001229426],"genre_scores_gemma":[0.937476,0.000045046476,0.061254587,0.00006144728,0.000010575307,0.00010913652,0.0001576109,0.00015675543,0.0007288713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962585,0.0014451641,0.00020677432,0.00059243955,0.0011715513,0.0003255167],"domain_scores_gemma":[0.9767934,0.016003579,0.002074891,0.0028169942,0.0020201502,0.00029101124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004775059,0.0008299035,0.00079617364,0.000807579,0.0004134353,0.0012769349,0.0016589897,0.0013165998,0.0026566288],"category_scores_gemma":[0.033412833,0.0005593544,0.0016253245,0.0002607785,0.002576875,0.0019722108,0.002040269,0.0017358128,0.00028457845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001163632,0.00003980771,0.0018784404,0.000090657544,0.000054568663,0.0002744865,0.00010390421,0.9285862,0.006778928,0.054752342,0.00023895105,0.007085307],"study_design_scores_gemma":[0.000004639983,0.000020871334,0.00004697815,0.0000072835296,0.000004781997,0.00001656029,0.000005363075,0.98387146,0.0022612354,0.013666346,0.000090295165,0.0000041825515],"about_ca_topic_score_codex":0.0017699113,"about_ca_topic_score_gemma":0.0017006295,"teacher_disagreement_score":0.004775059,"about_ca_system_score_codex":0.001209691,"about_ca_system_score_gemma":0.0015617559,"threshold_uncertainty_score":0.025253236},"labels":[],"label_agreement":null},{"id":"W4302082990","doi":"10.48550/arxiv.2208.01844","title":"Multiclass ASMA vs Targeted PGD Attack in Image Segmentation","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Generalization; Segmentation; Image (mathematics); Pattern recognition (psychology); Contextual image classification; Adversarial system; Image segmentation; Vulnerability (computing); Machine learning; Computer security; Mathematics","score_opus":0.05920745656320769,"score_gpt":0.22936376330426664,"score_spread":0.17015630674105894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302082990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45097756,0.0008714153,0.5342569,0.0016514421,0.00015764845,0.00009226808,0.00014765728,0.0018426235,0.010002594],"genre_scores_gemma":[0.97921604,0.000098334844,0.019243924,0.00012535426,0.000015929863,0.000018281296,0.000037674232,0.00005423837,0.0011902268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990988,0.0003354127,0.00003059553,0.00017994239,0.00023240928,0.00012293189],"domain_scores_gemma":[0.9980861,0.0010375092,0.0001899334,0.00048207538,0.00013152916,0.00007298281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011892647,0.000608402,0.0005683622,0.0003281066,0.00036664322,0.0006483368,0.0006667887,0.001177888,0.0009882926],"category_scores_gemma":[0.0033942228,0.00024768847,0.0005525774,0.0002558698,0.0016029823,0.0015822863,0.0016826094,0.0017985574,0.00021983428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008382488,0.00010627542,0.002778501,0.000093439296,0.00012670495,0.00042304158,0.00021890833,0.80629575,0.037572037,0.06296636,0.002981431,0.08559927],"study_design_scores_gemma":[0.0000075755775,0.0000708634,0.00029441167,0.0000056219224,0.0000072418593,0.000095760115,0.00001237267,0.9786718,0.0104042925,0.009923875,0.00049832347,0.000007935216],"about_ca_topic_score_codex":0.0010077913,"about_ca_topic_score_gemma":0.0009885031,"teacher_disagreement_score":0.0011892647,"about_ca_system_score_codex":0.00085340213,"about_ca_system_score_gemma":0.00045015034,"threshold_uncertainty_score":0.006289482},"labels":[],"label_agreement":null},{"id":"W4304140713","doi":"10.1109/socc56010.2022.9908113","title":"Inconspicuous Data Augmentation Based Backdoor Attack on Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation Singapore; National Research Foundation; Canadian Institute for Advanced Research","keywords":"Backdoor; Computer science; Interpretability; Artificial neural network; Robustness (evolution); Artificial intelligence; Deep neural networks; Enhanced Data Rates for GSM Evolution; Edge device; Trojan; Deep learning; Machine learning; Computer security; Cloud computing; Operating system","score_opus":0.05806069551596407,"score_gpt":0.31850247949278054,"score_spread":0.2604417839768165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304140713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3895243,0.0020080304,0.59618837,0.00077201694,0.0002392959,0.00012907678,0.00034361603,0.005788706,0.0050065625],"genre_scores_gemma":[0.95244277,0.00034408923,0.044909015,0.0001588141,0.00002425082,0.00005323854,0.00022416249,0.000079024525,0.0017645911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991155,0.00018321385,0.00006044899,0.0001559115,0.00033813468,0.00014664757],"domain_scores_gemma":[0.99881065,0.00045285362,0.00017174082,0.00035324067,0.00016680553,0.00004474486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007917371,0.0012304232,0.00079645513,0.00053976534,0.0003108644,0.000635547,0.0009435908,0.0007335606,0.00091414107],"category_scores_gemma":[0.0026147596,0.00029387476,0.0007610738,0.0003158031,0.0011452973,0.002124032,0.0016613307,0.0016256626,0.00030634968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013997345,0.0003107427,0.0038004357,0.0003468497,0.00027146452,0.0007980689,0.00028975483,0.49689594,0.09942771,0.012609588,0.004939835,0.37890998],"study_design_scores_gemma":[0.000014942028,0.00019815343,0.0005555432,0.000028455654,0.000026629732,0.00020677512,0.00003269075,0.9426595,0.051027633,0.0039868867,0.0012401858,0.000022717502],"about_ca_topic_score_codex":0.0014752847,"about_ca_topic_score_gemma":0.0015247901,"teacher_disagreement_score":0.0014752847,"about_ca_system_score_codex":0.0007280428,"about_ca_system_score_gemma":0.00050635735,"threshold_uncertainty_score":0.0052823424},"labels":[],"label_agreement":null},{"id":"W4304687331","doi":"10.20944/preprints202210.0157.v1","title":"Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Adversarial system; Underwriting; Intermediary; Actuarial science; Business; Robustness (evolution); Taxonomy (biology); Computer science; Computer security; Artificial intelligence; Finance","score_opus":0.1381291682684165,"score_gpt":0.36428737263720423,"score_spread":0.22615820436878772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304687331","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03436135,0.051962115,0.7213355,0.07091574,0.0014991754,0.00013935963,0.00018678886,0.0004096671,0.11919036],"genre_scores_gemma":[0.86475074,0.033130024,0.07799154,0.00429495,0.0021012106,0.0001546926,0.00011958655,0.00008973514,0.01736753],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982267,0.00089184183,0.00006927079,0.00017496811,0.00050098693,0.00013627285],"domain_scores_gemma":[0.9940234,0.004733386,0.000274288,0.00055767706,0.00030814274,0.000103192055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026126427,0.00071590923,0.00058356515,0.0008725658,0.0009794198,0.002457253,0.0010408276,0.003148585,0.002454342],"category_scores_gemma":[0.008399472,0.00027132608,0.00081211154,0.000639401,0.0055733225,0.0030420606,0.0023057733,0.004923471,0.00040647],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039871804,0.000053164404,0.0005485579,0.000214412,0.000042181873,0.0003378612,0.00022419154,0.051649574,0.00059879076,0.8937325,0.0070927036,0.045466226],"study_design_scores_gemma":[0.000013553541,0.00004516077,0.00037213883,0.0002685748,0.0000143186135,0.00029015445,0.00012173515,0.06947873,0.00064681424,0.90543413,0.023283204,0.000031568245],"about_ca_topic_score_codex":0.0009168228,"about_ca_topic_score_gemma":0.00061999465,"teacher_disagreement_score":0.003148585,"about_ca_system_score_codex":0.0009829107,"about_ca_system_score_gemma":0.00054754247,"threshold_uncertainty_score":0.0138171315},"labels":[],"label_agreement":null},{"id":"W4306407446","doi":"10.1016/j.micpro.2022.104710","title":"HARDeNN: Hardware-assisted attack-resilient deep neural network architectures","year":2022,"lang":"en","type":"article","venue":"Microprocessors and Microsystems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Embedded system; Artificial neural network; Computer architecture; Computer hardware; Artificial intelligence","score_opus":0.011790275075487699,"score_gpt":0.24149810026452648,"score_spread":0.2297078251890388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306407446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030590536,0.0014908807,0.94614226,0.0007825663,0.0004143674,0.000090329646,0.00038187898,0.0100887325,0.010018492],"genre_scores_gemma":[0.7240435,0.00072968553,0.24802111,0.00081301987,0.0001273005,0.00016000977,0.001032182,0.00060798955,0.024465188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996055,0.000066349465,0.000016375696,0.000087766166,0.00015932888,0.00006477664],"domain_scores_gemma":[0.9994696,0.00018035027,0.000053055606,0.00014897561,0.00011387388,0.000034164706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074829214,0.0009451885,0.0005370913,0.00040137774,0.0003123331,0.0008332355,0.0017639407,0.0009184605,0.007124828],"category_scores_gemma":[0.0021277433,0.00036446858,0.0003452471,0.00029271518,0.00067526376,0.0016957669,0.0022199377,0.0021687658,0.001485045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004207348,0.00018203247,0.0007170286,0.0002330739,0.00013383517,0.00016335158,0.000048297,0.5304035,0.022071771,0.030821158,0.024983417,0.38982186],"study_design_scores_gemma":[0.000013059111,0.000075971766,0.000099542944,0.000013323334,0.0000070560436,0.000034181914,0.0000058190467,0.981838,0.007002746,0.007925584,0.0029746885,0.000009965403],"about_ca_topic_score_codex":0.0013742985,"about_ca_topic_score_gemma":0.0033634733,"teacher_disagreement_score":0.007124828,"about_ca_system_score_codex":0.0006497756,"about_ca_system_score_gemma":0.00080450514,"threshold_uncertainty_score":0.023834944},"labels":[],"label_agreement":null},{"id":"W4306818976","doi":"10.1145/3510454.3516858","title":"HUDD","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Fonds National de la Recherche Luxembourg; European Commission","keywords":"Computer science","score_opus":0.02092173177462929,"score_gpt":0.2903201896606873,"score_spread":0.269398457886058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306818976","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005569118,0.0006283622,0.5747869,0.0005955997,0.0005135181,0.0002151371,0.010702954,0.3901776,0.016810806],"genre_scores_gemma":[0.21608049,0.001067186,0.62714773,0.002002564,0.00021923249,0.0007758776,0.046444483,0.058571797,0.04769066],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987795,0.00019126634,0.00009206601,0.00034158086,0.00048784248,0.00010779047],"domain_scores_gemma":[0.9976472,0.00092594215,0.00013895506,0.00084476883,0.00033190174,0.0001112249],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017045374,0.0013269914,0.0006759334,0.0016556492,0.00043099525,0.0019744497,0.0029944386,0.0011866762,0.06094031],"category_scores_gemma":[0.008478522,0.0009090192,0.0010229441,0.0005023288,0.0006985721,0.0026417258,0.0035564052,0.0016952029,0.017226947],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008729336,0.00020305162,0.007261132,0.0014813591,0.00022977163,0.00078335986,0.00039476826,0.04494791,0.012453639,0.027665386,0.3775767,0.52612996],"study_design_scores_gemma":[0.00036100828,0.0002721953,0.002039978,0.00029904814,0.000088629335,0.00095839385,0.00011948067,0.35633555,0.05572852,0.054811973,0.52880675,0.00017846288],"about_ca_topic_score_codex":0.0018782662,"about_ca_topic_score_gemma":0.0028031655,"teacher_disagreement_score":0.9390597,"about_ca_system_score_codex":0.00068473426,"about_ca_system_score_gemma":0.0009654735,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4307811455","doi":"10.1145/3569935","title":"Simulator-based Explanation and Debugging of Hazard-triggering Events in DNN-based Safety-critical Systems","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg; European Commission; Université du Luxembourg","keywords":"Debugging; Computer science; Hazard; Software engineering; Programming language","score_opus":0.04752392328673987,"score_gpt":0.3100093273240562,"score_spread":0.26248540403731635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307811455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21430057,0.000409033,0.77590644,0.0005996611,0.000109603716,0.00009715538,0.00024696588,0.0060854154,0.0022451465],"genre_scores_gemma":[0.9327576,0.0001085774,0.06599442,0.000113035145,0.000008886765,0.000044935696,0.00019483338,0.00012291878,0.0006547268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948275,0.00019743777,0.000034083536,0.000120378856,0.00011969885,0.000045587767],"domain_scores_gemma":[0.9971601,0.0017158532,0.000322927,0.00040166042,0.00030859376,0.00009091331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013055911,0.00080411544,0.00028638216,0.00037095076,0.00019234576,0.0004260976,0.0013521044,0.00072622974,0.0013586695],"category_scores_gemma":[0.007053859,0.00039832148,0.00032263636,0.00013610873,0.00067887397,0.0010359451,0.0008433737,0.0011863698,0.00020747003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020516352,0.0000595412,0.0020175588,0.000074605945,0.00002654703,0.00021306872,0.00015527995,0.9368846,0.008124391,0.0029635073,0.0008303709,0.048445374],"study_design_scores_gemma":[0.000005209825,0.000024553949,0.00014302623,0.0000055286096,0.0000035689873,0.000015475453,0.000007971963,0.99342185,0.004429131,0.0016864726,0.00025363723,0.0000036135652],"about_ca_topic_score_codex":0.0046351226,"about_ca_topic_score_gemma":0.0065135756,"teacher_disagreement_score":0.0046351226,"about_ca_system_score_codex":0.0011406173,"about_ca_system_score_gemma":0.0010559814,"threshold_uncertainty_score":0.009216309},"labels":[],"label_agreement":null},{"id":"W4308071056","doi":"10.48550/arxiv.2110.01954","title":"Continuous-Time Fitted Value Iteration for Robust Policies","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; Canadian Institute for Advanced Research; Nvidia","keywords":"Bellman equation; Discretization; Reinforcement learning; Leverage (statistics); Mathematical optimization; Optimal control; Computer science; Dynamic programming; Robustness (evolution); Mathematics; Artificial intelligence","score_opus":0.05323358205397503,"score_gpt":0.20114408677589748,"score_spread":0.14791050472192246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308071056","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007931715,0.00026619894,0.9891431,0.00017339978,0.00003683805,0.000035928733,0.000022773273,0.00025919906,0.00213091],"genre_scores_gemma":[0.7042421,0.0003548255,0.28856042,0.0002477835,0.000056074874,0.00032969768,0.000184301,0.00032077695,0.0057040746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989122,0.00043219453,0.00006144542,0.00021183542,0.00024250898,0.00013979198],"domain_scores_gemma":[0.9957391,0.0032109986,0.00029099907,0.0002098532,0.00040165495,0.00014725163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002439415,0.0013015592,0.0013936675,0.00062884577,0.00041764526,0.001374519,0.001062575,0.0015757912,0.0037572393],"category_scores_gemma":[0.010887995,0.0007276162,0.0009042826,0.00050998974,0.0019800598,0.0012730386,0.001670445,0.0028367862,0.00070833054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007382223,0.00003085338,0.00036944592,0.000062617946,0.00002935331,0.000049347967,0.0000692503,0.9463266,0.00073423114,0.032260645,0.00062669924,0.01936714],"study_design_scores_gemma":[0.0000060425623,0.000012740921,0.00001592439,0.0000069353478,0.0000017732214,0.0000058440655,0.0000037180314,0.9920101,0.000219211,0.0074869236,0.00022751467,0.0000032148478],"about_ca_topic_score_codex":0.0036477698,"about_ca_topic_score_gemma":0.0024278066,"teacher_disagreement_score":0.0037572393,"about_ca_system_score_codex":0.0016689133,"about_ca_system_score_gemma":0.0021192026,"threshold_uncertainty_score":0.012901008},"labels":[],"label_agreement":null},{"id":"W4308327073","doi":"10.1007/s10994-022-06263-z","title":"Adversarial examples for extreme multilabel text classification","year":2022,"lang":"fi","type":"article","venue":"Aaltodoc (Aalto University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Aalto-Yliopisto; Academy of Finland","keywords":"Adversarial system; Robustness (evolution); Computer science; Artificial intelligence; Similarity (geometry); Categorization; Text categorization; Machine learning; Pattern recognition (psychology); Data mining; Image (mathematics)","score_opus":0.07888814776326718,"score_gpt":0.2577371208845423,"score_spread":0.17884897312127512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308327073","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008730514,0.0003435075,0.98716426,0.00046120703,0.000099628036,0.000059470196,0.00021043257,0.001265875,0.0016650739],"genre_scores_gemma":[0.49596095,0.00047885894,0.47697812,0.0008839039,0.00056260027,0.00054753316,0.003230044,0.0009185341,0.02043947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613535,0.0017450883,0.00017921269,0.0007096559,0.000977563,0.00025321436],"domain_scores_gemma":[0.9909695,0.006545827,0.0004377556,0.0010646281,0.0007330471,0.00024922995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039535197,0.0011492053,0.0016589029,0.0015815271,0.00090662105,0.0015907405,0.0024377564,0.0028735853,0.005904712],"category_scores_gemma":[0.014064817,0.0006850861,0.0014113992,0.0011868316,0.0017484823,0.0027667456,0.0045044995,0.004234289,0.0025040384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005675997,0.00024018048,0.0007752969,0.0002727826,0.00013123636,0.00021188587,0.00016488347,0.5887283,0.0045129675,0.050648097,0.021400603,0.3323462],"study_design_scores_gemma":[0.000008342251,0.000024137033,0.00007674128,0.000013517766,0.000004940971,0.000029912608,0.0000080203845,0.9651326,0.0010743106,0.032808173,0.00081175583,0.0000076112237],"about_ca_topic_score_codex":0.0014697145,"about_ca_topic_score_gemma":0.0018520252,"teacher_disagreement_score":0.005904712,"about_ca_system_score_codex":0.0010385058,"about_ca_system_score_gemma":0.00076472183,"threshold_uncertainty_score":0.020908415},"labels":[],"label_agreement":null},{"id":"W4309447783","doi":"10.21203/rs.3.rs-2261000/v1","title":"Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Compute Canada","keywords":"Reinforcement learning; Adversarial system; Computer science; Artificial intelligence; Inference; Reinforcement; Deep learning; Machine learning; Adversary; Vulnerability (computing); Temporal difference learning; Set (abstract data type); Computer security; Engineering","score_opus":0.13763043122558846,"score_gpt":0.4333917443313675,"score_spread":0.295761313105779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309447783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20162198,0.00021338109,0.7948272,0.0007746916,0.00006132466,0.00008174215,0.00009055689,0.0007368345,0.0015923275],"genre_scores_gemma":[0.98178226,0.000039660772,0.017517498,0.00012284091,0.0000151180275,0.000033345335,0.000036243477,0.000023044084,0.0004300093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9937919,0.0029534663,0.0002877091,0.0009721913,0.0014278407,0.0005669903],"domain_scores_gemma":[0.9750738,0.01701124,0.0023077615,0.003996225,0.0010727042,0.0005382073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006548084,0.0007633036,0.0010199405,0.00052866683,0.0006326552,0.0010307899,0.0014383367,0.0013204034,0.0010918321],"category_scores_gemma":[0.026642947,0.00039859212,0.0006755869,0.00039550674,0.0025434534,0.0023654297,0.0031182575,0.0030457128,0.00016665118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062964763,0.00017699493,0.0042167553,0.00007759381,0.000117853575,0.00017961237,0.00015350466,0.9199585,0.0066408315,0.030975908,0.00094682345,0.035926033],"study_design_scores_gemma":[0.000010528677,0.000048733258,0.00018807537,0.0000068596532,0.0000044706994,0.000020004898,0.000008454078,0.9877418,0.0026270726,0.009221114,0.00011699557,0.000005870764],"about_ca_topic_score_codex":0.0015429454,"about_ca_topic_score_gemma":0.0010255025,"teacher_disagreement_score":0.006548084,"about_ca_system_score_codex":0.0014680912,"about_ca_system_score_gemma":0.0012194256,"threshold_uncertainty_score":0.03462994},"labels":[],"label_agreement":null},{"id":"W4309865903","doi":"10.1007/s00146-022-01591-z","title":"Toward safe AI","year":2022,"lang":"it","type":"article","venue":"AI & Society","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Risk analysis (engineering); Normative; Artificial intelligence; Management science; Machine learning; Data science; Engineering","score_opus":0.02187714235703819,"score_gpt":0.28005088675007095,"score_spread":0.25817374439303276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309865903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073652733,0.0033551264,0.75892025,0.034765013,0.0012048025,0.00008865891,0.00026018274,0.0013710099,0.19266972],"genre_scores_gemma":[0.6015095,0.005391646,0.23471126,0.013070358,0.0020939384,0.00039409692,0.00086855044,0.0012024959,0.14075811],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99743974,0.00092031254,0.000076263386,0.0004398587,0.00094838336,0.00017542811],"domain_scores_gemma":[0.99264044,0.0033044128,0.00029739516,0.002327363,0.0010481765,0.00038216935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004435843,0.0010113,0.00078636064,0.0011155327,0.0017425101,0.004690476,0.0018064119,0.0029017224,0.01615357],"category_scores_gemma":[0.016887952,0.0005686691,0.0008830648,0.00050853303,0.0077179093,0.0072330856,0.0060349577,0.008975397,0.0055879825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016271357,0.0000187534,0.00015021439,0.000037645426,0.000014756031,0.000020682302,0.00013277668,0.007351586,0.00034094864,0.9658974,0.0086301975,0.01738867],"study_design_scores_gemma":[0.0000043412265,0.000009689455,0.00004130169,0.00002905211,0.0000046269884,0.00001840704,0.000040438546,0.017738136,0.00041168107,0.9551803,0.026515277,0.000006771765],"about_ca_topic_score_codex":0.001660608,"about_ca_topic_score_gemma":0.0011586628,"teacher_disagreement_score":0.01615357,"about_ca_system_score_codex":0.0015846353,"about_ca_system_score_gemma":0.0018235719,"threshold_uncertainty_score":0.05403906},"labels":[],"label_agreement":null},{"id":"W4311990442","doi":"10.48550/arxiv.2108.05075","title":"Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Deep neural networks; False positive paradox; Generative grammar; Bounded function; Artificial intelligence; Artificial neural network; Computer security; Machine learning; Mathematics","score_opus":0.04675489863498341,"score_gpt":0.2123571774155322,"score_spread":0.16560227878054878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311990442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114564545,0.0015589828,0.8660906,0.0009306559,0.00028790385,0.00028851212,0.00019538787,0.009782814,0.0063006235],"genre_scores_gemma":[0.8895789,0.00030859825,0.10567349,0.00060263334,0.00010079152,0.00015349242,0.00027876286,0.00026772718,0.0030356802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986424,0.0003445981,0.000056496403,0.00024542818,0.00050320564,0.0002078128],"domain_scores_gemma":[0.99711716,0.0013024715,0.00028776308,0.0009206521,0.00023212025,0.00013975266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017714141,0.0014135917,0.0011406813,0.0006810792,0.0005678217,0.0010412501,0.0019723882,0.0021071024,0.0013923448],"category_scores_gemma":[0.006076773,0.0004903147,0.0009064884,0.00027317106,0.002012442,0.0019939172,0.0036484424,0.0027239528,0.00047894922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067670643,0.00024233176,0.0038606487,0.00022574494,0.00028765207,0.00038256345,0.00020719689,0.73396456,0.042395167,0.026888872,0.011449001,0.17941953],"study_design_scores_gemma":[0.000024013716,0.00015567604,0.00028321787,0.0000142922245,0.000015849604,0.0001068614,0.000012779741,0.9832835,0.009026481,0.0057921098,0.0012694263,0.000015672467],"about_ca_topic_score_codex":0.0010486316,"about_ca_topic_score_gemma":0.0013985464,"teacher_disagreement_score":0.0021071024,"about_ca_system_score_codex":0.000724114,"about_ca_system_score_gemma":0.0008427596,"threshold_uncertainty_score":0.0093683},"labels":[],"label_agreement":null},{"id":"W4312076523","doi":"10.1007/978-3-031-26409-2_18","title":"MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"","keywords":"Computer science; Adversarial system; Detector; Metric (unit); Artificial intelligence; Machine learning; Computer security; Data mining; Telecommunications","score_opus":0.07078720065733597,"score_gpt":0.31251728003652635,"score_spread":0.2417300793791904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312076523","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062444527,0.0003120531,0.9862726,0.00011952587,0.000074498734,0.00014446407,0.00022130116,0.004809578,0.0018015788],"genre_scores_gemma":[0.11099521,0.00013788298,0.8830902,0.00022700756,0.000052743784,0.00035798896,0.0008095969,0.0007077766,0.003621641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963607,0.0017970941,0.00019436779,0.0005085232,0.000915196,0.00022420549],"domain_scores_gemma":[0.9951574,0.0030184363,0.00021397446,0.0006204597,0.0008229857,0.00016671103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006960776,0.0023076762,0.0019204554,0.0023417706,0.00077448937,0.0020287712,0.003858438,0.004573532,0.008959327],"category_scores_gemma":[0.013871023,0.001234291,0.0014056728,0.0010958988,0.0010434481,0.0022459636,0.0034492349,0.0025178504,0.0021601582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009870157,0.0002505922,0.0013722986,0.00032777083,0.0004670663,0.00014398644,0.000094598414,0.5755744,0.007714341,0.016050339,0.017761836,0.37925577],"study_design_scores_gemma":[0.000023716007,0.000057770358,0.000083082756,0.000011637077,0.00001002861,0.000027793802,0.0000070751794,0.9936009,0.0018955554,0.0034185431,0.0008534946,0.0000103998],"about_ca_topic_score_codex":0.0027189662,"about_ca_topic_score_gemma":0.004650923,"teacher_disagreement_score":0.008959327,"about_ca_system_score_codex":0.0010563632,"about_ca_system_score_gemma":0.0012850049,"threshold_uncertainty_score":0.036812544},"labels":[],"label_agreement":null},{"id":"W4312250727","doi":"10.1007/978-3-031-19775-8_3","title":"You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Rounding; NIST; Random number generation; Generator (circuit theory); Artificial neural network; Application-specific integrated circuit; Stochastic computing; Field-programmable gate array; Artificial intelligence; Algorithm; Computer engineering; Speech recognition; Computer hardware","score_opus":0.04120259046185975,"score_gpt":0.29585350691733087,"score_spread":0.2546509164554711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312250727","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016814416,0.00017618296,0.9938082,0.00011547322,0.00008743142,0.000026823236,0.00008248329,0.0014624019,0.0025595874],"genre_scores_gemma":[0.1273487,0.00040366035,0.85641944,0.00037066266,0.00015712022,0.00014337072,0.0005723364,0.0009647526,0.013619841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954057,0.00010533747,0.000023875946,0.00011496085,0.00016805467,0.00004734453],"domain_scores_gemma":[0.9994949,0.00021088634,0.000026105325,0.00011413956,0.00011460881,0.000039279956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011686856,0.00094920944,0.0010254551,0.00039384354,0.0005430311,0.0013127615,0.0023219825,0.0017698448,0.011275229],"category_scores_gemma":[0.0027503176,0.00071943563,0.00070439925,0.0004633662,0.00071837305,0.0016160941,0.0022581243,0.0036932635,0.0053756367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002832925,0.000075973716,0.0001952314,0.00014546992,0.00006314681,0.00016020382,0.00006393689,0.49190572,0.013367022,0.09356842,0.014563801,0.38560775],"study_design_scores_gemma":[0.000011627921,0.000021135958,0.000020980098,0.000014568972,0.0000064968344,0.000027071874,0.0000042137804,0.9738764,0.0020286168,0.021817654,0.0021631636,0.000008112597],"about_ca_topic_score_codex":0.0040189573,"about_ca_topic_score_gemma":0.007268443,"teacher_disagreement_score":0.011275229,"about_ca_system_score_codex":0.0006680036,"about_ca_system_score_gemma":0.0010674247,"threshold_uncertainty_score":0.03771943},"labels":[],"label_agreement":null},{"id":"W4312292505","doi":"10.1145/3522664.3528617","title":"Identification of out-of-distribution cases of CNN using class-based surprise adequacy","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Surprise; Computer science; Identification (biology); Class (philosophy); Artificial intelligence; Calibration; Pattern recognition (psychology); Distribution (mathematics); Machine learning; Mathematics; Statistics","score_opus":0.03799936767009324,"score_gpt":0.31084270620987214,"score_spread":0.27284333853977893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312292505","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58614045,0.00044002032,0.40030012,0.0014684972,0.00020726021,0.00010608409,0.00041974237,0.002545035,0.008372793],"genre_scores_gemma":[0.98822635,0.000043811386,0.010532518,0.00018283825,0.000024732268,0.000021454676,0.00023096372,0.00013444053,0.00060291385],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99758744,0.00046166076,0.00013590805,0.0006739278,0.00082739454,0.00031369034],"domain_scores_gemma":[0.9884189,0.005555223,0.0014659343,0.0027486898,0.0014510805,0.0003602305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038942324,0.0008397208,0.0007962063,0.0011577009,0.00058107893,0.0015887779,0.0012858902,0.0016845553,0.0020215684],"category_scores_gemma":[0.029257616,0.0004623554,0.0005878144,0.00041498808,0.0020219474,0.0024014567,0.0022119673,0.0020094002,0.00059945043],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001962883,0.0003324819,0.22054452,0.00039946023,0.0004135985,0.006411241,0.0011952979,0.29857603,0.08304105,0.06302679,0.018885406,0.30521128],"study_design_scores_gemma":[0.0000217697,0.00008907531,0.018493827,0.00006295692,0.00004369549,0.0019503767,0.00017766564,0.90883917,0.03245079,0.035343647,0.0024872033,0.000039838655],"about_ca_topic_score_codex":0.0014767015,"about_ca_topic_score_gemma":0.0016670949,"teacher_disagreement_score":0.0038942324,"about_ca_system_score_codex":0.0011591348,"about_ca_system_score_gemma":0.0007085017,"threshold_uncertainty_score":0.020594954},"labels":[],"label_agreement":null},{"id":"W4312326921","doi":"10.1109/ijcnn55064.2022.9892485","title":"Adversarial Fine-tune with Dynamically Regulated Adversary","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adversarial system; Robustness (evolution); Computer science; Adversary; Artificial intelligence; Machine learning; Robot; Training set; Computer security","score_opus":0.015183575455262962,"score_gpt":0.2337178866282841,"score_spread":0.21853431117302113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312326921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019827634,0.00021767954,0.9763713,0.0002768001,0.000057703597,0.0000540741,0.000042732343,0.00070223876,0.0024498857],"genre_scores_gemma":[0.9131884,0.00023302317,0.08180246,0.00035734355,0.00006975517,0.00012200711,0.00013559546,0.00016500588,0.003926355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990445,0.0003117653,0.000044695706,0.00023589995,0.00023484898,0.00012837688],"domain_scores_gemma":[0.9972276,0.0016880034,0.0002303965,0.0005643383,0.00019564746,0.000093995855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018365706,0.0013139145,0.0009745915,0.00037838603,0.00046568003,0.00071696413,0.0014758699,0.0011043467,0.0020499963],"category_scores_gemma":[0.006369205,0.00039375437,0.0007058669,0.00030254907,0.0018524976,0.0016076748,0.0027628215,0.0025968691,0.00058386446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011883596,0.000049148042,0.00065267406,0.000052635005,0.000050920622,0.000101396276,0.000062285806,0.9396585,0.009384825,0.014590861,0.0013947574,0.033883043],"study_design_scores_gemma":[0.0000043223004,0.00003227126,0.000082196726,0.000005289246,0.000005413598,0.000030308931,0.0000050685517,0.99232763,0.0020047955,0.005109282,0.00038628408,0.0000070869864],"about_ca_topic_score_codex":0.0012399819,"about_ca_topic_score_gemma":0.0010273037,"teacher_disagreement_score":0.0020499963,"about_ca_system_score_codex":0.0006781307,"about_ca_system_score_gemma":0.00066198915,"threshold_uncertainty_score":0.009712815},"labels":[],"label_agreement":null},{"id":"W4313484756","doi":"10.1109/dsd57027.2022.00126","title":"Blind Data Adversarial Bit-flip Attack against Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"2022 25th Euromicro Conference on Digital System Design (DSD)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Normalization (sociology); Adversarial system; Artificial neural network; Bit (key); Deep neural networks; Artificial intelligence; Pattern recognition (psychology); Computer security","score_opus":0.08764770272569537,"score_gpt":0.27963736246526877,"score_spread":0.19198965973957338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313484756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1841181,0.000997296,0.80517673,0.0012223601,0.0002951931,0.00014872134,0.00039268952,0.0022340522,0.005414822],"genre_scores_gemma":[0.958321,0.00021825585,0.03934331,0.00030509295,0.000031788262,0.00008153215,0.00019021171,0.00007363372,0.0014353496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981592,0.00064567954,0.00010436324,0.00028351502,0.00060388853,0.0002034022],"domain_scores_gemma":[0.99442965,0.0034419452,0.0005586088,0.0010396461,0.00042002375,0.00011008611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002200042,0.0011420866,0.00069522613,0.00068673893,0.00038644543,0.00058600947,0.0008153089,0.0011296619,0.0015584574],"category_scores_gemma":[0.011258767,0.00031316857,0.00059735414,0.000394375,0.0016231284,0.0017541895,0.0018692049,0.0017850099,0.00044587124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012375595,0.00013133578,0.0037681211,0.00024645292,0.00020967552,0.00045802325,0.00015112504,0.76575655,0.06421819,0.03696613,0.0059865215,0.1208703],"study_design_scores_gemma":[0.000029856013,0.000145353,0.00048370758,0.000037523983,0.000022426431,0.00018375638,0.000020186453,0.93888867,0.044197217,0.014752701,0.0012143592,0.000024285087],"about_ca_topic_score_codex":0.00068637583,"about_ca_topic_score_gemma":0.0007559602,"teacher_disagreement_score":0.002200042,"about_ca_system_score_codex":0.0008102924,"about_ca_system_score_gemma":0.00049460726,"threshold_uncertainty_score":0.011635065},"labels":[],"label_agreement":null},{"id":"W4315629933","doi":"10.1109/globecom48099.2022.10000757","title":"Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Adversary; Adversarial machine learning; Artificial intelligence; Machine learning; Adversarial system; Classifier (UML); Attack model; Inference; Computer security","score_opus":0.028662259857975204,"score_gpt":0.29763317429265296,"score_spread":0.26897091443467774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19297954,0.0004479937,0.7986402,0.00097504054,0.00011670587,0.000103558494,0.00008712594,0.00047540196,0.0061745467],"genre_scores_gemma":[0.99125296,0.00008016523,0.007529671,0.00009368485,0.000019466761,0.00002231596,0.000024077313,0.000013750001,0.0009638566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998765,0.00046309127,0.00003660554,0.00022393907,0.00027209066,0.00023944343],"domain_scores_gemma":[0.99368423,0.0047181495,0.0006526401,0.0004016832,0.00037748358,0.00016572184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002121822,0.00086870865,0.0006959862,0.00037427558,0.00034919332,0.0007936486,0.0008165521,0.00092652696,0.0013041364],"category_scores_gemma":[0.008680999,0.0002589061,0.0005247066,0.00020158899,0.0014019975,0.0018333234,0.0016246298,0.0013886066,0.00019132749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008724072,0.000020337817,0.00086461863,0.000021965043,0.000020011496,0.00006869135,0.000025210295,0.9844333,0.0015328494,0.0062935078,0.00024610537,0.006386224],"study_design_scores_gemma":[0.0000023778914,0.00005050512,0.00017875386,0.0000034116338,0.0000033179208,0.00002485649,0.000010313361,0.99646497,0.00072247034,0.0024104125,0.00012480149,0.0000038204334],"about_ca_topic_score_codex":0.0013841011,"about_ca_topic_score_gemma":0.000752849,"teacher_disagreement_score":0.002121822,"about_ca_system_score_codex":0.0008885425,"about_ca_system_score_gemma":0.0006117646,"threshold_uncertainty_score":0.011221409},"labels":[],"label_agreement":null},{"id":"W4315780111","doi":"10.1007/978-3-031-20096-0_31","title":"Adversarial Attack and Defense on Natural Language Processing in Deep Learning: A Survey and Perspective","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Robustness (evolution); Perspective (graphical); Deep learning; Natural language; Natural language processing; Natural language understanding; Granularity; Deep neural networks; Programming language","score_opus":0.019071998195791237,"score_gpt":0.2869613327146658,"score_spread":0.26788933451887453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315780111","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063744546,0.31055686,0.63041943,0.006920524,0.0012478227,0.0001047191,0.0001414584,0.0003449687,0.043889754],"genre_scores_gemma":[0.34820607,0.43254018,0.16245,0.0044998466,0.0081206905,0.00038692376,0.0004631202,0.00030112077,0.043032162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99797016,0.000601702,0.00011572371,0.00031294907,0.00081889855,0.00018061012],"domain_scores_gemma":[0.9953432,0.003495981,0.00021762673,0.0005000884,0.00035819382,0.00008491888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028512962,0.0015388861,0.0017306915,0.0018223537,0.00070739014,0.0039743874,0.002167728,0.0032377401,0.0030208044],"category_scores_gemma":[0.0060815155,0.0009895905,0.0010277609,0.003183835,0.0035519742,0.0075280927,0.003622978,0.00587676,0.0011754278],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096034615,0.00019814092,0.0006840234,0.0011759788,0.00011391598,0.00011638588,0.00013361471,0.0704879,0.0015769062,0.5351416,0.016468639,0.37380695],"study_design_scores_gemma":[0.000016359865,0.00018299626,0.00054444,0.00069685426,0.00005582125,0.0005033182,0.00010919966,0.26597804,0.00301407,0.66535,0.0634868,0.00006205026],"about_ca_topic_score_codex":0.0010121174,"about_ca_topic_score_gemma":0.000705839,"teacher_disagreement_score":0.0039743874,"about_ca_system_score_codex":0.0019620322,"about_ca_system_score_gemma":0.0011631768,"threshold_uncertainty_score":0.01507926},"labels":[],"label_agreement":null},{"id":"W4315786905","doi":"10.3390/risks11010020","title":"Adversarial Artificial Intelligence in Insurance: From an Example to Some Potential Remedies","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Underwriting; Adversarial system; Intermediary; Actuarial science; Business; Robustness (evolution); Taxonomy (biology); Financial services; Computer science; Artificial intelligence; Finance","score_opus":0.08929388574500674,"score_gpt":0.3478451552789508,"score_spread":0.2585512695339441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315786905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034313127,0.024053242,0.7464979,0.07574079,0.001306072,0.00013303511,0.00017137115,0.00039673087,0.1173876],"genre_scores_gemma":[0.88043845,0.01587892,0.08142098,0.004767266,0.0016355938,0.0001550394,0.000082498555,0.00009428264,0.015526905],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997778,0.0011116735,0.000079270256,0.00021185698,0.0006572753,0.00016199672],"domain_scores_gemma":[0.9916264,0.0066560097,0.00040318663,0.0007682875,0.00041441392,0.0001315893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034251823,0.0007555075,0.00059337553,0.00077735935,0.001136314,0.0024717965,0.0012275483,0.0038432865,0.0024455914],"category_scores_gemma":[0.011409026,0.00027518885,0.00081542466,0.0005426738,0.007186228,0.0034043384,0.0026044373,0.0060232305,0.00040455148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003640516,0.000038642378,0.00043926557,0.000118571894,0.000028241131,0.00028926862,0.0002187881,0.041558985,0.00050070684,0.9268465,0.00497962,0.024944965],"study_design_scores_gemma":[0.000015730882,0.000039296752,0.00025518835,0.00018352747,0.000011902752,0.00026063126,0.00010336207,0.06339014,0.00060792995,0.9174262,0.017675757,0.000030405876],"about_ca_topic_score_codex":0.0010412356,"about_ca_topic_score_gemma":0.0007205389,"teacher_disagreement_score":0.0038432865,"about_ca_system_score_codex":0.0010596163,"about_ca_system_score_gemma":0.00066251523,"threshold_uncertainty_score":0.018114328},"labels":[],"label_agreement":null},{"id":"W4317207036","doi":"10.3390/app13031252","title":"Explainable Artificial Intelligence (XAI) for Intrusion Detection and Mitigation in Intelligent Connected Vehicles: A Review","year":2023,"lang":"en","type":"review","venue":"Applied Sciences","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Conestoga College","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Ministry of Education, Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Transparency (behavior); Computer security; Scope (computer science); Intrusion detection system; Internet of Things","score_opus":0.10063672507511486,"score_gpt":0.36797133523422526,"score_spread":0.2673346101591104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317207036","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004419712,0.98857015,0.0057538827,0.00077226694,0.00026419148,0.000016347167,0.000024730774,0.000022544553,0.004133915],"genre_scores_gemma":[0.007364082,0.98832625,0.002785986,0.00027946048,0.00036469387,0.00002687557,0.00004305547,0.000008002368,0.00080155826],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995627,0.000117102114,0.00005503183,0.00008688359,0.00014787784,0.00003036261],"domain_scores_gemma":[0.9980714,0.0015240468,0.00011311958,0.00005420357,0.00020356243,0.000033690896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001001782,0.0011195163,0.0009379399,0.0025510958,0.00033782126,0.0017701406,0.0010474824,0.0014479051,0.0028111662],"category_scores_gemma":[0.0023511115,0.00035455058,0.00087876554,0.0031236857,0.00088974164,0.0023289055,0.00077012763,0.0015822531,0.0009444139],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027444907,0.00010224306,0.0007906087,0.021253299,0.00021382855,0.00023462041,0.0002964999,0.006861944,0.00079781207,0.10739099,0.018071081,0.8439597],"study_design_scores_gemma":[0.000010918054,0.00019207218,0.0016878247,0.012879435,0.00029529943,0.0011314965,0.00034339444,0.0075627333,0.0010445324,0.07358035,0.9011839,0.00008814654],"about_ca_topic_score_codex":0.0017258495,"about_ca_topic_score_gemma":0.0015951018,"teacher_disagreement_score":0.0028111662,"about_ca_system_score_codex":0.001002212,"about_ca_system_score_gemma":0.0015095221,"threshold_uncertainty_score":0.009404302},"labels":[],"label_agreement":null},{"id":"W4318477412","doi":"10.1145/3582276","title":"The Generation of Visually Credible Adversarial Examples with Genetic Algorithms","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Evolutionary Learning and Optimization","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; MNIST database; Artificial neural network; Similarity (geometry); Machine learning; Perspective (graphical); Quality (philosophy); Perception; Image (mathematics); Psychology","score_opus":0.021469433226095777,"score_gpt":0.25957791831143495,"score_spread":0.23810848508533916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318477412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123487726,0.0004740393,0.8670696,0.0006619119,0.0000796644,0.00022043,0.000090423935,0.00092305837,0.006993087],"genre_scores_gemma":[0.6454132,0.00021742967,0.35133383,0.00033484594,0.000030307016,0.00023518836,0.00016497792,0.00014878353,0.0021213696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889284,0.00047387637,0.00004741227,0.00019706548,0.00029997074,0.00008889234],"domain_scores_gemma":[0.9941889,0.004220827,0.000420154,0.00061626616,0.0004401266,0.000113702336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022971062,0.001119116,0.0007671118,0.0009796073,0.0005694871,0.0009511477,0.0013916834,0.0015729317,0.00140299],"category_scores_gemma":[0.011515665,0.00048515882,0.0007576675,0.0004849022,0.001656697,0.0011623905,0.0015065868,0.0015508934,0.00029753408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007696269,0.0000562293,0.0010743819,0.00006508136,0.000038099588,0.00009870564,0.00011771963,0.93810886,0.0024891614,0.01352773,0.0010827153,0.043264322],"study_design_scores_gemma":[0.000016013102,0.000032279713,0.00012291185,0.00001621806,0.000008323697,0.000037316287,0.000023879189,0.9874938,0.0013364368,0.010266234,0.0006378472,0.000008734898],"about_ca_topic_score_codex":0.0027136663,"about_ca_topic_score_gemma":0.0028747332,"teacher_disagreement_score":0.0027136663,"about_ca_system_score_codex":0.001219443,"about_ca_system_score_gemma":0.0008655178,"threshold_uncertainty_score":0.01214838},"labels":[],"label_agreement":null},{"id":"W4318541558","doi":"10.1145/3575693.3575738","title":"HuffDuff: Stealing Pruned DNNs from Sparse Accelerators","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Exploit; Computer science; Edge device; Enhanced Data Rates for GSM Evolution; Side channel attack; Channel (broadcasting); State (computer science); Edge computing; Deep learning; Power (physics); Distributed computing; Computer network; Artificial intelligence; Computer security; Operating system; Cloud computing; Cryptography; Programming language","score_opus":0.0318325200345579,"score_gpt":0.2771317073615917,"score_spread":0.24529918732703382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318541558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12795389,0.00090210885,0.84954363,0.0012212106,0.0003168765,0.00008728297,0.00029167766,0.006131796,0.013551555],"genre_scores_gemma":[0.9138813,0.00019298583,0.07833169,0.0004827442,0.000044432647,0.000061323546,0.00029205566,0.00039487216,0.0063185478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999483,0.000113845876,0.000022107884,0.000082972474,0.00019400143,0.00010408907],"domain_scores_gemma":[0.9988273,0.0005897659,0.00008052589,0.00033073217,0.00011518729,0.00005662279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000883803,0.0007395511,0.00062191545,0.000376855,0.0003654764,0.0006903813,0.0014614452,0.0012311426,0.004252095],"category_scores_gemma":[0.0049250335,0.0004023193,0.00046004468,0.00028015222,0.0012509384,0.0019536568,0.0022075325,0.001786715,0.00088859414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050148164,0.000073412724,0.0010489115,0.00011434118,0.000075175885,0.00041873514,0.00010024134,0.82872623,0.013172042,0.05114698,0.007895148,0.09672735],"study_design_scores_gemma":[0.000012715311,0.000035721492,0.00005118273,0.00001073012,0.0000053670383,0.000035932953,0.000007699659,0.97722226,0.0037917169,0.01803861,0.000782362,0.0000057056304],"about_ca_topic_score_codex":0.002293971,"about_ca_topic_score_gemma":0.004219102,"teacher_disagreement_score":0.004252095,"about_ca_system_score_codex":0.0007989202,"about_ca_system_score_gemma":0.00079163106,"threshold_uncertainty_score":0.014224708},"labels":[],"label_agreement":null},{"id":"W4318719663","doi":"10.48550/arxiv.2301.11990","title":"Alignment with human representations supports robust few-shot learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Artificial intelligence; Domain (mathematical analysis); Shot (pellet); Machine learning; Mathematics","score_opus":0.1226970041717997,"score_gpt":0.23976317855043927,"score_spread":0.11706617437863957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318719663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24493253,0.0005677908,0.74543875,0.0014533802,0.00008615128,0.00007403984,0.00028098826,0.0012616954,0.005904674],"genre_scores_gemma":[0.97009003,0.00010002111,0.028551519,0.00016217264,0.000040534193,0.000025189094,0.00029148595,0.00006230622,0.000676616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604994,0.0018188871,0.00012096996,0.0010358103,0.00068583654,0.0002885953],"domain_scores_gemma":[0.9834825,0.008532525,0.0018420266,0.004846214,0.0007153731,0.0005812651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00465566,0.00095690775,0.001330662,0.0007996305,0.0007490327,0.002045609,0.001505256,0.0018836654,0.0019238776],"category_scores_gemma":[0.031683333,0.00056499266,0.00072110357,0.0007426225,0.0026760153,0.0050523244,0.003816812,0.002944467,0.0007250005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009779836,0.00047851485,0.01382043,0.0003186672,0.000490582,0.0002903941,0.0007400888,0.7457253,0.02040804,0.074635714,0.004346355,0.13776794],"study_design_scores_gemma":[0.000021375723,0.00018001081,0.0022190867,0.00001850322,0.000028243172,0.00011010989,0.0000969273,0.88887507,0.0054406147,0.10204875,0.0009348949,0.000026393958],"about_ca_topic_score_codex":0.0021378198,"about_ca_topic_score_gemma":0.0018061693,"teacher_disagreement_score":0.00465566,"about_ca_system_score_codex":0.0009058094,"about_ca_system_score_gemma":0.0010382812,"threshold_uncertainty_score":0.024621785},"labels":[],"label_agreement":null},{"id":"W4318977982","doi":"10.1007/978-3-031-10602-6_21","title":"Adversarial Autoencoders","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Generative grammar; Computer science; Point (geometry); Artificial intelligence; Noise (video); Sample (material); Quality (philosophy); Pattern recognition (psychology); Machine learning; Mathematics; Image (mathematics); Epistemology; Philosophy","score_opus":0.015036445180927066,"score_gpt":0.2299525585263899,"score_spread":0.21491611334546284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318977982","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013218874,0.0049469974,0.8555296,0.00072445127,0.000779035,0.000032625387,0.00020949072,0.001249166,0.13520673],"genre_scores_gemma":[0.13592388,0.016595226,0.26851052,0.0014592306,0.001727225,0.00018975473,0.0014779501,0.0016404623,0.57247585],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99971646,0.000049464914,0.000010280797,0.000052705615,0.00015574352,0.0000153317],"domain_scores_gemma":[0.99958485,0.00021208807,0.000017768281,0.00010536173,0.00007073455,0.000009210216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003910103,0.00096472545,0.00053696946,0.00048319733,0.00023160844,0.0010698278,0.00069397996,0.00089111103,0.0158703],"category_scores_gemma":[0.0016230489,0.00035912034,0.00039022425,0.0005588165,0.00079665554,0.0014919193,0.0010989431,0.0019613693,0.010221295],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002540389,0.000038842918,0.00011757141,0.00019163324,0.000047293855,0.000075955075,0.000045767927,0.1167038,0.004525992,0.2917922,0.08300213,0.5034334],"study_design_scores_gemma":[0.000005813904,0.000037494614,0.00032113155,0.00015648403,0.000030146366,0.00031029468,0.000018833727,0.37407297,0.009151377,0.33036843,0.28548414,0.000042957094],"about_ca_topic_score_codex":0.00048710225,"about_ca_topic_score_gemma":0.00072763494,"teacher_disagreement_score":0.0158703,"about_ca_system_score_codex":0.00038623586,"about_ca_system_score_gemma":0.00027010677,"threshold_uncertainty_score":0.053091466},"labels":[],"label_agreement":null},{"id":"W4319430780","doi":"10.1145/3551902.3565070","title":"Security Patterns for Machine Learning: The Data-Oriented Stages","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computer security; Artificial intelligence; Data science; Human–computer interaction","score_opus":0.02830365899215078,"score_gpt":0.2898578745624073,"score_spread":0.26155421557025654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319430780","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005808604,0.00049051386,0.98408544,0.0040718718,0.00009354276,0.0002642757,0.0001222245,0.0007147093,0.0043487796],"genre_scores_gemma":[0.09778117,0.0009464884,0.8966118,0.0007986544,0.000104926236,0.00043184194,0.00029946255,0.00037947623,0.0026461082],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9819579,0.00676642,0.0025886046,0.0022590996,0.0056520356,0.0007759051],"domain_scores_gemma":[0.96172225,0.015937619,0.002777531,0.01448804,0.0044310554,0.00064342574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01916337,0.0017187995,0.00092763285,0.0026336499,0.0020373284,0.0071814875,0.0028210254,0.0029681511,0.0030684131],"category_scores_gemma":[0.044845477,0.0015967846,0.001870741,0.001742175,0.008379182,0.014394481,0.0066661267,0.007955,0.0017086099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001679616,0.00011757308,0.004955517,0.00057276123,0.00008490905,0.00047621666,0.0021073062,0.016505752,0.0057975296,0.8076452,0.0061239377,0.15544534],"study_design_scores_gemma":[0.000024687237,0.00012340628,0.0011300863,0.00057052256,0.00006837798,0.0009624361,0.00063405605,0.0816447,0.018187705,0.83678496,0.05978239,0.00008672769],"about_ca_topic_score_codex":0.0012486314,"about_ca_topic_score_gemma":0.00092791795,"teacher_disagreement_score":0.01916337,"about_ca_system_score_codex":0.0019223987,"about_ca_system_score_gemma":0.0031471294,"threshold_uncertainty_score":0.10134679},"labels":[],"label_agreement":null},{"id":"W4319451761","doi":"10.1145/3583564","title":"Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Hong Kong University of Science and Technology; University of Waterloo; Cisco Systems","keywords":"Computer science; Artificial intelligence; Task (project management); Image (mathematics); Machine learning; Markov chain; Fitness function; Artificial neural network; Pattern recognition (psychology); Data mining; Genetic algorithm","score_opus":0.1374384112985051,"score_gpt":0.34852953556130783,"score_spread":0.21109112426280274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319451761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6567275,0.0015781122,0.3192862,0.0015100603,0.00024494994,0.00022099582,0.0009859035,0.014522641,0.004923607],"genre_scores_gemma":[0.9081795,0.0002649821,0.087611675,0.00042133307,0.000029622483,0.00012725929,0.0016088878,0.0003777068,0.0013789588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998982,0.00021715993,0.00007367652,0.00029657737,0.00030350397,0.00012692013],"domain_scores_gemma":[0.9952484,0.0028549836,0.00037757927,0.0007676631,0.00059598114,0.00015556865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019207406,0.0018729914,0.00077537436,0.0008672229,0.00047353373,0.0010821456,0.0020424519,0.0015064455,0.0017782181],"category_scores_gemma":[0.013020454,0.0006530997,0.0009605799,0.00050080515,0.0008988621,0.00299729,0.0012319565,0.002681914,0.00065269024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008454837,0.00045034674,0.016400902,0.00033773814,0.00025127034,0.00065195106,0.00028470988,0.6445417,0.02904478,0.0042944797,0.0077838697,0.29511276],"study_design_scores_gemma":[0.000015019484,0.00005316416,0.00057313894,0.000010748303,0.00001362051,0.000045668326,0.000022184786,0.99001837,0.0075811404,0.0013405961,0.0003167194,0.000009547821],"about_ca_topic_score_codex":0.012194132,"about_ca_topic_score_gemma":0.013611826,"teacher_disagreement_score":0.012194132,"about_ca_system_score_codex":0.0016274807,"about_ca_system_score_gemma":0.0016216232,"threshold_uncertainty_score":0.024246275},"labels":[],"label_agreement":null},{"id":"W4319663674","doi":"10.1109/tse.2023.3243522","title":"Black-Box Testing of Deep Neural Networks through Test Case Diversity","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Black box; Artificial neural network; White-box testing; Test (biology); Diversity (politics); Software testing; Artificial intelligence; Machine learning; Software engineering; Software; Programming language; Software development; Software construction","score_opus":0.02156941011280877,"score_gpt":0.23671552213532804,"score_spread":0.21514611202251926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319663674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87533295,0.0011203757,0.11948566,0.0003360079,0.000051207804,0.000094778356,0.00052418467,0.0015249815,0.0015298729],"genre_scores_gemma":[0.97160983,0.000094975425,0.027123831,0.00008977945,0.000020749952,0.000084370324,0.0006373148,0.00009197805,0.0002472018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929224,0.0026434667,0.0006068602,0.001445215,0.001918922,0.00046309963],"domain_scores_gemma":[0.92885566,0.054989837,0.0063725286,0.0050754384,0.0035818666,0.0011246372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065192985,0.0014387339,0.00084518024,0.0027192067,0.00038831812,0.0012053519,0.002055702,0.0012377034,0.0007586982],"category_scores_gemma":[0.046381094,0.0004723933,0.00079732656,0.0010948532,0.0013593095,0.0031075014,0.0018733774,0.0012428754,0.00016813156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012906842,0.0005188225,0.10954087,0.00038698706,0.00051128474,0.00065588555,0.0005118351,0.6672203,0.015461934,0.005416331,0.002007034,0.196478],"study_design_scores_gemma":[0.000035263358,0.00029020253,0.0056914836,0.000035766654,0.00004316562,0.00015222379,0.00007504083,0.9787965,0.009196274,0.005246384,0.00041632436,0.00002144429],"about_ca_topic_score_codex":0.0029680692,"about_ca_topic_score_gemma":0.0041303043,"teacher_disagreement_score":0.0065192985,"about_ca_system_score_codex":0.001389049,"about_ca_system_score_gemma":0.0010316157,"threshold_uncertainty_score":0.03447777},"labels":[],"label_agreement":null},{"id":"W4320717670","doi":"10.1016/j.knosys.2023.110384","title":"Label noise analysis meets adversarial training: A defense against label poisoning in federated learning","year":2023,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backdoor; Computer science; Noise (video); Adversarial system; Artificial intelligence; Machine learning; Artificial noise; The Internet; Differential privacy; Trojan; Data mining; Key (lock); Computer security; Computer network; World Wide Web","score_opus":0.04187831450664831,"score_gpt":0.2963231359441755,"score_spread":0.25444482143752717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320717670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019783752,0.00024309848,0.9763326,0.00096839236,0.000079545454,0.000058921916,0.000059605674,0.0010857929,0.0013882933],"genre_scores_gemma":[0.842241,0.00015316221,0.15349182,0.0009092636,0.00014780095,0.00010282354,0.00016000337,0.00017878834,0.0026153524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923955,0.002968403,0.0002702245,0.0014183632,0.002366737,0.00058080966],"domain_scores_gemma":[0.9699358,0.016833436,0.0019910622,0.008591408,0.0021288441,0.0005194059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009309467,0.0013614609,0.0021276018,0.0012817108,0.0017453266,0.002525182,0.0035646409,0.005312326,0.0011582131],"category_scores_gemma":[0.03523502,0.00084965397,0.0012387696,0.0010770977,0.0045107473,0.004908845,0.008278188,0.0060180486,0.00058234995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015739559,0.00046899542,0.0040959776,0.0002732651,0.00035775205,0.0004881066,0.00060707016,0.5859758,0.020945495,0.12559557,0.009753394,0.24986471],"study_design_scores_gemma":[0.00001668363,0.000058265872,0.00017035652,0.000018411001,0.000019746734,0.00009773716,0.000026208043,0.93864834,0.005168267,0.055187214,0.0005750822,0.000013706459],"about_ca_topic_score_codex":0.0013155306,"about_ca_topic_score_gemma":0.0010445556,"teacher_disagreement_score":0.009309467,"about_ca_system_score_codex":0.0017382643,"about_ca_system_score_gemma":0.0020875013,"threshold_uncertainty_score":0.049233794},"labels":[],"label_agreement":null},{"id":"W4321790328","doi":"10.1145/3584666","title":"Generative Adversarial Networks for Cyber Threat Hunting in Ethereum Blockchain","year":2023,"lang":"en","type":"article","venue":"Distributed Ledger Technologies Research and Practice","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Adversarial system; Blockchain; Exploit; Computer science; Computer security; Generative grammar; Internet of Things; Artificial intelligence","score_opus":0.07968166379482058,"score_gpt":0.3951902380515994,"score_spread":0.3155085742567788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321790328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21129404,0.0013935638,0.77424175,0.0014066183,0.00015806447,0.00009523253,0.00021804534,0.0013173685,0.009875398],"genre_scores_gemma":[0.97956526,0.00020658909,0.015610694,0.0001452074,0.000021700738,0.00004261341,0.00011147983,0.000036201596,0.0042602667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996867,0.00010350629,0.0000124180115,0.00007210593,0.00006681167,0.000058461424],"domain_scores_gemma":[0.99914193,0.0005632468,0.000097213786,0.00006465676,0.00009550986,0.00003738579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008359919,0.0006277245,0.000585596,0.00037332874,0.00032393917,0.000592908,0.0007793438,0.00091954676,0.0019188146],"category_scores_gemma":[0.0018049438,0.0003598725,0.0006076611,0.00023916365,0.0009303188,0.0010044752,0.0009579308,0.0014361284,0.00025224203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036402005,0.000015779313,0.0006340076,0.000016319513,0.000015921158,0.00007056067,0.00003171653,0.9829547,0.00082235393,0.004320267,0.00030207075,0.0107799545],"study_design_scores_gemma":[0.0000011955124,0.0000074505137,0.00005862642,0.0000016907957,0.0000021194733,0.000007681303,0.0000021618516,0.9980361,0.00018256181,0.0016096241,0.00008898913,0.0000018979496],"about_ca_topic_score_codex":0.0065238927,"about_ca_topic_score_gemma":0.0070113507,"teacher_disagreement_score":0.0065238927,"about_ca_system_score_codex":0.0009187046,"about_ca_system_score_gemma":0.000537831,"threshold_uncertainty_score":0.012971878},"labels":[],"label_agreement":null},{"id":"W4322772100","doi":"10.3390/s23052697","title":"Evaluation of GAN-Based Model for Adversarial Training","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Training (meteorology); Computer science; Training set; Artificial intelligence; Engineering; Physics","score_opus":0.1395842735723996,"score_gpt":0.3546751449782767,"score_spread":0.21509087140587707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322772100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23815908,0.0032371883,0.7328173,0.0013975395,0.0005234529,0.0003601757,0.00045911016,0.0026525266,0.020393632],"genre_scores_gemma":[0.9506383,0.0005812429,0.04580206,0.0002167224,0.000031859523,0.00012290046,0.0003800897,0.000118834636,0.0021079516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998623,0.0005356197,0.000053776697,0.00017592355,0.00048133894,0.00013033394],"domain_scores_gemma":[0.99724627,0.0016785221,0.00017065126,0.00032084287,0.00046837368,0.00011536175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025709257,0.001223175,0.0007570985,0.000550032,0.00028849888,0.00078359403,0.0011016857,0.0008867338,0.0017653416],"category_scores_gemma":[0.0059704203,0.00022933783,0.00047642709,0.00031160115,0.0008241817,0.0011176888,0.0011098266,0.0015220093,0.00036307066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027181703,0.000080012054,0.0012161264,0.00013652108,0.000060538816,0.00007747538,0.000029851992,0.9548668,0.0044302745,0.0059787575,0.0017589604,0.03109282],"study_design_scores_gemma":[0.000004789216,0.00006999489,0.00014920454,0.000009090553,0.000005743048,0.000024648669,0.0000053144813,0.995915,0.0027720646,0.000742034,0.00029677735,0.0000053780086],"about_ca_topic_score_codex":0.0028789095,"about_ca_topic_score_gemma":0.0021377727,"teacher_disagreement_score":0.0028789095,"about_ca_system_score_codex":0.0011605778,"about_ca_system_score_gemma":0.00068523694,"threshold_uncertainty_score":0.013596535},"labels":[],"label_agreement":null},{"id":"W4323266611","doi":"10.1016/j.artint.2023.103897","title":"Temporal logic explanations for dynamic decision systems using anchors and Monte Carlo Tree Search","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Black box; Monte Carlo tree search; Artificial intelligence; Decision tree; Machine learning; Monte Carlo method; State (computer science); Artificial neural network; Tree (set theory); Perception; Control (management); Complex system; Theoretical computer science; Algorithm; Mathematics","score_opus":0.1235831946043025,"score_gpt":0.38312002421067154,"score_spread":0.25953682960636903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323266611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015989067,0.00018777275,0.9792867,0.0007565477,0.00007140854,0.000044475233,0.00020869759,0.00037913,0.0030761415],"genre_scores_gemma":[0.790867,0.00029501467,0.20300747,0.00030679934,0.00015777932,0.00018148913,0.0006505211,0.0001863312,0.004347588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984799,0.00064797845,0.00009819316,0.00027605952,0.00035150006,0.00014637283],"domain_scores_gemma":[0.9851452,0.012639535,0.00066044676,0.0006555059,0.0006265281,0.00027271974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028387506,0.00075756,0.0012344546,0.0016617562,0.00088929455,0.002057065,0.0017835577,0.0020418833,0.010751455],"category_scores_gemma":[0.022661567,0.0006574838,0.0014907754,0.0011051102,0.0018196885,0.0043675895,0.002224086,0.0031035405,0.0006756361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014908488,0.000079233825,0.0008186585,0.00010970313,0.00006693418,0.00018151685,0.00020986075,0.57073915,0.0004354419,0.39096376,0.0026998501,0.03354674],"study_design_scores_gemma":[0.000010199821,0.000007776661,0.00004324079,0.000011852667,0.0000065241384,0.000009501414,0.000010662971,0.8459181,0.000092139584,0.15359981,0.0002840853,0.0000060999732],"about_ca_topic_score_codex":0.0057402994,"about_ca_topic_score_gemma":0.0070441673,"teacher_disagreement_score":0.010751455,"about_ca_system_score_codex":0.0017953046,"about_ca_system_score_gemma":0.0016699885,"threshold_uncertainty_score":0.03596723},"labels":[],"label_agreement":null},{"id":"W4323545679","doi":"10.1016/j.asoc.2023.110173","title":"A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of New Brunswick","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Intrusion detection system; Deep learning; Machine learning; Transferability; Artificial neural network; Deep neural networks; Jacobian matrix and determinant; Data mining; Pattern recognition (psychology); Mathematics","score_opus":0.02341448391837548,"score_gpt":0.26293871996507034,"score_spread":0.23952423604669487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323545679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03289385,0.00043360787,0.9633274,0.00031367748,0.000060588893,0.00007176263,0.00003201723,0.0012569053,0.0016101869],"genre_scores_gemma":[0.86299115,0.0002444988,0.13369244,0.00026524204,0.000060676746,0.00009112542,0.00011522969,0.000117581985,0.0024219758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993007,0.0001596293,0.000036697125,0.00012635933,0.0002798465,0.000096812866],"domain_scores_gemma":[0.9990472,0.0004124706,0.00011707366,0.00011113249,0.00023752371,0.00007459333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013619012,0.0010703962,0.00095077127,0.0009241011,0.00039078936,0.0005743469,0.0012610327,0.00077351386,0.0011556597],"category_scores_gemma":[0.0032709653,0.00038048092,0.00059752184,0.00043599785,0.0009793931,0.0011651381,0.0014674189,0.00153265,0.00023932243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013408804,0.000078566685,0.00073891296,0.00004551143,0.000042120108,0.00007973699,0.000044687036,0.87834257,0.0054118703,0.008501616,0.0015236012,0.1050567],"study_design_scores_gemma":[0.0000020702998,0.000013115531,0.000037770948,0.0000011239614,0.0000017848905,0.0000072752546,9.933701e-7,0.99834883,0.00051673996,0.0009720672,0.00009659444,0.0000016562905],"about_ca_topic_score_codex":0.0039383974,"about_ca_topic_score_gemma":0.0030708164,"teacher_disagreement_score":0.0039383974,"about_ca_system_score_codex":0.0011285954,"about_ca_system_score_gemma":0.0009027975,"threshold_uncertainty_score":0.008188605},"labels":[],"label_agreement":null},{"id":"W4323556281","doi":"10.48550/arxiv.2303.02322","title":"Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Robustness (evolution); Computer science; Machine learning; Artificial intelligence; Convolutional neural network","score_opus":0.08886710133815569,"score_gpt":0.2166347461521532,"score_spread":0.1277676448139975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323556281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1533117,0.0006411732,0.83783346,0.00053556095,0.00015922148,0.000057395384,0.000116545045,0.00077402935,0.0065709506],"genre_scores_gemma":[0.9475024,0.00027642486,0.05031767,0.00013051872,0.000058921356,0.000043772194,0.000092315546,0.000069760965,0.0015082408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980332,0.00052552036,0.00010115613,0.00033511218,0.00074467855,0.00026033126],"domain_scores_gemma":[0.99272704,0.003306123,0.00069814053,0.0020932907,0.0009991814,0.00017633452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002151902,0.000904486,0.00072730734,0.0006176243,0.0005857476,0.0009570126,0.0009895298,0.0011824387,0.0011158793],"category_scores_gemma":[0.011769942,0.00028138168,0.0005634272,0.0005431818,0.001392384,0.0015864538,0.0022762713,0.0020611961,0.00038098218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015592187,0.00004036212,0.0025962195,0.00007195435,0.00012730512,0.00014216223,0.00009249623,0.88703626,0.021506215,0.036421932,0.001007269,0.05080189],"study_design_scores_gemma":[0.000006994102,0.00009847658,0.00045805585,0.000019721854,0.000022262171,0.00011576739,0.000021917027,0.9655246,0.015469334,0.017065208,0.001177409,0.000020373738],"about_ca_topic_score_codex":0.0008311175,"about_ca_topic_score_gemma":0.000869198,"teacher_disagreement_score":0.002151902,"about_ca_system_score_codex":0.00056501175,"about_ca_system_score_gemma":0.00059840095,"threshold_uncertainty_score":0.011380494},"labels":[],"label_agreement":null},{"id":"W4323651448","doi":"10.48550/arxiv.2303.04075","title":"Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots (evolved version)","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Canadian Institute for Advanced Research","keywords":"Robot; Exploit; Computer science; Adversarial system; Artificial intelligence; Communication source; Task (project management); Statistical hypothesis testing; Computer security; Machine learning; Computer network; Mathematics","score_opus":0.1575077665701807,"score_gpt":0.21140974359120748,"score_spread":0.05390197702102678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323651448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008442741,0.00011299613,0.99006736,0.0002494846,0.000027110073,0.000051792933,0.00003821165,0.0003661361,0.0006442075],"genre_scores_gemma":[0.6732546,0.00015700926,0.32390216,0.00039470973,0.00011372069,0.00028009954,0.00016193118,0.00012060967,0.001615082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99328125,0.0027425697,0.000319174,0.001233935,0.0018381354,0.0005848765],"domain_scores_gemma":[0.9807827,0.012412036,0.0018296425,0.0029118257,0.0015668167,0.0004969319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009067711,0.0012535212,0.0015514436,0.0010974548,0.00062653836,0.0016473965,0.003197186,0.0021112585,0.001833992],"category_scores_gemma":[0.040076535,0.00069904036,0.0015861992,0.00072270515,0.003402302,0.0031419129,0.005148242,0.003017833,0.00057876215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041476448,0.00010760813,0.002329303,0.00014362462,0.0001708246,0.0004665816,0.0003361839,0.8122551,0.0055639865,0.094051935,0.001334197,0.08282595],"study_design_scores_gemma":[0.000023698765,0.000062332096,0.00013592279,0.000010478172,0.000011715261,0.000059528036,0.000009587448,0.96737516,0.001530454,0.030381216,0.00038692582,0.000012995354],"about_ca_topic_score_codex":0.001927239,"about_ca_topic_score_gemma":0.0010211198,"teacher_disagreement_score":0.009067711,"about_ca_system_score_codex":0.0017151419,"about_ca_system_score_gemma":0.0018071126,"threshold_uncertainty_score":0.047955215},"labels":[],"label_agreement":null},{"id":"W4324125491","doi":"10.1109/tnnls.2023.3252175","title":"Adversarial Danger Identification on Temporally Dynamic Graphs","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Computer science; Adversarial system; Artificial intelligence; Multivariate statistics; Identification (biology); Machine learning; Generalization; Data mining; Mathematics","score_opus":0.01214025023442221,"score_gpt":0.24937975096022522,"score_spread":0.237239500725803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324125491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10364437,0.0003015438,0.8926534,0.000497981,0.00007600946,0.0000365107,0.0001007494,0.00047164824,0.002217705],"genre_scores_gemma":[0.96711785,0.00020724526,0.030334264,0.00015294414,0.000036257577,0.000027895838,0.00015924833,0.000046867568,0.0019175255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99936026,0.0001797423,0.000028557968,0.00016974317,0.00016085498,0.00010079534],"domain_scores_gemma":[0.9966169,0.0022572125,0.00043196985,0.00027492788,0.00029057616,0.00012842243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001272476,0.00086052506,0.000638007,0.0007558485,0.00044552897,0.00064940326,0.0008591339,0.0009677897,0.0009401454],"category_scores_gemma":[0.0059436383,0.00027337478,0.0006154182,0.00042661026,0.001025633,0.0017384207,0.0013329071,0.0016741148,0.00017378309],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009101941,0.000016096428,0.001070592,0.00002522711,0.000022143317,0.00012448497,0.00005328786,0.95644414,0.0021421337,0.012547533,0.00076547224,0.026697863],"study_design_scores_gemma":[0.0000011175626,0.00000759681,0.00012185304,0.0000020822627,0.000002547254,0.000016513333,0.0000056151966,0.9946648,0.0004363848,0.004611884,0.00012661291,0.00000300022],"about_ca_topic_score_codex":0.0037754872,"about_ca_topic_score_gemma":0.002667689,"teacher_disagreement_score":0.0037754872,"about_ca_system_score_codex":0.0009591267,"about_ca_system_score_gemma":0.0006186061,"threshold_uncertainty_score":0.007507026},"labels":[],"label_agreement":null},{"id":"W4327919584","doi":"10.1016/j.micpro.2023.104800","title":"EAM: Ensemble of approximate multipliers for robust DNNs","year":2023,"lang":"en","type":"article","venue":"Microprocessors and Microsystems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Robustness (evolution); Deep neural networks; Transferability; Exploit; Adversarial system; Multiplier (economics); Range (aeronautics); Deep learning; Artificial intelligence; Computer engineering; Machine learning; Computer security","score_opus":0.021347535203661363,"score_gpt":0.25520956410081114,"score_spread":0.23386202889714977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327919584","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037859385,0.000390616,0.99361223,0.00007975062,0.000080788945,0.000039510414,0.00011291918,0.0012294356,0.00066891266],"genre_scores_gemma":[0.13714842,0.00039559862,0.85490435,0.00024635534,0.00015023422,0.00028209778,0.00077327056,0.00041014646,0.0056894612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993599,0.00018104888,0.00003706072,0.00016471534,0.00019279832,0.000064571774],"domain_scores_gemma":[0.9990644,0.0003840054,0.00007459731,0.000177413,0.00025011966,0.00004956889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019751978,0.0016263849,0.0013741389,0.00093864265,0.00041803485,0.0009872674,0.0021370791,0.0019873164,0.0058843647],"category_scores_gemma":[0.0045831613,0.00084346975,0.0008633153,0.00083549187,0.00044604315,0.0018020929,0.0019309069,0.0025696738,0.001969371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024140408,0.000068414614,0.0004202017,0.00011470784,0.00016846301,0.00005880029,0.000034376433,0.5587046,0.0053743003,0.008936076,0.006225103,0.4196535],"study_design_scores_gemma":[0.000008584534,0.000034505687,0.00005233819,0.000009414375,0.0000101518635,0.000018655137,0.000003287468,0.9942356,0.0015748659,0.002863559,0.0011837622,0.0000052724513],"about_ca_topic_score_codex":0.0024746798,"about_ca_topic_score_gemma":0.0050596143,"teacher_disagreement_score":0.0058843647,"about_ca_system_score_codex":0.000645274,"about_ca_system_score_gemma":0.0011888638,"threshold_uncertainty_score":0.019685209},"labels":[],"label_agreement":null},{"id":"W4362606928","doi":"10.1007/s10664-023-10291-1","title":"Bugs in machine learning-based systems: a faultload benchmark","year":2023,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Polytechnique Montréal","funders":"","keywords":"Benchmark (surveying); Computer science; Software portability; Debugging; Software bug; Software quality; Usability; Software engineering; Software; Relevance (law); Benchmarking; Machine learning; Software development; Operating system","score_opus":0.017192324944400338,"score_gpt":0.26297108381207424,"score_spread":0.2457787588676739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362606928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9597038,0.0011273556,0.030170726,0.0012767354,0.00020295933,0.00008055145,0.001064521,0.0021037685,0.004269484],"genre_scores_gemma":[0.98811406,0.00012344135,0.009742257,0.00008884154,0.000039103907,0.000040093808,0.00088569435,0.00017071204,0.000795804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99603385,0.00159428,0.0002751331,0.00054241304,0.0012448616,0.0003095715],"domain_scores_gemma":[0.92675006,0.059215926,0.0023497317,0.0072104842,0.0036368587,0.000836858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004833115,0.00083510816,0.00054357434,0.00157792,0.00061237393,0.0006558035,0.0014568348,0.0018491122,0.002227243],"category_scores_gemma":[0.050519135,0.00032477968,0.00055213436,0.0011695019,0.0017432601,0.0019191735,0.0014054023,0.001350043,0.00032217745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023245534,0.0023155957,0.025259994,0.0008620161,0.00029391117,0.0006188678,0.000343471,0.82974476,0.0065531465,0.023947496,0.024118425,0.08361784],"study_design_scores_gemma":[0.00028614487,0.0007395287,0.0047200355,0.000036063066,0.00004902907,0.00019094434,0.00008493661,0.97157884,0.005104201,0.015917365,0.0012740724,0.000018856801],"about_ca_topic_score_codex":0.0026405952,"about_ca_topic_score_gemma":0.0028330053,"teacher_disagreement_score":0.004833115,"about_ca_system_score_codex":0.0011536666,"about_ca_system_score_gemma":0.0010129695,"threshold_uncertainty_score":0.02556026},"labels":[],"label_agreement":null},{"id":"W4362721716","doi":"10.1145/3591870","title":"PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program; National Key Research and Development Program of China; Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Transformer; Convolutional neural network; Computer security; Software deployment; Computer engineering; Machine learning; Real-time computing; Software engineering; Electrical engineering","score_opus":0.13330348676148915,"score_gpt":0.33313640744718626,"score_spread":0.1998329206856971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362721716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08708649,0.0023197576,0.8522493,0.001089546,0.00049344136,0.00066060515,0.0010376738,0.04225977,0.012803308],"genre_scores_gemma":[0.80239546,0.000648586,0.1855823,0.00079870125,0.00016385746,0.00044885153,0.0024584117,0.0027868575,0.0047170524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99450374,0.001316194,0.00038086477,0.0010850584,0.0021154864,0.0005986715],"domain_scores_gemma":[0.9823601,0.0085227,0.0012434222,0.0050570406,0.0022296829,0.0005869571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051216413,0.0019654038,0.001565165,0.0018448435,0.00070688745,0.0013479461,0.0033831159,0.0021402114,0.011923277],"category_scores_gemma":[0.032948557,0.0007353997,0.0016930689,0.000662706,0.0028030027,0.004531494,0.0035724943,0.002491381,0.003412743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00252742,0.0006745541,0.015516168,0.0017890633,0.0005094242,0.0011465122,0.00030646738,0.26173392,0.053823195,0.044276718,0.044998,0.5726986],"study_design_scores_gemma":[0.0002518859,0.0008043732,0.0015236796,0.00014862999,0.00008216672,0.00063182175,0.00011188397,0.9323044,0.02379449,0.033028275,0.007259633,0.00005889936],"about_ca_topic_score_codex":0.0017684147,"about_ca_topic_score_gemma":0.0024984395,"teacher_disagreement_score":0.011923277,"about_ca_system_score_codex":0.0011329758,"about_ca_system_score_gemma":0.0026540842,"threshold_uncertainty_score":0.03988737},"labels":[],"label_agreement":null},{"id":"W4364305273","doi":"10.1109/aipr57179.2022.10092213","title":"Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Robustness (evolution); Computer science; Adversarial system; Artificial intelligence; Machine learning; Overhead (engineering); Deep learning; Object detection; Pipeline (software); Adversarial machine learning; Pattern recognition (psychology)","score_opus":0.006728922991036009,"score_gpt":0.23386590071352278,"score_spread":0.22713697772248678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364305273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0986791,0.00073576276,0.8947192,0.0005974275,0.00007434718,0.000041559793,0.000075367185,0.0013943735,0.0036829],"genre_scores_gemma":[0.9516272,0.00024161808,0.045757495,0.0002720168,0.000041457813,0.000032926953,0.0001114372,0.000092787144,0.0018231795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921584,0.00024594687,0.000027645947,0.00012734854,0.00023369388,0.000149521],"domain_scores_gemma":[0.9975508,0.0015450874,0.00027810948,0.00034257464,0.00019415913,0.0000892555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022495217,0.0009861995,0.00076229067,0.00053030957,0.00033831794,0.00076261756,0.0013061166,0.0009764647,0.0010013016],"category_scores_gemma":[0.0057190945,0.00042475158,0.00057959737,0.00032140347,0.0019054317,0.0019581125,0.002104967,0.0022592056,0.00030797554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011061896,0.00005129171,0.0006949706,0.00003760273,0.00003440434,0.00005743446,0.000048898986,0.95033145,0.0052243383,0.009809409,0.00089696725,0.032702606],"study_design_scores_gemma":[0.0000023336067,0.000020637095,0.00008034285,0.000005275013,0.0000030963981,0.000013759402,0.000004131601,0.9944132,0.001706208,0.0035847346,0.00016291576,0.0000033481708],"about_ca_topic_score_codex":0.0020095876,"about_ca_topic_score_gemma":0.0018503184,"teacher_disagreement_score":0.0022495217,"about_ca_system_score_codex":0.001046278,"about_ca_system_score_gemma":0.0007800991,"threshold_uncertainty_score":0.011896789},"labels":[],"label_agreement":null},{"id":"W4365787961","doi":"10.36227/techrxiv.20085902","title":"Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Robustness (evolution); Computer security; Cover (algebra); Field (mathematics); Artificial intelligence; Deep learning; Data science; Engineering","score_opus":0.024039486634767498,"score_gpt":0.3164262645323233,"score_spread":0.2923867778975558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365787961","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010005766,0.09054887,0.8751847,0.0026297586,0.00069720886,0.00021629153,0.00013268575,0.0009228522,0.019661777],"genre_scores_gemma":[0.6124836,0.125584,0.2361625,0.0032931315,0.0026804314,0.0005762896,0.00068102416,0.00062662706,0.017912429],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976708,0.00060949376,0.00019925354,0.00041967124,0.0009066606,0.0001941188],"domain_scores_gemma":[0.99338794,0.004652329,0.00044347232,0.0009810998,0.00039383856,0.0001412969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031882734,0.001923904,0.0020541218,0.0017082706,0.0006564091,0.0021095613,0.0022126106,0.0031195218,0.0027363175],"category_scores_gemma":[0.009387573,0.00087142835,0.0018224674,0.0013870847,0.0027046583,0.003976381,0.003340577,0.0043507805,0.0012390888],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023346797,0.00023470337,0.0025015208,0.0018853948,0.00039695646,0.00031959286,0.00028762824,0.19152276,0.0061394554,0.15412699,0.019904677,0.6224469],"study_design_scores_gemma":[0.000050609764,0.0005806122,0.0022639195,0.0009573079,0.00019349782,0.0016640039,0.00018162603,0.7499353,0.008902861,0.16950308,0.06564725,0.000119819415],"about_ca_topic_score_codex":0.000821638,"about_ca_topic_score_gemma":0.000591692,"teacher_disagreement_score":0.0031882734,"about_ca_system_score_codex":0.00094532315,"about_ca_system_score_gemma":0.0007125237,"threshold_uncertainty_score":0.01686138},"labels":[],"label_agreement":null},{"id":"W4366087622","doi":"10.1145/3593045","title":"<i>XploreNAS</i> : Explore Adversarially Robust and Hardware-efficient Neural Architectures for Non-ideal Xbars","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Embedded Computing Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Robustness (evolution); Crossbar switch; Benchmark (surveying); Software deployment; Artificial neural network; Deep neural networks; Ideal (ethics); Computer engineering; Hardware acceleration; Distributed computing; Embedded system; Artificial intelligence","score_opus":0.036079654297941334,"score_gpt":0.281352260344326,"score_spread":0.24527260604638465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366087622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13471007,0.0011633538,0.8182919,0.0017144328,0.00034404616,0.00023973551,0.00037612012,0.0124355545,0.030724771],"genre_scores_gemma":[0.63310283,0.0004953708,0.35166103,0.0008264566,0.000066301465,0.0002935203,0.0007358555,0.00092500204,0.011893697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988914,0.00002281358,0.0000049258188,0.000021245867,0.000039206992,0.00002268694],"domain_scores_gemma":[0.99977213,0.00008402136,0.000027101702,0.000054123466,0.000042974483,0.000019621186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047808053,0.0009098536,0.0002688899,0.0002827583,0.0002532242,0.0006338647,0.0011538519,0.0006115417,0.006100233],"category_scores_gemma":[0.0009989071,0.00024526048,0.0003600602,0.00016484414,0.0004835256,0.0008974924,0.0009110733,0.0010267578,0.0012908882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022452365,0.00012662477,0.0020336087,0.0002095199,0.00012650611,0.00020498714,0.00006881529,0.754261,0.039387643,0.020187648,0.021289138,0.16187994],"study_design_scores_gemma":[0.000016540851,0.000111544156,0.00015115691,0.000015465333,0.0000104521605,0.000038797174,0.000014651531,0.978066,0.013225755,0.003760378,0.004581727,0.000007560051],"about_ca_topic_score_codex":0.0015821544,"about_ca_topic_score_gemma":0.003678222,"teacher_disagreement_score":0.006100233,"about_ca_system_score_codex":0.00053627486,"about_ca_system_score_gemma":0.0005971219,"threshold_uncertainty_score":0.02040732},"labels":[],"label_agreement":null},{"id":"W4366817438","doi":"10.1007/s40747-023-01060-0","title":"SGMA: a novel adversarial attack approach with improved transferability","year":2023,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Transferability; Adversarial system; Computer science; Transformation (genetics); Grid; Vulnerability (computing); Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Machine learning; Data mining; Computer security; Mathematics","score_opus":0.08224180129885214,"score_gpt":0.2956072811599041,"score_spread":0.21336547986105195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366817438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023347523,0.0002549907,0.97227263,0.0002781722,0.00007378945,0.00006283266,0.000027348931,0.0013938427,0.0022888784],"genre_scores_gemma":[0.8494771,0.00025508634,0.14447376,0.00038918288,0.00010735609,0.0001235231,0.0001100404,0.00020317678,0.004860708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890244,0.00030717193,0.000049683706,0.00018961764,0.00041681665,0.00013437495],"domain_scores_gemma":[0.99846053,0.0006505789,0.0001971373,0.0003986191,0.00018701128,0.00010618748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010874833,0.0010793302,0.0010393007,0.00068722275,0.00039864262,0.0008309805,0.001476935,0.0012751063,0.002578384],"category_scores_gemma":[0.003179919,0.0003428269,0.0009580946,0.00035555896,0.0014807006,0.0016436826,0.0026975025,0.002055163,0.0006361248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003076222,0.00013094382,0.0008738514,0.00009407715,0.00013755093,0.00026569967,0.00011474751,0.72814536,0.03688275,0.03961705,0.0044932473,0.1889371],"study_design_scores_gemma":[0.000005719355,0.00004235489,0.000060732345,0.000003279861,0.000006012875,0.00004512285,0.0000037294437,0.9913468,0.0029279469,0.0049885414,0.0005639197,0.0000058816363],"about_ca_topic_score_codex":0.0010340333,"about_ca_topic_score_gemma":0.0007819739,"teacher_disagreement_score":0.002578384,"about_ca_system_score_codex":0.0005458603,"about_ca_system_score_gemma":0.0006984746,"threshold_uncertainty_score":0.008625507},"labels":[],"label_agreement":null},{"id":"W4367682491","doi":"10.1016/j.engappai.2023.106220","title":"Learning asymmetric encryption using adversarial neural networks","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Japan Society for the Promotion of Science; Mitacs; Telecommunications Advancement Foundation; Kyushu University; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Computer science; Communication source; Alice and Bob; Plaintext; Encryption; Computer security; Public-key cryptography; Secure communication; Computer network; Alice (programming language); Artificial neural network; Information leakage; Artificial intelligence","score_opus":0.0242679939324492,"score_gpt":0.2830559774342701,"score_spread":0.25878798350182086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367682491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035935435,0.00032080803,0.95936733,0.0006186899,0.00011676639,0.00004383289,0.000064642205,0.0003587092,0.003173829],"genre_scores_gemma":[0.91865236,0.00030999872,0.075636715,0.00028433977,0.0001341343,0.00007806469,0.00014341695,0.000073305906,0.0046876464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851936,0.000492376,0.000097948054,0.00021619229,0.00046981586,0.00020431657],"domain_scores_gemma":[0.99589264,0.0024169674,0.000394345,0.0008781176,0.0003240161,0.000093867246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002490824,0.00067230343,0.0010394744,0.0005768646,0.00042569643,0.0013958652,0.0011983744,0.0014997848,0.0025107157],"category_scores_gemma":[0.009300416,0.00042189774,0.00060931913,0.0004493722,0.001429646,0.0035657876,0.0025163002,0.0025049674,0.0006199341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042255552,0.00013840674,0.000956266,0.00012611394,0.00011377528,0.00018509902,0.00008150297,0.7637565,0.008880479,0.13500535,0.002722381,0.08761161],"study_design_scores_gemma":[0.000009938949,0.000024528506,0.000054820215,0.0000072947796,0.0000063273483,0.000034381173,0.000005038025,0.96987075,0.0016250464,0.028112976,0.00024207543,0.0000067788733],"about_ca_topic_score_codex":0.00039793528,"about_ca_topic_score_gemma":0.0003978748,"teacher_disagreement_score":0.0025107157,"about_ca_system_score_codex":0.0006858906,"about_ca_system_score_gemma":0.0006508646,"threshold_uncertainty_score":0.013172865},"labels":[],"label_agreement":null},{"id":"W4367721852","doi":"10.1109/mmul.2023.3272513","title":"Interpretability of Machine Learning: Recent Advances and Future Prospects","year":2023,"lang":"en","type":"article","venue":"IEEE Multimedia","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Interpretability; Computer science; Black box; Representation (politics); Artificial intelligence; Machine learning; Deep learning; Multimedia; Data science","score_opus":0.01164153988526008,"score_gpt":0.27056473191199515,"score_spread":0.2589231920267351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367721852","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039700395,0.92855954,0.04143521,0.014389376,0.000654293,0.00001554357,0.000048649017,0.00008245368,0.01084485],"genre_scores_gemma":[0.11025977,0.85706,0.020279458,0.0022787112,0.0075465054,0.000048363192,0.00012515982,0.000056332443,0.0023456984],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985055,0.00066247705,0.00009475139,0.00026806304,0.00038759387,0.000081705584],"domain_scores_gemma":[0.9839864,0.013995185,0.0004127617,0.00049313455,0.0009334136,0.00017915115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005521724,0.00086239696,0.001091581,0.001731805,0.0004331176,0.0034433722,0.001355061,0.0021673392,0.0031750058],"category_scores_gemma":[0.01133683,0.0005079826,0.00064469123,0.0016044452,0.0034267255,0.0061705937,0.001954343,0.004012408,0.0008055458],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016502,0.00011781318,0.0016201044,0.0031960993,0.0001040387,0.00021820772,0.0003879979,0.011433109,0.0011024645,0.28448537,0.012828771,0.684341],"study_design_scores_gemma":[0.000036773126,0.0002791594,0.001919059,0.0025691302,0.00010319624,0.00080377056,0.0005698962,0.056262344,0.0021021848,0.6920959,0.24313153,0.00012707707],"about_ca_topic_score_codex":0.0006230643,"about_ca_topic_score_gemma":0.00041247183,"teacher_disagreement_score":0.005521724,"about_ca_system_score_codex":0.0013230174,"about_ca_system_score_gemma":0.0007611296,"threshold_uncertainty_score":0.029202044},"labels":[],"label_agreement":null},{"id":"W4376470742","doi":"10.23977/acss.2023.070310","title":"A Robust Combinatorial Defensive Method Based on GCN","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Robustness (evolution); Computer science; Interpretability; Adversarial system; Machine learning; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.027842435149676163,"score_gpt":0.29981514649436236,"score_spread":0.2719727113446862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376470742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01260593,0.00013628966,0.98492116,0.00014856424,0.000037618312,0.000030329496,0.000018741302,0.0006824239,0.0014188015],"genre_scores_gemma":[0.72467583,0.00027228947,0.26861128,0.0004083768,0.00008501049,0.00012749228,0.0001744342,0.0003632677,0.005282042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993368,0.00013966238,0.000026581953,0.00017956161,0.000237188,0.00008025531],"domain_scores_gemma":[0.9991818,0.00028943396,0.0001392203,0.00016559614,0.00014896298,0.00007486951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010506986,0.001309358,0.001067259,0.00086212956,0.00054579607,0.0008680418,0.0018606314,0.0013533417,0.0014697753],"category_scores_gemma":[0.0028841551,0.00040685126,0.0010089576,0.0005536236,0.0014293996,0.0017750172,0.0018244651,0.001820813,0.00041714494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009202893,0.000043757555,0.00064141216,0.00006572641,0.00006680941,0.00015786475,0.00008365868,0.84821963,0.013184365,0.030482868,0.0018381122,0.10512378],"study_design_scores_gemma":[0.0000025708125,0.000019711846,0.000042314714,0.0000025819575,0.0000060905268,0.000027609858,0.0000037739842,0.9949543,0.0010507943,0.003632551,0.0002527039,0.0000048479483],"about_ca_topic_score_codex":0.0027126677,"about_ca_topic_score_gemma":0.002334133,"teacher_disagreement_score":0.0027126677,"about_ca_system_score_codex":0.0010477114,"about_ca_system_score_gemma":0.0011782235,"threshold_uncertainty_score":0.007601738},"labels":[],"label_agreement":null},{"id":"W4376606309","doi":"10.1109/aero55745.2023.10115684","title":"Trusting Machine-Learning Applications in Aeronautics","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Standardization; Computer science; Categorization; Process (computing); Machine learning; Reinforcement learning; Artificial intelligence; Software; Space (punctuation); Software engineering","score_opus":0.01826776087013781,"score_gpt":0.27365033074853845,"score_spread":0.25538256987840063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376606309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008303572,0.048105057,0.8321291,0.043007035,0.004742373,0.00011841428,0.00039522184,0.0013826215,0.06181663],"genre_scores_gemma":[0.5859004,0.08316402,0.1915945,0.009218245,0.00982903,0.0004060571,0.0014966754,0.00085678784,0.117534295],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971553,0.0012668631,0.00015769816,0.0003323435,0.0009521542,0.00013556873],"domain_scores_gemma":[0.99045265,0.0062584435,0.00028281042,0.0012517024,0.0015375682,0.00021695494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042294622,0.0006429884,0.00081951066,0.00075473613,0.00054399855,0.0026666108,0.001121067,0.0023182717,0.008755886],"category_scores_gemma":[0.016312253,0.00036106215,0.00069898105,0.0009665056,0.0015044414,0.004024651,0.0014640953,0.0035642376,0.0031659172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001404999,0.000054723896,0.00081061735,0.00078961405,0.000089887784,0.00019910683,0.00020156396,0.047919232,0.0013066111,0.3913048,0.10479674,0.45238662],"study_design_scores_gemma":[0.000038598897,0.00011746134,0.00086661085,0.0006031633,0.000043530843,0.000286264,0.00011919724,0.27568513,0.0027117552,0.41505274,0.30442774,0.00004772122],"about_ca_topic_score_codex":0.0027547234,"about_ca_topic_score_gemma":0.0024773274,"teacher_disagreement_score":0.008755886,"about_ca_system_score_codex":0.0015807991,"about_ca_system_score_gemma":0.00093926705,"threshold_uncertainty_score":0.029291391},"labels":[],"label_agreement":null},{"id":"W4376615930","doi":"10.1002/smr.2571","title":"CodeBERT‐Attack: Adversarial attack against source code deep learning models via pre‐trained model","year":2023,"lang":"en","type":"article","venue":"Journal of Software Evolution and Process","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program; Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Source code; Adversarial system; Code (set theory); Artificial intelligence; Machine learning; Masking (illustration); Vulnerability (computing); Deep learning; Inference; Programming language; Computer security","score_opus":0.02888597047409828,"score_gpt":0.28905566858260295,"score_spread":0.26016969810850465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376615930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15380478,0.00055344496,0.83540255,0.0011324061,0.00014839011,0.0001037981,0.00012496428,0.0053279484,0.003401619],"genre_scores_gemma":[0.950242,0.00012356704,0.046810694,0.00040767886,0.000029207396,0.000058783153,0.00014067309,0.0001619894,0.0020253903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908304,0.00029835268,0.000044138626,0.00017331721,0.00026940092,0.00013169344],"domain_scores_gemma":[0.99715555,0.0015846647,0.00025145148,0.00061525626,0.00029914046,0.000093946044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012695754,0.0012339507,0.00057443214,0.00044720352,0.00035664308,0.00051926525,0.0010997447,0.0011908414,0.001424758],"category_scores_gemma":[0.006136385,0.0003554132,0.0007378841,0.00020845691,0.0011984406,0.0014694716,0.0018577335,0.002735857,0.0004277574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028224196,0.00007218385,0.0021563428,0.000072810384,0.00008282899,0.00022335077,0.00008801945,0.9066708,0.014420858,0.00828963,0.0026819184,0.06495898],"study_design_scores_gemma":[0.0000029193607,0.00002327401,0.00006360069,0.0000037689729,0.000004237324,0.000017608683,0.000002992684,0.9952141,0.0030181548,0.0014871279,0.00015915686,0.0000030255937],"about_ca_topic_score_codex":0.0028237284,"about_ca_topic_score_gemma":0.0024367014,"teacher_disagreement_score":0.0028237284,"about_ca_system_score_codex":0.00082519895,"about_ca_system_score_gemma":0.00088485546,"threshold_uncertainty_score":0.0067142844},"labels":[],"label_agreement":null},{"id":"W4378188526","doi":"10.4271/01-16-03-0019","title":"A Novel Flight Dynamics Modeling Using Robust Support Vector Regression against Adversarial Attacks","year":2023,"lang":"en","type":"article","venue":"SAE International Journal of Aerospace","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Support vector machine; Flight dynamics; Artificial intelligence; Mathematics; Algorithm; Computer science; Engineering; Aerodynamics; Aerospace engineering","score_opus":0.038718001994047985,"score_gpt":0.3151873098887777,"score_spread":0.2764693078947297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378188526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031359803,0.0002491068,0.9650445,0.00015519522,0.000047723795,0.000037318492,0.00014015476,0.0008413298,0.002124935],"genre_scores_gemma":[0.8903381,0.00036128427,0.10215722,0.000109278386,0.000058271635,0.00016889186,0.0005456572,0.00010932937,0.0061519477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997923,0.00003912137,0.0000123910595,0.00007063187,0.000058391648,0.000027143844],"domain_scores_gemma":[0.9997181,0.00009712523,0.000061172585,0.00003254557,0.00007697496,0.000014080995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041743176,0.00078097114,0.00054485165,0.00034379555,0.00025514944,0.0005902509,0.00077063823,0.00081399037,0.0014332435],"category_scores_gemma":[0.0009739844,0.0002931813,0.00083571277,0.0002509345,0.0003009616,0.00055690255,0.00063516153,0.0011520458,0.0005285197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030912484,0.00001854244,0.0007140558,0.00003423557,0.00002365845,0.0000632845,0.000024358287,0.9702509,0.0037828218,0.0012835537,0.0004677741,0.023305874],"study_design_scores_gemma":[7.8990263e-7,0.000009110519,0.000078261735,0.0000019636457,0.0000015976694,0.000004847599,0.0000015757088,0.9992538,0.00031989472,0.00013598677,0.00019001168,0.0000020951236],"about_ca_topic_score_codex":0.0064402255,"about_ca_topic_score_gemma":0.003164025,"teacher_disagreement_score":0.0064402255,"about_ca_system_score_codex":0.00025077208,"about_ca_system_score_gemma":0.00056314527,"threshold_uncertainty_score":0.012805462},"labels":[],"label_agreement":null},{"id":"W4378376437","doi":"10.1145/3600094","title":"Taxonomy and Recent Advance of Game Theoretical Approaches in Adversarial Machine Learning: A Survey","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Computer science; Adversary; Adversarial machine learning; Scope (computer science); Artificial intelligence; Set (abstract data type); Game theory; Taxonomy (biology); Machine learning; Game design; Computer security; Data science; Mathematical economics","score_opus":0.06003432928758818,"score_gpt":0.2653364159620433,"score_spread":0.20530208667445515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378376437","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047877138,0.48858166,0.45117548,0.00949612,0.0014514866,0.00022183126,0.00021962584,0.00022869403,0.043837436],"genre_scores_gemma":[0.1448558,0.68839145,0.14860515,0.0041396446,0.005667464,0.0005878565,0.00047199818,0.00019433464,0.007086316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961157,0.0014550211,0.00035687,0.00068632275,0.0011771047,0.00020901162],"domain_scores_gemma":[0.98171693,0.015285061,0.0004999067,0.0007498435,0.0014702114,0.0002779845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064633884,0.0023703903,0.0021714908,0.0041743517,0.0011087016,0.004719668,0.0032574187,0.003167245,0.003936508],"category_scores_gemma":[0.015294213,0.001317976,0.0016970566,0.005908125,0.003973715,0.008459439,0.0028831845,0.0066767316,0.0014246698],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079226294,0.00023075205,0.001991771,0.0041750246,0.00016942582,0.00017492005,0.0005105726,0.03763983,0.0006301015,0.63152075,0.012093334,0.31078437],"study_design_scores_gemma":[0.00003372374,0.00031246687,0.0016333931,0.0028864022,0.00013871837,0.0008247651,0.000592203,0.13564427,0.0009994183,0.6466046,0.21015091,0.00017915439],"about_ca_topic_score_codex":0.0023962131,"about_ca_topic_score_gemma":0.001504564,"teacher_disagreement_score":0.0064633884,"about_ca_system_score_codex":0.0028425327,"about_ca_system_score_gemma":0.0021262022,"threshold_uncertainty_score":0.03418207},"labels":[],"label_agreement":null},{"id":"W4378906632","doi":"10.21810/jicw.v6i1.5274","title":"Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology","year":2023,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Drone; Computer security; Computer science; Authorization","score_opus":0.013668144289032864,"score_gpt":0.2631486316386547,"score_spread":0.24948048734962183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378906632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07675871,0.1672201,0.51495624,0.060566645,0.0034806146,0.00015231913,0.00020308637,0.00042273724,0.1762395],"genre_scores_gemma":[0.81276023,0.08070475,0.0775597,0.0027871698,0.0015877015,0.000096490774,0.00016397628,0.0001110959,0.024228849],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991062,0.00031296726,0.00003324757,0.00013188225,0.00030153908,0.00011415351],"domain_scores_gemma":[0.9980387,0.0008748448,0.00014083125,0.00034758708,0.000413237,0.00018486496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002437441,0.0005986656,0.0003279793,0.00072957424,0.0006063389,0.0031933025,0.0008326172,0.0014591995,0.0058259554],"category_scores_gemma":[0.0037595546,0.00023385025,0.00043188382,0.00048066085,0.0026532337,0.0065233395,0.0021458727,0.0028877682,0.0008878468],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010043516,0.000085517895,0.002191969,0.00043634427,0.00004546458,0.00010449472,0.0004199389,0.0232052,0.005709396,0.6520668,0.005516906,0.31011757],"study_design_scores_gemma":[0.0000305345,0.00082430296,0.0029639709,0.00093765435,0.00005007602,0.00053196086,0.0014073309,0.11011501,0.012840455,0.5673476,0.30283234,0.0001187496],"about_ca_topic_score_codex":0.0007085841,"about_ca_topic_score_gemma":0.0008029936,"teacher_disagreement_score":0.0058259554,"about_ca_system_score_codex":0.0011640447,"about_ca_system_score_gemma":0.0010612078,"threshold_uncertainty_score":0.019489825},"labels":[],"label_agreement":null},{"id":"W4379540121","doi":"10.48550/arxiv.2306.02879","title":"Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"JST-Mirai Program; Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canadian Institute for Advanced Research; City University of Hong Kong","keywords":"Robustness (evolution); Computer science; Generalization; Artificial neural network; Neuron; Measure (data warehouse); Deep neural networks; Artificial intelligence; Biological neuron model; Distribution (mathematics); Machine learning; Data mining; Mathematics; Neuroscience; Psychology","score_opus":0.07808514671438535,"score_gpt":0.21566352145291068,"score_spread":0.13757837473852533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379540121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2899249,0.0012564096,0.7015936,0.001641397,0.00010288688,0.00010669981,0.00036215148,0.002032157,0.0029798103],"genre_scores_gemma":[0.96805173,0.00019968483,0.029942766,0.00036678833,0.00008002272,0.000059944305,0.0004421958,0.00019218873,0.00066469755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684566,0.0012903713,0.00021645325,0.00066770095,0.0007150662,0.0002648002],"domain_scores_gemma":[0.97825646,0.014211294,0.0020474922,0.0035258797,0.0012809763,0.00067795283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00732248,0.001488583,0.0015901771,0.0018836324,0.0006768694,0.0015392584,0.0024909878,0.0019793212,0.0009171741],"category_scores_gemma":[0.037396774,0.00057820085,0.0010202024,0.00086612545,0.0027997834,0.0033199056,0.0046154396,0.0032166035,0.00031861497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061787036,0.00019933298,0.045843035,0.00019014029,0.0003725906,0.00051715574,0.00054979476,0.73235756,0.00970161,0.015280006,0.003918545,0.19045234],"study_design_scores_gemma":[0.000009729575,0.00006736187,0.0020256927,0.000025494563,0.000019450588,0.0000885694,0.000035758938,0.98159975,0.002912201,0.012873428,0.00032717644,0.000015455156],"about_ca_topic_score_codex":0.0027247765,"about_ca_topic_score_gemma":0.0028296018,"teacher_disagreement_score":0.00732248,"about_ca_system_score_codex":0.0012067489,"about_ca_system_score_gemma":0.00095860596,"threshold_uncertainty_score":0.038725436},"labels":[],"label_agreement":null},{"id":"W4380089110","doi":"10.2139/ssrn.4474509","title":"Calibration Attack: Adversarial Attacks Against Model Calibration","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Queen's University","funders":"","keywords":"Adversarial system; Calibration; Computer science; Computer security; Artificial intelligence; Mathematics; Statistics","score_opus":0.03142698594566107,"score_gpt":0.2950429123845449,"score_spread":0.2636159264388838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380089110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019168537,0.00031467667,0.96972233,0.0011950415,0.00020704695,0.00007219743,0.0001486284,0.001679719,0.007491824],"genre_scores_gemma":[0.88657135,0.00038079458,0.10419834,0.0011806607,0.00025090316,0.00012736199,0.00039218002,0.00046978536,0.006428704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959294,0.0015772894,0.00014377724,0.0007292888,0.0012372349,0.0003830409],"domain_scores_gemma":[0.98984516,0.0052737133,0.0007797467,0.0034183378,0.00046712862,0.00021588843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034990301,0.0015680575,0.0013006836,0.0008894586,0.00071725494,0.0015003585,0.0016338667,0.0039981036,0.0048011076],"category_scores_gemma":[0.023215283,0.00068455975,0.001177025,0.0008726086,0.002431453,0.0036139386,0.0071579814,0.0053550103,0.0014468107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007103709,0.0001456213,0.0015300642,0.00017149288,0.000271812,0.00054596615,0.00019305528,0.6204017,0.019411482,0.21916234,0.014986814,0.122469395],"study_design_scores_gemma":[0.000026187723,0.000072173796,0.00022589692,0.000024388746,0.000019354711,0.00024569823,0.00002033454,0.89823127,0.0070214304,0.09177983,0.0023103731,0.000023094213],"about_ca_topic_score_codex":0.00036016043,"about_ca_topic_score_gemma":0.00021578599,"teacher_disagreement_score":0.0048011076,"about_ca_system_score_codex":0.00064584555,"about_ca_system_score_gemma":0.00069962884,"threshold_uncertainty_score":0.018504858},"labels":[],"label_agreement":null},{"id":"W4380875858","doi":"10.1145/3575813.3597352","title":"Adversarial Attacks on Machine Learning-Based State Estimation in Power Distribution Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Robustness (evolution); Computer science; Adversarial machine learning; Artificial intelligence; Machine learning; Adversarial system","score_opus":0.012067012849457675,"score_gpt":0.2650432535339254,"score_spread":0.2529762406844677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380875858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09436528,0.00023196633,0.90212494,0.0006247072,0.000041693762,0.000030889845,0.00004210822,0.00030168862,0.0022367092],"genre_scores_gemma":[0.98676294,0.00009749708,0.012565864,0.00006519187,0.00002058885,0.000015363032,0.000017363696,0.00001841104,0.00043678892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787486,0.001175003,0.00006446235,0.00025412044,0.00042586846,0.00020563391],"domain_scores_gemma":[0.98122555,0.015474546,0.0013810061,0.0012193772,0.00056103786,0.00013853802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031360537,0.0007783303,0.0007090326,0.00047694074,0.00040845544,0.00077169354,0.00071335025,0.00088669534,0.0006786867],"category_scores_gemma":[0.01776321,0.00037214762,0.00044368408,0.00042059447,0.0020755404,0.0018118522,0.0015015261,0.0017911956,0.00012145476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044407494,0.000009716362,0.00048180256,0.000010034528,0.000017248049,0.000033658376,0.000020695052,0.9831033,0.00073689217,0.011179443,0.00012578421,0.0042369803],"study_design_scores_gemma":[0.0000018412302,0.000011921361,0.00006611036,0.0000019823717,0.0000012638029,0.0000068467043,0.0000020502187,0.9954904,0.00040196071,0.0039695306,0.000043204505,0.0000028953498],"about_ca_topic_score_codex":0.002418309,"about_ca_topic_score_gemma":0.0011186721,"teacher_disagreement_score":0.0031360537,"about_ca_system_score_codex":0.0010468414,"about_ca_system_score_gemma":0.0005867861,"threshold_uncertainty_score":0.01658523},"labels":[],"label_agreement":null},{"id":"W4381053704","doi":"10.1007/s11432-022-3580-5","title":"Toward actionable testing of deep learning models","year":2023,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Data science","score_opus":0.06458478689624302,"score_gpt":0.3011856642803998,"score_spread":0.23660087738415675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381053704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048066027,0.00022804494,0.9447088,0.0016029611,0.000086011525,0.00007734127,0.00022191479,0.002130653,0.0028781462],"genre_scores_gemma":[0.8677555,0.00009717879,0.12787674,0.00062733324,0.00009226659,0.0001713889,0.0005565269,0.00037151235,0.0024515793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926162,0.0038796163,0.0003495215,0.0011063176,0.0014541675,0.0005942182],"domain_scores_gemma":[0.9460824,0.04246091,0.0020374916,0.0059612296,0.0022713358,0.0011866015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010601793,0.001799322,0.0016599093,0.001380153,0.0006498849,0.0020405818,0.004226581,0.003653403,0.003800986],"category_scores_gemma":[0.056785274,0.001067825,0.0014637497,0.00061881775,0.00423633,0.0042836843,0.0067563555,0.0048516216,0.0004975392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006230421,0.00019644978,0.0033620442,0.00017271348,0.00015926354,0.00026959114,0.00014661274,0.82911384,0.0053510168,0.0918229,0.0035236927,0.06525893],"study_design_scores_gemma":[0.000014383399,0.00002576126,0.000080941085,0.000011093745,0.00000515261,0.000011212138,0.000006634336,0.96349835,0.0010148642,0.035198893,0.00012810409,0.0000044808685],"about_ca_topic_score_codex":0.002721635,"about_ca_topic_score_gemma":0.0026607711,"teacher_disagreement_score":0.010601793,"about_ca_system_score_codex":0.002167496,"about_ca_system_score_gemma":0.0024655848,"threshold_uncertainty_score":0.0560683},"labels":[],"label_agreement":null},{"id":"W4381194140","doi":"10.1016/j.infsof.2023.107281","title":"Robustness assessment of hyperspectral image CNNs using metamorphic testing","year":2023,"lang":"en","type":"article","venue":"Information and Software Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Robustness (evolution); Computer science; Convolutional neural network; Artificial intelligence; Hyperspectral imaging; Machine learning; Deep learning; Leverage (statistics); Pattern recognition (psychology)","score_opus":0.025520263598895292,"score_gpt":0.2976550759759764,"score_spread":0.2721348123770811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381194140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4958724,0.00066871336,0.49465653,0.00057434186,0.00013787742,0.00010300869,0.00031063127,0.001205571,0.0064709666],"genre_scores_gemma":[0.9771014,0.00009029731,0.021725798,0.00006014719,0.000014051422,0.000019830655,0.00018472229,0.000053587624,0.0007501457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992899,0.0001638431,0.000036446592,0.00014263525,0.00028270535,0.00008449216],"domain_scores_gemma":[0.99698,0.0015126347,0.00037151435,0.00047159026,0.0005599069,0.00010436865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020498703,0.00075547834,0.0004083768,0.0008322559,0.00025199036,0.0007669923,0.00090702967,0.0009604096,0.0013500211],"category_scores_gemma":[0.009287259,0.00024643014,0.0005537402,0.00036413484,0.00090289523,0.0009690436,0.0011145438,0.00073921535,0.00018918651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055207714,0.000077836754,0.004874304,0.00011935555,0.00018300582,0.00016360462,0.00004437869,0.89027023,0.028883563,0.0066947294,0.00095132185,0.067185655],"study_design_scores_gemma":[0.0000036485917,0.000050061248,0.0011181415,0.000007638537,0.000011463762,0.000038920945,0.0000059171452,0.9896535,0.008005418,0.000982361,0.000117239004,0.000005671133],"about_ca_topic_score_codex":0.0023556992,"about_ca_topic_score_gemma":0.0018582889,"teacher_disagreement_score":0.0023556992,"about_ca_system_score_codex":0.0008524371,"about_ca_system_score_gemma":0.0005080909,"threshold_uncertainty_score":0.010840833},"labels":[],"label_agreement":null},{"id":"W4382317858","doi":"10.1609/aaai.v37i8.26150","title":"DisGUIDE: Disagreement-Guided Data-Free Model Extraction","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"University of Waterloo; Purdue University; National Science Foundation","keywords":"Computer science; Generator (circuit theory); Data mining; Machine learning; Data extraction; Scheme (mathematics); Artificial intelligence; Power (physics)","score_opus":0.2242784790751523,"score_gpt":0.38195678724580584,"score_spread":0.15767830817065354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046286147,0.00045044138,0.9371069,0.0009602673,0.00012941325,0.00016542138,0.00039990558,0.012248957,0.0022524453],"genre_scores_gemma":[0.7090938,0.0002160613,0.2808828,0.001200257,0.00013289497,0.00025933413,0.0022052443,0.0011894925,0.004820131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611735,0.0014759296,0.0002141633,0.00070997514,0.0012083168,0.00027415948],"domain_scores_gemma":[0.9910782,0.0031212044,0.0005071017,0.004454737,0.00063948386,0.00019927387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004237098,0.001538763,0.0015562818,0.0009120248,0.00083460734,0.0015320902,0.0032267447,0.0022607967,0.0025988854],"category_scores_gemma":[0.018019715,0.0007238963,0.0012205167,0.0007327554,0.002108631,0.0059799305,0.0065972335,0.003989219,0.002170681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013655779,0.0005255935,0.007789437,0.00032150297,0.0004279864,0.0007446003,0.0007671892,0.44405788,0.036981743,0.046858374,0.03270234,0.4274577],"study_design_scores_gemma":[0.000049544735,0.00010268785,0.0002097025,0.000011325365,0.000016785663,0.000196753,0.0000419223,0.9614763,0.014589649,0.020410413,0.0028744151,0.000020432311],"about_ca_topic_score_codex":0.0015141389,"about_ca_topic_score_gemma":0.0018427381,"teacher_disagreement_score":0.004237098,"about_ca_system_score_codex":0.001075177,"about_ca_system_score_gemma":0.0014376459,"threshold_uncertainty_score":0.022408187},"labels":[],"label_agreement":null},{"id":"W4382317875","doi":"10.1609/aaai.v37i12.26748","title":"Revisiting the Importance of Amplifying Bias for Debiasing","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; Institute for Information and Communications Technology Promotion; Electronics and Telecommunications Research Institute","keywords":"Debiasing; Selection bias; Computer science; Artificial intelligence; Classifier (UML); Sampling bias; Non-response bias; Training set; Sample (material); Statistics; Machine learning; Sample size determination; Psychology; Mathematics; Social psychology","score_opus":0.18407786617459618,"score_gpt":0.35457081368631754,"score_spread":0.17049294751172137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124821946,0.0031680255,0.86564213,0.000914019,0.0002681911,0.0002034644,0.00015814784,0.0025701246,0.0022539601],"genre_scores_gemma":[0.6021904,0.00103385,0.3895822,0.0010437398,0.00024887832,0.00023947775,0.0006090305,0.0006693007,0.0043830834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977525,0.00053777115,0.00023361082,0.0006342853,0.00065150746,0.00019026798],"domain_scores_gemma":[0.9906288,0.0044273282,0.0007549581,0.0021100373,0.0018718957,0.00020697177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005136572,0.001703432,0.0013847788,0.0010412952,0.0006999646,0.0013460226,0.0023617675,0.0015633054,0.0018707705],"category_scores_gemma":[0.020840954,0.0006136464,0.000966246,0.0008013513,0.001897899,0.0033489813,0.0023013372,0.0027862117,0.00093927205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009815111,0.00043732455,0.014675311,0.00069702184,0.00026100408,0.00028344968,0.00078820577,0.13347232,0.09910589,0.010987541,0.0051027876,0.73320764],"study_design_scores_gemma":[0.00009847152,0.00058889686,0.0050279377,0.00016171299,0.00015477333,0.00039583223,0.00016564502,0.90131056,0.07146418,0.010767299,0.00978982,0.0000748789],"about_ca_topic_score_codex":0.0032979427,"about_ca_topic_score_gemma":0.0052175936,"teacher_disagreement_score":0.005136572,"about_ca_system_score_codex":0.00074792857,"about_ca_system_score_gemma":0.0012076809,"threshold_uncertainty_score":0.027165055},"labels":[],"label_agreement":null},{"id":"W4382317969","doi":"10.1609/aaai.v37i12.26779","title":"DeepGemini: Verifying Dependency Fairness for Deep Neural Network","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Japan Society for the Promotion of Science; Canada First Research Excellence Fund; JST-Mirai Program; University of Alberta; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Counterexample; Heuristics; Certification; Scalability; Deep neural networks; Benchmark (surveying); Key (lock); Fairness measure; Artificial neural network; Artificial intelligence; Computer security; Mathematics","score_opus":0.08482360610997083,"score_gpt":0.31933126912160475,"score_spread":0.23450766301163392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0501229,0.00057515135,0.93908715,0.0008616158,0.00017219625,0.00019692867,0.00056848506,0.00570887,0.00270672],"genre_scores_gemma":[0.7480003,0.00032031166,0.24635346,0.00083164574,0.00010333956,0.0003872656,0.0010541428,0.0006864857,0.0022629737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99251705,0.0023565013,0.00049045583,0.0016128778,0.0022100746,0.00081311393],"domain_scores_gemma":[0.97744274,0.014722084,0.0015290984,0.0034050746,0.0022850782,0.00061588973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009021072,0.0017235318,0.0012485583,0.0013017941,0.0013586171,0.002192729,0.0038924094,0.001799967,0.0039294846],"category_scores_gemma":[0.04352696,0.000952695,0.0022308582,0.0006893822,0.003537555,0.0046573486,0.0048524663,0.0052424907,0.0005160307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009478742,0.00022506152,0.009682186,0.00052835647,0.0001851062,0.00029336274,0.00027942378,0.7413438,0.01012705,0.10345192,0.005102355,0.1278335],"study_design_scores_gemma":[0.000031702955,0.000057616704,0.00020684836,0.000026837426,0.000018146213,0.000031805193,0.000018030943,0.9477547,0.00523602,0.045836043,0.00076967623,0.0000125865],"about_ca_topic_score_codex":0.008609929,"about_ca_topic_score_gemma":0.014080643,"teacher_disagreement_score":0.009021072,"about_ca_system_score_codex":0.005049775,"about_ca_system_score_gemma":0.008002029,"threshold_uncertainty_score":0.04770857},"labels":[],"label_agreement":null},{"id":"W4382318719","doi":"10.1609/aaai.v37i12.26737","title":"Redactor: A Data-Centric and Individualized Defense against Inference Attacks","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Inference; Machine learning; Artificial intelligence; Process (computing); SAFER; Probabilistic logic; Mistake; Information leakage; Data mining; Private information retrieval; Computer security","score_opus":0.13448805924779417,"score_gpt":0.35398669900702107,"score_spread":0.2194986397592269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382318719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011133209,0.00030561088,0.97988325,0.0008821059,0.00011383656,0.00011783405,0.00014796568,0.004948579,0.002467563],"genre_scores_gemma":[0.50945836,0.0003340443,0.47865355,0.0019233275,0.0003170146,0.00027783107,0.00083453837,0.0008384277,0.0073629776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9915455,0.002454144,0.00031997036,0.0016784631,0.003364705,0.00063721393],"domain_scores_gemma":[0.98167795,0.0058287084,0.001625966,0.008814397,0.001493264,0.00055970356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008580742,0.0017342237,0.002063299,0.0020674455,0.0014345169,0.0031207188,0.005015669,0.0038427336,0.0025505375],"category_scores_gemma":[0.024559896,0.001003532,0.0017541217,0.0011307762,0.0028198627,0.006440113,0.009562231,0.0066568945,0.0021422796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013561503,0.00066458277,0.0053431545,0.00024053665,0.00036444844,0.00049574394,0.00060190517,0.30898914,0.032145385,0.12616342,0.033070706,0.4905648],"study_design_scores_gemma":[0.000044401404,0.00013680705,0.0003459471,0.000026022777,0.000031240852,0.000228281,0.000042425327,0.94141245,0.010067097,0.042708065,0.004919116,0.00003819556],"about_ca_topic_score_codex":0.0012904751,"about_ca_topic_score_gemma":0.0014101638,"teacher_disagreement_score":0.008580742,"about_ca_system_score_codex":0.0016098821,"about_ca_system_score_gemma":0.0022442343,"threshold_uncertainty_score":0.045379877},"labels":[],"label_agreement":null},{"id":"W4382449344","doi":"10.1609/aaai.v37i7.25968","title":"Confidence-Aware Training of Smoothed Classifiers for Certified Robustness","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration; Agency for Defense Development","keywords":"Computer science; Robustness (evolution); Smoothing; Classifier (UML); Artificial intelligence; Machine learning; Gaussian; Adversarial system; Pattern recognition (psychology); Computer vision","score_opus":0.18571410764410629,"score_gpt":0.33755705963724886,"score_spread":0.15184295199314257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382449344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02176566,0.0001909446,0.9742347,0.00035999116,0.00006398882,0.000059190254,0.00008674844,0.001768951,0.0014698175],"genre_scores_gemma":[0.7435962,0.00018556273,0.2509424,0.0006374378,0.00019678612,0.00021727671,0.0005886086,0.0005825847,0.0030531257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960226,0.0010416666,0.0002385265,0.001015751,0.001283158,0.00039828397],"domain_scores_gemma":[0.9819385,0.009918015,0.0015713274,0.0038639691,0.0021137386,0.0005945397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052781957,0.001201398,0.0015520037,0.00094310514,0.00080907566,0.0017665313,0.0024677352,0.0024322616,0.002734612],"category_scores_gemma":[0.03865501,0.0007020266,0.0010545201,0.00061705924,0.002403788,0.0029968682,0.0036072533,0.0042127264,0.0013453793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000462926,0.000128938,0.0039857957,0.00016199957,0.00011155558,0.00016478248,0.00025362402,0.76163965,0.015705165,0.04120142,0.0061365785,0.17004752],"study_design_scores_gemma":[0.000013782313,0.000047458532,0.00026288608,0.00001793171,0.000010265017,0.000045727953,0.000012313275,0.98112595,0.0045923395,0.013127261,0.00073162455,0.000012493924],"about_ca_topic_score_codex":0.001975636,"about_ca_topic_score_gemma":0.0024424663,"teacher_disagreement_score":0.0052781957,"about_ca_system_score_codex":0.0016736377,"about_ca_system_score_gemma":0.002317135,"threshold_uncertainty_score":0.027914107},"labels":[],"label_agreement":null},{"id":"W4382463991","doi":"10.1609/aaai.v37i12.26771","title":"Conflicting Interactions among Protection Mechanisms for Machine Learning Models","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Pairwise comparison; Robustness (evolution); Popularity; Mechanism (biology); Evasion (ethics); Computer security; Machine learning; Private information retrieval; Risk analysis (engineering); Artificial intelligence; Business","score_opus":0.1333083317816195,"score_gpt":0.3360014266141462,"score_spread":0.20269309483252673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382463991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2150703,0.0010357013,0.7752094,0.00253712,0.00008484917,0.00019608672,0.00016445789,0.0011953139,0.004506697],"genre_scores_gemma":[0.94337326,0.00015878596,0.055043317,0.00025198178,0.000039399816,0.0001059861,0.00010832083,0.00013781704,0.00078112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9662896,0.018999822,0.002078022,0.003933618,0.00637155,0.0023274275],"domain_scores_gemma":[0.80889225,0.12755638,0.013649001,0.042124536,0.0054026674,0.002375167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028357351,0.0013476536,0.0015599524,0.0013521134,0.0013995504,0.0041199694,0.003650131,0.0030420795,0.0034203692],"category_scores_gemma":[0.16310222,0.0011169146,0.0018154192,0.00088519655,0.0034545395,0.008521039,0.0068048527,0.0057445597,0.00050904317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014594188,0.00041968172,0.023728605,0.0004915147,0.000536516,0.00055936887,0.0010173676,0.63186336,0.016817387,0.16767597,0.0024818566,0.1529489],"study_design_scores_gemma":[0.00006282622,0.00027743288,0.0021729458,0.00009244011,0.000120071454,0.0003257481,0.00017057426,0.8653936,0.011342537,0.1182995,0.0016892685,0.000053000826],"about_ca_topic_score_codex":0.0008423662,"about_ca_topic_score_gemma":0.0007968928,"teacher_disagreement_score":0.028357351,"about_ca_system_score_codex":0.0028589172,"about_ca_system_score_gemma":0.0023905423,"threshold_uncertainty_score":0.14996982},"labels":[],"label_agreement":null},{"id":"W4382464093","doi":"10.1609/aaai.v37i12.26738","title":"Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adversarial system; Computer science; Robustness (evolution); Discriminative model; Artificial intelligence; Class (philosophy); Deep neural networks; Machine learning; Theoretical computer science; Deep learning","score_opus":0.04198952150844852,"score_gpt":0.2643398717135558,"score_spread":0.22235035020510724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382464093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04910265,0.00033420316,0.9450218,0.00037769513,0.00010445074,0.00008573655,0.00006905597,0.0016688753,0.0032355743],"genre_scores_gemma":[0.88545626,0.00019391,0.10875614,0.0004452448,0.000087789034,0.00013555496,0.00024024975,0.00033859248,0.004346169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988746,0.00032030654,0.000051453357,0.00028543614,0.0003169419,0.00015116551],"domain_scores_gemma":[0.9975956,0.0011173881,0.0002991481,0.0006124586,0.00024594416,0.00012952871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021510075,0.001604345,0.0010484803,0.0005712656,0.00048103856,0.0007857408,0.0019959584,0.0013258815,0.002310096],"category_scores_gemma":[0.0076777195,0.0005074859,0.0007645404,0.00038863978,0.0015929503,0.0024517274,0.0033408136,0.00321326,0.0007177838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000164495,0.00011555382,0.0012532255,0.000058298327,0.00007317641,0.00010287396,0.00007500252,0.8926602,0.011210409,0.012446831,0.002746206,0.079093784],"study_design_scores_gemma":[0.0000064481387,0.00003304217,0.0000927123,0.000004872052,0.00000593437,0.000026555274,0.0000048543216,0.99287146,0.0021673576,0.004444721,0.0003364298,0.000005677183],"about_ca_topic_score_codex":0.001367053,"about_ca_topic_score_gemma":0.0015906049,"teacher_disagreement_score":0.002310096,"about_ca_system_score_codex":0.0008416256,"about_ca_system_score_gemma":0.00075808354,"threshold_uncertainty_score":0.011375785},"labels":[],"label_agreement":null},{"id":"W4383221307","doi":"10.1145/3579856.3582836","title":"FLAIR: Defense against Model Poisoning Attack in Federated Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Laboratory; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Intuition; Fluid-attenuated inversion recovery; Robustness (evolution); Artificial intelligence; Reputation; Computer security; Machine learning; Law","score_opus":0.031572075255123705,"score_gpt":0.29054060769566614,"score_spread":0.2589685324405424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383221307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0636595,0.00075314933,0.91524154,0.0022636945,0.00020215515,0.00027042706,0.00021604785,0.013724118,0.0036693683],"genre_scores_gemma":[0.8841358,0.00015663392,0.11111141,0.0014078581,0.000076786026,0.00020476371,0.00034429884,0.00035276293,0.0022095393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9922031,0.002978273,0.00045623706,0.0014167378,0.0022281357,0.0007175043],"domain_scores_gemma":[0.9798719,0.0057230466,0.0014513455,0.010697199,0.0016377933,0.0006186994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00859221,0.0016260751,0.0020462272,0.0011067073,0.0017578983,0.0022170332,0.0041602305,0.003598872,0.0013898807],"category_scores_gemma":[0.025143133,0.00068655604,0.0014100758,0.0009263358,0.003276463,0.0060960134,0.007529963,0.0064825267,0.0009432948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019404828,0.0007209469,0.009015124,0.0003176012,0.00047226634,0.0005804241,0.0007134799,0.60335374,0.0152982315,0.0722817,0.023690017,0.27161598],"study_design_scores_gemma":[0.00006460922,0.00017921114,0.0002454432,0.000021947453,0.000021067222,0.00017504887,0.000036682755,0.96393627,0.006067682,0.027187154,0.0020403054,0.000024573865],"about_ca_topic_score_codex":0.0019175009,"about_ca_topic_score_gemma":0.0017593807,"teacher_disagreement_score":0.00859221,"about_ca_system_score_codex":0.0017285864,"about_ca_system_score_gemma":0.0021612572,"threshold_uncertainty_score":0.045440435},"labels":[],"label_agreement":null},{"id":"W4383221314","doi":"10.1145/3579856.3582816","title":"Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Deep neural networks; Computer science; Bounded function; Artificial neural network; Artificial intelligence; Computer security; Mathematics","score_opus":0.018922751196278436,"score_gpt":0.28245008119465553,"score_spread":0.2635273299983771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383221314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050535124,0.0018756316,0.9212202,0.0017218288,0.00069912,0.00032375494,0.00016869773,0.0077552833,0.015700413],"genre_scores_gemma":[0.83725166,0.00063687476,0.14924175,0.0014594881,0.00025213917,0.000361528,0.00031006552,0.00041104315,0.01007553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832577,0.0003460605,0.000056870507,0.00024499683,0.00073007133,0.0002961266],"domain_scores_gemma":[0.9979545,0.0008950404,0.00016236707,0.0005842915,0.00024415314,0.00015969621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019753848,0.0012057809,0.001359109,0.0007262411,0.00087810063,0.0014984052,0.0023811585,0.0028727655,0.002766955],"category_scores_gemma":[0.006195368,0.00055188785,0.000983432,0.00030763863,0.0021338908,0.0019950278,0.005572366,0.0034993044,0.00084257853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014840564,0.00043556426,0.0023848058,0.00044331898,0.0005014896,0.00074933143,0.000359199,0.4307726,0.06943399,0.16177756,0.04071325,0.29094478],"study_design_scores_gemma":[0.000070280556,0.00028685803,0.000300312,0.000041209558,0.00004057585,0.00020023015,0.000023261184,0.9445783,0.012016508,0.036161404,0.0062435484,0.000037533755],"about_ca_topic_score_codex":0.0007009225,"about_ca_topic_score_gemma":0.0009981181,"teacher_disagreement_score":0.0028727655,"about_ca_system_score_codex":0.00064408814,"about_ca_system_score_gemma":0.0011053982,"threshold_uncertainty_score":0.010446966},"labels":[],"label_agreement":null},{"id":"W4383468997","doi":"10.48550/arxiv.2307.01610","title":"Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overconfidence effect; Computer science; HAMP; Exploit; Inference; Machine learning; Training set; Artificial intelligence; Benchmark (surveying); Entropy (arrow of time); Data mining; Computer security","score_opus":0.1314094578956852,"score_gpt":0.24322739030846818,"score_spread":0.11181793241278298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383468997","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12829562,0.00091126113,0.86018807,0.0026487103,0.00014082207,0.00010966103,0.00020463634,0.0022213107,0.005279881],"genre_scores_gemma":[0.9527663,0.0002172869,0.044981107,0.0005464194,0.000117038995,0.00006028019,0.00015905965,0.00009426292,0.0010583726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.990284,0.0047667427,0.00036800964,0.0013476178,0.0026138008,0.00061983056],"domain_scores_gemma":[0.97414374,0.011919719,0.0023609912,0.010049436,0.0010173448,0.00050870824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075220247,0.0010828241,0.0011988921,0.00068424497,0.00120988,0.002170053,0.0024454112,0.0021272525,0.0011147828],"category_scores_gemma":[0.03163365,0.0005549936,0.0010067454,0.00070959504,0.0025452226,0.00519683,0.0059778783,0.0053510317,0.00056137115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013580944,0.00047835597,0.017152103,0.000299157,0.0003964912,0.0008390045,0.0011391183,0.47798368,0.03289923,0.15474774,0.013323061,0.29938394],"study_design_scores_gemma":[0.0000403651,0.00014010086,0.0011258317,0.00004392648,0.00004195773,0.00038891716,0.00007854556,0.9358061,0.016760644,0.042500485,0.003033657,0.00003946725],"about_ca_topic_score_codex":0.0007481392,"about_ca_topic_score_gemma":0.00069558935,"teacher_disagreement_score":0.0075220247,"about_ca_system_score_codex":0.0011707067,"about_ca_system_score_gemma":0.0014300974,"threshold_uncertainty_score":0.039780796},"labels":[],"label_agreement":null},{"id":"W4383604828","doi":"10.48550/arxiv.2307.02849","title":"NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Inference; Artificial intelligence; Spurious relationship; Natural language; Formalism (music); Natural language understanding; Non-monotonic logic; Natural (archaeology); Cognitive science; Machine learning; Psychology","score_opus":0.1167031844234035,"score_gpt":0.2654593571134707,"score_spread":0.1487561726900672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383604828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023466814,0.00011694039,0.99206585,0.0006482352,0.000048191243,0.0000987399,0.000122022895,0.0017311876,0.0028222268],"genre_scores_gemma":[0.27357653,0.00055862067,0.7155114,0.0011675011,0.00016457315,0.0005750907,0.0005977126,0.00081489823,0.007033714],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963624,0.0015955195,0.0001842474,0.0005665221,0.0010467175,0.00024455276],"domain_scores_gemma":[0.9946621,0.003213107,0.00040126833,0.0012270581,0.0002913368,0.0002052136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050535714,0.0010639501,0.0007933767,0.0015352747,0.001154913,0.003355376,0.0038111578,0.0020852766,0.0071972287],"category_scores_gemma":[0.012750103,0.0008239151,0.0026424583,0.00074628746,0.0048234276,0.005242195,0.006143307,0.006071157,0.0012765548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001286386,0.00007692681,0.00044477917,0.0001686899,0.00009604486,0.00023485604,0.00028496966,0.15898927,0.002734454,0.7908148,0.005121633,0.040904976],"study_design_scores_gemma":[0.00002473695,0.00004305885,0.000052035175,0.000036573347,0.000027522836,0.000105175306,0.000030737196,0.5105833,0.001738443,0.47680044,0.010534933,0.000023095356],"about_ca_topic_score_codex":0.0030524547,"about_ca_topic_score_gemma":0.004186824,"teacher_disagreement_score":0.0071972287,"about_ca_system_score_codex":0.002381323,"about_ca_system_score_gemma":0.0023947456,"threshold_uncertainty_score":0.026726127},"labels":[],"label_agreement":null},{"id":"W4384115435","doi":"10.48550/arxiv.2307.05422","title":"Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tamkeen; York University; Army Research Office; New York University Abu Dhabi","keywords":"Backdoor; Novelty; Computer science; Novelty detection; Artificial intelligence; Detector; Metric (unit); Black box; Artificial neural network; Pattern recognition (psychology); Data mining; Deep learning; Machine learning; Intuition; Engineering","score_opus":0.056999211889464274,"score_gpt":0.21446935501882636,"score_spread":0.15747014312936208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384115435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12525907,0.00048609386,0.87010497,0.00021625403,0.000070887996,0.00009781377,0.00019847008,0.0020218901,0.0015445688],"genre_scores_gemma":[0.89550835,0.00015399742,0.10271234,0.000121389516,0.000041402804,0.000087725995,0.0003208763,0.00012376033,0.0009301205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981812,0.00036563742,0.00012043691,0.00039761036,0.00076436583,0.00017070441],"domain_scores_gemma":[0.99517787,0.0022061113,0.0010153838,0.0007570259,0.00063228415,0.00021127952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002256445,0.0012019273,0.000861203,0.0012694915,0.00035725764,0.0010356525,0.0011501532,0.0010099895,0.0010122982],"category_scores_gemma":[0.011147782,0.00034523362,0.00061219564,0.0005182904,0.0009410635,0.001981569,0.0017882934,0.0017240118,0.00037033737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011488604,0.00036269723,0.024398886,0.00032698992,0.00031310276,0.00056447653,0.0002329221,0.45279416,0.078035146,0.017432084,0.0029487738,0.4214419],"study_design_scores_gemma":[0.000008287858,0.0001094601,0.001738762,0.0000132069035,0.000021112746,0.00011845668,0.000015041747,0.9758741,0.017461194,0.004178096,0.00044606236,0.000016192163],"about_ca_topic_score_codex":0.00062193495,"about_ca_topic_score_gemma":0.00095951464,"teacher_disagreement_score":0.002256445,"about_ca_system_score_codex":0.0010166576,"about_ca_system_score_gemma":0.0007012435,"threshold_uncertainty_score":0.011933327},"labels":[],"label_agreement":null},{"id":"W4384345647","doi":"10.1109/icse48619.2023.00152","title":"Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program; Japan Society for the Promotion of Science; Department of Forestry and Natural Resources, Purdue University; Japan Science and Technology Corporation; Japan Society for the Promotion of Science London; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Labeled data; Artificial neural network; Machine learning; Deep neural networks; Deep learning; Test data; Key (lock); De facto; Set (abstract data type); Data mining; Transformation (genetics); Data set; Test set","score_opus":0.020823300054337385,"score_gpt":0.2748311056085461,"score_spread":0.25400780555420877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384345647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15962206,0.0018288769,0.7973962,0.0005439571,0.00035022676,0.00033512258,0.0019958394,0.03451805,0.0034096092],"genre_scores_gemma":[0.6436622,0.00032757665,0.3463024,0.00045343657,0.00011792333,0.0004974964,0.00514668,0.0012402942,0.0022519007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913317,0.0029394447,0.0007977778,0.0020354248,0.0024291954,0.00046646604],"domain_scores_gemma":[0.97274405,0.015238064,0.0025162548,0.005682104,0.0031949766,0.0006246801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008795697,0.0027317565,0.0014571907,0.0023331505,0.000711311,0.0016595387,0.0054259216,0.0022870568,0.0021403101],"category_scores_gemma":[0.043385744,0.0008684265,0.0015206981,0.0011996935,0.0013506841,0.0036089986,0.0030682192,0.0031288809,0.0013720165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019066519,0.0007199994,0.03244467,0.0006833904,0.0008439872,0.00038404454,0.00026202656,0.49790058,0.017426725,0.007980457,0.013700853,0.42574665],"study_design_scores_gemma":[0.000056756664,0.0001780811,0.0013887075,0.000025515521,0.000031297706,0.00007940101,0.00003409486,0.98688203,0.0071244333,0.003236722,0.0009383445,0.000024647634],"about_ca_topic_score_codex":0.005512141,"about_ca_topic_score_gemma":0.0075232135,"teacher_disagreement_score":0.008795697,"about_ca_system_score_codex":0.0014204145,"about_ca_system_score_gemma":0.0019028888,"threshold_uncertainty_score":0.046516657},"labels":[],"label_agreement":null},{"id":"W4384616170","doi":"10.48550/arxiv.2307.07457","title":"Structured Pruning of Neural Networks for Constraints Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pruning; Computer science; Artificial neural network; Scalability; Machine learning; Context (archaeology); Artificial intelligence; Process (computing)","score_opus":0.07493871090284321,"score_gpt":0.21811051011584703,"score_spread":0.14317179921300383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384616170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009308833,0.0013299615,0.9823704,0.00045025066,0.00008821871,0.000077034354,0.0001558999,0.0006235093,0.005595925],"genre_scores_gemma":[0.43565786,0.0021269058,0.5508999,0.0007575664,0.00027273782,0.0005663795,0.0011390741,0.0004883703,0.008091119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876046,0.00043824333,0.00007461772,0.00018725112,0.00042770375,0.00011170724],"domain_scores_gemma":[0.9960162,0.0028804536,0.00024777633,0.00030231592,0.00047962574,0.00007369686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016256255,0.0013182485,0.0012712477,0.0009577615,0.000528218,0.001152318,0.0016071682,0.0015723326,0.0053504505],"category_scores_gemma":[0.01074335,0.00071391783,0.000790259,0.0009710069,0.0008524948,0.0015760297,0.0015908238,0.0026689211,0.000917403],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000814108,0.00005200372,0.0005247949,0.00020657355,0.000053156597,0.00016013249,0.000063332875,0.8247925,0.0015481038,0.031116072,0.003712359,0.13768953],"study_design_scores_gemma":[0.0000057145144,0.000013686775,0.00006160526,0.000032983382,0.0000057814673,0.000024010595,0.000007134286,0.98631114,0.00058660924,0.01143639,0.0015112692,0.000003757182],"about_ca_topic_score_codex":0.004384834,"about_ca_topic_score_gemma":0.0070624775,"teacher_disagreement_score":0.0053504505,"about_ca_system_score_codex":0.0010654716,"about_ca_system_score_gemma":0.0012844965,"threshold_uncertainty_score":0.017899036},"labels":[],"label_agreement":null},{"id":"W4384663134","doi":"10.1109/tnnls.2023.3290592","title":"Asymptotic Behavior of Adversarial Training in Binary Linear Classification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"King Abdullah University of Science and Technology; National Science Foundation","keywords":"Adversarial system; Training (meteorology); Binary classification; Binary number; Artificial intelligence; Pattern recognition (psychology); Training set; Computer science; Machine learning; Mathematics; Geography; Support vector machine; Arithmetic","score_opus":0.035130990505624696,"score_gpt":0.2757076308336993,"score_spread":0.24057664032807463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384663134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072332144,0.001886341,0.9102272,0.0021537144,0.000089215355,0.000069709306,0.00018993695,0.000847965,0.012203892],"genre_scores_gemma":[0.93477976,0.0011646892,0.056818634,0.00059386174,0.000153981,0.00019612526,0.0003921183,0.0003121452,0.0055886046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632394,0.0017362098,0.00013901273,0.00048405566,0.0009848528,0.00033195782],"domain_scores_gemma":[0.94686955,0.045076977,0.0025498234,0.0026930012,0.0020542778,0.00075636397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009283881,0.0014148226,0.0012524483,0.0011178231,0.0006589929,0.0014389203,0.0016416055,0.0016078914,0.0024417392],"category_scores_gemma":[0.07373228,0.0007358834,0.00069050165,0.00059736526,0.0046639424,0.0037968808,0.0043806992,0.0040392512,0.00058588997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028666653,0.00007335958,0.0038670783,0.00024537314,0.00009407428,0.00021792515,0.00025029242,0.7111938,0.0036781959,0.25238162,0.0025058046,0.025205761],"study_design_scores_gemma":[0.0000072351277,0.000034568813,0.0005497263,0.000051268766,0.000009088464,0.00006623184,0.000022543083,0.93191284,0.0009566371,0.06606343,0.00031276385,0.000013584727],"about_ca_topic_score_codex":0.0019891965,"about_ca_topic_score_gemma":0.0012296055,"teacher_disagreement_score":0.009283881,"about_ca_system_score_codex":0.0022583124,"about_ca_system_score_gemma":0.00088623335,"threshold_uncertainty_score":0.04909849},"labels":[],"label_agreement":null},{"id":"W4385060545","doi":"10.5220/0012139900003555","title":"Multi-Environment Training Against Reward Poisoning Attacks on Deep Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Reinforcement learning; Computer science; Training (meteorology); Reinforcement; Artificial intelligence; Psychology; Social psychology","score_opus":0.04147161911139761,"score_gpt":0.2807891303442872,"score_spread":0.2393175112328896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385060545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22992079,0.00042285057,0.756774,0.0010451773,0.00017663055,0.00008461227,0.0000833829,0.0032976752,0.008194929],"genre_scores_gemma":[0.9780367,0.000037212747,0.020153882,0.00011429684,0.000012302678,0.000025629639,0.00003354772,0.00007095235,0.0015154744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989882,0.00034513485,0.000037133243,0.00016280425,0.00024850905,0.00021818343],"domain_scores_gemma":[0.9970394,0.0017241485,0.00023435238,0.00053039304,0.00031169495,0.0001600339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020003144,0.0008597403,0.00081028574,0.00037372703,0.00036491675,0.0004457002,0.0010382467,0.0012692236,0.0017382056],"category_scores_gemma":[0.007186667,0.00037323657,0.00037879948,0.00019165101,0.0010917307,0.0010959608,0.0024558369,0.0019998355,0.00031061523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033293758,0.00008606683,0.0010899189,0.00003226946,0.000053534888,0.00010330079,0.000037981255,0.93986785,0.0054463525,0.008958172,0.0014102433,0.04258137],"study_design_scores_gemma":[0.0000068901727,0.000033936394,0.000091381386,0.0000033255428,0.000003285621,0.000012936828,0.0000030205406,0.9961838,0.0010624207,0.002496999,0.000099765144,0.0000022725906],"about_ca_topic_score_codex":0.0013841387,"about_ca_topic_score_gemma":0.0014609485,"teacher_disagreement_score":0.0020003144,"about_ca_system_score_codex":0.0006311057,"about_ca_system_score_gemma":0.0009103137,"threshold_uncertainty_score":0.010578811},"labels":[],"label_agreement":null},{"id":"W4385222146","doi":"10.1145/3585005","title":"<i>ArchRepair</i> : Block-Level Architecture-Oriented Repairing for Deep Neural Networks","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program","keywords":"Computer science; Robustness (evolution); Block (permutation group theory); Deep neural networks; Overfitting; Architecture; Artificial neural network; Artificial intelligence; Network architecture; Machine learning; Retraining; Distributed computing; Computer engineering; Computer architecture; Computer network","score_opus":0.06776237405796683,"score_gpt":0.31016859040160144,"score_spread":0.24240621634363463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385222146","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008349636,0.0013461608,0.9770735,0.0011584546,0.0004663121,0.00007572007,0.0002504905,0.006161443,0.0051182387],"genre_scores_gemma":[0.4406445,0.002922006,0.5218148,0.0022475112,0.00058521604,0.00031913308,0.0025226986,0.0028116205,0.02613249],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897206,0.00016536427,0.000088087516,0.00018861219,0.0004795353,0.00010626488],"domain_scores_gemma":[0.9974968,0.0004245411,0.0002579477,0.0012532942,0.00047775885,0.00008965598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015215324,0.0012769264,0.00066965615,0.0006586635,0.00075404486,0.001776526,0.0031164286,0.0017082195,0.0075925174],"category_scores_gemma":[0.0061769984,0.0005235306,0.00089365843,0.0006918916,0.0016655567,0.0031141657,0.0036878632,0.003442863,0.0034556496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037371955,0.00010788774,0.0024372155,0.0004873104,0.00019987099,0.0006474789,0.00032704603,0.2891122,0.047010083,0.08330197,0.074690364,0.50130475],"study_design_scores_gemma":[0.000017801956,0.0001188815,0.00043298875,0.00009133129,0.00004830571,0.0004392004,0.000051474686,0.88103443,0.039974954,0.040725086,0.03701906,0.000046484573],"about_ca_topic_score_codex":0.0034820905,"about_ca_topic_score_gemma":0.0047303126,"teacher_disagreement_score":0.0075925174,"about_ca_system_score_codex":0.0011673104,"about_ca_system_score_gemma":0.0012905145,"threshold_uncertainty_score":0.025399566},"labels":[],"label_agreement":null},{"id":"W4385236985","doi":"10.1109/jiot.2023.3298663","title":"A GNN-Based Adversarial Internet of Things Malware Detection Framework for Critical Infrastructure: Studying Gafgyt, Mirai, and Tsunami Campaigns","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Brandon University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Malware; Computer science; Artificial intelligence; Machine learning; Detector; Classifier (UML); Data mining; Computer security","score_opus":0.020850652600527878,"score_gpt":0.30087432436716227,"score_spread":0.2800236717666344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385236985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13049462,0.001698822,0.8570265,0.0016110392,0.00016195633,0.00009915517,0.00017042151,0.001407476,0.0073300456],"genre_scores_gemma":[0.92522866,0.00070359383,0.06808971,0.0003810663,0.00009514174,0.00005183423,0.0002898422,0.0001241332,0.0050358833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968493,0.00006328004,0.000009806198,0.00008481609,0.00009511171,0.000062119856],"domain_scores_gemma":[0.99943405,0.00027327496,0.00007589389,0.00004974638,0.000120494966,0.000046559355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072238705,0.0007614183,0.00058626925,0.0010670476,0.0004299038,0.0006249421,0.0009494048,0.00088598684,0.0008804657],"category_scores_gemma":[0.0021595468,0.0003156857,0.0007102086,0.00040346425,0.0009829501,0.0014403152,0.0009497965,0.0012053887,0.00022149071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011314547,0.000088627334,0.007496894,0.00008426078,0.00008721519,0.00039832885,0.00018984333,0.85783505,0.0046814624,0.039299004,0.003608103,0.086118005],"study_design_scores_gemma":[0.0000012811248,0.000008705047,0.00027799106,0.0000036692225,0.0000045453576,0.000031071715,0.000007792928,0.9950028,0.00043653478,0.0038303249,0.00039063644,0.000004588992],"about_ca_topic_score_codex":0.011473528,"about_ca_topic_score_gemma":0.009300025,"teacher_disagreement_score":0.011473528,"about_ca_system_score_codex":0.001166729,"about_ca_system_score_gemma":0.0007143697,"threshold_uncertainty_score":0.022813499},"labels":[],"label_agreement":null},{"id":"W4385244318","doi":"10.1145/3586102.3586109","title":"AuthAttLyzer: A Robust defensive distillation-based Authorship Attribution framework","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Source code; Computer science; Robustness (evolution); Code (set theory); Attribution; Exploit; Open source; Authorship attribution; Computer security; Artificial intelligence; Data mining; Programming language; Software","score_opus":0.027738921631267423,"score_gpt":0.2586027499810975,"score_spread":0.23086382834983007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385244318","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028269727,0.00035146743,0.9603503,0.00050145906,0.00012611182,0.00012005898,0.00021822444,0.008112218,0.0019504456],"genre_scores_gemma":[0.70156443,0.0002742032,0.28691727,0.0005372018,0.0001562938,0.00022735518,0.0007986556,0.0007162695,0.008808312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778676,0.00048743404,0.0001106163,0.00060243945,0.0007530543,0.0002596264],"domain_scores_gemma":[0.9970727,0.0008817312,0.00039251504,0.001034172,0.0004507492,0.00016814715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026012831,0.0011633665,0.0010093369,0.0013969404,0.00087165507,0.0015320071,0.002837427,0.0017667633,0.002325813],"category_scores_gemma":[0.008168826,0.00049080217,0.00092827604,0.00076646876,0.002627902,0.004028079,0.0048848586,0.0031652777,0.0013095553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083088264,0.00049886986,0.005706585,0.00023686387,0.00019921795,0.0005101555,0.0005303574,0.3384425,0.033426177,0.07738436,0.010996544,0.53123754],"study_design_scores_gemma":[0.000024334266,0.00008251747,0.0002814984,0.00001564067,0.000018207853,0.00010441468,0.000025935282,0.9451695,0.012111674,0.03931707,0.0028182273,0.000030838528],"about_ca_topic_score_codex":0.0016215784,"about_ca_topic_score_gemma":0.002248751,"teacher_disagreement_score":0.002837427,"about_ca_system_score_codex":0.0010392249,"about_ca_system_score_gemma":0.001939326,"threshold_uncertainty_score":0.01375705},"labels":[],"label_agreement":null},{"id":"W4385322377","doi":"10.1109/tdsc.2023.3299522","title":"False Data Detector for Electrical Vehicles Temporal-Spatial Charging Coordination Secure Against Evasion and Privacy Adversarial Attacks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Detector; Robustness (evolution); Evasion (ethics); Computer security; Deep learning; Adversarial system; Artificial intelligence; Data mining; Real-time computing; Machine learning; Telecommunications","score_opus":0.03240345502596371,"score_gpt":0.292634949808259,"score_spread":0.2602314947822953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385322377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096341565,0.00026607845,0.8964334,0.0010595769,0.00010918204,0.00010928048,0.0001796196,0.0020626166,0.0034386863],"genre_scores_gemma":[0.9692835,0.000061205435,0.02880543,0.00024618272,0.000018281797,0.00004244577,0.00011774704,0.000037784754,0.0013873833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702615,0.00079419103,0.0001375013,0.0006034654,0.0009619074,0.00047684627],"domain_scores_gemma":[0.9940222,0.0022685854,0.0007833904,0.0015690394,0.0010884429,0.0002683757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027308916,0.000740609,0.0010274388,0.0006233847,0.0006082692,0.0012116521,0.002134942,0.0013829739,0.0012422374],"category_scores_gemma":[0.0121620735,0.00037241075,0.00064358715,0.00047951107,0.0015098965,0.0029935075,0.003057747,0.0022942524,0.00043035255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007518929,0.00033477572,0.017482102,0.00016655188,0.00016763083,0.00065559876,0.00032713497,0.7130919,0.015219409,0.04143346,0.006770606,0.20359893],"study_design_scores_gemma":[0.0000091019065,0.000054323198,0.00046636126,0.0000064930505,0.000009541135,0.00009478436,0.0000225881,0.9864893,0.006134592,0.0060292063,0.00067208335,0.000011634367],"about_ca_topic_score_codex":0.0019977847,"about_ca_topic_score_gemma":0.0020251386,"teacher_disagreement_score":0.0027308916,"about_ca_system_score_codex":0.0015660796,"about_ca_system_score_gemma":0.0018987057,"threshold_uncertainty_score":0.014442503},"labels":[],"label_agreement":null},{"id":"W4385484696","doi":"10.1109/ijcnn54540.2023.10191246","title":"NSA: Naturalistic Support Artifact to Boost Network Confidence","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of British Columbia","funders":"","keywords":"Computer science; Leverage (statistics); Artifact (error); Artificial intelligence; Adversarial system; Machine learning","score_opus":0.020681209791193613,"score_gpt":0.2929278825001699,"score_spread":0.2722466727089763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385484696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28136787,0.0008876601,0.7026368,0.0012669171,0.0003314818,0.00015494277,0.00061361346,0.005527496,0.007213223],"genre_scores_gemma":[0.93503463,0.0001286601,0.0620252,0.00026783015,0.00006953598,0.000059327318,0.00055698137,0.00018743692,0.0016704374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939287,0.000170563,0.000024033048,0.00012626039,0.00020342319,0.000082851846],"domain_scores_gemma":[0.9970898,0.001307895,0.00031007352,0.0006609094,0.00044398065,0.00018737109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014813098,0.0011131024,0.0006368716,0.0005447743,0.0004479416,0.0007808975,0.0018253278,0.0009556959,0.0019958369],"category_scores_gemma":[0.007867106,0.00033168946,0.0005440608,0.00029450105,0.0010060928,0.0014953293,0.0017550931,0.001695574,0.00045786466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042411743,0.00018036681,0.005755411,0.0001278351,0.00009907663,0.00029668811,0.0001340651,0.84591734,0.012085704,0.008275763,0.0085781505,0.11812542],"study_design_scores_gemma":[0.000011566406,0.000059181693,0.00034452658,0.000007633941,0.00000814091,0.000042328254,0.000009929018,0.9935581,0.0026648352,0.002681572,0.0006055539,0.000006602821],"about_ca_topic_score_codex":0.0029895226,"about_ca_topic_score_gemma":0.0056861877,"teacher_disagreement_score":0.0029895226,"about_ca_system_score_codex":0.00080288417,"about_ca_system_score_gemma":0.0007317024,"threshold_uncertainty_score":0.007834017},"labels":[],"label_agreement":null},{"id":"W4385565400","doi":"10.18653/v1/2023.acl-long.554","title":"NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adversarial system; Inference; Artificial intelligence; Natural language; Spurious relationship; Formalism (music); Non-monotonic logic; Natural language understanding; Commonsense reasoning; Theoretical computer science; Machine learning","score_opus":0.03507218224831566,"score_gpt":0.3438307329829823,"score_spread":0.3087585507346666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385565400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024775143,0.00013114896,0.99146867,0.0006626373,0.000055952027,0.00011496739,0.00014113047,0.0019221749,0.0030257783],"genre_scores_gemma":[0.26209056,0.0005724113,0.7268558,0.0011609406,0.0001547833,0.0005909495,0.0006333986,0.00085220253,0.0070889024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654466,0.0014580553,0.000184024,0.0005593353,0.0009949933,0.0002588599],"domain_scores_gemma":[0.99494714,0.002946667,0.0003880395,0.0011913993,0.00031238215,0.00021427678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004800167,0.0010892033,0.0008226337,0.0015593605,0.0012354644,0.003523164,0.003864131,0.0020251444,0.0077828183],"category_scores_gemma":[0.012509034,0.0008415034,0.002804576,0.00073608436,0.0045597116,0.0053638583,0.006082666,0.0059623206,0.0013760364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014127721,0.00008528003,0.00050639024,0.00019546071,0.00010411675,0.0002816515,0.00033240538,0.14987953,0.002916549,0.79754144,0.0055502015,0.04246565],"study_design_scores_gemma":[0.000027768318,0.000048192225,0.000055272732,0.000043129497,0.000032076136,0.0001292784,0.000038409442,0.5103822,0.0017836023,0.47561055,0.011822987,0.000026573187],"about_ca_topic_score_codex":0.0035089883,"about_ca_topic_score_gemma":0.0051857065,"teacher_disagreement_score":0.0077828183,"about_ca_system_score_codex":0.0022995784,"about_ca_system_score_gemma":0.0025400743,"threshold_uncertainty_score":0.026036143},"labels":[],"label_agreement":null},{"id":"W4385571199","doi":"10.18653/v1/2023.repl4nlp-1.4","title":"A Multilingual Evaluation of NER Robustness to Adversarial Inputs","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Adversarial system; Hindi; Robustness (evolution); Computer science; German; Training set; Artificial intelligence; Natural language processing; Language model; Test data; Focus (optics); Labeled data; Machine learning; Linguistics","score_opus":0.04854937151821755,"score_gpt":0.3470135699869087,"score_spread":0.29846419846869116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385571199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71703917,0.00411199,0.2391896,0.0010684385,0.0010403304,0.00050363346,0.0038167806,0.0071655572,0.026064575],"genre_scores_gemma":[0.9458589,0.00046528436,0.039744824,0.00038597363,0.00010205541,0.00014919267,0.0062377667,0.00060658366,0.0064495383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99500316,0.0021118047,0.0003599482,0.0011563766,0.0009957629,0.000372894],"domain_scores_gemma":[0.9910323,0.0051032016,0.00043799882,0.0017238392,0.0013549188,0.00034776243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006935846,0.0020747308,0.0011367302,0.0010570538,0.0008355452,0.0011872002,0.0010254551,0.0013449078,0.0032700081],"category_scores_gemma":[0.015250362,0.00032630417,0.0010316711,0.00064944994,0.0011981073,0.0019380971,0.0028917263,0.0020628152,0.0015286074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019289334,0.0008393488,0.011770477,0.0008956422,0.0011549173,0.0006768365,0.00034118525,0.75098217,0.05200005,0.004015847,0.01106382,0.16433077],"study_design_scores_gemma":[0.00009642261,0.0019582766,0.011366313,0.00013515375,0.0002495611,0.0008072843,0.00027401422,0.87185556,0.099227145,0.004150551,0.00969813,0.00018150313],"about_ca_topic_score_codex":0.004127499,"about_ca_topic_score_gemma":0.0053391648,"teacher_disagreement_score":0.006935846,"about_ca_system_score_codex":0.0010160045,"about_ca_system_score_gemma":0.000663858,"threshold_uncertainty_score":0.0366807},"labels":[],"label_agreement":null},{"id":"W4385572282","doi":"10.18653/v1/2023.findings-acl.496","title":"Impact of Adversarial Training on Robustness and Generalizability of Language Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Adversarial system; Generalizability theory; Computer science; Embedding; Robustness (evolution); Artificial intelligence; Generalization; Transformer; Machine learning; Training set; Mathematics; Engineering","score_opus":0.042190235318372814,"score_gpt":0.3193435283298187,"score_spread":0.2771532930114459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385572282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27386642,0.0020079452,0.70595104,0.0025059,0.00018809835,0.00018528989,0.00032862232,0.0016927809,0.013273932],"genre_scores_gemma":[0.974256,0.0005978733,0.022970555,0.0003038935,0.00005526864,0.00008391532,0.00018924338,0.00024305518,0.0013001703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968836,0.0014956524,0.00016606049,0.0005036208,0.000591461,0.0003596395],"domain_scores_gemma":[0.9626355,0.029557664,0.0016378204,0.0048128488,0.0008895398,0.0004665834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007468306,0.0016310012,0.0009751957,0.0007424771,0.0006001582,0.0013733938,0.0013235429,0.0015162323,0.002195161],"category_scores_gemma":[0.051456153,0.0006006792,0.0010182224,0.00037149663,0.0031470936,0.0026770337,0.0037529382,0.0036533924,0.00041034358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023517024,0.000052515523,0.0020249756,0.00014941957,0.00012214953,0.00016156155,0.00014324143,0.9527508,0.0067930045,0.015104882,0.0005826873,0.021879472],"study_design_scores_gemma":[0.000014359882,0.00023990656,0.0010843492,0.000074983815,0.000033096734,0.00017346427,0.0000683555,0.9645668,0.007093055,0.0260088,0.00061923213,0.000023653078],"about_ca_topic_score_codex":0.0018568469,"about_ca_topic_score_gemma":0.0013241254,"teacher_disagreement_score":0.007468306,"about_ca_system_score_codex":0.001147654,"about_ca_system_score_gemma":0.000789498,"threshold_uncertainty_score":0.03949666},"labels":[],"label_agreement":null},{"id":"W4385584185","doi":"10.1049/cvi2.12231","title":"Visual privacy behaviour recognition for social robots based on an improved generative adversarial network","year":2023,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Petroleum Technology Research Centre; National Natural Science Foundation of China","keywords":"Computer science; Discriminator; Robot; Artificial intelligence; Feature extraction; Machine learning; Layer (electronics); Feature (linguistics); Pattern recognition (psychology)","score_opus":0.032579903667120366,"score_gpt":0.33630904672829526,"score_spread":0.3037291430611749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385584185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058985233,0.0002902363,0.9350427,0.00034386187,0.00006234913,0.00006362878,0.000076429205,0.0009815834,0.0041539236],"genre_scores_gemma":[0.9451806,0.00011175909,0.05042168,0.00022145337,0.000018406789,0.00005803639,0.00013924332,0.00004659514,0.0038023503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994234,0.00019978316,0.000016459444,0.00014002541,0.00014564865,0.00007485411],"domain_scores_gemma":[0.99941933,0.0002467625,0.00007344502,0.00012797806,0.000102936276,0.000029666207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007522714,0.00059033756,0.00044896305,0.00026265718,0.00018039814,0.00039964277,0.00082175457,0.00057043065,0.0010702463],"category_scores_gemma":[0.001584903,0.00019452153,0.0005491069,0.0001592189,0.0008200301,0.0006237226,0.00080240273,0.0009645678,0.0003145396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019097996,0.000071361275,0.0020819034,0.000059350226,0.000050844013,0.00019673476,0.00010603545,0.8487231,0.016140185,0.008520647,0.0024919328,0.1213669],"study_design_scores_gemma":[0.0000024061662,0.000021671756,0.00020653212,0.0000031821546,0.0000034665911,0.000037516067,0.0000056118497,0.9958026,0.0022770094,0.0013394026,0.00029667225,0.0000039185766],"about_ca_topic_score_codex":0.0020712998,"about_ca_topic_score_gemma":0.0016559842,"teacher_disagreement_score":0.0020712998,"about_ca_system_score_codex":0.00056738406,"about_ca_system_score_gemma":0.00035823695,"threshold_uncertainty_score":0.004118502},"labels":[],"label_agreement":null},{"id":"W4385680786","doi":"10.48550/arxiv.2308.02594","title":"SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland","keywords":"Reinforcement learning; Reinforcement; Computer science; Safety monitoring; Artificial intelligence; Psychology; Social psychology","score_opus":0.11588068759823777,"score_gpt":0.23198194017460502,"score_spread":0.11610125257636725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385680786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023403091,0.0002603518,0.9673665,0.00040467735,0.00007174651,0.00014930073,0.0001634664,0.006195075,0.0019856005],"genre_scores_gemma":[0.73455864,0.00013374785,0.26098603,0.0004414359,0.000050724793,0.0003974365,0.0003066925,0.00029253837,0.0028328397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904186,0.00030160477,0.000063393556,0.00022181604,0.00024310504,0.00012822518],"domain_scores_gemma":[0.9975006,0.0012398707,0.00041963384,0.00022259292,0.00044233786,0.00017496044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022179403,0.0013444666,0.0011058763,0.0007569571,0.00047992606,0.0009603012,0.0027347798,0.0014342217,0.0032040332],"category_scores_gemma":[0.0065884516,0.00075841375,0.00083056884,0.00026907862,0.0008849895,0.0013721747,0.0019264495,0.0025619222,0.0006040251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017726659,0.00013734636,0.0025486485,0.000100942314,0.00008209276,0.00011241304,0.00009716937,0.9049966,0.0022219205,0.004969448,0.002004323,0.08255181],"study_design_scores_gemma":[0.000010631428,0.000025537276,0.000071694456,0.00000550189,0.000005013361,0.0000067557235,0.0000033253773,0.9975078,0.0003887814,0.0016976503,0.00027350942,0.000003715756],"about_ca_topic_score_codex":0.0071049235,"about_ca_topic_score_gemma":0.008240048,"teacher_disagreement_score":0.0071049235,"about_ca_system_score_codex":0.0013049291,"about_ca_system_score_gemma":0.0023739743,"threshold_uncertainty_score":0.014127135},"labels":[],"label_agreement":null},{"id":"W4385695492","doi":"10.1109/dsn58367.2023.00048","title":"vWitness: Certifying Web Page Interactions with Computer Vision","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Toronto","funders":"","keywords":"Computer science; World Wide Web; Web page; Information retrieval; Computer graphics (images)","score_opus":0.02191165408595461,"score_gpt":0.29589361060850455,"score_spread":0.2739819565225499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385695492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13274775,0.0004027761,0.82997364,0.00030682542,0.00013344505,0.0002557198,0.00022673221,0.027774835,0.008178248],"genre_scores_gemma":[0.8936695,0.00010956986,0.10192696,0.00020770884,0.00003734937,0.000073204814,0.00043372807,0.00041019754,0.0031318222],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977214,0.00036656833,0.000107167434,0.0004298348,0.0010995409,0.00027543076],"domain_scores_gemma":[0.99491185,0.0012254604,0.00075329194,0.0021738217,0.00074177765,0.0001938813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018785837,0.0006538386,0.0006140185,0.00083197287,0.00038696005,0.0014366906,0.001666009,0.0012756949,0.0018830156],"category_scores_gemma":[0.01028502,0.00039807285,0.00047610025,0.0003478147,0.001225058,0.002508443,0.0022405398,0.0014653662,0.0012834832],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001352894,0.00064721715,0.013370644,0.00038245055,0.00022942394,0.0008531514,0.00047467774,0.21171741,0.17288451,0.029180752,0.01886374,0.5500431],"study_design_scores_gemma":[0.000026059288,0.00023473609,0.0022807843,0.000028915536,0.000016768401,0.0004211931,0.0000476433,0.8800927,0.10311906,0.009793599,0.0038986849,0.00003985035],"about_ca_topic_score_codex":0.0018462243,"about_ca_topic_score_gemma":0.0016004102,"teacher_disagreement_score":0.0018830156,"about_ca_system_score_codex":0.00075582357,"about_ca_system_score_gemma":0.00083049963,"threshold_uncertainty_score":0.009935021},"labels":[],"label_agreement":null},{"id":"W4385849361","doi":"10.48550/arxiv.2308.07233","title":"A Unifying Generator Loss Function for Generative Adversarial Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generator (circuit theory); Divergence (linguistics); Discriminator; Function (biology); Mathematics; MNIST database; Alpha (finance); Combinatorics; Discrete mathematics; Applied mathematics; Pure mathematics; Topology (electrical circuits); Artificial neural network; Computer science; Physics; Power (physics); Artificial intelligence; Quantum mechanics; Optics; Statistics","score_opus":0.0937237138490774,"score_gpt":0.21864442658026728,"score_spread":0.12492071273118988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385849361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025738387,0.0003446242,0.9923389,0.00032407467,0.00006974986,0.000050934406,0.00012168874,0.0004469868,0.003729079],"genre_scores_gemma":[0.4596133,0.002029013,0.51143825,0.0014354947,0.00049448916,0.00061895617,0.0019608457,0.0009836638,0.021425897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990344,0.00038545465,0.000042091542,0.00020310855,0.00025028203,0.0000847091],"domain_scores_gemma":[0.999243,0.00036919612,0.00006885737,0.00014524415,0.00011921428,0.000054436907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002011211,0.001976756,0.0009510643,0.0006854809,0.0003824523,0.0011910308,0.0019337365,0.001508137,0.0035638057],"category_scores_gemma":[0.0035838939,0.00046610174,0.00078082085,0.00070377154,0.0012667767,0.002067925,0.0021327944,0.0043945955,0.0016451199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010587606,0.00010957927,0.0007065972,0.0001446176,0.00007909766,0.00019406271,0.000099866826,0.6529141,0.00886601,0.19951105,0.013202764,0.124066375],"study_design_scores_gemma":[0.000009185761,0.000035723027,0.0001156946,0.000019221587,0.000010352422,0.00007999934,0.0000062890454,0.9498039,0.0016298408,0.04427802,0.0039982223,0.000013515617],"about_ca_topic_score_codex":0.001299605,"about_ca_topic_score_gemma":0.0023964457,"teacher_disagreement_score":0.0035638057,"about_ca_system_score_codex":0.0013442617,"about_ca_system_score_gemma":0.0010236996,"threshold_uncertainty_score":0.011922061},"labels":[],"label_agreement":null},{"id":"W4385894382","doi":"10.2139/ssrn.4525468","title":"Receptive Field  Refinement for Convolutional Neural Networks Reliably Improves Predictive Performancefield Refinement for Convolutional Neural Networks Reliably Improve","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Convolutional neural network; Computer science; Receptive field; Field (mathematics); Artificial intelligence; Artificial neural network; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.014270519701313046,"score_gpt":0.2662697371035933,"score_spread":0.25199921740228026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385894382","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051370166,0.0014963427,0.93615586,0.0011201933,0.00029912873,0.00009641882,0.0002721495,0.0043807956,0.0048089246],"genre_scores_gemma":[0.83498853,0.00065360166,0.15110525,0.00070814654,0.00022581482,0.0001087548,0.0008680614,0.0011113622,0.010230482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985083,0.0003919402,0.000080147795,0.00037820596,0.00044591803,0.00019550398],"domain_scores_gemma":[0.99339116,0.0036647178,0.00038238362,0.0017359306,0.0006232804,0.00020262238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037371346,0.002006521,0.0015071593,0.00079095695,0.00053549156,0.0011847233,0.0017919171,0.002106168,0.0042717075],"category_scores_gemma":[0.017186742,0.000871807,0.0009365326,0.0005962324,0.0013678988,0.0030259138,0.002984114,0.0038048232,0.0014552459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048242242,0.00012578562,0.0015362542,0.000202318,0.00012630901,0.00013524022,0.0000728631,0.8120588,0.027186958,0.023189493,0.006891228,0.12799229],"study_design_scores_gemma":[0.000009706499,0.000046068777,0.0001934121,0.000010755219,0.000010570183,0.000018027187,0.0000051562383,0.98322356,0.007669527,0.008331567,0.0004745792,0.000006963243],"about_ca_topic_score_codex":0.00510059,"about_ca_topic_score_gemma":0.004826929,"teacher_disagreement_score":0.00510059,"about_ca_system_score_codex":0.0012446039,"about_ca_system_score_gemma":0.0015806658,"threshold_uncertainty_score":0.019764125},"labels":[],"label_agreement":null},{"id":"W4385894685","doi":"10.18653/v1/2022.blackboxnlp-1.18","title":"Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information","year":2022,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Classifier (UML); Debiasing; Salient; Leverage (statistics); Artificial intelligence; Sentiment analysis; Machine learning; Natural language processing; Psychology; Social psychology","score_opus":0.017757430420927037,"score_gpt":0.26061384125069287,"score_spread":0.24285641082976583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385894685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40535662,0.00031432198,0.5844009,0.0027537118,0.000108591856,0.0001718652,0.00015200589,0.00042719074,0.006314792],"genre_scores_gemma":[0.9765385,0.00005266924,0.02179505,0.00048980233,0.000036847963,0.000046146,0.00007863313,0.00007023807,0.0008921261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99011433,0.005628941,0.0003279081,0.0014783818,0.0017288271,0.00072162115],"domain_scores_gemma":[0.9399279,0.041252606,0.005714054,0.009244831,0.0028373993,0.001023351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026807994,0.0009219609,0.0012385926,0.0010877531,0.0012854718,0.003619738,0.0016679538,0.002054901,0.0017046729],"category_scores_gemma":[0.09195284,0.0004726368,0.0007592482,0.00064362516,0.0043783705,0.0055710715,0.003575697,0.0040133535,0.00042469223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027014944,0.0006461618,0.11051632,0.00036998617,0.00066921854,0.0006429558,0.0036896185,0.4413311,0.026330156,0.22551978,0.0057657002,0.18181746],"study_design_scores_gemma":[0.000039319522,0.00015692465,0.0045859185,0.0000456868,0.000046991943,0.00010396041,0.0002278546,0.8262696,0.008086191,0.15942776,0.0009604763,0.000049243517],"about_ca_topic_score_codex":0.0020714141,"about_ca_topic_score_gemma":0.0019829131,"teacher_disagreement_score":0.026807994,"about_ca_system_score_codex":0.0018833597,"about_ca_system_score_gemma":0.0019984285,"threshold_uncertainty_score":0.1417759},"labels":[],"label_agreement":null},{"id":"W4386136237","doi":"10.1145/3617168","title":"Faire: Repairing Fairness of Neural Networks via Neuron Condition Synthesis","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation; Bộ Giáo dục và Ðào tạo; Ministry of Education - Singapore; National Research Foundation Singapore; Canadian Institute for Advanced Research","keywords":"Computer science; Deep neural networks; Retraining; Robustness (evolution); Overhead (engineering); Trustworthiness; Artificial intelligence; Machine learning; Artificial neural network; Computer security; Programming language","score_opus":0.04843400212564353,"score_gpt":0.30030590532651313,"score_spread":0.2518719032008696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386136237","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031200582,0.0002891309,0.9623219,0.0002454819,0.00013097726,0.0001136085,0.00009943956,0.0035289372,0.0020700223],"genre_scores_gemma":[0.76457274,0.00019888929,0.23049535,0.000297684,0.000075816984,0.00027322906,0.0002570185,0.00046111917,0.0033681851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983228,0.00027648453,0.00012859183,0.0004927421,0.00055359903,0.00022570656],"domain_scores_gemma":[0.99610436,0.0022063064,0.00038758866,0.00064605224,0.0005186504,0.00013709017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026106504,0.0012178905,0.0010664769,0.00082181726,0.00072296866,0.0011007157,0.0019642874,0.0012632458,0.004812771],"category_scores_gemma":[0.012721211,0.00054831983,0.001058636,0.0003343366,0.0019816058,0.0021280695,0.0022753682,0.0020546936,0.00046597555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044501593,0.00010942,0.0020163925,0.00022292169,0.0000871185,0.0003920613,0.00019613218,0.74020344,0.01824021,0.03708401,0.0024200575,0.19858328],"study_design_scores_gemma":[0.000024263192,0.000055889843,0.00008437199,0.0000151503655,0.000018624005,0.000037450445,0.000012931178,0.9739595,0.0075417873,0.017402165,0.00083744345,0.000010445107],"about_ca_topic_score_codex":0.0038231032,"about_ca_topic_score_gemma":0.004042197,"teacher_disagreement_score":0.004812771,"about_ca_system_score_codex":0.0013517998,"about_ca_system_score_gemma":0.0023581088,"threshold_uncertainty_score":0.016100347},"labels":[],"label_agreement":null},{"id":"W4386162746","doi":"10.1145/3617502","title":"Black-box Attack against Self-supervised Video Object Segmentation Models with Contrastive Loss","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Black box; Feature (linguistics); Adversarial system; Object (grammar); Deep learning; Metric (unit); Focus (optics); Frame (networking); Pattern recognition (psychology); Pixel; Machine learning; Computer vision","score_opus":0.028624551226466195,"score_gpt":0.29631492429823625,"score_spread":0.26769037307177007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386162746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19432776,0.0005398956,0.79642147,0.0009044887,0.00012972616,0.00013152036,0.00015587432,0.0034266855,0.0039624986],"genre_scores_gemma":[0.9125397,0.00012908438,0.08444658,0.00035091644,0.000034937788,0.00007560472,0.00019003998,0.0001672722,0.0020659035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985669,0.00044532926,0.000060910435,0.000308807,0.00044165473,0.0001764012],"domain_scores_gemma":[0.9971066,0.00152428,0.00032975746,0.00070881884,0.00022650328,0.00010397377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020842666,0.0010325187,0.00085426914,0.0004614609,0.00039284874,0.0007158748,0.0012465827,0.0015023499,0.0011289642],"category_scores_gemma":[0.0060609854,0.00036943183,0.0009200882,0.00026620488,0.0014929689,0.0017315833,0.002029144,0.002449918,0.00039592112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070319686,0.00017433317,0.0025068223,0.000096241674,0.00016909525,0.00030661796,0.00021706171,0.83508736,0.028814191,0.018297946,0.0039489674,0.1096781],"study_design_scores_gemma":[0.0000072084176,0.000058691134,0.00018508901,0.0000057330867,0.0000046289306,0.000051485666,0.0000075045455,0.98969984,0.0067750346,0.0028230818,0.00037565923,0.0000060244615],"about_ca_topic_score_codex":0.0023696586,"about_ca_topic_score_gemma":0.002591704,"teacher_disagreement_score":0.0023696586,"about_ca_system_score_codex":0.001312382,"about_ca_system_score_gemma":0.000872149,"threshold_uncertainty_score":0.011022806},"labels":[],"label_agreement":null},{"id":"W4386179076","doi":"10.22259/2638-5201.0301004","title":"Rogers's RS und SC Malingering Scales Derived from the SIMS","year":2020,"lang":"en","type":"article","venue":"Archives of Psychiatry and Behavioral Sciences","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Malingering; Psychology; Clinical psychology","score_opus":0.03598919045717314,"score_gpt":0.3108703117338568,"score_spread":0.2748811212766837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386179076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99160016,0.00035221886,0.002137381,0.00013094775,0.000033367403,0.00015586375,0.00037803463,0.000039505125,0.005172512],"genre_scores_gemma":[0.9963368,0.000122065554,0.0027880517,0.0000217305,0.000016900392,0.000122045996,0.0003088643,0.0000051876104,0.00027841236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971361,0.0007252298,0.0006891799,0.00015218754,0.0011369534,0.00016033452],"domain_scores_gemma":[0.9904197,0.0035394402,0.0028088551,0.00083405105,0.0018354444,0.0005625484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026406029,0.00045798733,0.00042383908,0.0028253805,0.00034065807,0.00058836926,0.00044485697,0.00027155798,0.0021700582],"category_scores_gemma":[0.01533937,0.000116946336,0.0008189003,0.0008135766,0.0009387728,0.000623271,0.0016898223,0.0006464466,0.00049384456],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029850277,0.00012899748,0.93398577,0.00024450533,0.00016905877,0.00024273357,0.003099674,0.0005995237,0.0029802665,0.0009128184,0.0010658172,0.05627226],"study_design_scores_gemma":[0.000019809724,0.0006285113,0.9876266,0.00008540741,0.00008271956,0.0022684827,0.0026616142,0.001189607,0.001750381,0.0013744748,0.0022756874,0.000036693214],"about_ca_topic_score_codex":0.00066662097,"about_ca_topic_score_gemma":0.0010283495,"teacher_disagreement_score":0.0028253805,"about_ca_system_score_codex":0.0003706095,"about_ca_system_score_gemma":0.00062103226,"threshold_uncertainty_score":0.013965011},"labels":[],"label_agreement":null},{"id":"W4386242325","doi":"10.1145/3600211.3604690","title":"Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Open Philanthropy Project; Canadian Institute for Advanced Research","keywords":"Computer science; Adversarial system; Artificial intelligence; Machine learning; Reinforcement learning; Foundation (evidence); Dual (grammatical number)","score_opus":0.044621462242707674,"score_gpt":0.28541686404650773,"score_spread":0.24079540180380005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386242325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12974338,0.00051702885,0.85644895,0.0026144325,0.00013539563,0.00015996785,0.00020833322,0.004350345,0.00582218],"genre_scores_gemma":[0.8757085,0.00027050922,0.118464604,0.0008437575,0.00006853484,0.00022854186,0.00027194916,0.0009422521,0.0032012626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99382365,0.003173214,0.00028988862,0.0009320792,0.0012459236,0.0005351317],"domain_scores_gemma":[0.94782925,0.026790133,0.0026171072,0.020059142,0.0018261444,0.00087834505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011850272,0.0015753913,0.0012346684,0.0006488285,0.0010889517,0.002485891,0.0036088699,0.0023536212,0.0031394986],"category_scores_gemma":[0.05473512,0.0011444684,0.0014955869,0.0004932064,0.004686907,0.007984308,0.007242789,0.0075333957,0.0012916895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008468221,0.0003101738,0.012683999,0.00025931367,0.00023852069,0.0005147411,0.000925694,0.6956021,0.010062161,0.14160056,0.0066362144,0.13031961],"study_design_scores_gemma":[0.000038991646,0.00012614549,0.00039503543,0.000056906123,0.00004260812,0.00014373797,0.00006904546,0.89793724,0.006900643,0.09114015,0.003113183,0.00003618809],"about_ca_topic_score_codex":0.002158498,"about_ca_topic_score_gemma":0.0024982032,"teacher_disagreement_score":0.011850272,"about_ca_system_score_codex":0.0014697005,"about_ca_system_score_gemma":0.0020681196,"threshold_uncertainty_score":0.062670946},"labels":[],"label_agreement":null},{"id":"W4386363433","doi":"10.1007/978-3-031-36938-4_8","title":"Bayesian-Based Parameter Estimation to Quantify Trust in Medical Devices","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Toronto Metropolitan University; McMaster University","funders":"","keywords":"Generalizability theory; Robustness (evolution); Computer science; Bayesian probability; Reliability (semiconductor); Bayesian network; Data mining; Wearable computer; Machine learning; Artificial intelligence; Trustworthiness; Statistics; Mathematics","score_opus":0.10850900503504561,"score_gpt":0.40602480290772064,"score_spread":0.297515797872675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386363433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022021143,0.000823738,0.9945859,0.00018390012,0.000040119874,0.000013976521,0.00004050926,0.000106082705,0.0020036297],"genre_scores_gemma":[0.60626423,0.0036499642,0.3781892,0.000344217,0.00036849047,0.00016316326,0.00032784272,0.00032738844,0.010365416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982864,0.00067833596,0.000094000585,0.00023362886,0.00063900737,0.000068549925],"domain_scores_gemma":[0.99528414,0.003460184,0.00034302258,0.00041423444,0.00042306446,0.00007537154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028460016,0.0009829093,0.001134886,0.0009806349,0.00033113905,0.002101564,0.0013647466,0.0019277398,0.0025672049],"category_scores_gemma":[0.016672442,0.0007420806,0.00085696595,0.0009167652,0.0016735808,0.0036704927,0.0019020268,0.0022742501,0.00079779234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010062292,0.000047885147,0.0009166738,0.00019851913,0.00011464395,0.000093721756,0.00013883434,0.7047697,0.005845328,0.16193789,0.0033520702,0.12248411],"study_design_scores_gemma":[0.000003354143,0.000024817513,0.00028635794,0.00003776835,0.000014178443,0.0000712649,0.0000112887965,0.9390262,0.0011116208,0.058149002,0.0012470458,0.000017201148],"about_ca_topic_score_codex":0.0014961247,"about_ca_topic_score_gemma":0.0012661096,"teacher_disagreement_score":0.0028460016,"about_ca_system_score_codex":0.0011820485,"about_ca_system_score_gemma":0.0005789966,"threshold_uncertainty_score":0.015051305},"labels":[],"label_agreement":null},{"id":"W4386525921","doi":"10.3390/e25091306","title":"Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of California Merced; China Scholarship Council; Canadian Institute for Advanced Research","keywords":"Autoencoder; Artificial intelligence; Computer science; Adversarial system; Image (mathematics); Representation (politics); Pattern recognition (psychology); Deep learning; Noise (video); Artificial neural network; Image restoration; Computer vision; Image processing","score_opus":0.044708054722360405,"score_gpt":0.3491046305429865,"score_spread":0.3043965758206261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386525921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013308058,0.00016537333,0.98544335,0.0001485648,0.000019022895,0.00001786821,0.000022043847,0.0002528086,0.0006228142],"genre_scores_gemma":[0.7805102,0.0003902967,0.21336174,0.0003293757,0.00011765966,0.000090515365,0.00030643417,0.00014679629,0.0047470303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925214,0.00019149214,0.000033179884,0.00016390535,0.00026609743,0.000093132614],"domain_scores_gemma":[0.99907494,0.000416643,0.00016570446,0.00014470212,0.00014627886,0.00005176216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012762825,0.0007215968,0.000896387,0.00066135166,0.00031000315,0.0005624442,0.0011190819,0.0008929423,0.0011243911],"category_scores_gemma":[0.0026873644,0.00031022282,0.0008499966,0.00043276578,0.0009946915,0.0010657837,0.0013616022,0.0015157928,0.00032963287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022244148,0.00008731327,0.001406564,0.00008599527,0.00011663412,0.00015572355,0.00012240041,0.74381566,0.029735578,0.027438404,0.0026116048,0.19420157],"study_design_scores_gemma":[0.0000029285839,0.00002030308,0.00011306602,0.0000031849745,0.000006601787,0.000030406996,0.000004019954,0.9946197,0.0024599375,0.0024570432,0.00027746824,0.000005379278],"about_ca_topic_score_codex":0.0013956412,"about_ca_topic_score_gemma":0.0014283621,"teacher_disagreement_score":0.0013956412,"about_ca_system_score_codex":0.0005820917,"about_ca_system_score_gemma":0.0006070666,"threshold_uncertainty_score":0.0067496896},"labels":[],"label_agreement":null},{"id":"W4386528685","doi":"10.48550/arxiv.2309.02159","title":"The Adversarial Implications of Variable-Time Inference","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ben-Gurion University of the Negev; Defense Advanced Research Projects Agency; Government of Canada; Canadian Institute for Advanced Research; Alfred P. Sloan Foundation","keywords":"Computer science; Adversary; Inference; Exploit; Adversarial system; Information leakage; Side channel attack; Artificial intelligence; Machine learning; Computer security; Cryptography","score_opus":0.07381278144022783,"score_gpt":0.22246008321094993,"score_spread":0.1486473017707221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386528685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10996957,0.00036719063,0.8795893,0.0018155694,0.00014305093,0.00012424843,0.0002039452,0.0013153986,0.006471736],"genre_scores_gemma":[0.9514273,0.00015780007,0.046519086,0.00036213096,0.00007255159,0.00006860143,0.0000859396,0.0001168433,0.0011897298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99123114,0.0036317904,0.00039584874,0.0012285309,0.002712914,0.0007997681],"domain_scores_gemma":[0.9552258,0.029737249,0.0034586878,0.0101446705,0.0010341349,0.00039926544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060058897,0.0009300187,0.00072099915,0.0005576483,0.00077435427,0.0017782799,0.0016727351,0.0016161304,0.0022474558],"category_scores_gemma":[0.038935993,0.00062048144,0.0010277227,0.00048106103,0.003346118,0.004085496,0.0034112078,0.004557052,0.00048460418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009891036,0.00015755766,0.0063977824,0.0001930656,0.00019531736,0.0006572088,0.00040583516,0.69180983,0.029906737,0.20756015,0.0024121823,0.059315182],"study_design_scores_gemma":[0.000028266026,0.00013517092,0.00064624095,0.00003303707,0.000030635118,0.00022996769,0.00003297733,0.8880864,0.01979885,0.08942274,0.0015251578,0.000030657386],"about_ca_topic_score_codex":0.000615468,"about_ca_topic_score_gemma":0.00063034915,"teacher_disagreement_score":0.0060058897,"about_ca_system_score_codex":0.0013196843,"about_ca_system_score_gemma":0.0013412986,"threshold_uncertainty_score":0.03176254},"labels":[],"label_agreement":null},{"id":"W4386578476","doi":"","title":"Towards Privacy Aware Deep Learning for Embedded Systems","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Deep learning; Information privacy; Computer security; Human–computer interaction; Internet privacy; Artificial intelligence","score_opus":0.01377738016322066,"score_gpt":0.2438185168851677,"score_spread":0.23004113672194704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386578476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011554554,0.0003726501,0.98493147,0.000793388,0.000056168094,0.000025751262,0.000089375746,0.0007037909,0.0014728158],"genre_scores_gemma":[0.75662273,0.00077780575,0.23002933,0.0010257405,0.00016164557,0.00009991087,0.00042922137,0.00034028493,0.010513354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986582,0.00042109878,0.000051576902,0.00021544634,0.0004730524,0.00018066139],"domain_scores_gemma":[0.9966415,0.0018893243,0.00019150875,0.00078013644,0.00038758985,0.00010996049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002336466,0.0008157771,0.0010049662,0.0003600643,0.00035763334,0.001433457,0.0014149217,0.0015047996,0.0029430087],"category_scores_gemma":[0.0077895788,0.0005290754,0.0005180464,0.00041683688,0.0013535313,0.0029437307,0.0037418033,0.0039827637,0.0006993019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037080952,0.00015488909,0.0010936502,0.00027963676,0.000121380945,0.00011325467,0.0001414485,0.67518044,0.01727909,0.09159052,0.007857666,0.20581712],"study_design_scores_gemma":[0.000004301661,0.00001852663,0.00006193067,0.000008920215,0.0000045776715,0.00001204666,0.000007127078,0.9647947,0.0027003165,0.03145419,0.0009303821,0.0000029482867],"about_ca_topic_score_codex":0.0013455618,"about_ca_topic_score_gemma":0.0020136472,"teacher_disagreement_score":0.0029430087,"about_ca_system_score_codex":0.001054381,"about_ca_system_score_gemma":0.0011061492,"threshold_uncertainty_score":0.012356579},"labels":[],"label_agreement":null},{"id":"W4386585729","doi":"10.1007/978-3-031-40923-3_7","title":"A Low-Cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Computer science; Anomaly detection; Inference; Scalability; Overhead (engineering); Artificial intelligence; Categorization; Artificial neural network; Precision and recall; Recall; Component (thermodynamics); Fault detection and isolation; Detector; Pattern recognition (psychology); Machine learning; Data mining; Programming language","score_opus":0.02756579282268076,"score_gpt":0.26612443447366696,"score_spread":0.2385586416509862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386585729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044384045,0.00018581799,0.99313074,0.00014625888,0.000067134526,0.000034172903,0.00005615272,0.0010481054,0.00089315715],"genre_scores_gemma":[0.3817094,0.00029521863,0.6085647,0.00037464823,0.00020028303,0.00019074323,0.00044088013,0.0003533908,0.007870812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988808,0.0002325368,0.00006817064,0.00027328994,0.0004159465,0.00012929064],"domain_scores_gemma":[0.9980141,0.0008617813,0.0001727732,0.00044346973,0.00039751272,0.000110364555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015927418,0.0012933121,0.0012800778,0.0006018681,0.00046256807,0.0012731908,0.0029474795,0.0015591104,0.0036704454],"category_scores_gemma":[0.0056238915,0.0006877548,0.0006612738,0.0006590698,0.00090056687,0.0022234959,0.0037131913,0.0028082978,0.0010724565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038775493,0.00020718499,0.0008869461,0.00013032249,0.000101778474,0.00015710329,0.00010818692,0.2763551,0.021422274,0.044561617,0.010457379,0.6452244],"study_design_scores_gemma":[0.000006008555,0.000027728405,0.000070974565,0.000005651528,0.000007116118,0.000021280452,0.000005147164,0.98408186,0.0028283608,0.012402751,0.00053623994,0.00000673235],"about_ca_topic_score_codex":0.0026833096,"about_ca_topic_score_gemma":0.004895897,"teacher_disagreement_score":0.0036704454,"about_ca_system_score_codex":0.0008530689,"about_ca_system_score_gemma":0.001557132,"threshold_uncertainty_score":0.012278914},"labels":[],"label_agreement":null},{"id":"W4386712148","doi":"10.1007/978-3-031-40953-0_34","title":"Safety Integrity Levels for Artificial Intelligence","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Critical Systems Labs","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.06908601725453448,"score_gpt":0.3164454676170098,"score_spread":0.24735945036247534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386712148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005725247,0.002607867,0.9287404,0.0030535595,0.0005025975,0.00009027449,0.00024172537,0.0009787338,0.058059637],"genre_scores_gemma":[0.53329146,0.0043240413,0.40094844,0.0016811801,0.0017041945,0.0005336773,0.0010928323,0.0007566555,0.055667505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976253,0.0006485899,0.00020809947,0.00034855155,0.0010101793,0.00015927931],"domain_scores_gemma":[0.99542797,0.002120208,0.0002851558,0.0013800042,0.0006560038,0.0001306444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029573403,0.0010353418,0.00093222334,0.0014044779,0.0008079015,0.003922257,0.0017637619,0.0017882591,0.013942738],"category_scores_gemma":[0.007828398,0.000523591,0.00094817637,0.00094262493,0.0036288078,0.0073363474,0.0028624837,0.0057363296,0.0034414514],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041915067,0.000025545265,0.00006612498,0.00014547822,0.000016158636,0.00002743758,0.00006743677,0.010072726,0.0009788179,0.9172854,0.005563693,0.06570924],"study_design_scores_gemma":[0.000004783223,0.00001855577,0.000023888946,0.00004187997,0.0000066470066,0.000020025263,0.000015948533,0.018426217,0.0010290124,0.9714328,0.008974444,0.0000058445867],"about_ca_topic_score_codex":0.00028131864,"about_ca_topic_score_gemma":0.00018100014,"teacher_disagreement_score":0.013942738,"about_ca_system_score_codex":0.0014664375,"about_ca_system_score_gemma":0.0007747551,"threshold_uncertainty_score":0.046643138},"labels":[],"label_agreement":null},{"id":"W4386722210","doi":"10.48550/arxiv.2010.12995","title":"Out-of-distribution detection for regression tasks: parameter versus predictor entropy","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Machine learning; Computer science; Entropy (arrow of time); Artificial neural network; Regression; Principle of maximum entropy; Data mining; Mathematics; Statistics","score_opus":0.09141412361104741,"score_gpt":0.2364910310149689,"score_spread":0.14507690740392148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386722210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055152837,0.00048127625,0.941426,0.00085616286,0.000039147275,0.00004675924,0.00011487911,0.00045903772,0.0014238829],"genre_scores_gemma":[0.87441486,0.00039785885,0.1225346,0.00035399882,0.00016997382,0.00010319978,0.00041748505,0.0003038623,0.0013041015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99513465,0.002514463,0.00022214565,0.000889006,0.0009959894,0.00024366837],"domain_scores_gemma":[0.9509372,0.040215064,0.0024683736,0.0044515105,0.0011287689,0.00079904636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011617477,0.0012748841,0.0016282364,0.001643734,0.00082738744,0.002396999,0.0019458864,0.0023440253,0.0013132154],"category_scores_gemma":[0.053511906,0.00071901816,0.00096753705,0.00091101864,0.0040148897,0.0041993815,0.004350281,0.004720367,0.00035448908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040869284,0.00013116661,0.016061166,0.00015585232,0.00023826363,0.0002537818,0.00038145494,0.82323045,0.005803174,0.06650282,0.002187105,0.08464615],"study_design_scores_gemma":[0.000010272216,0.00003879778,0.0012282438,0.000028603408,0.0000124822545,0.000076541146,0.0000305537,0.94999456,0.0026032464,0.045621794,0.00033005205,0.000024834892],"about_ca_topic_score_codex":0.001363882,"about_ca_topic_score_gemma":0.0012017934,"teacher_disagreement_score":0.011617477,"about_ca_system_score_codex":0.0014228942,"about_ca_system_score_gemma":0.0010946811,"threshold_uncertainty_score":0.06143987},"labels":[],"label_agreement":null},{"id":"W4386796458","doi":"10.2139/ssrn.4574177","title":"Improving Adversarial Transferability Through Hybrid Augmentation","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Transferability; Adversarial system; Computer science; Data science; Artificial intelligence; Machine learning","score_opus":0.020624595050595555,"score_gpt":0.2824837748828986,"score_spread":0.26185917983230306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386796458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016882159,0.00025149714,0.97641355,0.0002959057,0.00009680206,0.00004017332,0.00006787442,0.0012063502,0.004745706],"genre_scores_gemma":[0.8569377,0.0003041972,0.13162667,0.000404505,0.00020834399,0.00016003381,0.0002934138,0.0004752891,0.009589799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986547,0.0004545128,0.00005910984,0.0003306495,0.000366297,0.00013457488],"domain_scores_gemma":[0.9953347,0.0028259135,0.00026928625,0.0011287117,0.00031473386,0.00012658101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021692128,0.0018507643,0.0012556865,0.0007046175,0.0004690315,0.0014377334,0.001731867,0.00198137,0.005988425],"category_scores_gemma":[0.00980552,0.0006252266,0.0009992622,0.00060353416,0.002027278,0.0030992003,0.0053102276,0.0031181136,0.0014397517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033590625,0.00012347993,0.00067617017,0.00015195072,0.000104584404,0.0001761582,0.000099945326,0.8062013,0.01809672,0.053395033,0.003575296,0.11706336],"study_design_scores_gemma":[0.0000069893836,0.000041520565,0.00008458014,0.0000083782825,0.000009540909,0.000043380216,0.00000522855,0.9781051,0.0026392178,0.0185794,0.00046890482,0.000007791647],"about_ca_topic_score_codex":0.00083835045,"about_ca_topic_score_gemma":0.00080487435,"teacher_disagreement_score":0.005988425,"about_ca_system_score_codex":0.0005887662,"about_ca_system_score_gemma":0.00059676066,"threshold_uncertainty_score":0.02003324},"labels":[],"label_agreement":null},{"id":"W4386811899","doi":"10.1007/978-3-031-43424-2_3","title":"Exploring the Training Robustness of Distributional Reinforcement Learning Against Noisy State Observations","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Robustness (evolution); Bounded function; Mathematical optimization; Artificial intelligence; Markov decision process; Suite; Algorithm; Markov process; Mathematics; Statistics","score_opus":0.10254552285347575,"score_gpt":0.2689238549056029,"score_spread":0.16637833205212715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386811899","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08213944,0.0005928885,0.91210234,0.00069593923,0.00005609168,0.00004244805,0.00007240771,0.00063049246,0.0036679858],"genre_scores_gemma":[0.9552046,0.000261672,0.040597018,0.00017608065,0.00007718606,0.00007764053,0.0001645945,0.00022521251,0.0032158867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808633,0.00094045623,0.00007469205,0.00037742226,0.0003108716,0.00021024658],"domain_scores_gemma":[0.96922565,0.027677266,0.0009925758,0.0011313496,0.0006524355,0.00032065943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061583365,0.0010424411,0.001336949,0.0006787491,0.00041759992,0.0012432656,0.0021479249,0.0017918599,0.0021517507],"category_scores_gemma":[0.038170833,0.00084163685,0.0007174442,0.0005663529,0.0025567706,0.0027835446,0.0033368578,0.0028832133,0.0003122697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012711914,0.0000367906,0.0005426401,0.000050716142,0.0000443325,0.000040372594,0.000051328116,0.96541756,0.0012370141,0.014462188,0.00035966444,0.017630132],"study_design_scores_gemma":[0.0000042578336,0.00002568706,0.000088432294,0.000005389725,0.0000038556486,0.000007729501,0.0000042860747,0.98968875,0.00030545407,0.00981105,0.00005154778,0.000003471353],"about_ca_topic_score_codex":0.0031582953,"about_ca_topic_score_gemma":0.0014024636,"teacher_disagreement_score":0.0061583365,"about_ca_system_score_codex":0.0014286,"about_ca_system_score_gemma":0.0010377478,"threshold_uncertainty_score":0.032568812},"labels":[],"label_agreement":null},{"id":"W4386827564","doi":"10.1016/j.cose.2023.103482","title":"Transferable adversarial distribution learning: Query-efficient adversarial attack against large language models","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Overfitting; Machine learning; Language model; Artificial intelligence; Leverage (statistics); Regularization (linguistics); Adversarial system; Black box; Artificial neural network","score_opus":0.013819036654897237,"score_gpt":0.26314159993484443,"score_spread":0.2493225632799472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386827564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014892993,0.00045864892,0.97960895,0.0010442176,0.000100556696,0.00008804904,0.00019028953,0.0015397761,0.0020765937],"genre_scores_gemma":[0.8475943,0.00057590054,0.141145,0.0010477011,0.00029333372,0.0002764251,0.00065562275,0.00038039754,0.008031323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969382,0.001095468,0.0001360448,0.00045754196,0.0009780738,0.0003946672],"domain_scores_gemma":[0.9909938,0.006521919,0.000352384,0.0014610996,0.00046547837,0.00020526601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004041353,0.0014142691,0.001977409,0.000762542,0.0006278139,0.0014398819,0.002595407,0.0023502626,0.0036539375],"category_scores_gemma":[0.015114125,0.0006823559,0.0009982184,0.0012183284,0.0023240834,0.0042709894,0.006267514,0.0046551013,0.0011431848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072644395,0.00021474247,0.0006898674,0.00017609318,0.0001272039,0.00020898024,0.0001265756,0.7759552,0.008506301,0.07303438,0.01211786,0.12811641],"study_design_scores_gemma":[0.00001675153,0.000029174118,0.000045824116,0.0000037641137,0.0000056277154,0.000030478433,0.0000064553465,0.97693175,0.0009947848,0.021581164,0.00034792334,0.0000063485904],"about_ca_topic_score_codex":0.0020242473,"about_ca_topic_score_gemma":0.001544958,"teacher_disagreement_score":0.004041353,"about_ca_system_score_codex":0.0015552634,"about_ca_system_score_gemma":0.0018900738,"threshold_uncertainty_score":0.021373034},"labels":[],"label_agreement":null},{"id":"W4386954264","doi":"10.1016/j.media.2023.102965","title":"Backdoor attack and defense in federated generative adversarial network-based medical image synthesis","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Public Safety Canada; Nvidia","keywords":"Backdoor; Computer science; Generator (circuit theory); Adversarial system; Computer security; Artificial intelligence; Deep learning; Machine learning; Data mining","score_opus":0.014849875500631417,"score_gpt":0.29797350023547114,"score_spread":0.2831236247348397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386954264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043854985,0.0004193161,0.95164716,0.00042214282,0.000066489156,0.00005132722,0.00006274546,0.0011932019,0.0022825787],"genre_scores_gemma":[0.9322359,0.00017819072,0.064966194,0.0003455681,0.00002670528,0.00006212481,0.00008009679,0.00007979357,0.0020254645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879587,0.00045848434,0.000052446438,0.00022162442,0.00033171457,0.00013990639],"domain_scores_gemma":[0.9981406,0.0010794838,0.00016405187,0.00038033284,0.00016467526,0.00007082899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017514402,0.00076966215,0.0007432926,0.00035467668,0.00033412248,0.0006992552,0.0009281284,0.0010743942,0.0011246953],"category_scores_gemma":[0.0045053144,0.0003581673,0.0007612043,0.0002232422,0.0015177454,0.0012644335,0.0023463916,0.0018516936,0.0002599219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034291175,0.000072808,0.0015722367,0.00009287991,0.00011153511,0.0004197694,0.00021672926,0.86793023,0.020351602,0.030142104,0.0018908259,0.07685652],"study_design_scores_gemma":[0.0000074827644,0.000032092266,0.00012684001,0.000009753279,0.000007637447,0.000100371435,0.000010197875,0.98663735,0.005223635,0.0073818304,0.0004531466,0.000009630416],"about_ca_topic_score_codex":0.00093249604,"about_ca_topic_score_gemma":0.00087452313,"teacher_disagreement_score":0.0017514402,"about_ca_system_score_codex":0.0006977993,"about_ca_system_score_gemma":0.0005614619,"threshold_uncertainty_score":0.009262621},"labels":[],"label_agreement":null},{"id":"W4387068365","doi":"10.1109/comst.2023.3319492","title":"Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Adversarial system; Computer science; Robustness (evolution); Artificial intelligence; Machine learning; Deep learning; Transferability; Categorization; Deep neural networks; Decision tree; Adversarial machine learning; Software deployment; Data science; Field (mathematics); Computer security; Software engineering","score_opus":0.07554101023633406,"score_gpt":0.3193680409457303,"score_spread":0.24382703070939624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387068365","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0094394805,0.20955423,0.7404352,0.004546661,0.0014711111,0.00019120151,0.00018434427,0.0006433548,0.03353448],"genre_scores_gemma":[0.4312058,0.4019322,0.13490663,0.0043580295,0.005293244,0.0005814719,0.00072132354,0.00038388037,0.020617446],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974412,0.00076901703,0.00021262614,0.00033969103,0.0010409714,0.00019654726],"domain_scores_gemma":[0.9938723,0.0045344126,0.00040446763,0.0005707055,0.0005269751,0.00009118352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034105321,0.0017962432,0.0015471611,0.0018256144,0.00092673267,0.0029680268,0.0015667067,0.002600484,0.0037078077],"category_scores_gemma":[0.007167293,0.00072966045,0.0013241192,0.0017486637,0.0023095587,0.005134625,0.0027596885,0.00384908,0.0012590243],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013480317,0.00013535982,0.002171625,0.0026967812,0.00025276525,0.00026938147,0.00027254788,0.13611835,0.0042393887,0.24179757,0.021924404,0.589987],"study_design_scores_gemma":[0.000038821963,0.0005618239,0.0021248232,0.0025317818,0.0002550762,0.002370162,0.00041954764,0.48110443,0.010703891,0.2936559,0.20603265,0.00020114418],"about_ca_topic_score_codex":0.00074091856,"about_ca_topic_score_gemma":0.00052199437,"teacher_disagreement_score":0.0037078077,"about_ca_system_score_codex":0.0011731767,"about_ca_system_score_gemma":0.00089105754,"threshold_uncertainty_score":0.018036842},"labels":[],"label_agreement":null},{"id":"W4387185400","doi":"10.3233/faia230311","title":"Worrisome Properties of Neural Network Controllers and Their Symbolic Representations","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"Narodowa Agencja Wymiany Akademickiej; Infrastruktura PL-Grid; Uniwersytet Warszawski","keywords":"Robustness (evolution); Computer science; Artificial neural network; Benchmark (surveying); Property (philosophy); Control theory (sociology); Artificial intelligence; Control (management)","score_opus":0.047352966318324294,"score_gpt":0.2652768182628564,"score_spread":0.21792385194453207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387185400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04052673,0.0042919717,0.81535214,0.0028997774,0.000309518,0.000064938235,0.00028098476,0.0008881258,0.13538583],"genre_scores_gemma":[0.7883894,0.004359367,0.16127984,0.0006886497,0.0002614201,0.00021950867,0.00042317435,0.00040888475,0.04396975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996086,0.00006555316,0.00002369905,0.00009179424,0.00018040682,0.000029987508],"domain_scores_gemma":[0.99863774,0.000868479,0.000094575975,0.00026964548,0.00010369768,0.000025911864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070948584,0.0005504634,0.00042288078,0.0003530964,0.00040959188,0.0018398444,0.0007850422,0.0008794256,0.0066145156],"category_scores_gemma":[0.0036338007,0.00023892343,0.0004606553,0.0004195027,0.002254996,0.0023281511,0.0008445237,0.0023998225,0.00092254364],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042010757,0.000025657182,0.00021483561,0.00035176516,0.000025926518,0.00013332351,0.00019433712,0.10815417,0.008095176,0.7928819,0.0037619576,0.08611895],"study_design_scores_gemma":[0.000008343923,0.00006092694,0.00017880138,0.00008771337,0.000014019437,0.00019146655,0.00003784624,0.20848463,0.007608406,0.76381487,0.01949612,0.00001682156],"about_ca_topic_score_codex":0.0006011996,"about_ca_topic_score_gemma":0.000489389,"teacher_disagreement_score":0.0066145156,"about_ca_system_score_codex":0.0008666814,"about_ca_system_score_gemma":0.0004305692,"threshold_uncertainty_score":0.022127748},"labels":[],"label_agreement":null},{"id":"W4387250554","doi":"10.1109/asap57973.2023.00016","title":"COIN: Combinational Intelligent Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fulbright Canada","keywords":"Computer science; Artificial neural network; Field-programmable gate array; Backpropagation; Throughput; Inference; Enhanced Data Rates for GSM Evolution; Deep learning; Combinational logic; Edge device; Artificial intelligence; Computer engineering; Computer architecture; Logic gate; Embedded system; Machine learning; Algorithm","score_opus":0.01815461593179084,"score_gpt":0.26991832789364034,"score_spread":0.2517637119618495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387250554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006571513,0.0007775579,0.9265623,0.00057935546,0.0003209149,0.00018364206,0.0013434662,0.045109022,0.018552164],"genre_scores_gemma":[0.35625,0.0015236384,0.6023011,0.0011831259,0.00017572018,0.000690293,0.0040531578,0.0060873567,0.027735574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993923,0.00009921378,0.00003421035,0.00012061685,0.00028212665,0.00007160931],"domain_scores_gemma":[0.9994172,0.00026212822,0.00004575324,0.00015369098,0.00009444655,0.000026738478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006167993,0.0010423611,0.0004322883,0.00055245624,0.00037876677,0.0012774402,0.001966304,0.0007882756,0.021315446],"category_scores_gemma":[0.0033712476,0.0005544463,0.0007218981,0.00048037714,0.00070841325,0.0024116714,0.001328343,0.0016668822,0.004362341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049958896,0.00013408414,0.0014769799,0.0008123068,0.00022102184,0.0004115358,0.00012880673,0.37169653,0.017101098,0.093796425,0.09761641,0.41610527],"study_design_scores_gemma":[0.00005337674,0.00008253217,0.00018279819,0.00007628233,0.000042687145,0.00021010908,0.000013874144,0.87632215,0.016889056,0.044784855,0.061308037,0.00003422294],"about_ca_topic_score_codex":0.002576261,"about_ca_topic_score_gemma":0.0046808957,"teacher_disagreement_score":0.021315446,"about_ca_system_score_codex":0.0006610624,"about_ca_system_score_gemma":0.00078773795,"threshold_uncertainty_score":0.0713073},"labels":[],"label_agreement":null},{"id":"W4387390093","doi":"10.48550/arxiv.2310.02779","title":"Expected flow networks in stochastic environments and two-player zero-sum games","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; Samsung; Nvidia","keywords":"Computer science; Set (abstract data type); Zero (linguistics); Flow (mathematics); Adversarial system; Generative grammar; Sampling (signal processing); Object (grammar); Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics; Computer vision","score_opus":0.05003588794772058,"score_gpt":0.19911757568519903,"score_spread":0.14908168773747846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387390093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12136878,0.0003905787,0.8668906,0.0014745186,0.00009577481,0.00010977287,0.00032468222,0.00034607065,0.008999245],"genre_scores_gemma":[0.9249775,0.00037954186,0.06484616,0.00044378507,0.00007339074,0.0003071278,0.00033670518,0.000117731026,0.008517879],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987068,0.00067495974,0.00004091958,0.00026520962,0.0001400011,0.00017199256],"domain_scores_gemma":[0.9945831,0.0042419373,0.0004504675,0.00022068016,0.00018504824,0.0003188276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025471286,0.0011272191,0.0012209237,0.00082722935,0.0008107951,0.0014802819,0.0016232885,0.0018089734,0.0046371864],"category_scores_gemma":[0.0102641005,0.0006409721,0.000980728,0.0005098075,0.0027177613,0.0027562408,0.0019357861,0.002155532,0.0004765236],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008619899,0.00004279451,0.0006929036,0.00004593909,0.000033433767,0.00008228767,0.00006587805,0.85546154,0.0005702839,0.13490123,0.0010533032,0.0069643403],"study_design_scores_gemma":[0.000015940008,0.000021123988,0.00009075118,0.0000075495295,0.0000042894503,0.0000138312325,0.000008927918,0.91225934,0.00016489776,0.08702124,0.00038400953,0.000008058567],"about_ca_topic_score_codex":0.002798105,"about_ca_topic_score_gemma":0.0031391364,"teacher_disagreement_score":0.0046371864,"about_ca_system_score_codex":0.001724496,"about_ca_system_score_gemma":0.0010722869,"threshold_uncertainty_score":0.015512943},"labels":[],"label_agreement":null},{"id":"W4387475865","doi":"10.1016/j.iot.2023.100965","title":"A federated and explainable approach for insider threat detection in IoT","year":2023,"lang":"en","type":"article","venue":"Internet of Things","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Insider threat; Insider; Computer science; Computer security; Process (computing); Trustworthiness; Internet of Things; Internet privacy","score_opus":0.02062771944478478,"score_gpt":0.26305290104667817,"score_spread":0.24242518160189339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387475865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0122589795,0.0001205176,0.98559314,0.0003579358,0.00003353247,0.000041556454,0.00008794791,0.0006157295,0.0008905067],"genre_scores_gemma":[0.76212525,0.00020927913,0.23358211,0.00033154635,0.00014113185,0.00012073005,0.00037096333,0.00013626262,0.0029828304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826366,0.00054496236,0.00007943851,0.00039702005,0.00053037825,0.00018454953],"domain_scores_gemma":[0.99587065,0.0021949317,0.00046707992,0.00083418604,0.0004906509,0.00014248828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002086138,0.0007901746,0.0010569199,0.001277455,0.0007601659,0.001647307,0.0021209891,0.002034694,0.0022085833],"category_scores_gemma":[0.008481978,0.0004949565,0.0013176284,0.00067404466,0.0015581094,0.0033255273,0.0042932685,0.0025470646,0.00036452716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028152653,0.0002165671,0.0032774091,0.0001452106,0.00026925365,0.0006524828,0.00033858255,0.7481135,0.007437278,0.09646001,0.0033934477,0.1394148],"study_design_scores_gemma":[0.0000034928148,0.00001974217,0.00022020844,0.000009418655,0.000014853591,0.000044010616,0.0000150386295,0.9670346,0.0008531905,0.03134503,0.00043146286,0.000008911351],"about_ca_topic_score_codex":0.0021641355,"about_ca_topic_score_gemma":0.0024803092,"teacher_disagreement_score":0.0022085833,"about_ca_system_score_codex":0.0009168853,"about_ca_system_score_gemma":0.0012276763,"threshold_uncertainty_score":0.011032641},"labels":[],"label_agreement":null},{"id":"W4387634923","doi":"10.48550/arxiv.2310.07856","title":"Assessing Evaluation Metrics for Neural Test Oracle Generation","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Oracle; Computer science; Test (biology); Metric (unit); Assertion; Machine learning; Test case; Artificial intelligence; Data mining; Programming language; Regression analysis; Engineering","score_opus":0.334270648900014,"score_gpt":0.29876270420289863,"score_spread":0.03550794469711538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387634923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73587394,0.009962993,0.22446635,0.0016252821,0.00040232026,0.0005759249,0.0031725238,0.015436324,0.008484359],"genre_scores_gemma":[0.91784614,0.0005523133,0.07379158,0.00030405595,0.0000571532,0.0002541367,0.0057265377,0.0004889246,0.0009791122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.980838,0.008912308,0.0019042284,0.0024331561,0.005261826,0.0006504534],"domain_scores_gemma":[0.8944715,0.07485919,0.006729351,0.0093967,0.012762395,0.0017808301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017063351,0.0021397267,0.0009991627,0.004824183,0.0004423424,0.002528213,0.0026670177,0.0016856623,0.0012989814],"category_scores_gemma":[0.1216332,0.0004587583,0.0007632621,0.002504391,0.0011785524,0.0042063165,0.0020144465,0.002167629,0.00067944615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013843658,0.0010288395,0.14611934,0.0014744946,0.00080897834,0.00028228862,0.00070828875,0.319778,0.006349955,0.0041861054,0.010613592,0.50726575],"study_design_scores_gemma":[0.00009378499,0.0010576871,0.020399805,0.0002626072,0.000146019,0.00018975504,0.0002560705,0.9611423,0.009016591,0.00451546,0.002854267,0.00006568036],"about_ca_topic_score_codex":0.0070253876,"about_ca_topic_score_gemma":0.009132746,"teacher_disagreement_score":0.017063351,"about_ca_system_score_codex":0.0031836818,"about_ca_system_score_gemma":0.0021833656,"threshold_uncertainty_score":0.09024072},"labels":[],"label_agreement":null},{"id":"W4387700646","doi":"10.1145/3617946.3617953","title":"Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)","year":2023,"lang":"en","type":"article","venue":"ACM SIGSOFT Software Engineering Notes","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Deep learning; Dependability; Interpretability; Software engineering; Computer science; Verification and validation; Intersection (aeronautics); Correctness; Software testing; Software system; Artificial intelligence; Software; Engineering; Programming language","score_opus":0.02896485804727705,"score_gpt":0.2640472140121904,"score_spread":0.23508235596491334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387700646","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015374196,0.07008097,0.6700914,0.050887108,0.07002063,0.0016484841,0.009856489,0.010816319,0.10122448],"genre_scores_gemma":[0.10673741,0.046072867,0.28552037,0.015114723,0.023942487,0.0018563436,0.05580083,0.0074919774,0.457463],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952434,0.0012145439,0.0003514959,0.0010408765,0.0016300147,0.00051962264],"domain_scores_gemma":[0.9908421,0.0016194528,0.00015901917,0.0010758555,0.004455711,0.0018478266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010768778,0.0024315813,0.0016359558,0.0026072916,0.00088784134,0.004798222,0.002727491,0.002624622,0.0448667],"category_scores_gemma":[0.010286151,0.0009880394,0.0017851433,0.0017858794,0.0009171867,0.0054028654,0.0042150435,0.005220601,0.020423988],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040323797,0.00035320752,0.0008295973,0.00041916518,0.00012283563,0.0002540967,0.00016858504,0.0066566034,0.0045321416,0.007366902,0.57961404,0.3992796],"study_design_scores_gemma":[0.00018332624,0.0005774046,0.0021011843,0.0007298396,0.00014882587,0.0005265191,0.00018668617,0.051175892,0.01006445,0.026693368,0.9074755,0.00013703866],"about_ca_topic_score_codex":0.0063413186,"about_ca_topic_score_gemma":0.009102645,"teacher_disagreement_score":0.0448667,"about_ca_system_score_codex":0.0025003122,"about_ca_system_score_gemma":0.0034865988,"threshold_uncertainty_score":0.15009409},"labels":[],"label_agreement":null},{"id":"W4387735260","doi":"10.1145/3627817","title":"It Is All about Data: A Survey on the Effects of Data on Adversarial Robustness","year":2023,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Robustness (evolution); Computer science; Machine learning; Artificial intelligence; Mistake; Vulnerability (computing); Adversarial machine learning; Computer security; Threat model; Data science","score_opus":0.2351525607181049,"score_gpt":0.4152004103574418,"score_spread":0.18004784963933693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387735260","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014358513,0.9460791,0.023949588,0.00270883,0.000656481,0.000051575247,0.00007576784,0.00011050608,0.0249324],"genre_scores_gemma":[0.018151416,0.96901155,0.0065837656,0.0010891848,0.001235381,0.00006232372,0.000122339,0.000050786293,0.0036931203],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998259,0.00047567434,0.00012840064,0.0002298917,0.00079351413,0.00011349228],"domain_scores_gemma":[0.98940444,0.008475471,0.00038249022,0.0005319312,0.0010726631,0.00013300806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027774533,0.0011762645,0.0014134598,0.0031032574,0.00069211476,0.002540717,0.00143072,0.0019954883,0.0060204305],"category_scores_gemma":[0.007758799,0.00083307986,0.0008550565,0.004093381,0.0020251367,0.005196302,0.0017193658,0.0028185276,0.0029753],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054388307,0.00010115159,0.0012213227,0.006208421,0.000109036846,0.0001793036,0.00021836975,0.00882746,0.00076682155,0.13675335,0.036647566,0.80891275],"study_design_scores_gemma":[0.00001609911,0.00024135297,0.0021289082,0.007599773,0.00012991812,0.0017205143,0.00037713526,0.009349279,0.0018188083,0.101689264,0.87482494,0.00010400241],"about_ca_topic_score_codex":0.0011366301,"about_ca_topic_score_gemma":0.0010653082,"teacher_disagreement_score":0.0060204305,"about_ca_system_score_codex":0.0011370318,"about_ca_system_score_gemma":0.0012239814,"threshold_uncertainty_score":0.02014035},"labels":[],"label_agreement":null},{"id":"W4387817937","doi":"10.47852/bonviewaaes32021471","title":"Machine Learning Insights into Hypersonics Research Evolution: A 21st Century Perspective","year":2023,"lang":"en","type":"article","venue":"Archives of Advanced Engineering Science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; National Research Council Canada","funders":"","keywords":"Data science; Field (mathematics); Computer science; Management science; Engineering","score_opus":0.012927668261380762,"score_gpt":0.28796640287966624,"score_spread":0.2750387346182855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387817937","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10652268,0.2830292,0.105169214,0.40239578,0.0026913034,0.00017527865,0.004835583,0.0006172869,0.094563685],"genre_scores_gemma":[0.72788066,0.15938742,0.077666104,0.01735827,0.0078011993,0.0002492438,0.0032738636,0.0002890314,0.006094234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9941095,0.0027260568,0.0005318194,0.00085282716,0.0013906702,0.00038907368],"domain_scores_gemma":[0.9349207,0.047061205,0.006942931,0.0029231124,0.0060936376,0.0020583852],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.016310357,0.00058727566,0.0007476143,0.02509593,0.0018376312,0.01145833,0.0018101471,0.0020171537,0.0035137867],"category_scores_gemma":[0.038398206,0.00036644927,0.00060618547,0.03074011,0.007384855,0.015672848,0.0037148849,0.0029612798,0.00080217683],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010362348,0.00014286245,0.05617891,0.0026821003,0.00022480759,0.00065663044,0.012277792,0.0057310406,0.0011411958,0.5433433,0.027684346,0.3498333],"study_design_scores_gemma":[0.000017775888,0.00007175136,0.041793548,0.0032746291,0.00005949707,0.00060689263,0.013075667,0.01586005,0.00070504117,0.60846883,0.31596538,0.00010088741],"about_ca_topic_score_codex":0.0053728153,"about_ca_topic_score_gemma":0.0074118106,"teacher_disagreement_score":0.98368967,"about_ca_system_score_codex":0.0042512068,"about_ca_system_score_gemma":0.0051877587,"threshold_uncertainty_score":0.08625841},"labels":[],"label_agreement":null},{"id":"W4387896026","doi":"10.54254/2755-2721/19/20231029","title":"Review of Adversarial Attacks in Object Detection","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Object (grammar); Exploit; Object detection; Computer security; Artificial intelligence; Deep learning; Masking (illustration); Adversarial machine learning; Risk analysis (engineering); Data science; Pattern recognition (psychology); Business","score_opus":0.00712527573519359,"score_gpt":0.23422256478764214,"score_spread":0.22709728905244855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896026","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007694655,0.9531455,0.02863907,0.002295832,0.0012236766,0.000033719007,0.00007253803,0.00009223183,0.013727924],"genre_scores_gemma":[0.012607652,0.9721395,0.007121809,0.0013740154,0.0022846607,0.000035874087,0.00014761227,0.000039381724,0.004249545],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99904174,0.00022908102,0.000099710545,0.00016742513,0.0003981754,0.00006395689],"domain_scores_gemma":[0.9969035,0.0021803523,0.00019423717,0.00017092822,0.0004897855,0.00006127947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018191237,0.0012959637,0.0010654766,0.0018125975,0.00054760167,0.0016790361,0.0015328323,0.0018702976,0.0035042062],"category_scores_gemma":[0.00402205,0.00066778687,0.00071968045,0.0021007492,0.0012700362,0.0029134178,0.0010133712,0.0023104204,0.0020618946],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007337577,0.00008978654,0.0007328028,0.008328674,0.0001952076,0.00027281546,0.0001476561,0.015912598,0.0016718404,0.0949286,0.060458023,0.81718856],"study_design_scores_gemma":[0.000012330577,0.00019345486,0.0011404692,0.0045600757,0.00013664289,0.0013419774,0.00009404476,0.010034828,0.0021903464,0.04971879,0.9305059,0.00007129402],"about_ca_topic_score_codex":0.00139652,"about_ca_topic_score_gemma":0.0011053028,"teacher_disagreement_score":0.0035042062,"about_ca_system_score_codex":0.0010798989,"about_ca_system_score_gemma":0.0012403481,"threshold_uncertainty_score":0.0117227435},"labels":[],"label_agreement":null},{"id":"W4387928556","doi":"10.48550/arxiv.2310.13786","title":"Fundamental Limits of Membership Inference Attacks on Machine Learning Models","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut Universitaire de France; Agence Nationale de la Recherche","keywords":"Overfitting; Inference; Computer science; Machine learning; Statistical inference; Artificial intelligence; Point (geometry); Statistical model; Data mining; Mathematics; Artificial neural network; Statistics","score_opus":0.25136149970872107,"score_gpt":0.2536368033275691,"score_spread":0.0022753036188480347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387928556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09726758,0.0013660778,0.87479365,0.007957512,0.00016863098,0.00018874396,0.00032912535,0.0006493868,0.01727936],"genre_scores_gemma":[0.94711995,0.0009885218,0.047911674,0.0009957538,0.0004618405,0.00043665047,0.0001988641,0.00023799851,0.0016488056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9723551,0.015311053,0.001135682,0.0032989928,0.0061058234,0.0017932532],"domain_scores_gemma":[0.63296276,0.32221794,0.013579104,0.024085198,0.0042788857,0.0028760838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037152994,0.00167137,0.0027043768,0.002280326,0.0028612562,0.0063571967,0.0042308844,0.0051722312,0.0034863288],"category_scores_gemma":[0.21963632,0.0016371547,0.0020148263,0.0016057028,0.011596307,0.01369785,0.010824451,0.012441108,0.0005872343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030626112,0.000110201385,0.0024389725,0.00020308301,0.00013793696,0.00021186745,0.00043281796,0.21153946,0.0023412628,0.76405084,0.0020954784,0.01613186],"study_design_scores_gemma":[0.00003378886,0.00008604368,0.0003348736,0.000068262794,0.0000177353,0.00014520486,0.000055650216,0.45224786,0.0011557989,0.5450811,0.00073862245,0.000035064964],"about_ca_topic_score_codex":0.00072566414,"about_ca_topic_score_gemma":0.00045149887,"teacher_disagreement_score":0.037152994,"about_ca_system_score_codex":0.004462025,"about_ca_system_score_gemma":0.0026356408,"threshold_uncertainty_score":0.19648612},"labels":[],"label_agreement":null},{"id":"W4387969815","doi":"10.1145/3581783.3612018","title":"Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Robustness (evolution); Computer science; Adversarial system; Distortion (music); Noise reduction; Algorithm; Prior probability; Noise (video); Mathematical optimization; Artificial intelligence; Computer vision; Mathematics; Image (mathematics); Telecommunications","score_opus":0.01734292886604292,"score_gpt":0.2515388911011982,"score_spread":0.2341959622351553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387969815","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011799986,0.00018464461,0.9853962,0.00020867896,0.000048741484,0.000033691507,0.000045629145,0.0012932908,0.000989205],"genre_scores_gemma":[0.70028895,0.00047111372,0.29225463,0.0006713204,0.00009846248,0.00018574797,0.00035179706,0.0005814431,0.005096493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990132,0.00017855011,0.000040674062,0.00021420557,0.00044471194,0.00010858226],"domain_scores_gemma":[0.9984988,0.0005548106,0.00020256644,0.00044809902,0.0001915718,0.000104194834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014643702,0.001162592,0.0011713197,0.00070051284,0.00057205104,0.001003882,0.0025765416,0.0019354941,0.0015847031],"category_scores_gemma":[0.0044831485,0.00067931693,0.0012394559,0.00044416756,0.0020249537,0.001895376,0.0047191507,0.0030209995,0.00079098373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017098396,0.000048344238,0.0010375505,0.000066071436,0.00009113963,0.00012787423,0.00012311651,0.86488414,0.018110473,0.024524532,0.0028214806,0.08799441],"study_design_scores_gemma":[0.000007995643,0.000021878424,0.000066718014,0.000004623368,0.0000043404366,0.00003453408,0.0000060739653,0.98942155,0.0029518686,0.0067645833,0.0007083282,0.000007549727],"about_ca_topic_score_codex":0.002992614,"about_ca_topic_score_gemma":0.00275239,"teacher_disagreement_score":0.002992614,"about_ca_system_score_codex":0.00094077707,"about_ca_system_score_gemma":0.0009096738,"threshold_uncertainty_score":0.0077444315},"labels":[],"label_agreement":null},{"id":"W4387993744","doi":"10.1145/3623652.3623665","title":"DINAR: Enabling Distribution Agnostic Noise Injection in Machine Learning Hardware","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Noise (video); Robustness (evolution); Gaussian noise; Embedded system; Artificial intelligence; Image (mathematics)","score_opus":0.015664368406405494,"score_gpt":0.25969977554319995,"score_spread":0.24403540713679445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387993744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044417027,0.0010889655,0.9271434,0.00079948606,0.00042116677,0.00018999973,0.00035039373,0.013377034,0.012212601],"genre_scores_gemma":[0.8177038,0.0004211774,0.17017612,0.0013373212,0.00014841449,0.0003152121,0.00048721232,0.00088238914,0.008528338],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99786574,0.00036448505,0.00013323939,0.00045101435,0.000898869,0.00028670896],"domain_scores_gemma":[0.99718386,0.0011304196,0.000274171,0.0008484225,0.00043658703,0.00012658372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001395509,0.0011243938,0.0007359855,0.0006562089,0.0005827156,0.0014455066,0.0027423122,0.0010532762,0.0071355016],"category_scores_gemma":[0.0066495235,0.00043028448,0.0005421708,0.00042643462,0.0012053364,0.002559777,0.002823743,0.002399336,0.0020957193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020615333,0.0009198828,0.005898236,0.0010270943,0.00021645053,0.0008670854,0.00038578955,0.28487492,0.17011392,0.14932436,0.03427963,0.35003117],"study_design_scores_gemma":[0.00013339339,0.00057300716,0.00044669447,0.000107172535,0.000060390834,0.00045189922,0.000065073014,0.80544126,0.13320373,0.03778142,0.02165717,0.000078721685],"about_ca_topic_score_codex":0.00048786128,"about_ca_topic_score_gemma":0.001230966,"teacher_disagreement_score":0.0071355016,"about_ca_system_score_codex":0.0009073915,"about_ca_system_score_gemma":0.0011642743,"threshold_uncertainty_score":0.023870647},"labels":[],"label_agreement":null},{"id":"W4388115665","doi":"10.1109/isi58743.2023.10297141","title":"An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; York University","funders":"","keywords":"SQL injection; Adversarial system; Computer science; Cross-site scripting; Scripting language; Vulnerability (computing); Adversarial machine learning; SQL; Machine learning; Artificial intelligence; Web application security; Software engineering; Computer security; Database; Web service; World Wide Web; Query by Example; Programming language; Web development; Search engine","score_opus":0.03944939451725725,"score_gpt":0.32235910456138195,"score_spread":0.2829097100441247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02270462,0.00021658184,0.972971,0.00037010052,0.00005659026,0.00011333103,0.000035198696,0.00042333387,0.00310925],"genre_scores_gemma":[0.5214133,0.00021486821,0.47202843,0.00044929492,0.000051852967,0.0003875971,0.00015765602,0.00014741305,0.0051495438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938023,0.00020885776,0.00003453508,0.00010496711,0.00017569399,0.00009568016],"domain_scores_gemma":[0.9975278,0.0017927684,0.00012670529,0.00012905605,0.0003217759,0.000101846876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017184427,0.00092504366,0.0010386889,0.00092724845,0.0005497322,0.00069246383,0.0015546741,0.0018107513,0.0024257563],"category_scores_gemma":[0.006258721,0.0005190374,0.0006278313,0.00055760203,0.0010307953,0.0009065982,0.0015025316,0.0018068632,0.000399711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048779508,0.00006332405,0.0008277978,0.000030910276,0.00003538724,0.00008843862,0.000064334265,0.9263358,0.0016479162,0.011122794,0.0012300666,0.058504507],"study_design_scores_gemma":[0.000007828152,0.000018131406,0.000036766778,0.0000035423525,0.0000039948095,0.000016858652,0.000004542267,0.9970811,0.00020926488,0.0023850598,0.00023030731,0.000002602764],"about_ca_topic_score_codex":0.002457396,"about_ca_topic_score_gemma":0.002532607,"teacher_disagreement_score":0.002457396,"about_ca_system_score_codex":0.000825392,"about_ca_system_score_gemma":0.0012322553,"threshold_uncertainty_score":0.009088099},"labels":[],"label_agreement":null},{"id":"W4388212577","doi":"10.1109/issrew60843.2023.00032","title":"Building Resilient ML Applications using Ensembles against Faulty Training Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia Hospital; University of British Columbia","funders":"","keywords":"Computer science; Resilience (materials science); Reuse; Process (computing); Training set; Training (meteorology); Machine learning; Artificial intelligence; Data modeling; Software engineering; Engineering","score_opus":0.14165828444133746,"score_gpt":0.3757090690191169,"score_spread":0.23405078457777942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388212577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14088973,0.0002729702,0.8520386,0.0005761452,0.000092591064,0.000104481376,0.00008323487,0.0039281384,0.00201423],"genre_scores_gemma":[0.83327156,0.00015739936,0.1639609,0.00029875306,0.000060583923,0.00012596993,0.00023138835,0.00036438534,0.0015290242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983559,0.00062193046,0.00009213595,0.0003809375,0.0003680805,0.00018090547],"domain_scores_gemma":[0.99134785,0.0045853313,0.0005977349,0.0022637888,0.00093567604,0.00026954256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033179908,0.001120791,0.00095421606,0.0007682399,0.0008008051,0.0012550892,0.0017853327,0.0014260488,0.0015960687],"category_scores_gemma":[0.017525092,0.00077130686,0.00092651456,0.00045491767,0.0011087254,0.0026210619,0.003236003,0.0021925005,0.0010047633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113169175,0.00009084454,0.005326533,0.000058168727,0.000120907505,0.00019523564,0.00024947693,0.90514725,0.009972098,0.0027171108,0.0012251107,0.07478399],"study_design_scores_gemma":[0.000004351283,0.000048916518,0.0003808273,0.000013746748,0.000016023918,0.00005909153,0.000045012854,0.9881335,0.0055964296,0.0049199504,0.0007717304,0.000010347665],"about_ca_topic_score_codex":0.0015719442,"about_ca_topic_score_gemma":0.0021020712,"teacher_disagreement_score":0.0033179908,"about_ca_system_score_codex":0.0006951132,"about_ca_system_score_gemma":0.00077658636,"threshold_uncertainty_score":0.017547429},"labels":[],"label_agreement":null},{"id":"W4388282590","doi":"10.1016/j.cmpb.2023.107910","title":"Assessing privacy leakage in synthetic 3-D PET imaging using transversal GAN","year":2023,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; BC Cancer Foundation","keywords":"Computer science; Machine learning; Artificial intelligence; Inference; Fidelity; Generative model; Segmentation; Generative grammar; Data mining","score_opus":0.08766871363078618,"score_gpt":0.4167928898280828,"score_spread":0.3291241761972966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388282590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4406539,0.0015330527,0.55062497,0.0017593935,0.00014300166,0.0000814002,0.0007574201,0.0010972399,0.003349688],"genre_scores_gemma":[0.9739526,0.00027775607,0.024169022,0.00025872115,0.000029003912,0.000022791346,0.0004914706,0.00007290394,0.0007256225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988859,0.00059235014,0.0000379953,0.00015519306,0.00023702763,0.000091528906],"domain_scores_gemma":[0.99512863,0.0037025749,0.00029105734,0.0005475828,0.00021918406,0.000110814995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028028972,0.0005601309,0.00048191956,0.0002961592,0.00021740342,0.0009771163,0.00054953265,0.0014027882,0.0006664257],"category_scores_gemma":[0.012888026,0.00033148524,0.0004370737,0.00034879975,0.0010538975,0.00087717234,0.0010642796,0.0008715339,0.00018345157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016232206,0.00013740595,0.0066280877,0.00026215144,0.00020403082,0.00077457284,0.00014628837,0.9155542,0.020822177,0.0073947767,0.0021702547,0.044282887],"study_design_scores_gemma":[0.000022827076,0.00011150324,0.0019205642,0.00002340534,0.000028370823,0.0007911954,0.000034343306,0.98203295,0.009085871,0.0053435676,0.0005863658,0.000019071518],"about_ca_topic_score_codex":0.0013449424,"about_ca_topic_score_gemma":0.0010127361,"teacher_disagreement_score":0.0028028972,"about_ca_system_score_codex":0.00055319926,"about_ca_system_score_gemma":0.0006033541,"threshold_uncertainty_score":0.0148233175},"labels":[],"label_agreement":null},{"id":"W4388427718","doi":"10.1109/iv60283.2023.00062","title":"Responsible Artificial Intelligence and Bias Mitigation in Deep Learning Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Key (lock); Computer science; Trustworthiness; Deep learning; Artificial intelligence; Domain (mathematical analysis); Data science; Computer security","score_opus":0.05837492449784346,"score_gpt":0.2997693397210008,"score_spread":0.24139441522315733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388427718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021747477,0.0046517327,0.94405127,0.008901012,0.0003895654,0.000092251204,0.00007894743,0.00029460844,0.019793134],"genre_scores_gemma":[0.9079803,0.0044281688,0.07696094,0.0015500827,0.00057607284,0.00019305528,0.000075898664,0.00011444717,0.008120923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962548,0.0018840156,0.00018066232,0.0003879445,0.00091925566,0.00037330863],"domain_scores_gemma":[0.98978627,0.007170565,0.0007787847,0.0011744789,0.0008072049,0.00028270372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00782545,0.0008038833,0.0009073845,0.0006722545,0.0009378667,0.0040738774,0.0016561026,0.0029117588,0.0030384895],"category_scores_gemma":[0.02254367,0.00053207384,0.0006167123,0.000569714,0.0049076406,0.0044150655,0.0044858307,0.003705524,0.0004677307],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010620602,0.00004750656,0.0012072246,0.00030184988,0.000118644886,0.00013111468,0.0002914732,0.19869123,0.0024302318,0.71764123,0.004286858,0.07474643],"study_design_scores_gemma":[0.000018042503,0.000050615738,0.0002145286,0.00012340871,0.00001968641,0.000075628835,0.000050594503,0.35216162,0.0018185339,0.6400151,0.0054287026,0.000023546856],"about_ca_topic_score_codex":0.0009424095,"about_ca_topic_score_gemma":0.0008677242,"teacher_disagreement_score":0.00782545,"about_ca_system_score_codex":0.002090191,"about_ca_system_score_gemma":0.0019406916,"threshold_uncertainty_score":0.041385412},"labels":[],"label_agreement":null},{"id":"W4388514504","doi":"10.48550/arxiv.2311.03683","title":"Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Government of Canada; Canadian Institute for Advanced Research","keywords":"Discriminative model; Computer science; Artificial neural network; Training set; Class (philosophy); Machine learning; Test data; Artificial intelligence; Logit; De facto; Point (geometry); Set (abstract data type); Data set; Mathematics","score_opus":0.16183786839215936,"score_gpt":0.2594925872341089,"score_spread":0.09765471884194954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388514504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09174901,0.0011124501,0.9006879,0.0012293819,0.00011426094,0.00008567253,0.00025364017,0.0018004015,0.0029673048],"genre_scores_gemma":[0.8770046,0.00039671914,0.116487406,0.001174384,0.00013664595,0.00019807357,0.0008543857,0.0004151438,0.0033326724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99192315,0.003678192,0.00040853015,0.0014384774,0.0020791567,0.00047250272],"domain_scores_gemma":[0.94855493,0.03902144,0.0025193717,0.00692885,0.002243705,0.0007318258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011781956,0.0018928158,0.0021548436,0.00094091694,0.0009912311,0.0014167859,0.0037095556,0.003209392,0.0017919095],"category_scores_gemma":[0.061789103,0.0012573423,0.0011761099,0.00089329877,0.0045243474,0.0041709375,0.009776874,0.006585712,0.0009910219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011461321,0.00022752912,0.0065059876,0.00035549732,0.00015706226,0.00056884316,0.0003323437,0.8227345,0.00996754,0.024566263,0.005413555,0.12802473],"study_design_scores_gemma":[0.00004021025,0.00013275656,0.00072189263,0.000068192174,0.000014869298,0.00021816895,0.00003762304,0.9709882,0.0074333097,0.019584185,0.00073145254,0.0000291845],"about_ca_topic_score_codex":0.002121399,"about_ca_topic_score_gemma":0.002521483,"teacher_disagreement_score":0.011781956,"about_ca_system_score_codex":0.0015766713,"about_ca_system_score_gemma":0.00093607005,"threshold_uncertainty_score":0.062309682},"labels":[],"label_agreement":null},{"id":"W4388621503","doi":"10.1007/978-981-99-8248-6_24","title":"Combating Computer Vision-Based Aim Assist Tools in Competitive Online Games","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Adversarial system; Domain (mathematical analysis); Set (abstract data type); Human–computer interaction; Artificial intelligence; Object (grammar); Cheating; Black box; Computer game; Competitor analysis; Computer vision; Computer security; Multimedia; Programming language","score_opus":0.024851314607340833,"score_gpt":0.2910374366453593,"score_spread":0.26618612203801845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388621503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060638037,0.0006948068,0.8952402,0.0004364023,0.0002805337,0.00016463407,0.0000539081,0.0012265883,0.041264936],"genre_scores_gemma":[0.8896881,0.00036016697,0.089591734,0.0002411412,0.000067427914,0.00013644983,0.000067380664,0.000117368705,0.019730214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995926,0.00010279764,0.000010711442,0.000054952972,0.0001506426,0.0000883491],"domain_scores_gemma":[0.9994816,0.00026989292,0.000050048548,0.000046197812,0.000089834626,0.000062460174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007829779,0.0010547512,0.000535779,0.00035801984,0.00034791883,0.0009697293,0.0011214515,0.001088189,0.0063750953],"category_scores_gemma":[0.001824824,0.0003225158,0.00034447957,0.00016277411,0.00075137545,0.0010571032,0.0025862653,0.0013591562,0.0013080804],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009958126,0.00067900715,0.0011322394,0.00041418677,0.00011784267,0.0004614106,0.00043655318,0.4668409,0.06331794,0.0593547,0.013430848,0.39281857],"study_design_scores_gemma":[0.000022265933,0.00042336993,0.00070098095,0.000046169225,0.000015689424,0.0001728033,0.00009175002,0.9674004,0.008959357,0.017281532,0.0048627863,0.000022901002],"about_ca_topic_score_codex":0.0007753515,"about_ca_topic_score_gemma":0.0012359557,"teacher_disagreement_score":0.0063750953,"about_ca_system_score_codex":0.00031760507,"about_ca_system_score_gemma":0.00042106182,"threshold_uncertainty_score":0.02132684},"labels":[],"label_agreement":null},{"id":"W4388662069","doi":"10.1145/3581784.3607084","title":"Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Meta; Alliance de recherche numérique du Canada; Intel Corporation","keywords":"Dram; Granularity; Computer science; Convolutional neural network; Coding (social sciences); Scheme (mathematics); Embedded system; Distributed computing; Computer engineering; Computer hardware; Artificial intelligence; Operating system","score_opus":0.0242408235099306,"score_gpt":0.29187736930199604,"score_spread":0.2676365457920654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388662069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07050341,0.00077049364,0.9166946,0.00077328394,0.00032967888,0.00012875197,0.00022236291,0.0026494903,0.0079279775],"genre_scores_gemma":[0.9336001,0.00034752805,0.060316876,0.00036300306,0.00009769293,0.00008951283,0.00020384774,0.000101695274,0.0048798257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956673,0.000052827494,0.00002486908,0.00007863245,0.0001869503,0.00008998952],"domain_scores_gemma":[0.9987675,0.00032886432,0.00018815392,0.00042111473,0.00024241535,0.000052076437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005571741,0.0007443449,0.00043406305,0.0004401101,0.0004814225,0.00056315126,0.0016378438,0.0008681969,0.0026619732],"category_scores_gemma":[0.0029824644,0.00023308273,0.00035600766,0.0003243601,0.00095212547,0.0015682216,0.0017695802,0.0012976032,0.0005737069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007998909,0.00015845531,0.0012634762,0.00034706268,0.00012956183,0.00040555737,0.0002496992,0.3668434,0.16319853,0.13719615,0.01816551,0.31124273],"study_design_scores_gemma":[0.000044971068,0.00028884446,0.00034333576,0.00004175462,0.00005436748,0.00016342374,0.00003175991,0.90408134,0.052203055,0.037420038,0.0052951565,0.000032012547],"about_ca_topic_score_codex":0.0013208241,"about_ca_topic_score_gemma":0.0023053642,"teacher_disagreement_score":0.0026619732,"about_ca_system_score_codex":0.0006657258,"about_ca_system_score_gemma":0.001067437,"threshold_uncertainty_score":0.008905172},"labels":[],"label_agreement":null},{"id":"W4388886508","doi":"10.1145/3605764.3623912","title":"The Adversarial Implications of Variable-Time Inference","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Ben-Gurion University of the Negev; Defense Advanced Research Projects Agency; Government of Canada; Canadian Institute for Advanced Research; Alfred P. Sloan Foundation","keywords":"Computer science; Adversary; Inference; Exploit; Adversarial system; Information leakage; Side channel attack; Artificial intelligence; Machine learning; Robustness (evolution); Computer security; Data mining; Cryptography","score_opus":0.014611685934245042,"score_gpt":0.28339920155070375,"score_spread":0.2687875156164587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388886508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10996957,0.00036719063,0.8795893,0.0018155694,0.00014305093,0.00012424843,0.0002039452,0.0013153986,0.006471736],"genre_scores_gemma":[0.9514273,0.00015780007,0.046519086,0.00036213096,0.00007255159,0.00006860143,0.0000859396,0.0001168433,0.0011897298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99123114,0.0036317904,0.00039584874,0.0012285309,0.002712914,0.0007997681],"domain_scores_gemma":[0.9552258,0.029737249,0.0034586878,0.0101446705,0.0010341349,0.00039926544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060058897,0.0009300187,0.00072099915,0.0005576483,0.00077435427,0.0017782799,0.0016727351,0.0016161304,0.0022474558],"category_scores_gemma":[0.038935993,0.00062048144,0.0010277227,0.00048106103,0.003346118,0.004085496,0.0034112078,0.004557052,0.00048460418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009891036,0.00015755766,0.0063977824,0.0001930656,0.00019531736,0.0006572088,0.00040583516,0.69180983,0.029906737,0.20756015,0.0024121823,0.059315182],"study_design_scores_gemma":[0.000028266026,0.00013517092,0.00064624095,0.00003303707,0.000030635118,0.00022996769,0.00003297733,0.8880864,0.01979885,0.08942274,0.0015251578,0.000030657386],"about_ca_topic_score_codex":0.000615468,"about_ca_topic_score_gemma":0.00063034915,"teacher_disagreement_score":0.0060058897,"about_ca_system_score_codex":0.0013196843,"about_ca_system_score_gemma":0.0013412986,"threshold_uncertainty_score":0.03176254},"labels":[],"label_agreement":null},{"id":"W4388894069","doi":"10.1109/pst58708.2023.10320191","title":"Building Trust in Deep Learning Models via a Self- Interpretable Visual Architecture","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Interpretability; Computer science; Artificial intelligence; Deep learning; Robustness (evolution); Machine learning; Convolutional neural network; Consistency (knowledge bases); Architecture; Feature (linguistics); Feature learning; Context (archaeology)","score_opus":0.010986690342346547,"score_gpt":0.2685908941211028,"score_spread":0.25760420377875626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388894069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036467887,0.00012448373,0.95909816,0.00040788786,0.000038536575,0.000043326716,0.000033871784,0.001384036,0.0024018483],"genre_scores_gemma":[0.8848031,0.00016767663,0.11193223,0.00019639704,0.000031118387,0.00007940949,0.00008691884,0.0001853243,0.0025176802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988776,0.00037733276,0.000062876206,0.00022807145,0.00033960195,0.00011451486],"domain_scores_gemma":[0.99754155,0.00073762826,0.00031169082,0.00096017966,0.0003633371,0.000085663734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016787611,0.0008767729,0.00053535047,0.0003854131,0.0003656085,0.0015028064,0.0013853968,0.0011189432,0.0015740155],"category_scores_gemma":[0.007669946,0.0006324397,0.00071765116,0.00019994023,0.001956746,0.0030653644,0.0027112013,0.003054966,0.00042409354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002708971,0.00012212674,0.0024647978,0.000127085,0.00011176967,0.00036713207,0.0005319613,0.73548514,0.043702614,0.08980599,0.0026059074,0.124404676],"study_design_scores_gemma":[0.000008072353,0.00003977218,0.00014745034,0.000011450347,0.000012827108,0.00003741082,0.00001537199,0.9718865,0.0063015814,0.020616889,0.00091379404,0.000008801089],"about_ca_topic_score_codex":0.0017108464,"about_ca_topic_score_gemma":0.0020475597,"teacher_disagreement_score":0.0017108464,"about_ca_system_score_codex":0.0010584737,"about_ca_system_score_gemma":0.0008422438,"threshold_uncertainty_score":0.008878291},"labels":[],"label_agreement":null},{"id":"W4389072922","doi":"10.4230/lipics.itcs.2024.47","title":"Distribution Testing with a Confused Collector","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Staatssekretariat für Bildung, Forschung und Innovation; Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Cluster analysis; Oracle; Computer science; Mathematics; Property testing; Sample (material); Artificial intelligence; Combinatorics; Algorithm; Physics","score_opus":0.1153048172818834,"score_gpt":0.19988674430314313,"score_spread":0.08458192702125973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389072922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119216606,0.0002728107,0.8718,0.0018963843,0.00010351329,0.00025979718,0.00028728365,0.0032975515,0.0028659995],"genre_scores_gemma":[0.81995183,0.00009963895,0.17495677,0.0011151922,0.00012233287,0.00034798097,0.00068925635,0.00046301764,0.0022539836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96895593,0.013278411,0.001633154,0.0073033073,0.0067573604,0.002071765],"domain_scores_gemma":[0.84725446,0.102875605,0.0076377895,0.03359704,0.0055677807,0.0030673726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022382986,0.0019338111,0.0028338376,0.0013816159,0.001475079,0.00384112,0.0066799675,0.004266087,0.0037133726],"category_scores_gemma":[0.12994726,0.0010914947,0.0032804422,0.0013998835,0.009720647,0.013586585,0.009256754,0.0070969327,0.0014084366],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0076489537,0.0011595536,0.041039042,0.0006491471,0.0006866712,0.002168052,0.002148569,0.3216101,0.029741997,0.40751126,0.009432666,0.17620401],"study_design_scores_gemma":[0.0004321185,0.0008874952,0.0018672425,0.000080800804,0.00009349176,0.0007785963,0.00033133855,0.6755354,0.035647817,0.2818344,0.0024020062,0.00010930555],"about_ca_topic_score_codex":0.0016690071,"about_ca_topic_score_gemma":0.0013550054,"teacher_disagreement_score":0.022382986,"about_ca_system_score_codex":0.0035132223,"about_ca_system_score_gemma":0.0042283656,"threshold_uncertainty_score":0.11837399},"labels":[],"label_agreement":null},{"id":"W4389209067","doi":"10.1145/3611643.3616333","title":"DecompoVision: Reliability Analysis of Machine Vision Components through Decomposition and Reuse","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Reliability (semiconductor); Artificial intelligence; Benchmark (surveying); Reuse; Object detection; Machine vision; Task (project management); Segmentation; Modular design; Machine learning; Computer vision; Reliability engineering; Engineering; Programming language","score_opus":0.017148995022709237,"score_gpt":0.3299928873664652,"score_spread":0.312843892343756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389209067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014969526,0.00032008498,0.9811566,0.00008184635,0.000019787483,0.00009954352,0.000103694045,0.0019486338,0.0013002141],"genre_scores_gemma":[0.3835923,0.00051525637,0.6106909,0.00011530732,0.000075018455,0.00040101694,0.0008985369,0.0016088478,0.0021027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611807,0.0006006357,0.00023500582,0.000738499,0.001883405,0.00042433207],"domain_scores_gemma":[0.9884127,0.0035998959,0.0013375459,0.0040336223,0.0023595842,0.00025664817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004368778,0.001957058,0.001126707,0.0032398154,0.000666184,0.0023105599,0.0027228699,0.00096862816,0.0024708407],"category_scores_gemma":[0.016436223,0.0011556036,0.0022970485,0.0014067615,0.0021098254,0.0038767755,0.0034959253,0.0021958265,0.0007727893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036011162,0.00016560244,0.010801754,0.00079111644,0.00024660473,0.00065272185,0.0009830208,0.42066464,0.03482824,0.10814589,0.0055755847,0.4167847],"study_design_scores_gemma":[0.000024476518,0.00016696239,0.0021575447,0.00009600627,0.00009410687,0.00027217277,0.0001277818,0.8929767,0.0174783,0.08035454,0.0061976733,0.000053795757],"about_ca_topic_score_codex":0.004958962,"about_ca_topic_score_gemma":0.0032675238,"teacher_disagreement_score":0.004958962,"about_ca_system_score_codex":0.0015705663,"about_ca_system_score_gemma":0.0027773436,"threshold_uncertainty_score":0.023104608},"labels":[],"label_agreement":null},{"id":"W4389352648","doi":"10.1109/access.2023.3339542","title":"A Novel Semi-Supervised Adversarially Learned Meta-Classifier for Detecting Neural Trojan Attacks","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; MNIST database; Artificial intelligence; Trojan; Classifier (UML); Artificial neural network; Machine learning; Inference; Pattern recognition (psychology); Adversary; Training set; Deep learning; Computer security","score_opus":0.1723322725974216,"score_gpt":0.37433516982004017,"score_spread":0.20200289722261858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389352648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10064177,0.0015654167,0.8885686,0.0005525527,0.00020990963,0.00014861645,0.00029978744,0.004709669,0.0033036682],"genre_scores_gemma":[0.9019481,0.0003077834,0.09149709,0.00054589455,0.00008779568,0.000115048424,0.0006996718,0.00012305286,0.004675546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991448,0.0001992416,0.000041490217,0.00022154966,0.00027197148,0.000121009434],"domain_scores_gemma":[0.9983272,0.00056011457,0.00029063737,0.0003116526,0.0004067885,0.00010357838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011899729,0.0013215446,0.0012728592,0.00082853454,0.00033741232,0.0006678575,0.002260714,0.001426955,0.00097586174],"category_scores_gemma":[0.0031275558,0.00041968052,0.0009529001,0.00029382546,0.00069760083,0.0015532833,0.0011176707,0.0019490033,0.0005479241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037204692,0.00030733226,0.006369901,0.00012766643,0.00025142945,0.00033437266,0.00007657484,0.7084273,0.017237354,0.0045745363,0.006422837,0.2554987],"study_design_scores_gemma":[0.000004004966,0.000052176827,0.00016876473,0.000005026146,0.000010387387,0.0000478785,0.0000030990357,0.99670213,0.0019215222,0.0008223442,0.00025772818,0.0000049733444],"about_ca_topic_score_codex":0.0016670711,"about_ca_topic_score_gemma":0.0028063757,"teacher_disagreement_score":0.002260714,"about_ca_system_score_codex":0.0008161137,"about_ca_system_score_gemma":0.00089767337,"threshold_uncertainty_score":0.006293297},"labels":[],"label_agreement":null},{"id":"W4389520058","doi":"10.18653/v1/2023.findings-emnlp.35","title":"Toward Stronger Textual Attack Detectors","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Grand Équipement National De Calcul Intensif","keywords":"Adversarial system; Computer science; Benchmark (surveying); Hyperparameter; Artificial intelligence; Machine learning; Trustworthiness; Computer security","score_opus":0.051490275223493,"score_gpt":0.30233792019258754,"score_spread":0.25084764496909456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389520058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011977005,0.002335102,0.9388371,0.012111277,0.0025078822,0.00023214995,0.0005266736,0.004798818,0.02667401],"genre_scores_gemma":[0.5675713,0.003906498,0.2943094,0.015254096,0.009528218,0.00040582955,0.003154605,0.0018785036,0.103991576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99586934,0.0013383903,0.00016331187,0.000933347,0.0014699736,0.00022553676],"domain_scores_gemma":[0.9831267,0.009833572,0.00086175994,0.0029360885,0.0026532027,0.0005886121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00533958,0.0025442566,0.0019648026,0.0018368337,0.0010317066,0.0037932394,0.0016788611,0.004196868,0.026445597],"category_scores_gemma":[0.028994022,0.0008041901,0.0011265046,0.00077604037,0.0025862763,0.0058324384,0.0047620265,0.0067578116,0.014169343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000901766,0.00056282256,0.0015404493,0.00097587326,0.00031634764,0.00046490843,0.0002515284,0.12671162,0.05076428,0.1990008,0.16230854,0.45620102],"study_design_scores_gemma":[0.00007572866,0.00027588807,0.00045627868,0.000167022,0.00009001993,0.00044310378,0.000047372792,0.80318385,0.027425962,0.13555425,0.03221714,0.00006341266],"about_ca_topic_score_codex":0.00048858055,"about_ca_topic_score_gemma":0.00048296596,"teacher_disagreement_score":0.026445597,"about_ca_system_score_codex":0.000924819,"about_ca_system_score_gemma":0.0006177282,"threshold_uncertainty_score":0.08846933},"labels":[],"label_agreement":null},{"id":"W4389575929","doi":"10.1109/csnet59123.2023.10339778","title":"Stealthy Attacks on Multi-Agent Reinforcement Learning in Mobile Cyber-Physical Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Science Foundation","keywords":"Computer science; Reinforcement learning; Adversarial system; Robustness (evolution); Scalability; Computer security; Distributed computing; Node (physics); Cyber-physical system; Artificial intelligence; Machine learning; Engineering","score_opus":0.03201959749298665,"score_gpt":0.3216053526677072,"score_spread":0.2895857551747205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389575929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39240247,0.0006493849,0.59990007,0.0007492992,0.00007738261,0.000085782594,0.000042998254,0.0005755901,0.00551709],"genre_scores_gemma":[0.9930976,0.000055064793,0.0063360943,0.000046276076,0.000006156513,0.00001802452,0.000007910458,0.0000072924404,0.00042558706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999271,0.0003108437,0.000029348459,0.00009794919,0.00016485971,0.00012602437],"domain_scores_gemma":[0.9970822,0.0019872421,0.00038840002,0.00018887434,0.00022490897,0.00012831853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013735448,0.0006666194,0.0007403141,0.0003123721,0.0003337592,0.0005555212,0.00057489873,0.000695256,0.0007443247],"category_scores_gemma":[0.0048614894,0.0002288407,0.0003946548,0.00015234009,0.0011131156,0.00069379486,0.0013434036,0.0010919647,0.0000893836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043078686,0.000023621049,0.00064039463,0.000015910655,0.000013894325,0.000047339217,0.000020420277,0.9912055,0.00092084816,0.0026120115,0.00010463804,0.0043524243],"study_design_scores_gemma":[0.000003060558,0.0000235986,0.00007899254,0.0000017948528,0.0000015810211,0.000006372549,0.0000026899309,0.9987072,0.00019930156,0.0009288452,0.000045019886,0.000001578186],"about_ca_topic_score_codex":0.0034784456,"about_ca_topic_score_gemma":0.002000065,"teacher_disagreement_score":0.0034784456,"about_ca_system_score_codex":0.00073300785,"about_ca_system_score_gemma":0.0007105322,"threshold_uncertainty_score":0.0072640777},"labels":[],"label_agreement":null},{"id":"W4389584550","doi":"10.1109/tai.2023.3340982","title":"Manipulation Attacks on Learned Image Compression","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Waterloo","funders":"Army Research Office; Science and Technology Program of Hubei Province; National Natural Science Foundation of China","keywords":"Computer science; Image compression; Lossy compression; Lossless compression; Artificial intelligence; JPEG; Deep learning; Robustness (evolution); Image quality; Computer vision; Data compression; Computer engineering; Image processing; Image (mathematics)","score_opus":0.10140778479716481,"score_gpt":0.357783726830343,"score_spread":0.25637594203317815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389584550","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66130257,0.0008493774,0.322655,0.00086001435,0.00009556042,0.00009159935,0.00026518994,0.0024693492,0.011411362],"genre_scores_gemma":[0.9866599,0.0001337864,0.011806608,0.000082334554,0.000014651016,0.000019238425,0.00008562313,0.000054051186,0.0011437503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994431,0.000111564405,0.0000248473,0.00007508504,0.00024448935,0.00010078975],"domain_scores_gemma":[0.9986929,0.0006599102,0.00017493553,0.00031810903,0.000117673866,0.000036504032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004968513,0.00052591285,0.00033272067,0.00044642494,0.00021467701,0.00046820642,0.0004754152,0.0005448815,0.0013298773],"category_scores_gemma":[0.0032330852,0.00014850462,0.0003373699,0.00023641335,0.0009763309,0.0010757872,0.0009668169,0.00086992804,0.00022251882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005120718,0.000097782446,0.0024663978,0.00012282717,0.00007521403,0.0003904329,0.00011451841,0.82358843,0.0709614,0.025570363,0.0015907838,0.07450983],"study_design_scores_gemma":[0.0000072290773,0.00008182104,0.00044927993,0.000010534418,0.0000073548963,0.00008386057,0.000008866717,0.9642404,0.030708458,0.0037486942,0.00064478203,0.000008651371],"about_ca_topic_score_codex":0.0010232257,"about_ca_topic_score_gemma":0.00065775303,"teacher_disagreement_score":0.0013298773,"about_ca_system_score_codex":0.00064727303,"about_ca_system_score_gemma":0.00029986125,"threshold_uncertainty_score":0.004696369},"labels":[],"label_agreement":null},{"id":"W4389989966","doi":"10.32469/10355/88910","title":"Fast and robust deep neural networks design","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Nvidia","keywords":"Computer science; Pixel; Artificial intelligence; Deep learning; Smoothing; Sensitivity (control systems); Artificial neural network; Norm (philosophy); Pattern recognition (psychology); Machine learning; Computer vision; Engineering","score_opus":0.019407889815016455,"score_gpt":0.24664151360115627,"score_spread":0.22723362378613982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389989966","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002139182,0.00042295663,0.99382037,0.00016059057,0.00005162262,0.00006403173,0.000047329133,0.0005442554,0.0027497467],"genre_scores_gemma":[0.32596928,0.0013472614,0.6597384,0.00049098267,0.00013070133,0.0008076552,0.00036570462,0.00036249825,0.010787484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938166,0.000108199245,0.000035075867,0.00015250567,0.00025060002,0.000072054034],"domain_scores_gemma":[0.9995085,0.00013447738,0.00005933853,0.000063800246,0.00020761273,0.000026262822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013495081,0.0013247732,0.0008015601,0.0005102506,0.00037078882,0.0011736881,0.0018226365,0.0015163457,0.004123902],"category_scores_gemma":[0.0024196992,0.00079754496,0.00065305084,0.00041385822,0.00069939013,0.0012618353,0.0014339084,0.0022786278,0.0017658948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007541181,0.000033355456,0.00024588525,0.0001679288,0.000047441856,0.00006984556,0.000034297373,0.8188734,0.009117558,0.029966338,0.0033807512,0.13798784],"study_design_scores_gemma":[0.0000051779098,0.000017712578,0.000022789976,0.000008627558,0.000004807568,0.000013342313,0.0000026016137,0.99307394,0.0012729644,0.004459908,0.001114285,0.0000037784043],"about_ca_topic_score_codex":0.002864458,"about_ca_topic_score_gemma":0.0036671252,"teacher_disagreement_score":0.004123902,"about_ca_system_score_codex":0.0012485167,"about_ca_system_score_gemma":0.0015408334,"threshold_uncertainty_score":0.013795793},"labels":[],"label_agreement":null},{"id":"W4390059484","doi":"10.3390/ai5010003","title":"AI Advancements: Comparison of Innovative Techniques","year":2023,"lang":"en","type":"article","venue":"AI","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Transformative learning; Generative grammar; Computer science; Artificial intelligence; Reinforcement learning; Transparency (behavior); Adversarial system; Field (mathematics); Cognitive science; Deep learning; Neuroevolution; Data science; Artificial neural network; Sociology; Psychology","score_opus":0.02405040763861786,"score_gpt":0.3753800590950696,"score_spread":0.35132965145645173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390059484","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014112657,0.82369256,0.042524684,0.0125381425,0.001938208,0.0002061517,0.00034080527,0.0002338031,0.104413055],"genre_scores_gemma":[0.20513892,0.7353916,0.046024702,0.002744838,0.0017523074,0.00050281105,0.0006598061,0.0002169045,0.007568054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936659,0.002230329,0.00055608846,0.00062471064,0.0025922703,0.0003307033],"domain_scores_gemma":[0.9818589,0.013777485,0.00095292385,0.0008137702,0.0021686044,0.00042830626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008447448,0.00081715646,0.0009595261,0.006445323,0.0008764096,0.0050756843,0.0017427746,0.0018732392,0.006758369],"category_scores_gemma":[0.025215957,0.00031556172,0.001487691,0.0047824634,0.0026388867,0.0059489035,0.003041556,0.0021832585,0.0012688163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037725296,0.00010794325,0.0027315712,0.009261373,0.00037026242,0.00013438445,0.0011192443,0.0048448094,0.00067353994,0.26757073,0.011882164,0.7009268],"study_design_scores_gemma":[0.00014659704,0.00074912194,0.008187896,0.01434289,0.0007168235,0.0011872039,0.0030137722,0.011955121,0.0034742646,0.30209827,0.65396035,0.00016772542],"about_ca_topic_score_codex":0.0011959655,"about_ca_topic_score_gemma":0.0012961858,"teacher_disagreement_score":0.008447448,"about_ca_system_score_codex":0.0031221611,"about_ca_system_score_gemma":0.0039148885,"threshold_uncertainty_score":0.044674933},"labels":[],"label_agreement":null},{"id":"W4390116303","doi":"10.1016/j.cose.2023.103674","title":"Improving adversarial transferability through hybrid augmentation","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Transferability; Computer science; Adversarial system; Domain (mathematical analysis); Focus (optics); Masking (illustration); Artificial intelligence; Diversity (politics); Scaling; Frequency domain; Image (mathematics); Machine learning; Pattern recognition (psychology); Algorithm; Computer vision; Mathematics","score_opus":0.014672022562037942,"score_gpt":0.26638045364820656,"score_spread":0.25170843108616864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390116303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01946295,0.0003032376,0.9736703,0.00033051518,0.00010367729,0.00004354036,0.00006230965,0.0013545913,0.00466879],"genre_scores_gemma":[0.87552446,0.00028913596,0.116062604,0.00034530836,0.00016281358,0.0001246963,0.00024458277,0.00028479914,0.0069616307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989241,0.00035121365,0.000046800582,0.00023755424,0.00031990692,0.00012053057],"domain_scores_gemma":[0.99645895,0.0021164431,0.00020074652,0.00082756666,0.0003066392,0.000089737456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019260094,0.0015049436,0.0010379683,0.00065982307,0.00043554918,0.0011108102,0.0016642459,0.0014892423,0.0045626103],"category_scores_gemma":[0.008178939,0.00050616194,0.00091829547,0.0005099142,0.0016960317,0.0029750213,0.003770105,0.0026637448,0.0010958038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002668261,0.00013414914,0.0006591966,0.000114438284,0.000086995315,0.00014229747,0.0000865407,0.81099707,0.017193416,0.0426602,0.003253032,0.12440591],"study_design_scores_gemma":[0.00000523384,0.00003552063,0.00008009234,0.000006714368,0.0000081662,0.000033292163,0.0000045502666,0.9851659,0.0024889277,0.011749067,0.0004156739,0.000006895951],"about_ca_topic_score_codex":0.0010434968,"about_ca_topic_score_gemma":0.0010312767,"teacher_disagreement_score":0.0045626103,"about_ca_system_score_codex":0.0005331549,"about_ca_system_score_gemma":0.0006003003,"threshold_uncertainty_score":0.015263498},"labels":[],"label_agreement":null},{"id":"W4390187158","doi":"10.1109/access.2023.3347498","title":"Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bounded function; Computer science; Divergence (linguistics); Dual (grammatical number); Deep neural networks; Artificial intelligence; Artificial neural network; Gating; Deep learning; Pattern recognition (psychology); Mathematics","score_opus":0.06996597923306581,"score_gpt":0.33673508839256755,"score_spread":0.26676910915950175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390187158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09187034,0.0011694632,0.900281,0.0009308355,0.00018347766,0.00006475142,0.00016050649,0.0018717023,0.0034678217],"genre_scores_gemma":[0.92899734,0.00043269858,0.0668533,0.0008229913,0.000068155045,0.000076872304,0.000461167,0.00026305811,0.002024545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798954,0.0006173331,0.00009779512,0.00038853445,0.0006537666,0.00025312623],"domain_scores_gemma":[0.99468404,0.0030546766,0.000495133,0.0009619844,0.0005446643,0.00025958358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039569084,0.0014976107,0.0011296157,0.00057090964,0.00047977924,0.0014490907,0.0018143713,0.0015774192,0.0014986342],"category_scores_gemma":[0.01309516,0.00050152506,0.0010042666,0.00037740901,0.0019637444,0.0032467425,0.0047585205,0.0036995695,0.00048795596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005036599,0.00021548677,0.0030050224,0.00014155569,0.00012776813,0.00022556535,0.00013634314,0.86362773,0.016019901,0.017900962,0.0035915694,0.0945044],"study_design_scores_gemma":[0.000012914794,0.000081433674,0.00023315361,0.000015546855,0.000011244263,0.000048075573,0.000012466519,0.9862748,0.0046584494,0.00820424,0.00043342254,0.000014302328],"about_ca_topic_score_codex":0.0015862974,"about_ca_topic_score_gemma":0.0017476686,"teacher_disagreement_score":0.0039569084,"about_ca_system_score_codex":0.0010125894,"about_ca_system_score_gemma":0.0013735866,"threshold_uncertainty_score":0.020926416},"labels":[],"label_agreement":null},{"id":"W4390189903","doi":"10.1109/iccvw60793.2023.00465","title":"Confusing Large Models by Confusing Small Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Robustness (evolution); Machine learning; Heuristics; Confusion; Artificial intelligence; Benchmark (surveying); Focus (optics); Upsampling; Image (mathematics)","score_opus":0.045205286628696086,"score_gpt":0.26440706618260756,"score_spread":0.21920177955391149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390189903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30697513,0.0016152282,0.6783299,0.0027903188,0.00034245307,0.00017573673,0.00036150977,0.002444704,0.006965005],"genre_scores_gemma":[0.9038601,0.00032486013,0.0912527,0.0010182272,0.00020797095,0.0000870585,0.0006267283,0.00050476566,0.0021175868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994662,0.0023786016,0.00024924797,0.0011885718,0.0011449466,0.00037667563],"domain_scores_gemma":[0.9788529,0.012133977,0.0015351691,0.0055250595,0.0012488217,0.0007040676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0081201065,0.002359314,0.0017248777,0.0016962852,0.0010809529,0.00279819,0.0017670289,0.002216164,0.001987341],"category_scores_gemma":[0.03717411,0.0008096861,0.001386291,0.00081955903,0.0036987488,0.0044644214,0.00482143,0.00519152,0.0007300705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008323963,0.00017858097,0.013708645,0.00025998199,0.0004965013,0.0005254106,0.0007168609,0.8013951,0.017635668,0.023536608,0.0063430397,0.13437112],"study_design_scores_gemma":[0.00001714145,0.00020311231,0.0023384192,0.000059179445,0.000069854286,0.00037113542,0.00012465671,0.94057083,0.01200902,0.04209142,0.0020847889,0.00006047605],"about_ca_topic_score_codex":0.0026217003,"about_ca_topic_score_gemma":0.0034534433,"teacher_disagreement_score":0.0081201065,"about_ca_system_score_codex":0.0015021431,"about_ca_system_score_gemma":0.00077271013,"threshold_uncertainty_score":0.042943716},"labels":[],"label_agreement":null},{"id":"W4390430482","doi":"10.18280/ts.400629","title":"COPYNet: Unveiling Suspicious Behaviour in Face-to-Face Exams","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Face (sociological concept); Artificial intelligence; Computer science; Computer vision; Philosophy; Linguistics","score_opus":0.02009802416461649,"score_gpt":0.2771890824196711,"score_spread":0.2570910582550546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390430482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47329026,0.0012641202,0.47891003,0.0019229988,0.00081247423,0.00028508317,0.0025304006,0.028418796,0.012565823],"genre_scores_gemma":[0.934181,0.00013038845,0.056318033,0.0002708408,0.00014245343,0.000050220347,0.0014299394,0.00041119911,0.007066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899966,0.00030028704,0.000030508001,0.00022025882,0.00034115263,0.000108182845],"domain_scores_gemma":[0.9979335,0.001070589,0.00016565237,0.0003383515,0.0003484495,0.00014357404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011426569,0.000870603,0.0006020307,0.0009323353,0.0003310715,0.00079544785,0.001015525,0.0014580999,0.0029514972],"category_scores_gemma":[0.007528132,0.00023153402,0.0003502893,0.00031592295,0.0004783634,0.0009257041,0.0011802724,0.0010745924,0.0014213008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024171716,0.00066306186,0.020286093,0.00028573716,0.00029005617,0.0018410904,0.000322937,0.24271768,0.040737215,0.008555274,0.07448463,0.60739917],"study_design_scores_gemma":[0.00002179952,0.000116706506,0.0027682807,0.0000120389295,0.000015337908,0.000314172,0.000039769922,0.98190194,0.0097810775,0.00261532,0.0023989251,0.00001467433],"about_ca_topic_score_codex":0.003786497,"about_ca_topic_score_gemma":0.00497852,"teacher_disagreement_score":0.003786497,"about_ca_system_score_codex":0.0005209175,"about_ca_system_score_gemma":0.0004626843,"threshold_uncertainty_score":0.009873748},"labels":[],"label_agreement":null},{"id":"W4390481971","doi":"10.1109/ssci52147.2023.10371863","title":"An Ensemble Learning to Detect Decision-Based Adversarial Attacks in Industrial Control Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Decision tree; Artificial intelligence; Adversarial system; Adversarial machine learning; Machine learning; Artificial neural network; Decision boundary; AdaBoost; Deep learning; Black box; Ensemble learning; Random forest; Support vector machine","score_opus":0.025483893811839048,"score_gpt":0.2963945167267564,"score_spread":0.2709106229149173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390481971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1815002,0.0005004589,0.8145543,0.00033893163,0.00009423005,0.00007488732,0.00011104157,0.00083370577,0.0019922683],"genre_scores_gemma":[0.9475273,0.000103935956,0.05128847,0.00006689467,0.000027303773,0.00003600546,0.00013974846,0.000021632746,0.00078863365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906045,0.00032586366,0.00005314061,0.00019840567,0.00025509996,0.00010709186],"domain_scores_gemma":[0.99744177,0.001421887,0.00021110282,0.00026252633,0.000568249,0.000094507406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025182634,0.0006921626,0.0011279102,0.0011273458,0.0004863321,0.0006250109,0.0007757827,0.0006515796,0.0007154867],"category_scores_gemma":[0.0046871384,0.00032707566,0.0007005429,0.0005472002,0.00038839827,0.0011027151,0.0008531005,0.0011970339,0.00014420816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010414074,0.00010039138,0.0041841515,0.000020725467,0.00008752652,0.000054078588,0.000040674426,0.9088188,0.0012846476,0.0020897097,0.0006410319,0.08257417],"study_design_scores_gemma":[8.6294654e-7,0.00001548985,0.00018091025,0.0000011765736,0.0000030741223,0.000004267451,0.0000018593076,0.99913496,0.00020706502,0.0004015703,0.000047306366,0.0000015426026],"about_ca_topic_score_codex":0.005156347,"about_ca_topic_score_gemma":0.0043562273,"teacher_disagreement_score":0.005156347,"about_ca_system_score_codex":0.0006786867,"about_ca_system_score_gemma":0.0006077862,"threshold_uncertainty_score":0.013318002},"labels":[],"label_agreement":null},{"id":"W4390540551","doi":"10.5121/csit.2023.132414","title":"3D Convolution for Proactive Défense Against Localized Adversary Attacks","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adversarial system; Computer science; Convolutional neural network; Convolution (computer science); Adversary; Deep learning; Artificial intelligence; Vulnerability (computing); Machine learning; Scale (ratio); Deep neural networks; Artificial neural network; Computer engineering; Computer security","score_opus":0.0246019375211531,"score_gpt":0.2930149065632314,"score_spread":0.26841296904207834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390540551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051987495,0.0005394299,0.93941945,0.00049530715,0.00008670206,0.000042458752,0.0000914209,0.002289729,0.005048116],"genre_scores_gemma":[0.91318464,0.00039280875,0.082610905,0.00029909887,0.000040714916,0.00004842787,0.00018057978,0.00013042621,0.0031124062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943405,0.00013097879,0.00002047593,0.00010389594,0.00020789525,0.000102647915],"domain_scores_gemma":[0.99913067,0.00025889187,0.00012733696,0.00035509837,0.0000736301,0.000054426113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008483402,0.0008791073,0.0005963223,0.0003639837,0.00038516056,0.00064393535,0.0010741553,0.0009485124,0.0020842934],"category_scores_gemma":[0.0024676155,0.00033067123,0.0006551519,0.0002579715,0.0010805478,0.0018631482,0.002942887,0.0018167433,0.00066978985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023613981,0.000087113236,0.0023069307,0.00010310755,0.00010283093,0.0002001113,0.00012753814,0.7377035,0.057156824,0.04220616,0.004672707,0.155097],"study_design_scores_gemma":[0.000005073014,0.000066075445,0.00024679233,0.000011760325,0.000011353291,0.00011310264,0.00001339772,0.9708213,0.017678123,0.008518018,0.0025054824,0.00000954243],"about_ca_topic_score_codex":0.0010990278,"about_ca_topic_score_gemma":0.0014660102,"teacher_disagreement_score":0.0020842934,"about_ca_system_score_codex":0.0006500515,"about_ca_system_score_gemma":0.000691884,"threshold_uncertainty_score":0.006972611},"labels":[],"label_agreement":null},{"id":"W4390575962","doi":"10.1137/1.9781611977912.42","title":"Sorting and Selection in Rounds with Adversarial Comparisons","year":2024,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sorting; Selection (genetic algorithm); Upper and lower bounds; Combinatorics; Constant (computer programming); Sorting algorithm; Binary logarithm; Mathematics; Sorting network; Computer science; Selection algorithm; Deterministic algorithm; Randomized algorithm; Algorithm; Discrete mathematics; Artificial intelligence","score_opus":0.0408580626423147,"score_gpt":0.2511247736164804,"score_spread":0.2102667109741657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390575962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015445805,0.0016342053,0.7968308,0.0049146116,0.0008984167,0.00022631069,0.0002757924,0.00067186827,0.17910226],"genre_scores_gemma":[0.5207467,0.003425643,0.31110016,0.002937794,0.0014227083,0.00068891,0.00042515862,0.0008447922,0.15840818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953904,0.001997046,0.00019647324,0.0007073097,0.0013188771,0.0003898259],"domain_scores_gemma":[0.9911765,0.005984012,0.00053252274,0.0017210827,0.00040226243,0.00018374014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004336146,0.0011241758,0.0010547041,0.0012023413,0.0014616263,0.004627919,0.0022567937,0.0023726649,0.014662581],"category_scores_gemma":[0.016781714,0.0005289942,0.0015240409,0.001841872,0.0045649474,0.010249086,0.003653,0.0052278223,0.0036777663],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064920794,0.000018237542,0.0000921555,0.000057268364,0.000013859525,0.000046871686,0.00006964831,0.01692049,0.0006247806,0.9527636,0.0047094356,0.024618711],"study_design_scores_gemma":[0.00001175967,0.000047884394,0.00006841771,0.00004261368,0.000009585934,0.00008485053,0.000033010423,0.05187707,0.0011252549,0.9295222,0.017158583,0.000018769762],"about_ca_topic_score_codex":0.00064498564,"about_ca_topic_score_gemma":0.00061701745,"teacher_disagreement_score":0.014662581,"about_ca_system_score_codex":0.0031536089,"about_ca_system_score_gemma":0.0014821424,"threshold_uncertainty_score":0.049051285},"labels":[],"label_agreement":null},{"id":"W4391272611","doi":"10.48550/arxiv.2401.14033","title":"Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; Air Force Office of Scientific Research; Tamkeen; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; Deutsche Forschungsgemeinschaft; Singapore Institute of Manufacturing Technology; York University; New York University Abu Dhabi; National Science Foundation","keywords":"Lipschitz continuity; Residual; Quadratic equation; Mathematical optimization; Semidefinite programming; Computer science; Quadratic programming; Activation function; Artificial neural network; Class (philosophy); Mathematics; Quadratic function; Upper and lower bounds; Applied mathematics; Algorithm; Pure mathematics; Artificial intelligence; Mathematical analysis; Geometry","score_opus":0.0772137013745093,"score_gpt":0.23207800523780875,"score_spread":0.15486430386329947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391272611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057631535,0.00023156327,0.9889508,0.0003496911,0.000039450442,0.000045625697,0.00015925936,0.00022033235,0.004240182],"genre_scores_gemma":[0.5140986,0.0011590356,0.46749684,0.0010862973,0.00023866806,0.0007175758,0.0010269799,0.0006833616,0.013492632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998781,0.00043023916,0.000076170494,0.00025170628,0.00035530364,0.00010548433],"domain_scores_gemma":[0.996807,0.002045857,0.00022544124,0.00026468135,0.0005422608,0.00011491134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025704962,0.0018026987,0.0009436135,0.00067669875,0.00044946774,0.0016194099,0.0017753621,0.0014814734,0.008727087],"category_scores_gemma":[0.009831601,0.00073966954,0.0007542572,0.0008370266,0.0012492922,0.0028184124,0.0027081952,0.003886734,0.0013329668],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010754736,0.00008361759,0.0005626128,0.0003142334,0.000038099282,0.00019543951,0.00015960394,0.78741527,0.005947089,0.12243855,0.0070524095,0.075685576],"study_design_scores_gemma":[0.0000089678215,0.00003161798,0.000057726138,0.00002194046,0.000004242661,0.000028756342,0.000010837662,0.97382927,0.0008829261,0.023300603,0.0018152283,0.000007964106],"about_ca_topic_score_codex":0.002044558,"about_ca_topic_score_gemma":0.0031806582,"teacher_disagreement_score":0.008727087,"about_ca_system_score_codex":0.0010663982,"about_ca_system_score_gemma":0.0015141901,"threshold_uncertainty_score":0.029194951},"labels":[],"label_agreement":null},{"id":"W4391334942","doi":"10.1145/3630106.3659037","title":"Black-Box Access is Insufficient for Rigorous AI Audits","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Toronto","funders":"","keywords":"Black box; Audit; Computer science; Business; Computer security; Accounting; Artificial intelligence","score_opus":0.0312995521654295,"score_gpt":0.3489467783865863,"score_spread":0.31764722622115676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391334942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1269667,0.0031513635,0.74576104,0.056363497,0.0016470624,0.0023572615,0.0009257988,0.00888794,0.053939268],"genre_scores_gemma":[0.84976965,0.0010252793,0.13136618,0.0076826084,0.000647816,0.0013342481,0.0003353295,0.001187508,0.0066513536],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8030759,0.11389322,0.012674776,0.01915927,0.043515734,0.0076810564],"domain_scores_gemma":[0.48449722,0.25137296,0.042986747,0.17381077,0.04177213,0.0055601085],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14590605,0.0015625063,0.0026476106,0.0027651254,0.003224102,0.013818199,0.0038579118,0.0056588645,0.007755456],"category_scores_gemma":[0.38356417,0.0017691895,0.0014921231,0.0020369086,0.013085563,0.022647137,0.010465446,0.010723542,0.003835893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002417867,0.00060871866,0.018395927,0.0015826174,0.00054761855,0.0008719962,0.010592517,0.022881016,0.011461346,0.42020032,0.038173493,0.47226658],"study_design_scores_gemma":[0.00070597045,0.00082577724,0.012468983,0.0026550896,0.00024194203,0.00072579883,0.002533787,0.06096282,0.018686706,0.78421503,0.11547264,0.0005054819],"about_ca_topic_score_codex":0.0029156902,"about_ca_topic_score_gemma":0.0016405564,"teacher_disagreement_score":0.85409397,"about_ca_system_score_codex":0.00378353,"about_ca_system_score_gemma":0.011235546,"threshold_uncertainty_score":0.77163416},"labels":[],"label_agreement":null},{"id":"W4391444574","doi":"10.1145/3641543","title":"Beyond Fidelity: Explaining Vulnerability Localization of Learning-Based Detectors","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fidelity; Vulnerability (computing); Detector; Computer security; Telecommunications","score_opus":0.04447907595058553,"score_gpt":0.31488102608238366,"score_spread":0.27040195013179813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391444574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15438074,0.0009353022,0.83634573,0.0011757443,0.00007136853,0.00012532281,0.00056567526,0.00441859,0.001981409],"genre_scores_gemma":[0.88906056,0.00030418145,0.10842996,0.00026047885,0.000039534414,0.00005189999,0.00085156166,0.0001994009,0.0008024033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769336,0.00077479944,0.00014977805,0.0005247498,0.00062960247,0.00022773135],"domain_scores_gemma":[0.97497004,0.016906492,0.0029424543,0.0032802105,0.0015965352,0.00030415333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042236885,0.0016721667,0.00089159637,0.003258075,0.00044525828,0.0015854299,0.0014822023,0.001840652,0.0014659746],"category_scores_gemma":[0.032102596,0.00044629409,0.0010156415,0.0013330874,0.0015902164,0.0047733597,0.0023601097,0.0024820263,0.0004319641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039099812,0.00020195114,0.056093905,0.00042943232,0.00028311563,0.00054667966,0.00061595114,0.66259027,0.0077697923,0.021464923,0.0046706772,0.24494235],"study_design_scores_gemma":[0.0000138130235,0.000073112096,0.0026332706,0.000039300667,0.000046277037,0.0001752034,0.00007783039,0.9688626,0.0050714216,0.021831643,0.0011492558,0.000026339561],"about_ca_topic_score_codex":0.0038423028,"about_ca_topic_score_gemma":0.0038983577,"teacher_disagreement_score":0.0042236885,"about_ca_system_score_codex":0.0012201953,"about_ca_system_score_gemma":0.0013150522,"threshold_uncertainty_score":0.022337258},"labels":[],"label_agreement":null},{"id":"W4391557801","doi":"10.1109/dessert61349.2023.10416455","title":"Consensus Protocols for Improved Survivability in Autonomous Groups of UAV","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Solana Networks (Canada)","funders":"","keywords":"Survivability; Computer science; Computer security; Distributed computing; Computer network","score_opus":0.037278778958776876,"score_gpt":0.3331101638272935,"score_spread":0.2958313848685167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391557801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03886306,0.0003766654,0.9535537,0.0005862539,0.00011960914,0.00007155502,0.000031252053,0.00014653978,0.0062513384],"genre_scores_gemma":[0.89778686,0.0005608016,0.09564294,0.00018946893,0.000105762825,0.00020844088,0.000077152334,0.00004937288,0.005379279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908423,0.00033794582,0.000063250336,0.00015983277,0.00026823915,0.000086485044],"domain_scores_gemma":[0.9981469,0.0010453372,0.00018643442,0.00022354194,0.00031488232,0.00008293429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016100729,0.0003875064,0.00043034455,0.00044306577,0.00048002557,0.00072703295,0.0008042434,0.0007293959,0.0015801137],"category_scores_gemma":[0.0059691747,0.00020784326,0.00036279927,0.0004075597,0.0011089387,0.0015785574,0.001564522,0.0013458988,0.0002550616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106964246,0.000037486374,0.00021136903,0.00013405785,0.00003376392,0.00024508845,0.00045761265,0.54043436,0.011974167,0.41477734,0.0018503485,0.029737495],"study_design_scores_gemma":[0.000035549234,0.000075431904,0.00007746812,0.000020000774,0.00000990046,0.00004188404,0.00006243132,0.8931523,0.0021802583,0.10167844,0.0026532705,0.0000129674],"about_ca_topic_score_codex":0.0009450178,"about_ca_topic_score_gemma":0.00076786877,"teacher_disagreement_score":0.0016100729,"about_ca_system_score_codex":0.0007143245,"about_ca_system_score_gemma":0.00086580735,"threshold_uncertainty_score":0.008514941},"labels":[],"label_agreement":null},{"id":"W4391613811","doi":"10.21203/rs.3.rs-3924726/v1","title":"Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wycliffe College","funders":"","keywords":"Computer science; Computer security; Artificial intelligence; Medicine","score_opus":0.05815428216874306,"score_gpt":0.44602292055923354,"score_spread":0.38786863839049046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391613811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03312813,0.023898236,0.9028652,0.00766503,0.0006224889,0.00019884153,0.00010745484,0.00030661814,0.03120802],"genre_scores_gemma":[0.85729355,0.028919235,0.09769533,0.002078502,0.0023501748,0.00016468126,0.00018493376,0.000132978,0.011180734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970009,0.0010250902,0.00013442186,0.00034342078,0.0011996324,0.0002965294],"domain_scores_gemma":[0.98287255,0.012708747,0.0013924616,0.0013261194,0.0014036023,0.0002965553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034352168,0.0010775288,0.00090695283,0.002206385,0.00070933974,0.0023546608,0.0012398615,0.0024468165,0.0024508354],"category_scores_gemma":[0.015679257,0.0004857238,0.0010950138,0.00085419137,0.0030683968,0.0042401794,0.0020116167,0.0035359734,0.0005505993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018959618,0.00019638715,0.0031829593,0.0006017439,0.00023229123,0.0006211636,0.00031891832,0.31972834,0.008088661,0.516206,0.009537342,0.14109659],"study_design_scores_gemma":[0.0000113272245,0.00014910733,0.00139006,0.00028924653,0.000049416332,0.0006853635,0.00015823758,0.76143235,0.0044708345,0.2183972,0.0129184555,0.00004838711],"about_ca_topic_score_codex":0.00079629273,"about_ca_topic_score_gemma":0.0003833698,"teacher_disagreement_score":0.0034352168,"about_ca_system_score_codex":0.001346885,"about_ca_system_score_gemma":0.00070236984,"threshold_uncertainty_score":0.018167317},"labels":[],"label_agreement":null},{"id":"W4391614448","doi":"10.1145/3643671","title":"Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg; Université du Luxembourg","keywords":"Computer science; Cluster analysis; Artificial intelligence; Data mining; Software engineering","score_opus":0.08917448517718657,"score_gpt":0.349973502449047,"score_spread":0.26079901727186044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391614448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040853646,0.00020910766,0.95260715,0.00020113659,0.000036072153,0.00011703976,0.00022519464,0.004165196,0.0015854648],"genre_scores_gemma":[0.60348123,0.00019368053,0.39307132,0.0002254841,0.000036208916,0.00016472574,0.0007346067,0.00047045795,0.0016222744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990427,0.00018957374,0.000062704064,0.0003004587,0.00030137014,0.00010320391],"domain_scores_gemma":[0.99639916,0.0014011074,0.00048959325,0.0005678683,0.0010598366,0.00008231231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019064113,0.0018681741,0.0006622476,0.0025564702,0.0006219557,0.0011625953,0.002233391,0.0013494138,0.0022522886],"category_scores_gemma":[0.0083551975,0.0005545314,0.0009145601,0.00078296184,0.0010819816,0.0017676449,0.00168954,0.0016999292,0.0007485036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002765169,0.00012665046,0.0035570257,0.00018447825,0.000116306386,0.00021744978,0.00020253901,0.7646832,0.017227197,0.0059362883,0.0026291271,0.20484321],"study_design_scores_gemma":[0.0000033442489,0.00001629951,0.00031875915,0.000008353158,0.000006918351,0.000019515846,0.000020662972,0.98753786,0.007281841,0.0044894796,0.00029087556,0.0000060675725],"about_ca_topic_score_codex":0.008583513,"about_ca_topic_score_gemma":0.011980025,"teacher_disagreement_score":0.008583513,"about_ca_system_score_codex":0.0021594441,"about_ca_system_score_gemma":0.0015077731,"threshold_uncertainty_score":0.017067134},"labels":[],"label_agreement":null},{"id":"W4391903608","doi":"10.24251/hicss.2023.799","title":"Safe Reinforcement Learning via Observation Shielding","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Science Foundation","keywords":"Robustness (evolution); Computer science; Adversarial system; Agile software development; SAFER; Artificial intelligence; Machine learning; Reinforcement learning; Deep neural networks; Artificial neural network; Computer security","score_opus":0.05429878908399725,"score_gpt":0.3038269335608544,"score_spread":0.24952814447685714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391903608","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018310802,0.00013745889,0.9782508,0.00019844339,0.000039367947,0.000042950785,0.000020847752,0.001166207,0.0018331384],"genre_scores_gemma":[0.9154555,0.000119141965,0.08134421,0.00026047704,0.000055750254,0.00012419654,0.0000774565,0.00020403645,0.002359245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988417,0.00034917129,0.00005382501,0.00028421343,0.0002988589,0.00017227764],"domain_scores_gemma":[0.99558556,0.002626306,0.0005814987,0.0006072693,0.00037692726,0.00022235846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019735582,0.0015261983,0.0010193309,0.00044758565,0.0005020251,0.00081283273,0.0014848907,0.0010857519,0.0022697689],"category_scores_gemma":[0.008717036,0.0006135038,0.0006463647,0.00020597717,0.0019616175,0.0015230522,0.0026499878,0.0027670446,0.00051232066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014699818,0.000058897007,0.00089607405,0.00005319947,0.00003734828,0.000089028305,0.00008772399,0.93902,0.0031399743,0.01082163,0.000958177,0.04469093],"study_design_scores_gemma":[0.000014566037,0.00004052072,0.000046951052,0.0000058719243,0.0000052600117,0.0000138361775,0.000004885078,0.99341387,0.0007916031,0.0054148617,0.0002437986,0.000004070091],"about_ca_topic_score_codex":0.0026129843,"about_ca_topic_score_gemma":0.002048242,"teacher_disagreement_score":0.0026129843,"about_ca_system_score_codex":0.0008298896,"about_ca_system_score_gemma":0.0018237187,"threshold_uncertainty_score":0.01043725},"labels":[],"label_agreement":null},{"id":"W4392005731","doi":"10.1016/j.neunet.2024.106199","title":"SecureNet: Proactive intellectual property protection and model security defense for DNNs based on backdoor learning","year":2024,"lang":"en","type":"article","venue":"Neural Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Backdoor; Computer science; Key (lock); License; Computer security; Intellectual property; Artificial intelligence","score_opus":0.021340185839489923,"score_gpt":0.25162750131679423,"score_spread":0.2302873154773043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392005731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045976695,0.0006543927,0.9340722,0.0007564883,0.00035673642,0.00013309713,0.0003214085,0.01153634,0.006192757],"genre_scores_gemma":[0.8766588,0.0003280595,0.11547711,0.0004974796,0.00007648584,0.00009670302,0.0005060895,0.00041701985,0.0059421957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933463,0.00014037771,0.00003146747,0.0001442328,0.0002394135,0.00010986003],"domain_scores_gemma":[0.99898344,0.0003532614,0.00009144431,0.00039507126,0.00012644842,0.00005023893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012938646,0.00080672256,0.00063378137,0.0005523055,0.0005099764,0.001412413,0.0014861984,0.0013350205,0.0037000456],"category_scores_gemma":[0.002681029,0.00032783457,0.0005972972,0.00028588402,0.0012084893,0.0029791712,0.002503874,0.0021322516,0.00083244505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013560371,0.00040454612,0.0015780657,0.00034868004,0.00028742274,0.00044511474,0.00014519496,0.5094759,0.044992793,0.14172317,0.024167644,0.2750755],"study_design_scores_gemma":[0.000029114479,0.00008496299,0.00008167226,0.000014942094,0.000016916989,0.00006748338,0.000010478024,0.95830685,0.013445245,0.025229394,0.0027000473,0.000012807054],"about_ca_topic_score_codex":0.0015635549,"about_ca_topic_score_gemma":0.0022628566,"teacher_disagreement_score":0.0037000456,"about_ca_system_score_codex":0.0009404679,"about_ca_system_score_gemma":0.0014156003,"threshold_uncertainty_score":0.012377918},"labels":[],"label_agreement":null},{"id":"W4392114235","doi":"10.1109/tvt.2024.3369100","title":"REAL-SAP: Real-Time Evidence Aware Liable Safety Assessment for Perception in Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Reliability engineering; Computer science; Real-time computing; Engineering; Psychology","score_opus":0.01283765923528781,"score_gpt":0.29982265714430745,"score_spread":0.28698499790901966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392114235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032929257,0.00026168863,0.96357155,0.00017238794,0.00005260033,0.00007649523,0.00007176953,0.0015802983,0.0012839516],"genre_scores_gemma":[0.8968409,0.00012796199,0.10128038,0.00013070651,0.000041141964,0.000091146096,0.00014338321,0.00008023388,0.0012642379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991779,0.00020924208,0.000049522165,0.00018843096,0.00028894108,0.00008594742],"domain_scores_gemma":[0.9974262,0.0010463926,0.00032396338,0.0002958104,0.0007004573,0.00020711048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023238035,0.0009822415,0.00073171844,0.0007694429,0.00029279024,0.0012411044,0.0018660004,0.0010555584,0.0019825802],"category_scores_gemma":[0.007326016,0.00034575438,0.00042929134,0.0003425061,0.0009415272,0.001962216,0.0023306555,0.0013175192,0.00038128643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009208809,0.00025876038,0.0068718805,0.00031640913,0.00015363669,0.0005092168,0.00029966346,0.56228614,0.021819342,0.014271581,0.0034187706,0.38887376],"study_design_scores_gemma":[0.000010965035,0.00008135651,0.00058920134,0.000010860236,0.000012474404,0.000053906515,0.000017960743,0.99101347,0.0035799788,0.004242046,0.00037527518,0.0000123974805],"about_ca_topic_score_codex":0.0020028595,"about_ca_topic_score_gemma":0.0024547742,"teacher_disagreement_score":0.0023238035,"about_ca_system_score_codex":0.0007562728,"about_ca_system_score_gemma":0.0013049596,"threshold_uncertainty_score":0.012289584},"labels":[],"label_agreement":null},{"id":"W4392191164","doi":"10.53819/81018102t7002","title":"Resistance to Fast Gradient Sign Method Using Block Switching Algorithm","year":2024,"lang":"en","type":"article","venue":"Journal of Information and Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Robustness (evolution); Dependability; Block (permutation group theory); Artificial intelligence; Adversarial system; Artificial neural network; Deep learning; Sign (mathematics); Algorithm; Machine learning; Resilience (materials science); Data mining; Mathematics","score_opus":0.008238789470627393,"score_gpt":0.2809420582462839,"score_spread":0.2727032687756565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392191164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14282538,0.0002746775,0.84824735,0.00047202816,0.00012947207,0.00013881268,0.00005637637,0.0019297034,0.0059261294],"genre_scores_gemma":[0.90359837,0.00013123787,0.092296794,0.0001743526,0.000028803055,0.00008699437,0.000093900584,0.00008377792,0.0035058768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992818,0.00015752047,0.000050520917,0.00012798521,0.0002689585,0.00011322512],"domain_scores_gemma":[0.99808407,0.0008125962,0.0002327492,0.0004241919,0.00033559348,0.0001108307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012481379,0.000552347,0.00061442424,0.00052409957,0.00039272854,0.0008046826,0.0010485512,0.0009201392,0.0022300698],"category_scores_gemma":[0.004802942,0.00019510629,0.00059124804,0.00024644213,0.0008539789,0.001368952,0.0011134025,0.0013747325,0.00050951797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071236363,0.00020136175,0.0041670655,0.00013600048,0.0001253201,0.00022574341,0.00021432192,0.60332024,0.04863733,0.051540982,0.0035877845,0.28713152],"study_design_scores_gemma":[0.000017488435,0.00013938178,0.0002219596,0.000007220753,0.000009547988,0.0000572769,0.000009745475,0.9850969,0.008783903,0.0049638,0.00068334735,0.000009419506],"about_ca_topic_score_codex":0.0018341237,"about_ca_topic_score_gemma":0.0014056916,"teacher_disagreement_score":0.0022300698,"about_ca_system_score_codex":0.0006579116,"about_ca_system_score_gemma":0.0011461884,"threshold_uncertainty_score":0.0074603558},"labels":[],"label_agreement":null},{"id":"W4392349457","doi":"10.18280/ts.410105","title":"Improving Explainability in CNN-Based Classification of Mask Images with HayCAM+: An Enhanced Visual Explanation Technique","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology)","score_opus":0.011944775822399115,"score_gpt":0.27629671719875265,"score_spread":0.26435194137635354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392349457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10888878,0.0006528143,0.8839779,0.00045904462,0.000108210166,0.00006244736,0.00017649643,0.0035863724,0.0020879193],"genre_scores_gemma":[0.7732044,0.0003924482,0.22049208,0.00037460148,0.00009939593,0.000060500763,0.0006464826,0.00022556799,0.004504554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999788,0.000041055868,0.000009519533,0.00005560031,0.00006368804,0.000042226988],"domain_scores_gemma":[0.9994772,0.00022973934,0.00008878654,0.00009122682,0.000088778244,0.000024313294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000720642,0.0009098198,0.00069085415,0.00057411386,0.0001827499,0.000676445,0.00097373856,0.00095234916,0.0021986659],"category_scores_gemma":[0.0019249016,0.00026798339,0.0007734726,0.00034307147,0.0004846559,0.0010069563,0.0009895463,0.0012443146,0.00040479103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042262292,0.000117768424,0.0026785035,0.00017708767,0.00021231499,0.0002638894,0.00014248188,0.30581853,0.056790516,0.010090884,0.0055342354,0.6177512],"study_design_scores_gemma":[0.00000922976,0.00004301557,0.000554754,0.00000703193,0.000016374397,0.0000530491,0.0000074358522,0.98992985,0.0067440644,0.0020538273,0.0005739781,0.000007309701],"about_ca_topic_score_codex":0.0027762584,"about_ca_topic_score_gemma":0.0026986906,"teacher_disagreement_score":0.0027762584,"about_ca_system_score_codex":0.00044323833,"about_ca_system_score_gemma":0.00047570077,"threshold_uncertainty_score":0.007355273},"labels":[],"label_agreement":null},{"id":"W4392543429","doi":"10.1109/tip.2024.3372456","title":"PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Adversarial system; Robustness (evolution); Artificial intelligence; Boosting (machine learning); Leverage (statistics); Pattern recognition (psychology); Machine learning","score_opus":0.03959340633078498,"score_gpt":0.2901404290749319,"score_spread":0.250547022744147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392543429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004739988,0.00012938061,0.9921898,0.00013722296,0.000042891992,0.000056113644,0.000096281394,0.0014792016,0.0011290555],"genre_scores_gemma":[0.5509699,0.00033021162,0.43920153,0.0007245677,0.000108277956,0.00046927255,0.0010318224,0.00073209946,0.006432318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918395,0.00024483586,0.000032807606,0.00019582182,0.00025576688,0.00008689624],"domain_scores_gemma":[0.9985631,0.0008084666,0.000119923345,0.00029304225,0.00015154817,0.000064014705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014643784,0.0012818414,0.0009257262,0.00048154828,0.00034420122,0.0006581076,0.0019323906,0.0013151993,0.0035828447],"category_scores_gemma":[0.004629213,0.0004769092,0.00080766523,0.0004332648,0.0013305431,0.0010234563,0.0026180136,0.0027984416,0.0011596545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001269377,0.00005254749,0.00062825985,0.00007658919,0.00006476453,0.00009054351,0.00003733353,0.89787096,0.0061030816,0.015674362,0.0050146407,0.07425998],"study_design_scores_gemma":[0.000004479972,0.000016391497,0.00004578069,0.000004389905,0.000002433699,0.000018790239,0.0000020216748,0.99478334,0.0012077163,0.003380112,0.00053047517,0.0000040131645],"about_ca_topic_score_codex":0.0024164624,"about_ca_topic_score_gemma":0.0026501983,"teacher_disagreement_score":0.0035828447,"about_ca_system_score_codex":0.0007525331,"about_ca_system_score_gemma":0.00086931157,"threshold_uncertainty_score":0.011985779},"labels":[],"label_agreement":null},{"id":"W4392669834","doi":"10.18653/v1/2023.artofsafety-1.5","title":"Discovering Safety Issues in Text-to-Image Models: Insights from Adversarial Nibbler Challenge","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Adversarial system; White (mutation); Content analysis; Content (measure theory); Psychology; Social psychology; Computer science; Internet privacy; Artificial intelligence; Sociology; Social science","score_opus":0.02314821895900354,"score_gpt":0.2776442431533271,"score_spread":0.25449602419432354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392669834","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76843596,0.0006314546,0.18668254,0.009291706,0.00021119241,0.0007144902,0.0014774426,0.0007823743,0.031772945],"genre_scores_gemma":[0.9587181,0.0001432286,0.034093942,0.00068138633,0.000065038956,0.00022531646,0.00086952193,0.0001520786,0.0050514354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974269,0.0016521112,0.00006658236,0.0003135977,0.00042324406,0.00011746441],"domain_scores_gemma":[0.9710586,0.024056617,0.0015180815,0.001534038,0.0013413059,0.0004913963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034906636,0.0006335938,0.00040222576,0.0006805417,0.00091418234,0.0023630115,0.001083742,0.0019763797,0.00427316],"category_scores_gemma":[0.029341768,0.00024273568,0.00063504535,0.00041179135,0.002238773,0.0033761791,0.0017311771,0.002192083,0.0008311429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031522957,0.0018826142,0.07483251,0.0020238303,0.00025297323,0.005497111,0.06361262,0.32198295,0.0343097,0.31844962,0.038009986,0.1359937],"study_design_scores_gemma":[0.00007677135,0.0003593164,0.0054449895,0.00010741209,0.00003607485,0.00065508677,0.006011082,0.8811203,0.0052568875,0.08191571,0.01892818,0.00008820357],"about_ca_topic_score_codex":0.0033823524,"about_ca_topic_score_gemma":0.00228732,"teacher_disagreement_score":0.00427316,"about_ca_system_score_codex":0.0015773734,"about_ca_system_score_gemma":0.0006030253,"threshold_uncertainty_score":0.018460572},"labels":[],"label_agreement":null},{"id":"W4392669880","doi":"10.18653/v1/2023.findings-ijcnlp.17","title":"A Novel Information Theoretic Objective to Disentangle Representations for Fair Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Entropy (arrow of time); Cross entropy; Representation (politics); Artificial intelligence; Mutual information; Artificial neural network; Machine learning; Set (abstract data type); Conditional entropy; Task (project management); Deep neural networks; Training set; Principle of maximum entropy; Theoretical computer science","score_opus":0.02840612711661232,"score_gpt":0.31752403497442955,"score_spread":0.2891179078578172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392669880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008431838,0.00024078184,0.9861974,0.00086839625,0.00013804683,0.000056196965,0.00012265665,0.00020580791,0.0037389316],"genre_scores_gemma":[0.7685721,0.0007329961,0.2116145,0.0012371186,0.0009352467,0.00032025666,0.0006007264,0.00036593576,0.01562113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656963,0.001531408,0.00013206757,0.00061470567,0.0008657524,0.00028645483],"domain_scores_gemma":[0.99128425,0.005165019,0.00078277267,0.0014928746,0.0007665026,0.0005086875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006444667,0.0016621897,0.0015549221,0.0015917305,0.0009141175,0.003245783,0.0024616271,0.0027311577,0.00641882],"category_scores_gemma":[0.024020843,0.0005843711,0.0010383506,0.0013155293,0.0030380504,0.0047634006,0.0050516464,0.0046876566,0.0010849042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033487933,0.00020037257,0.0008811522,0.00021017138,0.00012059887,0.00009717961,0.00010346018,0.39293158,0.0061779106,0.44982934,0.009488696,0.13962476],"study_design_scores_gemma":[0.000012608053,0.00007480051,0.00015587715,0.000031478547,0.000019309473,0.00004391927,0.000013344168,0.79874974,0.0017632692,0.1977326,0.0013842386,0.000018753368],"about_ca_topic_score_codex":0.00086004834,"about_ca_topic_score_gemma":0.00088471966,"teacher_disagreement_score":0.006444667,"about_ca_system_score_codex":0.001659413,"about_ca_system_score_gemma":0.001587906,"threshold_uncertainty_score":0.03408307},"labels":[],"label_agreement":null},{"id":"W4392902808","doi":"10.1109/icassp48485.2024.10446776","title":"Robustness Against Adversarial Attacks Via Learning Confined Adversarial Polytopes","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Polytope; Robustness (evolution); Bounded function; Deep neural networks; Computer science; Norm (philosophy); Artificial intelligence; Limiting; Deep learning; Mathematical optimization; Mathematics; Combinatorics; Engineering","score_opus":0.009320907631920512,"score_gpt":0.25375059565644137,"score_spread":0.24442968802452086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392902808","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026857551,0.00034458115,0.9690307,0.0002644796,0.000043015807,0.00008829798,0.000083043815,0.0010613922,0.002226928],"genre_scores_gemma":[0.86366856,0.00045891374,0.13186485,0.0004815837,0.00007864794,0.00025432822,0.00037226506,0.00031212173,0.0025086924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817073,0.0005809509,0.00009575064,0.0004617464,0.00049488596,0.00019588535],"domain_scores_gemma":[0.99297816,0.004529737,0.0007575659,0.0010802615,0.0004134397,0.00024083305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003013561,0.0021823249,0.0013944608,0.00073648925,0.00062238536,0.0013848738,0.0017672671,0.0016422231,0.0020788794],"category_scores_gemma":[0.012554297,0.0008012181,0.0013416026,0.00038235896,0.0025682587,0.0026931777,0.004436033,0.0042195013,0.000584976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069701826,0.000031764288,0.00043107942,0.000043904787,0.000040436807,0.00006186586,0.00002988387,0.9755785,0.0025090575,0.006141789,0.00055972463,0.014502262],"study_design_scores_gemma":[0.0000054919947,0.000046001456,0.00007480779,0.000012049945,0.0000060132134,0.000025365247,0.000007287642,0.99155575,0.0015628514,0.0064359363,0.0002618954,0.0000066074235],"about_ca_topic_score_codex":0.0017158819,"about_ca_topic_score_gemma":0.001584848,"teacher_disagreement_score":0.003013561,"about_ca_system_score_codex":0.0011678092,"about_ca_system_score_gemma":0.0009913641,"threshold_uncertainty_score":0.015937448},"labels":[],"label_agreement":null},{"id":"W4392906114","doi":"10.32920/25412851","title":"Security of Generative Adversarial Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"MNIST database; Discriminator; Adversarial system; Computer science; Generative grammar; Attack surface; Computer security; Generative adversarial network; Artificial intelligence; Artificial neural network; Machine learning; Deep learning; Telecommunications","score_opus":0.01168006359099469,"score_gpt":0.2705876212344714,"score_spread":0.25890755764347667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392906114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053653322,0.0012655308,0.92528605,0.0024615685,0.00020247103,0.0001166129,0.00026503703,0.0011927241,0.015556662],"genre_scores_gemma":[0.94729674,0.00073273375,0.04634758,0.0006723309,0.0001123778,0.00011876753,0.00028077804,0.00016491661,0.0042738556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99670607,0.0015754619,0.0001278972,0.0004931538,0.00078787934,0.00030960055],"domain_scores_gemma":[0.985609,0.010531147,0.0007349027,0.0021966605,0.00070342526,0.00022480763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052661123,0.0011747462,0.0010719232,0.00079261384,0.000738252,0.0019277977,0.0013962142,0.0018887529,0.0025615043],"category_scores_gemma":[0.0147435265,0.0005759883,0.00097290904,0.00038363467,0.0030070795,0.002510404,0.0033758634,0.0036917164,0.00075567956],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021569291,0.000039936636,0.0015068115,0.0001065619,0.0001076442,0.00015162454,0.000119151366,0.81848145,0.004328938,0.14390033,0.0025054186,0.02853645],"study_design_scores_gemma":[0.000008995758,0.000033171862,0.00015349752,0.000024800169,0.000009639486,0.00005961496,0.000011824792,0.93871987,0.001490647,0.058470923,0.0010042579,0.000012820397],"about_ca_topic_score_codex":0.0015099202,"about_ca_topic_score_gemma":0.000973983,"teacher_disagreement_score":0.0052661123,"about_ca_system_score_codex":0.0016133961,"about_ca_system_score_gemma":0.0010090931,"threshold_uncertainty_score":0.02785021},"labels":[],"label_agreement":null},{"id":"W4392920822","doi":"10.21203/rs.3.rs-3648954/v1","title":"LRCM: Enhancing Adversarial Purification through Latent Representation Compression","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Guangdong Power Grid Company; Canadian Institute for Advanced Research","keywords":"Adversarial system; Representation (politics); Compression (physics); Artificial intelligence; Computer science; Pattern recognition (psychology); Political science; Materials science; Law; Composite material","score_opus":0.0958234845702851,"score_gpt":0.4326338984710349,"score_spread":0.3368104139007498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392920822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042402027,0.00023183276,0.9921936,0.00023993055,0.00007257588,0.000032965116,0.000074205695,0.0015332412,0.0013815598],"genre_scores_gemma":[0.3683205,0.00056645094,0.6121663,0.0008728564,0.0003278052,0.00017552283,0.0008478584,0.0011295979,0.015593127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800414,0.00077444606,0.000058262707,0.0003226448,0.0007032357,0.00013729598],"domain_scores_gemma":[0.99704903,0.0013620786,0.00016650565,0.0010270699,0.00028699305,0.00010834335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021136769,0.0012191127,0.0011660369,0.0007959722,0.00045546851,0.0011386556,0.001865448,0.0018288296,0.005650939],"category_scores_gemma":[0.00818395,0.0005410678,0.0007144453,0.0009026698,0.0014567329,0.0023472703,0.0037696322,0.0030112034,0.002484761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041122298,0.00025054137,0.0005962556,0.00021897386,0.00013149751,0.00021676377,0.000117856056,0.47376552,0.030358167,0.068359904,0.017912084,0.40766123],"study_design_scores_gemma":[0.000013198124,0.000033940632,0.00007402958,0.000008931115,0.000008348056,0.00005763357,0.000005736222,0.97696334,0.0068241083,0.014274476,0.0017248685,0.000011365825],"about_ca_topic_score_codex":0.0016245935,"about_ca_topic_score_gemma":0.0019540845,"teacher_disagreement_score":0.005650939,"about_ca_system_score_codex":0.00049303763,"about_ca_system_score_gemma":0.00089633913,"threshold_uncertainty_score":0.018904269},"labels":[],"label_agreement":null},{"id":"W4392927053","doi":"10.32920/25412851.v1","title":"Security of Generative Adversarial Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"MNIST database; Discriminator; Adversarial system; Computer science; Generative grammar; Attack surface; Computer security; Generative adversarial network; Artificial intelligence; Artificial neural network; Machine learning; Deep learning; Telecommunications","score_opus":0.01168006359099469,"score_gpt":0.2705876212344714,"score_spread":0.25890755764347667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392927053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053653322,0.0012655308,0.92528605,0.0024615685,0.00020247103,0.0001166129,0.00026503703,0.0011927241,0.015556662],"genre_scores_gemma":[0.94729674,0.00073273375,0.04634758,0.0006723309,0.0001123778,0.00011876753,0.00028077804,0.00016491661,0.0042738556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670607,0.0015754619,0.0001278972,0.0004931538,0.00078787934,0.00030960055],"domain_scores_gemma":[0.985609,0.010531147,0.0007349027,0.0021966605,0.00070342526,0.00022480763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052661123,0.0011747462,0.0010719232,0.00079261384,0.000738252,0.0019277977,0.0013962142,0.0018887529,0.0025615043],"category_scores_gemma":[0.0147435265,0.0005759883,0.00097290904,0.00038363467,0.0030070795,0.002510404,0.0033758634,0.0036917164,0.00075567956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021569291,0.000039936636,0.0015068115,0.0001065619,0.0001076442,0.00015162454,0.000119151366,0.81848145,0.004328938,0.14390033,0.0025054186,0.02853645],"study_design_scores_gemma":[0.000008995758,0.000033171862,0.00015349752,0.000024800169,0.000009639486,0.00005961496,0.000011824792,0.93871987,0.001490647,0.058470923,0.0010042579,0.000012820397],"about_ca_topic_score_codex":0.0015099202,"about_ca_topic_score_gemma":0.000973983,"teacher_disagreement_score":0.0052661123,"about_ca_system_score_codex":0.0016133961,"about_ca_system_score_gemma":0.0010090931,"threshold_uncertainty_score":0.02785021},"labels":[],"label_agreement":null},{"id":"W4392943612","doi":"10.1109/icmla58977.2023.00161","title":"Towards Safe Online Machine Learning Model Training and Inference on Edge Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Training (meteorology); Inference; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Machine learning; Edge device; Online learning; Multimedia; Operating system","score_opus":0.05657319367236363,"score_gpt":0.3092488177099152,"score_spread":0.25267562403755156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019164706,0.0000759979,0.9785267,0.00015587393,0.000016520984,0.000026998188,0.000033825618,0.0013009137,0.00069844996],"genre_scores_gemma":[0.6434219,0.00014953729,0.35322553,0.00021421921,0.00004674203,0.000104029896,0.00032906097,0.000345046,0.0021639687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893755,0.0003432343,0.000045107037,0.0002165945,0.00028371988,0.0001737593],"domain_scores_gemma":[0.99669975,0.001594701,0.000290222,0.0009437426,0.00036198608,0.00010948649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013281008,0.0010425413,0.0009245595,0.0005477536,0.00064376293,0.0008656127,0.0017706562,0.0010929847,0.001653887],"category_scores_gemma":[0.006541992,0.0006911495,0.0007607859,0.0004125145,0.0011640232,0.0022465033,0.0022123042,0.002726446,0.0008137601],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002738966,0.00007840926,0.00087883195,0.00005652721,0.000036318594,0.00015512042,0.00009758702,0.89835405,0.007230754,0.013722437,0.0016492046,0.077466905],"study_design_scores_gemma":[0.0000026910704,0.000009459316,0.000032118776,0.0000016821845,0.0000019263618,0.000010156604,0.000006061888,0.9935347,0.0016860127,0.0045752195,0.00013828171,0.0000016998416],"about_ca_topic_score_codex":0.0030877774,"about_ca_topic_score_gemma":0.0035463027,"teacher_disagreement_score":0.0030877774,"about_ca_system_score_codex":0.0007548627,"about_ca_system_score_gemma":0.0013816054,"threshold_uncertainty_score":0.0070237517},"labels":[],"label_agreement":null},{"id":"W4392943806","doi":"10.1109/mosicom59118.2023.10458796","title":"Predicting Defective and Good Tyre Quality Status with Pre-Trained CNN Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Computer science; Quality (philosophy); Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.02334970009100873,"score_gpt":0.29225746599886127,"score_spread":0.26890776590785254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8489449,0.00085521926,0.14343426,0.00031399864,0.00023472821,0.00006507633,0.0011530996,0.0016036142,0.0033951267],"genre_scores_gemma":[0.9908819,0.0001018758,0.0063835173,0.000038923532,0.000016359178,0.000012861019,0.00071612815,0.000024320885,0.0018241267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998642,0.00001028934,0.0000062103895,0.000045262565,0.00002455676,0.00004951466],"domain_scores_gemma":[0.99963534,0.00009413909,0.000050444993,0.000048741003,0.0001466802,0.000024724754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003672141,0.0010090331,0.00040804528,0.0006532469,0.00012664328,0.0005376317,0.00069434685,0.0006984463,0.0010573756],"category_scores_gemma":[0.0012439485,0.00029918444,0.00051546685,0.00024956517,0.00024543688,0.0005024662,0.000395542,0.0007008841,0.00038667096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065070053,0.00027307676,0.048269928,0.00008889548,0.00016693931,0.00041753714,0.00006682041,0.7681791,0.020705389,0.00084962265,0.005552788,0.15477918],"study_design_scores_gemma":[0.0000018421498,0.000020989404,0.0033476455,0.000004314114,0.000008938133,0.000016304837,0.0000062405716,0.99442446,0.0019078149,0.00015647712,0.000101207916,0.0000036316364],"about_ca_topic_score_codex":0.013243095,"about_ca_topic_score_gemma":0.016277988,"teacher_disagreement_score":0.013243095,"about_ca_system_score_codex":0.0008056083,"about_ca_system_score_gemma":0.00034019802,"threshold_uncertainty_score":0.02633208},"labels":[],"label_agreement":null},{"id":"W4393005048","doi":"10.3390/app14062576","title":"Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Ministry of Education, India; National Research Foundation of Korea; Hanyang University; Korea Institute for Advancement of Technology; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Computer science; Segmentation; Artificial intelligence; Feature (linguistics); Adversarial system; Pattern recognition (psychology); Transformation (genetics); Statistics; Mathematics","score_opus":0.01648488935171837,"score_gpt":0.303485871632201,"score_spread":0.2870009822804826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393005048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04759392,0.00016491479,0.94804364,0.0005838468,0.000060061415,0.000043233562,0.000079295736,0.0005677216,0.0028633678],"genre_scores_gemma":[0.93833196,0.00023643399,0.058067303,0.00032948866,0.00006056962,0.00008841803,0.00013424878,0.00011371218,0.0026379828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988079,0.0004977741,0.000041212214,0.0002231441,0.00031792335,0.00011204116],"domain_scores_gemma":[0.9970771,0.0017898994,0.00041698763,0.0004995405,0.00013639685,0.00008005598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016844294,0.00095404114,0.0005278599,0.00046767696,0.00031099483,0.00070575834,0.00080738746,0.0010204022,0.0012163376],"category_scores_gemma":[0.0067636394,0.00032314306,0.00070145476,0.00029612193,0.0019538593,0.0012613648,0.0020417133,0.001906974,0.00031124635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011427487,0.000028615206,0.0009950487,0.000028520233,0.000039053833,0.00015801514,0.00006374345,0.9303037,0.007774849,0.036261488,0.0013436149,0.022889068],"study_design_scores_gemma":[0.000004175856,0.00002794372,0.00016698973,0.00000527375,0.000004227543,0.000048428145,0.0000052439846,0.9886242,0.0020817674,0.008619261,0.00040625068,0.0000061971705],"about_ca_topic_score_codex":0.0013364085,"about_ca_topic_score_gemma":0.0009529837,"teacher_disagreement_score":0.0016844294,"about_ca_system_score_codex":0.0009862366,"about_ca_system_score_gemma":0.0006224675,"threshold_uncertainty_score":0.008908212},"labels":[],"label_agreement":null},{"id":"W4393146083","doi":"10.1609/aaai.v38i21.30468","title":"Attacking CNNs in Histopathology with SNAP: Sporadic and Naturalistic Adversarial Patches (Student Abstract)","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Western University","funders":"","keywords":"Adversarial system; Histopathology; Artificial intelligence; Computer science; Medicine; Pathology","score_opus":0.04090473530556537,"score_gpt":0.3024550889130241,"score_spread":0.2615503536074587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393146083","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6680431,0.0011225961,0.31238744,0.004811513,0.0007841959,0.00013966125,0.00044659898,0.0026893544,0.009575609],"genre_scores_gemma":[0.9878321,0.000086470805,0.010482271,0.00035897378,0.000035057015,0.000018694882,0.000105254585,0.000039098413,0.0010421001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999521,0.00017416813,0.000016473365,0.00009967641,0.000115149625,0.000073552736],"domain_scores_gemma":[0.99756914,0.0015529438,0.0001513719,0.0004587885,0.00017012742,0.00009759855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012737978,0.00045689545,0.00030560404,0.00021789402,0.00025245015,0.0003786406,0.00051036105,0.00078407885,0.0013755535],"category_scores_gemma":[0.0055580237,0.0001720445,0.00039661073,0.00009720676,0.0010981257,0.0007433794,0.0013083315,0.0012051917,0.00023277293],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007476061,0.00013150189,0.01065603,0.00012711502,0.00020344641,0.0011714993,0.00021799239,0.8529057,0.028170194,0.014258657,0.0139997145,0.07741062],"study_design_scores_gemma":[0.000014642197,0.00021620015,0.0016614181,0.00002605337,0.00001562735,0.000279878,0.00004770053,0.97849405,0.011353077,0.006160358,0.0017175968,0.0000135119535],"about_ca_topic_score_codex":0.0014445488,"about_ca_topic_score_gemma":0.0021974067,"teacher_disagreement_score":0.0014445488,"about_ca_system_score_codex":0.00048276622,"about_ca_system_score_gemma":0.0002786976,"threshold_uncertainty_score":0.0067365766},"labels":[],"label_agreement":null},{"id":"W4393147413","doi":"10.1609/aaai.v38i10.29070","title":"Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Bundesministerium für Bildung und Forschung; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Adversarial system; Robustness (evolution); Computer science; Econometrics; Psychology; Mathematics; Artificial intelligence; Biology","score_opus":0.11249251735549742,"score_gpt":0.33436862176287313,"score_spread":0.2218761044073757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393147413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017228529,0.00014519984,0.98103714,0.00034719336,0.000030768882,0.000040670086,0.00003263003,0.00014595914,0.0009919633],"genre_scores_gemma":[0.8848751,0.00018631176,0.11258187,0.0002809173,0.00012156465,0.00013201885,0.00011672242,0.000072922754,0.0016326129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961165,0.0017568016,0.0001730493,0.00080781936,0.00088488887,0.00026099355],"domain_scores_gemma":[0.98422205,0.011298034,0.0013499255,0.002014223,0.00074121315,0.0003746165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006697183,0.00086798554,0.0010631608,0.0010036797,0.0009573067,0.0014343474,0.0014999427,0.0012166632,0.0011547909],"category_scores_gemma":[0.026472902,0.00035168344,0.00081624754,0.0006982287,0.0030697114,0.0026540323,0.002891864,0.0028044772,0.00018039056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012825815,0.00005882119,0.0025038193,0.00006471096,0.00007718329,0.00011526001,0.00012843711,0.85926193,0.0027534885,0.10015689,0.0009499136,0.033801217],"study_design_scores_gemma":[0.0000036924412,0.000021580452,0.00020152974,0.0000069527987,0.000006545636,0.000022313021,0.000009878584,0.9482461,0.00089349685,0.050220225,0.00035976962,0.000007820262],"about_ca_topic_score_codex":0.00162738,"about_ca_topic_score_gemma":0.0016539382,"teacher_disagreement_score":0.006697183,"about_ca_system_score_codex":0.001775318,"about_ca_system_score_gemma":0.0014775652,"threshold_uncertainty_score":0.03541851},"labels":[],"label_agreement":null},{"id":"W4393148469","doi":"10.1609/aaai.v38i6.28470","title":"FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Agency for Defense Development; National Research Foundation of Korea; National Research Foundation","keywords":"Adversarial system; Computer science; Computer security; Artificial intelligence","score_opus":0.06289783033739495,"score_gpt":0.3213497346852908,"score_spread":0.2584519043478959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393148469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021297555,0.0001961807,0.9740795,0.0003023798,0.000055115477,0.00007681638,0.000051945543,0.0013886342,0.0025518835],"genre_scores_gemma":[0.8316927,0.00015993865,0.16379616,0.0005045435,0.00006586901,0.00015441996,0.00013612979,0.00021835537,0.0032719234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904066,0.00031363632,0.000035463858,0.00018117875,0.0003186456,0.00011036476],"domain_scores_gemma":[0.99804354,0.001025923,0.00019716326,0.0005203122,0.00013563127,0.000077450575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018281206,0.0010645879,0.00069118507,0.0004756958,0.00035731008,0.000767747,0.0014609422,0.0013399981,0.0026516807],"category_scores_gemma":[0.006124247,0.000313392,0.0005832966,0.00024980062,0.0017244468,0.0017575953,0.0028704065,0.0026059279,0.00069387484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047172263,0.00022132316,0.0015738789,0.0001596786,0.00013016886,0.00025180142,0.00010506309,0.7079104,0.0482442,0.07897717,0.004358984,0.15759566],"study_design_scores_gemma":[0.000017571054,0.000095360454,0.0001385125,0.000010627473,0.000007578406,0.0000884493,0.000006469173,0.9704405,0.010876468,0.01712314,0.0011838375,0.00001158038],"about_ca_topic_score_codex":0.00040001917,"about_ca_topic_score_gemma":0.0004633247,"teacher_disagreement_score":0.0026516807,"about_ca_system_score_codex":0.00063535984,"about_ca_system_score_gemma":0.0005199879,"threshold_uncertainty_score":0.009668112},"labels":[],"label_agreement":null},{"id":"W4393319426","doi":"10.13052/2794-7254.005","title":"Adversarial Machine-Learning-Enabled Anonymization of OpenWiFi Data","year":2024,"lang":"en","type":"article","venue":"Wireless World Research and Trends Magazine","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Mitacs","keywords":"Adversarial system; Computer science; Artificial intelligence; Machine learning","score_opus":0.0712782366026268,"score_gpt":0.3635485785877823,"score_spread":0.29227034198515545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393319426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1646579,0.0004051029,0.8237281,0.0009456105,0.0001828413,0.000247857,0.0009878086,0.0017236316,0.0071211616],"genre_scores_gemma":[0.9221588,0.00021386467,0.07347562,0.00024048683,0.00004287806,0.00016341153,0.0013013175,0.00007611753,0.0023275176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690634,0.0014167604,0.00011729455,0.00044862487,0.000833212,0.0002777597],"domain_scores_gemma":[0.9923138,0.0031846836,0.0006551794,0.0032116484,0.00050144887,0.00013324467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032437812,0.0005289397,0.00059490505,0.00077133114,0.00092852995,0.0014506931,0.0012275056,0.00090296916,0.0011034481],"category_scores_gemma":[0.011266982,0.000202914,0.0005316247,0.0009910828,0.0015793098,0.0023530873,0.002248842,0.0015970044,0.00047174533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056061643,0.00018201418,0.0064755543,0.00017946835,0.00013198557,0.00041931926,0.0005337306,0.7881731,0.01589854,0.06228987,0.0051848297,0.119970985],"study_design_scores_gemma":[0.000009809671,0.000056355224,0.00084663666,0.000022911307,0.000010810538,0.00020299116,0.00011008834,0.9616327,0.011744481,0.02184535,0.0034968555,0.000020942809],"about_ca_topic_score_codex":0.0016961786,"about_ca_topic_score_gemma":0.0016800017,"teacher_disagreement_score":0.0032437812,"about_ca_system_score_codex":0.0010628899,"about_ca_system_score_gemma":0.00091692986,"threshold_uncertainty_score":0.017154932},"labels":[],"label_agreement":null},{"id":"W4393533947","doi":"10.5281/zenodo.4459195","title":"Verification Witnesses from Verification Tools (SV-COMP 2021)","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.040831439641406814,"score_gpt":0.26370577374418314,"score_spread":0.22287433410277632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393533947","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027979373,0.0008406028,0.5704935,0.0027486363,0.0020586238,0.001045972,0.03456714,0.2945407,0.090906814],"genre_scores_gemma":[0.08548754,0.0017093298,0.41265374,0.0023241607,0.00086670107,0.002502204,0.19679825,0.16741586,0.13024217],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98347837,0.0046809977,0.0014986336,0.0015278462,0.0078468,0.0009673262],"domain_scores_gemma":[0.972934,0.00896065,0.0009510582,0.009386419,0.0069490415,0.0008189124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013610404,0.0025047387,0.0011259601,0.0033013679,0.0015191126,0.006495577,0.0028870555,0.002395501,0.19665182],"category_scores_gemma":[0.04978785,0.0020249265,0.0019206034,0.0018465557,0.0011777539,0.0095371045,0.009860712,0.0040075365,0.11863241],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047408763,0.00008671099,0.00045936916,0.0008039377,0.000054459975,0.00030438002,0.00048135666,0.0015969162,0.0035582795,0.062065873,0.74036413,0.18975045],"study_design_scores_gemma":[0.0001842528,0.00011459713,0.00040600015,0.0005151526,0.000026734595,0.00046701325,0.00019141527,0.010164015,0.017280584,0.045467116,0.9250856,0.00009747823],"about_ca_topic_score_codex":0.0024583815,"about_ca_topic_score_gemma":0.00252358,"teacher_disagreement_score":0.19665182,"about_ca_system_score_codex":0.0016445724,"about_ca_system_score_gemma":0.004358534,"threshold_uncertainty_score":0.6578659},"labels":[],"label_agreement":null},{"id":"W4393674187","doi":"10.5281/zenodo.5831005","title":"Verification Witnesses from Verification Tools (SV-COMP 2022)","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.03973750652817714,"score_gpt":0.26206913464098625,"score_spread":0.2223316281128091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393674187","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002144602,0.00065382844,0.6007331,0.0022565848,0.0021076854,0.0007161054,0.025383832,0.27967098,0.08633331],"genre_scores_gemma":[0.09395882,0.0017141132,0.38128334,0.0027237395,0.0010186615,0.002060784,0.16329806,0.19235112,0.16159143],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98634166,0.0037672606,0.0012868914,0.001457675,0.006208587,0.00093779404],"domain_scores_gemma":[0.97903264,0.006514052,0.00072997966,0.007781023,0.0052433577,0.0006989803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0110918665,0.002602689,0.0011318,0.003038101,0.0015829282,0.006660074,0.0029638994,0.002487095,0.20856762],"category_scores_gemma":[0.041263733,0.0021252565,0.0020168494,0.0017172833,0.001259501,0.009304997,0.009570544,0.0042988206,0.12570775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044092783,0.000075042415,0.0004511735,0.00069267704,0.00006140721,0.00039099396,0.0004980504,0.001866641,0.0038453129,0.08328919,0.7407337,0.167655],"study_design_scores_gemma":[0.00016596557,0.00009013681,0.00031863034,0.0004365779,0.000028358209,0.0004965057,0.00016633318,0.010688516,0.01734639,0.059976075,0.91018695,0.00009955071],"about_ca_topic_score_codex":0.0025123835,"about_ca_topic_score_gemma":0.0025446657,"teacher_disagreement_score":0.20856762,"about_ca_system_score_codex":0.0015920685,"about_ca_system_score_gemma":0.00396294,"threshold_uncertainty_score":0.69772816},"labels":[],"label_agreement":null},{"id":"W4393699878","doi":"10.5281/zenodo.4459196","title":"Verification Witnesses from Verification Tools (SV-COMP 2021)","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Verification; Programming language; Software","score_opus":0.040831439641406814,"score_gpt":0.26370577374418314,"score_spread":0.22287433410277632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393699878","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027979373,0.0008406028,0.5704935,0.0027486363,0.0020586238,0.001045972,0.03456714,0.2945407,0.090906814],"genre_scores_gemma":[0.08548754,0.0017093298,0.41265374,0.0023241607,0.00086670107,0.002502204,0.19679825,0.16741586,0.13024217],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98347837,0.0046809977,0.0014986336,0.0015278462,0.0078468,0.0009673262],"domain_scores_gemma":[0.972934,0.00896065,0.0009510582,0.009386419,0.0069490415,0.0008189124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013610404,0.0025047387,0.0011259601,0.0033013679,0.0015191126,0.006495577,0.0028870555,0.002395501,0.19665182],"category_scores_gemma":[0.04978785,0.0020249265,0.0019206034,0.0018465557,0.0011777539,0.0095371045,0.009860712,0.0040075365,0.11863241],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047408763,0.00008671099,0.00045936916,0.0008039377,0.000054459975,0.00030438002,0.00048135666,0.0015969162,0.0035582795,0.062065873,0.74036413,0.18975045],"study_design_scores_gemma":[0.0001842528,0.00011459713,0.00040600015,0.0005151526,0.000026734595,0.00046701325,0.00019141527,0.010164015,0.017280584,0.045467116,0.9250856,0.00009747823],"about_ca_topic_score_codex":0.0024583815,"about_ca_topic_score_gemma":0.00252358,"teacher_disagreement_score":0.19665182,"about_ca_system_score_codex":0.0016445724,"about_ca_system_score_gemma":0.004358534,"threshold_uncertainty_score":0.6578659},"labels":[],"label_agreement":null},{"id":"W4393729196","doi":"10.5281/zenodo.5831004","title":"Verification Witnesses from Verification Tools (SV-COMP 2022)","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Reliability engineering; Engineering","score_opus":0.03973750652817714,"score_gpt":0.26206913464098625,"score_spread":0.2223316281128091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393729196","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002144602,0.00065382844,0.6007331,0.0022565848,0.0021076854,0.0007161054,0.025383832,0.27967098,0.08633331],"genre_scores_gemma":[0.09395882,0.0017141132,0.38128334,0.0027237395,0.0010186615,0.002060784,0.16329806,0.19235112,0.16159143],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98634166,0.0037672606,0.0012868914,0.001457675,0.006208587,0.00093779404],"domain_scores_gemma":[0.97903264,0.006514052,0.00072997966,0.007781023,0.0052433577,0.0006989803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0110918665,0.002602689,0.0011318,0.003038101,0.0015829282,0.006660074,0.0029638994,0.002487095,0.20856762],"category_scores_gemma":[0.041263733,0.0021252565,0.0020168494,0.0017172833,0.001259501,0.009304997,0.009570544,0.0042988206,0.12570775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044092783,0.000075042415,0.0004511735,0.00069267704,0.00006140721,0.00039099396,0.0004980504,0.001866641,0.0038453129,0.08328919,0.7407337,0.167655],"study_design_scores_gemma":[0.00016596557,0.00009013681,0.00031863034,0.0004365779,0.000028358209,0.0004965057,0.00016633318,0.010688516,0.01734639,0.059976075,0.91018695,0.00009955071],"about_ca_topic_score_codex":0.0025123835,"about_ca_topic_score_gemma":0.0025446657,"teacher_disagreement_score":0.20856762,"about_ca_system_score_codex":0.0015920685,"about_ca_system_score_gemma":0.00396294,"threshold_uncertainty_score":0.69772816},"labels":[],"label_agreement":null},{"id":"W4393754376","doi":"10.5281/zenodo.5838498","title":"Verification Witnesses from Verification Tools (SV-COMP 2022)","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.03973750652817714,"score_gpt":0.26206913464098625,"score_spread":0.2223316281128091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393754376","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002144602,0.00065382844,0.6007331,0.0022565848,0.0021076854,0.0007161054,0.025383832,0.27967098,0.08633331],"genre_scores_gemma":[0.09395882,0.0017141132,0.38128334,0.0027237395,0.0010186615,0.002060784,0.16329806,0.19235112,0.16159143],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98634166,0.0037672606,0.0012868914,0.001457675,0.006208587,0.00093779404],"domain_scores_gemma":[0.97903264,0.006514052,0.00072997966,0.007781023,0.0052433577,0.0006989803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0110918665,0.002602689,0.0011318,0.003038101,0.0015829282,0.006660074,0.0029638994,0.002487095,0.20856762],"category_scores_gemma":[0.041263733,0.0021252565,0.0020168494,0.0017172833,0.001259501,0.009304997,0.009570544,0.0042988206,0.12570775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044092783,0.000075042415,0.0004511735,0.00069267704,0.00006140721,0.00039099396,0.0004980504,0.001866641,0.0038453129,0.08328919,0.7407337,0.167655],"study_design_scores_gemma":[0.00016596557,0.00009013681,0.00031863034,0.0004365779,0.000028358209,0.0004965057,0.00016633318,0.010688516,0.01734639,0.059976075,0.91018695,0.00009955071],"about_ca_topic_score_codex":0.0025123835,"about_ca_topic_score_gemma":0.0025446657,"teacher_disagreement_score":0.20856762,"about_ca_system_score_codex":0.0015920685,"about_ca_system_score_gemma":0.00396294,"threshold_uncertainty_score":0.69772816},"labels":[],"label_agreement":null},{"id":"W4394825026","doi":"10.5121/ijaia.2024.15205","title":"Immunizing Image Classifiers Against Localized Adversary Attack","year":2024,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adversarial system; Convolutional neural network; Computer science; Adversary; Deep learning; Artificial intelligence; Vulnerability (computing); Machine learning; Convolution (computer science); Image (mathematics); Scale (ratio); Deep neural networks; Artificial neural network; Pattern recognition (psychology); Computer security; Geography","score_opus":0.03972670245325821,"score_gpt":0.356184689767243,"score_spread":0.3164579873139848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394825026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25367764,0.0009245627,0.7353367,0.0009331066,0.00021605346,0.00011724262,0.00010382268,0.0025170618,0.0061738286],"genre_scores_gemma":[0.96490663,0.00020467903,0.03253434,0.0002635055,0.00006653973,0.000042310916,0.00010704075,0.00005916529,0.0018159525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991393,0.0001934971,0.00003651506,0.00017128217,0.0002747461,0.00018455452],"domain_scores_gemma":[0.997698,0.00097584876,0.00034062203,0.000659288,0.00021689343,0.00010928554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014358917,0.0010002666,0.0009752018,0.00052741106,0.00040723503,0.00069153094,0.0009960429,0.0012629324,0.0011917095],"category_scores_gemma":[0.006200925,0.00036469297,0.0006208517,0.00024010269,0.0012100593,0.0021395283,0.0027746444,0.0016117248,0.0006588456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004544867,0.00015423962,0.004206792,0.00012327878,0.00015926249,0.00024961703,0.00016125634,0.72215426,0.06424871,0.01766317,0.0043715443,0.1860534],"study_design_scores_gemma":[0.000006155566,0.00011562559,0.00030982017,0.000011301959,0.000014185569,0.000069974536,0.000018812514,0.9804684,0.0144412415,0.0037619364,0.00077374064,0.000008804821],"about_ca_topic_score_codex":0.00081735547,"about_ca_topic_score_gemma":0.00081570557,"teacher_disagreement_score":0.0014358917,"about_ca_system_score_codex":0.0006842929,"about_ca_system_score_gemma":0.00062787376,"threshold_uncertainty_score":0.0075938106},"labels":[],"label_agreement":null},{"id":"W4394939057","doi":"10.1109/tdsc.2024.3387570","title":"Fast Generation-Based Gradient Leakage Attacks: An Approach to Generate Training Data Directly From the Gradient","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Computer science; Leakage (economics); Artificial intelligence","score_opus":0.07632921568485775,"score_gpt":0.2941784633937752,"score_spread":0.21784924770891745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394939057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014082928,0.00020426542,0.9825068,0.0002874068,0.000049465576,0.00008277552,0.00005994086,0.0017576023,0.0009688004],"genre_scores_gemma":[0.6918677,0.00022787982,0.30356616,0.0006134417,0.00008218732,0.00022795764,0.0002025333,0.00027287146,0.0029392957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981832,0.00068720896,0.0001045287,0.0003087481,0.0005610099,0.00015532381],"domain_scores_gemma":[0.9962794,0.001530012,0.00032223007,0.0015407559,0.00023452038,0.00009301224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022302298,0.00084309455,0.0007539209,0.0005933054,0.0004552567,0.00092861184,0.0013395267,0.0012327243,0.001634201],"category_scores_gemma":[0.008360189,0.00041568174,0.000740921,0.0005290028,0.0014761209,0.0023059477,0.0028074007,0.0022799356,0.0007293681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014246344,0.0004204602,0.0027421685,0.00023389437,0.00021044463,0.0003783696,0.0003898736,0.36092123,0.06731509,0.074540555,0.010129569,0.48129362],"study_design_scores_gemma":[0.00005631105,0.00016733972,0.00028992267,0.000019846486,0.000014728642,0.00028203524,0.000021336118,0.9405844,0.031231165,0.023977088,0.0033273639,0.00002837163],"about_ca_topic_score_codex":0.00045945588,"about_ca_topic_score_gemma":0.00048438204,"teacher_disagreement_score":0.0022302298,"about_ca_system_score_codex":0.00055795506,"about_ca_system_score_gemma":0.0009233596,"threshold_uncertainty_score":0.011794686},"labels":[],"label_agreement":null},{"id":"W4394949264","doi":"10.6339/24-jds1122","title":"BIE: Binary Image Encoding for the Classification of Tabular Data","year":2024,"lang":"en","type":"article","venue":"Journal of Data Science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Encoding (memory); Pattern recognition (psychology); Convolutional neural network; Contextual image classification; Benchmark (surveying); Image (mathematics); Deep learning; Feature (linguistics); Field (mathematics); Binary image; Image processing; Mathematics","score_opus":0.1344438530442302,"score_gpt":0.40138674335794516,"score_spread":0.266942890313715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394949264","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06598583,0.0028332972,0.89124894,0.00096855976,0.00069349154,0.00044613506,0.0070872903,0.021611176,0.009125199],"genre_scores_gemma":[0.2745471,0.0016267254,0.6986895,0.0007443694,0.00016683596,0.0005527565,0.014584676,0.0010098267,0.008078139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958354,0.000050615126,0.00004147239,0.00008449152,0.00017457933,0.000065377586],"domain_scores_gemma":[0.99907804,0.00022933637,0.00015516816,0.00024409562,0.00022968174,0.000063764266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000584412,0.0010475981,0.0006222587,0.001958009,0.00025206315,0.0011724008,0.0013152392,0.0007127872,0.0062041637],"category_scores_gemma":[0.0034033032,0.00027577035,0.0006006884,0.0018492567,0.0004698569,0.0028010895,0.0011641514,0.001462332,0.00300788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007517315,0.00024955347,0.0022710026,0.0004965406,0.00005789151,0.00016269991,0.00011498322,0.020896802,0.04245036,0.01508897,0.038039066,0.87942046],"study_design_scores_gemma":[0.00010008634,0.0004260703,0.0035842005,0.00018427365,0.0000509015,0.00064973295,0.00016305233,0.78113204,0.1413098,0.02530491,0.046990722,0.00010418882],"about_ca_topic_score_codex":0.0020017305,"about_ca_topic_score_gemma":0.0023017696,"teacher_disagreement_score":0.0062041637,"about_ca_system_score_codex":0.00059756055,"about_ca_system_score_gemma":0.0006903269,"threshold_uncertainty_score":0.020754993},"labels":[],"label_agreement":null},{"id":"W4395080000","doi":"10.36227/techrxiv.171392778.89337138/v1","title":"Semantic Stealth: Adversarial Text Attacks on NLP using Several Methods","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Adversarial system; Artificial intelligence; Computer science; Natural language processing","score_opus":0.06166764125969915,"score_gpt":0.41177476271631674,"score_spread":0.3501071214566176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395080000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10925477,0.0018258606,0.8706394,0.0026094515,0.00037240807,0.00053591793,0.00042941503,0.0037092154,0.0106236255],"genre_scores_gemma":[0.85291976,0.00063925487,0.13848583,0.0008031254,0.00020040142,0.00031397858,0.00063828664,0.00018265734,0.005816706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965289,0.0015425569,0.00016274535,0.00048526053,0.0010548182,0.0002256939],"domain_scores_gemma":[0.99483526,0.0030838163,0.0005248793,0.0011511879,0.00028146149,0.0001234183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036359057,0.0014239942,0.0010957025,0.0012871627,0.0009288666,0.0015187763,0.0014110478,0.0019283831,0.0017859038],"category_scores_gemma":[0.009060502,0.00030239223,0.0011888773,0.00072398386,0.002255131,0.0034521848,0.0034744462,0.0026376534,0.0006562656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011102753,0.0005319547,0.0043863435,0.00042820137,0.00031359342,0.00062871,0.00043902293,0.58876634,0.019831168,0.06872768,0.013753028,0.30108365],"study_design_scores_gemma":[0.000024158631,0.00016280389,0.00030139956,0.000021001957,0.000017441065,0.00017102493,0.000052178013,0.97180736,0.0061361124,0.01903093,0.0022566551,0.00001898768],"about_ca_topic_score_codex":0.0011072366,"about_ca_topic_score_gemma":0.0013340997,"teacher_disagreement_score":0.0036359057,"about_ca_system_score_codex":0.0011876363,"about_ca_system_score_gemma":0.0012090454,"threshold_uncertainty_score":0.019228697},"labels":[],"label_agreement":null},{"id":"W4395090193","doi":"10.58414/scientifictemper.2024.15.1.31","title":"Ensuring ethical integrity and bias reduction in machine learning models","year":2024,"lang":"en","type":"article","venue":"THE SCIENTIFIC TEMPER","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Reduction (mathematics); Computer science; Academic integrity; Artificial intelligence; Mathematics","score_opus":0.07394090712796714,"score_gpt":0.2949799228178198,"score_spread":0.22103901568985268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395090193","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017809307,0.0014463473,0.9105056,0.044521634,0.0006825073,0.0008169533,0.0003593398,0.00040321023,0.023455074],"genre_scores_gemma":[0.6337964,0.0015685764,0.34783086,0.007355465,0.0012288144,0.0022580263,0.00044341938,0.00042649164,0.005091956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7601909,0.18980476,0.008449002,0.01253387,0.02609286,0.002928528],"domain_scores_gemma":[0.34637928,0.456738,0.032649823,0.12558699,0.03516335,0.0034826193],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.19633476,0.0012112217,0.0019691198,0.0025428303,0.0052415337,0.014180437,0.0038671917,0.0077220225,0.0062374156],"category_scores_gemma":[0.56358683,0.0012803266,0.0014900941,0.002917514,0.02415027,0.015630761,0.013328495,0.010753185,0.0023748046],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018243345,0.00011541277,0.005986842,0.00077143824,0.00015869967,0.00025948934,0.006522473,0.013771942,0.0011467614,0.84735304,0.009871161,0.113860205],"study_design_scores_gemma":[0.00004992939,0.00008326952,0.0006173356,0.0006011783,0.000042408443,0.00023758566,0.00067002134,0.021423196,0.002177145,0.9528662,0.02117862,0.00005312093],"about_ca_topic_score_codex":0.0018799145,"about_ca_topic_score_gemma":0.0015064885,"teacher_disagreement_score":0.19633476,"about_ca_system_score_codex":0.0043825153,"about_ca_system_score_gemma":0.020592835,"threshold_uncertainty_score":0.9910623},"labels":[],"label_agreement":null},{"id":"W4396243913","doi":"10.1145/3658644.3690194","title":"Evaluations of Machine Learning Privacy Defenses are Misleading","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; Universitas Brawijaya; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Internet privacy; Computer science; Computer security; Business; Psychology; Artificial intelligence","score_opus":0.03983493120052315,"score_gpt":0.3299973261049216,"score_spread":0.29016239490439844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396243913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16306438,0.009853572,0.72470987,0.021669941,0.0007645556,0.00062729797,0.0012744142,0.0029480532,0.07508785],"genre_scores_gemma":[0.90882456,0.0013579238,0.08267672,0.0026533336,0.00026480263,0.00031724537,0.0006493298,0.0003319303,0.0029240593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.93385494,0.037598763,0.002117438,0.003845018,0.020468246,0.0021155486],"domain_scores_gemma":[0.8457704,0.096039645,0.005760264,0.043633085,0.0074314307,0.001365183],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03301297,0.001790553,0.0013447085,0.0017438303,0.0019211008,0.0048741708,0.0034536186,0.00377981,0.006296686],"category_scores_gemma":[0.15780422,0.0007629516,0.0012390616,0.0017773913,0.006047416,0.009979009,0.006159364,0.009319394,0.0015265348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001582102,0.0010256569,0.010300411,0.0017385326,0.00047532888,0.00020117051,0.0006282653,0.1430187,0.010049555,0.5423584,0.029116772,0.25950515],"study_design_scores_gemma":[0.0003784042,0.0016737577,0.0033557012,0.0008436449,0.00020266602,0.0011392959,0.00059550663,0.41091016,0.034885872,0.5103201,0.03558385,0.00011094786],"about_ca_topic_score_codex":0.000418758,"about_ca_topic_score_gemma":0.00064039236,"teacher_disagreement_score":0.966987,"about_ca_system_score_codex":0.0041526044,"about_ca_system_score_gemma":0.0024991503,"threshold_uncertainty_score":0.17459136},"labels":[],"label_agreement":null},{"id":"W4396790742","doi":"10.1109/satml59370.2024.00023","title":"Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Waterloo","funders":"","keywords":"Computer science; Feature (linguistics); Machine learning; Artificial intelligence; Extractor; Set (abstract data type); Labeled data; Downstream (manufacturing); Training set; Feature vector; Supervised learning; Process (computing); Artificial neural network; Engineering","score_opus":0.03982274016507497,"score_gpt":0.3439346978512736,"score_spread":0.30411195768619864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396790742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09697224,0.00054763583,0.8875405,0.0013366371,0.00015061084,0.00027626439,0.00041937715,0.0078124446,0.004944176],"genre_scores_gemma":[0.81844944,0.00030502808,0.17502539,0.00080259336,0.00007297894,0.00032785928,0.0005651815,0.00046832318,0.0039832364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948178,0.0015371388,0.00040246718,0.0009670812,0.0018152706,0.00046015452],"domain_scores_gemma":[0.98369944,0.00494787,0.0012125866,0.008815134,0.0011170306,0.00020796056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036862553,0.0016337585,0.0015141274,0.000788012,0.0006211104,0.0012719138,0.0020138437,0.0020035382,0.0018814604],"category_scores_gemma":[0.021382898,0.00086581806,0.0013700965,0.0008255214,0.0027011018,0.0049577644,0.004446807,0.005057344,0.0016088784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020220976,0.0005796294,0.012756657,0.0006168567,0.00039090207,0.001420711,0.0010678421,0.22262157,0.09705712,0.09702771,0.016549803,0.547889],"study_design_scores_gemma":[0.00008528068,0.0004027216,0.0018957651,0.00008681718,0.0000633479,0.00090056786,0.000109265966,0.82817847,0.09983262,0.060786232,0.0075925873,0.0000662973],"about_ca_topic_score_codex":0.0006555707,"about_ca_topic_score_gemma":0.0007061754,"teacher_disagreement_score":0.0036862553,"about_ca_system_score_codex":0.0009222588,"about_ca_system_score_gemma":0.0009342373,"threshold_uncertainty_score":0.01949501},"labels":[],"label_agreement":null},{"id":"W4396934766","doi":"10.1007/s11263-024-02096-6","title":"Benchmarking Object Detection Robustness against Real-World Corruptions","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Benchmarking; Robustness (evolution); Artificial intelligence; Computer science; Pattern recognition (psychology); Object detection; Computer vision; Data mining","score_opus":0.012028827665084337,"score_gpt":0.31425362835624837,"score_spread":0.30222480069116403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396934766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6963939,0.0015888275,0.28984424,0.0007123286,0.00053257943,0.00020068853,0.0009502086,0.004191675,0.005585522],"genre_scores_gemma":[0.96907884,0.00016124386,0.027795978,0.00011365215,0.00004039929,0.00003411591,0.0013243936,0.00022436785,0.00122696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960444,0.0010349979,0.00026814264,0.0010174771,0.0010918878,0.00054314866],"domain_scores_gemma":[0.98963976,0.0056146653,0.0008627349,0.0021267843,0.0014174879,0.0003386601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044814167,0.0011555535,0.001014978,0.0014734111,0.00041666365,0.0013274286,0.0013110297,0.0023420567,0.0019286319],"category_scores_gemma":[0.025567679,0.00043851061,0.0008806739,0.0008618154,0.0014076768,0.0012913616,0.0018251765,0.001159475,0.0010107091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019656888,0.00047216562,0.010203944,0.0003489126,0.000541825,0.0003348705,0.000102981605,0.79997754,0.055779904,0.003868418,0.0040386873,0.122365065],"study_design_scores_gemma":[0.00004304223,0.00038825665,0.005727469,0.0000246848,0.00008137059,0.00026501255,0.000031840304,0.9451695,0.04549175,0.0017286248,0.0010190972,0.000029403605],"about_ca_topic_score_codex":0.0024754829,"about_ca_topic_score_gemma":0.0011760069,"teacher_disagreement_score":0.0044814167,"about_ca_system_score_codex":0.0008314593,"about_ca_system_score_gemma":0.00073360786,"threshold_uncertainty_score":0.023700297},"labels":[],"label_agreement":null},{"id":"W4398134041","doi":"10.1145/3626205.3659150","title":"Building Detection-Resistant Reconnaissance Attacks Based on Adversarial Explainability","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Universitas Brawijaya","keywords":"Adversarial system; Computer science; Computer security; Artificial intelligence","score_opus":0.015776682873417824,"score_gpt":0.2898233357011232,"score_spread":0.27404665282770535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398134041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055940636,0.000076301374,0.9411116,0.00019736958,0.000026148678,0.00008700505,0.00004590777,0.00092210865,0.0015929091],"genre_scores_gemma":[0.9085964,0.00010271089,0.08962697,0.00011929429,0.00003096013,0.00009057982,0.00014902561,0.00010739144,0.0011766729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981963,0.0005507993,0.000073125484,0.0003816815,0.0005857402,0.00021235793],"domain_scores_gemma":[0.9934662,0.003984186,0.0007743532,0.0012441162,0.0003998656,0.00013136456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017563935,0.0011021092,0.00092779234,0.0011596909,0.00039549018,0.00076626067,0.0009578842,0.0009916887,0.0013178977],"category_scores_gemma":[0.008482263,0.00040382228,0.0015141987,0.00035684186,0.0017128941,0.0016293539,0.0023427221,0.0019066846,0.00025626383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017493995,0.00011782474,0.0028234902,0.00010382123,0.00020646765,0.00034953025,0.00018363526,0.86628836,0.027285192,0.053541187,0.0011653769,0.047760203],"study_design_scores_gemma":[0.0000053965855,0.000044553097,0.00018218401,0.0000045246265,0.000014181722,0.0000590254,0.000008328111,0.98842084,0.00351039,0.007431386,0.00031114006,0.000008135331],"about_ca_topic_score_codex":0.0008024899,"about_ca_topic_score_gemma":0.00062536873,"teacher_disagreement_score":0.0017563935,"about_ca_system_score_codex":0.00064531766,"about_ca_system_score_gemma":0.0005458525,"threshold_uncertainty_score":0.009288847},"labels":[],"label_agreement":null},{"id":"W4398239315","doi":"10.1145/3639478.3640028","title":"SAFE: Safety Analysis and Retraining of DNNs","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg","keywords":"Computer science; Retraining; Cluster analysis; Artificial intelligence; Deep neural networks; Root cause; Set (abstract data type); Machine learning; Feature (linguistics); Transfer of learning; Domain (mathematical analysis); Feature extraction; Artificial neural network; Root (linguistics); Pattern recognition (psychology); Reliability engineering","score_opus":0.007276204077550433,"score_gpt":0.26108915839883723,"score_spread":0.2538129543212868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398239315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042273927,0.00049472007,0.9293496,0.00045103816,0.00014146688,0.00015138094,0.0006045178,0.023197416,0.0033358815],"genre_scores_gemma":[0.721163,0.00030151074,0.26811957,0.00062057824,0.00007161392,0.00037531453,0.0017494494,0.002456014,0.005142935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990559,0.00017894522,0.000051131698,0.00023130128,0.00036034553,0.00012235901],"domain_scores_gemma":[0.99680907,0.0016577571,0.00036501084,0.00048831437,0.0005918507,0.00008791977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026433722,0.0021051443,0.0006946017,0.0016312654,0.0004823157,0.0008792086,0.0028083152,0.001515453,0.006018913],"category_scores_gemma":[0.01150637,0.0007367318,0.0010218628,0.0003743152,0.0013179973,0.0016130127,0.0018802152,0.001952625,0.0013568198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022427224,0.000060830545,0.0026306717,0.00017682898,0.0000800602,0.00042043586,0.000098410106,0.86928916,0.007304175,0.0059440653,0.0059139477,0.10785712],"study_design_scores_gemma":[0.000006528936,0.000031226304,0.00014628656,0.000015695041,0.000006594485,0.000040453833,0.000008987601,0.9905111,0.00450523,0.0041843075,0.00053805765,0.000005464263],"about_ca_topic_score_codex":0.005634968,"about_ca_topic_score_gemma":0.005790779,"teacher_disagreement_score":0.006018913,"about_ca_system_score_codex":0.0016836294,"about_ca_system_score_gemma":0.0016757302,"threshold_uncertainty_score":0.020135224},"labels":[],"label_agreement":null},{"id":"W4399019310","doi":"10.2139/ssrn.4840878","title":"Adversarial Defenses Via Vector Quantization","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adversarial system; Vector quantization; Quantization (signal processing); Vector (molecular biology); Computer science; Artificial intelligence; Algorithm; Biology; Genetics","score_opus":0.009311214859808124,"score_gpt":0.2604776954554638,"score_spread":0.25116648059565566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399019310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021388039,0.00085691083,0.9571287,0.002263506,0.0003289673,0.000078489844,0.00016403462,0.00085733994,0.016934028],"genre_scores_gemma":[0.92575455,0.00058613805,0.0571741,0.00095389615,0.00045074552,0.00014801459,0.0002241049,0.00021869066,0.014489744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974649,0.0008977692,0.000098440076,0.000397157,0.00085949246,0.00028230966],"domain_scores_gemma":[0.99253476,0.005041531,0.00048217265,0.0013005297,0.00043557535,0.00020548051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025659585,0.0010955292,0.0012904333,0.0010491847,0.0007088053,0.0017976057,0.0014472378,0.002497936,0.006716891],"category_scores_gemma":[0.014475274,0.00053027243,0.0006607808,0.000971459,0.0026517438,0.0035240967,0.004561526,0.0036860537,0.0010699837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022269461,0.00007422754,0.000376898,0.00010887905,0.00007251664,0.00009805991,0.00009011613,0.3913013,0.005537842,0.51640105,0.009582156,0.07613422],"study_design_scores_gemma":[0.000018433786,0.000048498383,0.00010357664,0.000017408058,0.000009585647,0.000059181835,0.000014647708,0.64725816,0.0011965063,0.3497436,0.0015148267,0.000015586218],"about_ca_topic_score_codex":0.00050803396,"about_ca_topic_score_gemma":0.0003746947,"teacher_disagreement_score":0.006716891,"about_ca_system_score_codex":0.0009766244,"about_ca_system_score_gemma":0.00070343865,"threshold_uncertainty_score":0.022470176},"labels":[],"label_agreement":null},{"id":"W4399062520","doi":"10.48550/arxiv.2405.15589","title":"Efficient Adversarial Training in LLMs with Continuous Attacks","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; European Commission; Canadian Institute for Advanced Research","keywords":"Adversarial system; Training (meteorology); Computer security; Computer science; Artificial intelligence; Geography; Meteorology","score_opus":0.046645961146312885,"score_gpt":0.20218578839141738,"score_spread":0.15553982724510448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399062520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029931093,0.0004974681,0.9610625,0.0008836323,0.00011237178,0.000099537654,0.00021407347,0.0036862758,0.0035129935],"genre_scores_gemma":[0.77767956,0.00031793228,0.21236393,0.0010323906,0.00012194182,0.00027654687,0.0009615472,0.0009608477,0.006285309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975624,0.0011738176,0.00011462998,0.00050405547,0.00041408496,0.0002310425],"domain_scores_gemma":[0.99452555,0.0034792917,0.00036202034,0.0011881447,0.00027066117,0.00017432572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029734208,0.0015779397,0.0012655647,0.0005084542,0.0006530713,0.0012252734,0.001700274,0.001923973,0.0034106825],"category_scores_gemma":[0.011772677,0.000732488,0.001101303,0.00040732432,0.002203914,0.00297253,0.004525273,0.004364468,0.0014678913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017482716,0.00005921739,0.0011737497,0.00009449728,0.000055699777,0.0001425479,0.000105728934,0.9168483,0.0031866308,0.021006918,0.004207217,0.052944604],"study_design_scores_gemma":[0.000009149666,0.000027138016,0.00008240433,0.000009582303,0.0000038161306,0.000027931594,0.000011746654,0.98421395,0.0011229992,0.013742265,0.0007428877,0.000006121408],"about_ca_topic_score_codex":0.0028163171,"about_ca_topic_score_gemma":0.003467582,"teacher_disagreement_score":0.0034106825,"about_ca_system_score_codex":0.0012718108,"about_ca_system_score_gemma":0.0013153107,"threshold_uncertainty_score":0.015725136},"labels":[],"label_agreement":null},{"id":"W4399174291","doi":"10.1145/3654963","title":"OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Management of Data","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Universitas Brawijaya","keywords":"Computer science; Scalability; Mathematical optimization; Independence (probability theory); Mathematics","score_opus":0.12434380659866183,"score_gpt":0.3641923593716973,"score_spread":0.23984855277303546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399174291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017414105,0.0000663337,0.997221,0.00013580089,0.000020327156,0.00003859871,0.00005106481,0.000540071,0.00018541292],"genre_scores_gemma":[0.15939814,0.00029452328,0.83517224,0.00043574275,0.00012316962,0.00042025957,0.0009657904,0.000968841,0.002221215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99364626,0.0023119836,0.0004228577,0.0012506263,0.001959227,0.0004090894],"domain_scores_gemma":[0.9832876,0.008246134,0.0016192176,0.0044114874,0.002053997,0.00038160058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009386571,0.0016054147,0.0020952506,0.0019304189,0.0017679493,0.002945765,0.0038068949,0.0026074317,0.0028277982],"category_scores_gemma":[0.032097597,0.0010656045,0.0026681519,0.0025633627,0.0036322847,0.0059973216,0.007385392,0.005768319,0.0011272002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036128197,0.00016224975,0.0026036573,0.00047874133,0.00022787377,0.00032823943,0.00059674546,0.6314083,0.012559094,0.09293812,0.009314306,0.2490214],"study_design_scores_gemma":[0.000023740322,0.000082329934,0.00029405562,0.00003725482,0.000020751471,0.00012427621,0.00011455949,0.9252272,0.00764299,0.06267842,0.0037222607,0.000031997806],"about_ca_topic_score_codex":0.0051218187,"about_ca_topic_score_gemma":0.004157647,"teacher_disagreement_score":0.009386571,"about_ca_system_score_codex":0.001953521,"about_ca_system_score_gemma":0.00621663,"threshold_uncertainty_score":0.04964149},"labels":[],"label_agreement":null},{"id":"W4399333297","doi":"10.1016/j.egyai.2024.100381","title":"Evasive attacks against autoencoder-based cyberattack detection systems in power systems","year":2024,"lang":"en","type":"article","venue":"Energy and AI","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Adversarial system; Computer science; Robustness (evolution); Computer security; Smart grid; Threat model; Adversarial machine learning; Deep learning; Situation awareness; Artificial intelligence; Engineering","score_opus":0.007464424445712928,"score_gpt":0.24604306009227248,"score_spread":0.23857863564655954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399333297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15948012,0.00020631822,0.8367425,0.000265887,0.000042316133,0.000047285095,0.000018554285,0.0006078505,0.0025891622],"genre_scores_gemma":[0.9687561,0.00006443441,0.030458339,0.000053992502,0.000009975681,0.000017753817,0.000013535277,0.000017838134,0.00060802454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999126,0.00028161128,0.00004475101,0.0001497233,0.00029469773,0.000103196275],"domain_scores_gemma":[0.99589914,0.0029166394,0.00035543396,0.00040385945,0.00036019005,0.00006473038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013409129,0.00058559654,0.0005436598,0.00038833552,0.00022799417,0.00053002534,0.00049362733,0.00067007774,0.00067067833],"category_scores_gemma":[0.0065187896,0.000240249,0.00035990402,0.00015995919,0.0011621702,0.00084405905,0.0008888668,0.00093515543,0.00013845052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018006457,0.000050616334,0.0012675016,0.000054733067,0.000060677077,0.00013751732,0.00010361411,0.91731316,0.01888435,0.009296085,0.0003492584,0.052302398],"study_design_scores_gemma":[0.000002723713,0.00003909765,0.00022362791,0.00000429987,0.0000037732802,0.000035927187,0.000005923016,0.9929414,0.0053214044,0.0013010824,0.00011650571,0.000004221427],"about_ca_topic_score_codex":0.001213169,"about_ca_topic_score_gemma":0.0010924262,"teacher_disagreement_score":0.0013409129,"about_ca_system_score_codex":0.00052195287,"about_ca_system_score_gemma":0.0003963586,"threshold_uncertainty_score":0.007091522},"labels":[],"label_agreement":null},{"id":"W4399358245","doi":"10.1016/j.cose.2024.103936","title":"FedIMP: Parameter Importance-based Model Poisoning attack against Federated learning system","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Boosting (machine learning); Federated learning; Vulnerability (computing); Convergence (economics); Similarity (geometry); Computer security; Machine learning; Artificial intelligence","score_opus":0.01843278694387394,"score_gpt":0.26595516966738575,"score_spread":0.24752238272351182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399358245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15014814,0.00035778453,0.834819,0.0009019028,0.00020250813,0.00013540656,0.00021753732,0.009234537,0.003983192],"genre_scores_gemma":[0.9609199,0.000052511037,0.036993645,0.00017492399,0.000017154138,0.000031264815,0.00011481766,0.00006124671,0.0016344681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992324,0.00018998551,0.000047643593,0.00013044532,0.00027348832,0.00012605774],"domain_scores_gemma":[0.99892104,0.00034108723,0.000104289604,0.0004322045,0.00015568401,0.00004578381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011895875,0.00062400114,0.00076170964,0.0005137341,0.00048680633,0.0006505229,0.0010444814,0.0014166369,0.0019902124],"category_scores_gemma":[0.0037008265,0.00023425528,0.00060309895,0.00030756122,0.00064970716,0.0013880427,0.0021525852,0.0015019958,0.00043119726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023585935,0.0003679124,0.006954913,0.00022397505,0.0004846663,0.001345078,0.00024654297,0.5644408,0.064280346,0.032450713,0.013680954,0.3131656],"study_design_scores_gemma":[0.000027686925,0.00014404579,0.00037396373,0.000009347506,0.000022454145,0.00023496457,0.000011182358,0.97360736,0.017613968,0.007234006,0.000709077,0.00001189962],"about_ca_topic_score_codex":0.0007245333,"about_ca_topic_score_gemma":0.00049463107,"teacher_disagreement_score":0.0019902124,"about_ca_system_score_codex":0.00050580816,"about_ca_system_score_gemma":0.00057743036,"threshold_uncertainty_score":0.0066578984},"labels":[],"label_agreement":null},{"id":"W4399529617","doi":"10.1109/csnt60213.2024.10545943","title":"The Impact of Adversarial Attacks on Medical Imaging AI Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Adversarial system; Computer science; Medical imaging; Artificial intelligence; Computer security","score_opus":0.008611239916290104,"score_gpt":0.32832839723896107,"score_spread":0.319717157322671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399529617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28205803,0.001764045,0.69100344,0.006040797,0.0003737205,0.00015350446,0.00021928773,0.0013486072,0.017038615],"genre_scores_gemma":[0.97995776,0.00027042947,0.017619407,0.00041445586,0.00009748709,0.000030059096,0.00007552259,0.00007418386,0.0014605476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99545777,0.002194709,0.00013898873,0.0005430615,0.0011658106,0.00049975835],"domain_scores_gemma":[0.97605735,0.017025126,0.0015844973,0.0034541802,0.0013504981,0.00052827405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056352597,0.00084795384,0.0006277544,0.00067938823,0.0009245595,0.0017147444,0.0010515362,0.0013222975,0.0018445494],"category_scores_gemma":[0.02684275,0.00037185042,0.0005905919,0.0004002902,0.0024347987,0.0026757787,0.0033836802,0.0024818198,0.00052421045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047860254,0.0000877612,0.007425189,0.00009556088,0.00018271303,0.0002973841,0.0003618229,0.8676905,0.008814894,0.038565267,0.0036717094,0.072328515],"study_design_scores_gemma":[0.000009630513,0.000092236405,0.0012587827,0.000021356042,0.000020903428,0.00014913308,0.00006193995,0.9737316,0.0039319275,0.019213213,0.0014875744,0.000021777334],"about_ca_topic_score_codex":0.001980121,"about_ca_topic_score_gemma":0.0010944152,"teacher_disagreement_score":0.0056352597,"about_ca_system_score_codex":0.0014304495,"about_ca_system_score_gemma":0.00077193364,"threshold_uncertainty_score":0.029802442},"labels":[],"label_agreement":null},{"id":"W4399531570","doi":"10.1109/csnt60213.2024.10546022","title":"Designing Explainable Defenses Against Sophisticated Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Adversarial system; Computer science; Interpretability; Robustness (evolution); Reinforcement learning; Artificial intelligence; Adaptability; Transparency (behavior); Flexibility (engineering); Machine learning; Computer security","score_opus":0.0162113499643965,"score_gpt":0.26580373613634056,"score_spread":0.24959238617194407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399531570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050712645,0.0003301981,0.9420057,0.00060680765,0.00004245611,0.0000999441,0.00007147793,0.0019228744,0.0042079063],"genre_scores_gemma":[0.84931654,0.00032549645,0.14684974,0.00027560187,0.00003824509,0.00012384214,0.00016986788,0.00019930434,0.002701231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897087,0.00036487167,0.000043211578,0.00019997188,0.00028341534,0.00013762097],"domain_scores_gemma":[0.99688953,0.001605826,0.000437884,0.00073048804,0.00022136496,0.00011485117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019711938,0.0010156909,0.00050526654,0.0005980651,0.00040360886,0.0012596685,0.0014943322,0.0012358715,0.0028998912],"category_scores_gemma":[0.0065923193,0.00038969654,0.0007652154,0.00019946003,0.0016764532,0.0024366525,0.0026351092,0.002048237,0.0005774788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030907334,0.00013452006,0.005628593,0.0003574385,0.0002626379,0.00028209173,0.00054199895,0.7079603,0.031950645,0.09128448,0.0028992295,0.15838896],"study_design_scores_gemma":[0.000017494396,0.00012462487,0.00057766354,0.000032537453,0.00004671891,0.00012792373,0.00006453429,0.94800586,0.008032303,0.039350156,0.0035978225,0.00002241149],"about_ca_topic_score_codex":0.0008341471,"about_ca_topic_score_gemma":0.001303397,"teacher_disagreement_score":0.0028998912,"about_ca_system_score_codex":0.0007487215,"about_ca_system_score_gemma":0.0010149325,"threshold_uncertainty_score":0.010424852},"labels":[],"label_agreement":null},{"id":"W4399601886","doi":"10.1145/3672457","title":"<i>GIST</i> : Generated Inputs Sets Transferability in Deep Learning","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Scratch; Test set; GiST; Testability; Test (biology); Set (abstract data type); Artificial intelligence; Property (philosophy); Artificial neural network; Machine learning; Transfer of learning; Data mining; Reliability engineering; Programming language","score_opus":0.04779516566455851,"score_gpt":0.31123246473109833,"score_spread":0.26343729906653984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399601886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018536162,0.00028916568,0.97440594,0.00048418934,0.00005188402,0.00013780444,0.00012677826,0.0037710594,0.002197132],"genre_scores_gemma":[0.6581245,0.00034580566,0.33666217,0.000649351,0.00009589452,0.00043511417,0.00068726,0.00084698235,0.0021529815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949999,0.0019446595,0.00033652526,0.00088217645,0.00153961,0.00029724246],"domain_scores_gemma":[0.98166245,0.010469561,0.001319248,0.005122036,0.00115137,0.00027532608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005787595,0.0013952391,0.00075191766,0.0012816199,0.00041899396,0.0015507687,0.002919201,0.0016250414,0.003737976],"category_scores_gemma":[0.031027248,0.0005961552,0.0011464789,0.00081888225,0.003451832,0.0036680591,0.0035957561,0.0034771531,0.0007125886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005541619,0.00026002838,0.0046275104,0.00044643946,0.00017306747,0.0006205254,0.00041416282,0.478938,0.027521197,0.078738965,0.0071542184,0.40055168],"study_design_scores_gemma":[0.000031772393,0.00020059984,0.00045554253,0.00006191641,0.000024456598,0.00016576347,0.0000235881,0.9196472,0.02626254,0.050759688,0.0023449413,0.000021960397],"about_ca_topic_score_codex":0.0015667984,"about_ca_topic_score_gemma":0.0012417329,"teacher_disagreement_score":0.005787595,"about_ca_system_score_codex":0.001588322,"about_ca_system_score_gemma":0.0011343743,"threshold_uncertainty_score":0.030608118},"labels":[],"label_agreement":null},{"id":"W4399838215","doi":"10.48550/arxiv.2406.12843","title":"Can Go AIs be adversarially robust?","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Valorisation des Données","keywords":"Econometrics; Mathematics","score_opus":0.06371097362501402,"score_gpt":0.20008069738052525,"score_spread":0.13636972375551124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399838215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14340045,0.0009899967,0.8155123,0.004345331,0.00022002016,0.00021709356,0.0003306089,0.0032760452,0.031708196],"genre_scores_gemma":[0.95367706,0.00034207897,0.040750016,0.0008645577,0.000061899125,0.00013737885,0.00021966983,0.00023432642,0.003713092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841917,0.00046096471,0.000060424718,0.00038117153,0.00040944974,0.0002688463],"domain_scores_gemma":[0.9907251,0.0048519005,0.0010515114,0.0025879259,0.00045972783,0.0003238298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028047094,0.0008224557,0.0007472973,0.000681091,0.0009113959,0.0015080199,0.0016438358,0.002641977,0.004382586],"category_scores_gemma":[0.01982919,0.00047025812,0.0007720061,0.00031385993,0.0050659818,0.004151346,0.0032927697,0.0027587556,0.0012976354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020417241,0.0000905006,0.0047371285,0.00031404858,0.00020474897,0.00026330672,0.00045141985,0.6848932,0.022700159,0.21546017,0.0053117466,0.06536948],"study_design_scores_gemma":[0.000028163242,0.00023568676,0.0014278091,0.00009382278,0.00004207639,0.0003424826,0.00019244356,0.65928155,0.009828316,0.31909567,0.009381205,0.000050799725],"about_ca_topic_score_codex":0.001149611,"about_ca_topic_score_gemma":0.0010223999,"teacher_disagreement_score":0.004382586,"about_ca_system_score_codex":0.00071640575,"about_ca_system_score_gemma":0.0008267565,"threshold_uncertainty_score":0.014832914},"labels":[],"label_agreement":null},{"id":"W4399882356","doi":"10.1109/icnc59896.2024.10556266","title":"Could Min-Max Optimization be a General Defense Against Adversarial Attacks?","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Science Foundation","keywords":"Adversarial system; Computer science; Computer security; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.015804376798738775,"score_gpt":0.2771514597200692,"score_spread":0.26134708292133046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399882356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015997866,0.00094965816,0.9757186,0.0018866775,0.00012790496,0.0000626795,0.00010453844,0.00034829235,0.0048038024],"genre_scores_gemma":[0.83557475,0.0016140527,0.15294394,0.0016825825,0.00030112072,0.00028090808,0.00029685372,0.00032079974,0.0069850767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801135,0.0008543238,0.00009842941,0.00050934474,0.00028953806,0.00023702436],"domain_scores_gemma":[0.9960743,0.0025399793,0.00046494088,0.0005685137,0.00023852615,0.00011368951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051990044,0.0019290715,0.0016261765,0.0005678261,0.0005590895,0.0018436823,0.0016217205,0.002214151,0.0028151579],"category_scores_gemma":[0.010316009,0.0007278371,0.0011912193,0.00055945624,0.0031206226,0.0038916753,0.0022506253,0.0041600405,0.0007051683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016419156,0.00007844231,0.0008872667,0.00020359409,0.0001766176,0.0001125918,0.00008352633,0.82421863,0.0032776522,0.13442089,0.0038035207,0.032572992],"study_design_scores_gemma":[0.000011486918,0.00011564791,0.00017996912,0.000034773708,0.000018800522,0.00007079521,0.000020973477,0.9016003,0.0017658351,0.09434404,0.0018202413,0.000017158769],"about_ca_topic_score_codex":0.0008688825,"about_ca_topic_score_gemma":0.00083458476,"teacher_disagreement_score":0.0051990044,"about_ca_system_score_codex":0.0012399015,"about_ca_system_score_gemma":0.0011942288,"threshold_uncertainty_score":0.027495265},"labels":[],"label_agreement":null},{"id":"W4400104927","doi":"10.52202/079017-0733","title":"Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université Laval; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Margin (machine learning); Computer science; Robustness (evolution); Consistency (knowledge bases); Logit; Artificial intelligence; Machine learning; Adversarial system","score_opus":0.0437626189196848,"score_gpt":0.28564797610252124,"score_spread":0.24188535718283644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400104927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11697054,0.00046127106,0.8782433,0.0008421571,0.000052485873,0.00007145706,0.00013155042,0.0015626984,0.0016643691],"genre_scores_gemma":[0.9505824,0.00014385553,0.0475622,0.00034067247,0.000049770548,0.00005593468,0.00019528467,0.00016518556,0.00090472796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743646,0.00084573973,0.0001552282,0.0005741013,0.00072136585,0.0002672013],"domain_scores_gemma":[0.9857949,0.0074509047,0.002497476,0.0028413518,0.0009981587,0.00041715606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052692755,0.0016611218,0.0014206229,0.0011535228,0.00059195596,0.0019547448,0.0021591338,0.00177527,0.001395864],"category_scores_gemma":[0.030221576,0.00078915496,0.0008360147,0.0006052773,0.0026682953,0.0049458803,0.004304991,0.004415447,0.0005364494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036206126,0.000121184574,0.0073965527,0.00009333631,0.00013274117,0.00023883478,0.00018721577,0.8792823,0.0067251096,0.014681757,0.0020902795,0.08868872],"study_design_scores_gemma":[0.000005949975,0.000058197013,0.00043520573,0.00001686652,0.000012215731,0.000050488477,0.000017666067,0.97845185,0.0028999194,0.01778466,0.0002526305,0.0000144204405],"about_ca_topic_score_codex":0.0013727215,"about_ca_topic_score_gemma":0.0014264847,"teacher_disagreement_score":0.0052692755,"about_ca_system_score_codex":0.0011712712,"about_ca_system_score_gemma":0.0010743574,"threshold_uncertainty_score":0.0278669},"labels":[],"label_agreement":null},{"id":"W4400363320","doi":"10.2139/ssrn.4883392","title":"Artificial Intelligence in the Context of Crime and Criminal Justice","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Criminology; Criminal justice; Context (archaeology); Economic Justice; Political science; Psychology; Law; Sociology; History","score_opus":0.02194024162269006,"score_gpt":0.30326539894601906,"score_spread":0.281325157323329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400363320","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20530814,0.07500428,0.2560835,0.21338335,0.0039474154,0.00009075847,0.00046968114,0.0001963766,0.24551648],"genre_scores_gemma":[0.9852899,0.0058154413,0.0040317997,0.0011494177,0.0011524603,0.000019329034,0.00003921017,0.000018275527,0.0024842026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974886,0.0016930527,0.00005975472,0.00023211063,0.0003700733,0.00015631098],"domain_scores_gemma":[0.9904179,0.0076197563,0.0007925733,0.00047559978,0.00042694598,0.00026724962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027382893,0.00038396436,0.00080988713,0.001276975,0.0011932339,0.0038472738,0.00088124967,0.0032728922,0.003338998],"category_scores_gemma":[0.015438347,0.00025976606,0.00034056927,0.0011007441,0.007548484,0.0037931507,0.002107299,0.004182697,0.00019885162],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000406002,0.000038202274,0.0020110607,0.00010920764,0.00007629289,0.00017223558,0.00022534328,0.042808995,0.00020952584,0.9350991,0.004075483,0.015133881],"study_design_scores_gemma":[0.000004005131,0.000018483297,0.0013723482,0.000050617073,0.000010154239,0.00006906798,0.00018626318,0.035193104,0.00008943152,0.95785403,0.0051375283,0.000014924184],"about_ca_topic_score_codex":0.002814834,"about_ca_topic_score_gemma":0.003412192,"teacher_disagreement_score":0.0038472738,"about_ca_system_score_codex":0.0019344691,"about_ca_system_score_gemma":0.0012314955,"threshold_uncertainty_score":0.014481604},"labels":[],"label_agreement":null},{"id":"W4400375395","doi":"10.48550/arxiv.2407.02551","title":"Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research; Alfred P. Sloan Foundation","keywords":"Leakage (economics); Computer security; Sense (electronics); Computer science; Business; Engineering; Electrical engineering; Economics","score_opus":0.029377219003230978,"score_gpt":0.20625632596426313,"score_spread":0.17687910696103215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400375395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15123507,0.00053730217,0.8272126,0.0053811776,0.00013074372,0.00019723421,0.00039621463,0.0033105966,0.011599127],"genre_scores_gemma":[0.9214912,0.00013670414,0.07479664,0.0010133275,0.00006215789,0.00011252024,0.00026767974,0.00027627376,0.0018435182],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.97181106,0.016335329,0.0011674794,0.0024322255,0.006869672,0.0013840812],"domain_scores_gemma":[0.8944706,0.06875,0.0049845874,0.02593277,0.004157417,0.0017046733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021657677,0.00087160734,0.001041508,0.0010722873,0.0013052411,0.004482776,0.0021994987,0.0031470621,0.004007841],"category_scores_gemma":[0.09933148,0.0005544907,0.0011795779,0.0007188445,0.0063463687,0.009724374,0.0067190644,0.0061222557,0.00096908095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002772614,0.00073345075,0.016362887,0.00070993905,0.00038787018,0.000753111,0.004454303,0.24362864,0.025714664,0.4635539,0.01194644,0.22898208],"study_design_scores_gemma":[0.00006733605,0.00046032976,0.0015920447,0.00014132418,0.00009393234,0.0003471987,0.00060252525,0.5221058,0.026808063,0.44048455,0.007211882,0.00008491682],"about_ca_topic_score_codex":0.0011778695,"about_ca_topic_score_gemma":0.0010452056,"teacher_disagreement_score":0.021657677,"about_ca_system_score_codex":0.002089313,"about_ca_system_score_gemma":0.0020666234,"threshold_uncertainty_score":0.11453813},"labels":[],"label_agreement":null},{"id":"W4400485071","doi":"10.1145/3664646.3664777","title":"Effectiveness of ChatGPT for Static Analysis: How Far Are We?","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science","score_opus":0.01700652742124443,"score_gpt":0.29738468871786167,"score_spread":0.28037816129661725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400485071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41460022,0.02102913,0.4695674,0.007720416,0.0017484559,0.0006482319,0.0018746123,0.06641728,0.01639427],"genre_scores_gemma":[0.90777636,0.0013790994,0.08295448,0.001497583,0.00044384462,0.00019295707,0.0012908666,0.0013764169,0.0030885148],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9896389,0.0051236055,0.00038283764,0.0020468943,0.0022918717,0.00051583716],"domain_scores_gemma":[0.951912,0.031523127,0.0022297513,0.010154544,0.0030968355,0.0010838539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01144251,0.002452062,0.0019505185,0.0021806527,0.00093349966,0.0025452527,0.002825516,0.0025418883,0.002525463],"category_scores_gemma":[0.05419636,0.00066780945,0.0009680668,0.0008445257,0.0019965903,0.007452202,0.0031035435,0.0030005563,0.0023388145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037081924,0.0008448785,0.04103728,0.0017044862,0.00076666294,0.0007463041,0.0010569104,0.11577929,0.03199326,0.008939608,0.03542013,0.758003],"study_design_scores_gemma":[0.00023185466,0.0017588794,0.010515894,0.00033681132,0.0003799196,0.0011141975,0.0004915163,0.93068075,0.024871603,0.013517395,0.015907612,0.00019361859],"about_ca_topic_score_codex":0.003190731,"about_ca_topic_score_gemma":0.0033490863,"teacher_disagreement_score":0.01144251,"about_ca_system_score_codex":0.0011655854,"about_ca_system_score_gemma":0.0017124842,"threshold_uncertainty_score":0.06051451},"labels":[],"label_agreement":null},{"id":"W4400649290","doi":"10.1109/iv55156.2024.10588456","title":"SITAR: Evaluating the Adversarial Robustness of Traffic Light Recognition in Level-4 Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Robustness (evolution); Computer science; Adversarial system; Artificial intelligence; Traffic signal; Computer vision; Computer security; Real-time computing","score_opus":0.05140210079399462,"score_gpt":0.3145013423670686,"score_spread":0.263099241573074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400649290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75678635,0.0009786244,0.22703905,0.000667068,0.00030756363,0.00024783463,0.0006839084,0.0039347056,0.009354965],"genre_scores_gemma":[0.981729,0.000084224426,0.015918247,0.0001390553,0.000018640396,0.0000449026,0.00056856783,0.00007703782,0.0014204052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989041,0.00025286552,0.000050957166,0.00024616858,0.00035285202,0.00019311016],"domain_scores_gemma":[0.9975943,0.0013266606,0.00024789054,0.00032299617,0.0003247013,0.00018358558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018865939,0.0011183743,0.00055782346,0.0006359178,0.00030876932,0.0005375325,0.0011383735,0.0010791427,0.0016576989],"category_scores_gemma":[0.0058646323,0.00021341087,0.00061931874,0.0002481546,0.0010922618,0.0010447955,0.0012531917,0.0012991064,0.00049219944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005953169,0.00022432843,0.0053395447,0.00012538822,0.00011616803,0.00011681907,0.000031643962,0.93844575,0.006880517,0.0023288524,0.0023577472,0.04343794],"study_design_scores_gemma":[0.000011795631,0.00018765844,0.00083026954,0.000007434872,0.000009589305,0.000033296703,0.000010751297,0.9937605,0.00422218,0.00064879726,0.00026982027,0.000007904665],"about_ca_topic_score_codex":0.0045912107,"about_ca_topic_score_gemma":0.0036752967,"teacher_disagreement_score":0.0045912107,"about_ca_system_score_codex":0.0009590203,"about_ca_system_score_gemma":0.0007670976,"threshold_uncertainty_score":0.009977341},"labels":[],"label_agreement":null},{"id":"W4400810146","doi":"10.1109/tii.2024.3410319","title":"A Stability-Enhanced Dynamic Backdoor Defense in Federated Learning for IIoT","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Backdoor; Stability (learning theory); Computer science; Computer security; Machine learning","score_opus":0.038239671183889754,"score_gpt":0.28905178385348973,"score_spread":0.25081211266959996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400810146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07644843,0.00020720843,0.92067885,0.0002598079,0.000051519455,0.000056103385,0.000052510244,0.0014188654,0.0008267279],"genre_scores_gemma":[0.9581578,0.000051221712,0.04091603,0.00013999279,0.000018545667,0.00003062742,0.000071454146,0.000032916883,0.0005815461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815315,0.0005167996,0.00009460566,0.00047353157,0.0004255556,0.00033643402],"domain_scores_gemma":[0.99607074,0.0013188015,0.00047300736,0.0014104077,0.00048761282,0.00023936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029397951,0.00081580627,0.000987712,0.000590608,0.00073448045,0.001059749,0.0014482271,0.0012472663,0.00065421977],"category_scores_gemma":[0.0076592322,0.00028562304,0.00070223433,0.0004967587,0.0012097826,0.002606261,0.003133991,0.0016544005,0.00026228483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005833856,0.0003347687,0.0054146308,0.00009363063,0.00013092326,0.0003087306,0.00020702143,0.7657443,0.01979349,0.017864052,0.002617886,0.18690728],"study_design_scores_gemma":[0.000010218515,0.00009803084,0.00031725332,0.0000057342713,0.000009318316,0.00007593673,0.000022816503,0.989876,0.0035641138,0.005711304,0.00029871342,0.000010531943],"about_ca_topic_score_codex":0.0013090869,"about_ca_topic_score_gemma":0.0012161076,"teacher_disagreement_score":0.0029397951,"about_ca_system_score_codex":0.0008204174,"about_ca_system_score_gemma":0.0012989239,"threshold_uncertainty_score":0.015547276},"labels":[],"label_agreement":null},{"id":"W4400934506","doi":"10.1145/3680463","title":"An Empirical Study of Testing Machine Learning in the Wild","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; York University; Polytechnique Montréal","funders":"","keywords":"Computer science; White-box testing; Regression testing; Oracle; Test strategy; Workflow; Quality assurance; Machine learning; Software reliability testing; Manual testing; Software quality; Empirical research; Non-regression testing; Software performance testing; Software engineering; Artificial intelligence; Software testing; Software; Software system; Software construction; Software development; Database; Programming language","score_opus":0.1074213531816717,"score_gpt":0.370455849240389,"score_spread":0.2630344960587173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400934506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98773164,0.000347329,0.008864909,0.00063303404,0.000028208982,0.00019473981,0.00045444138,0.000112810645,0.0016329238],"genre_scores_gemma":[0.99045753,0.00009381988,0.007901549,0.0002529354,0.00002785567,0.00023731326,0.00066429446,0.0000744976,0.00029013655],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9191273,0.054223515,0.005041789,0.0072514187,0.012611536,0.0017444863],"domain_scores_gemma":[0.34449747,0.55799323,0.04166053,0.029754164,0.022524476,0.0035701802],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04144661,0.00062401965,0.00063764903,0.0037640906,0.0013124808,0.0029287892,0.0030523066,0.0020282539,0.0017497237],"category_scores_gemma":[0.2779034,0.0005819688,0.0006022694,0.0032874031,0.0062811906,0.0060033565,0.0024106908,0.0026931122,0.00050650025],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093102374,0.002655725,0.8257572,0.0009823916,0.00035110308,0.0012856523,0.025232852,0.006494014,0.0030069454,0.007651851,0.0062814564,0.11936978],"study_design_scores_gemma":[0.00042613223,0.0051100934,0.72774965,0.0019010243,0.00034291108,0.0037008023,0.046354137,0.13708596,0.014851499,0.025367646,0.03673925,0.0003708078],"about_ca_topic_score_codex":0.0027802798,"about_ca_topic_score_gemma":0.0035282096,"teacher_disagreement_score":0.9585534,"about_ca_system_score_codex":0.002113375,"about_ca_system_score_gemma":0.0013470409,"threshold_uncertainty_score":0.21919328},"labels":[],"label_agreement":null},{"id":"W4401032320","doi":"10.1007/978-3-031-66064-1_3","title":"Pierce: A Testing Tool for Neural Network Verification Solvers","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Artificial neural network; Programming language; Software engineering; Artificial intelligence","score_opus":0.025490686614710076,"score_gpt":0.2695432457109795,"score_spread":0.2440525590962694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401032320","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028352276,0.00017168609,0.895996,0.00031430056,0.00013650596,0.00014285116,0.0012289434,0.09087916,0.008295359],"genre_scores_gemma":[0.17746659,0.00052254694,0.7508534,0.0010632022,0.00018412237,0.0010233861,0.0076083923,0.04254099,0.018737296],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960443,0.0011832407,0.00036845377,0.0005909805,0.0015151842,0.00029785428],"domain_scores_gemma":[0.98725617,0.008760191,0.0004589291,0.0022940526,0.001070015,0.00016068284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004050108,0.0027362849,0.0011168852,0.0023035684,0.00068181084,0.002186758,0.005116879,0.0019664248,0.067558825],"category_scores_gemma":[0.022551551,0.0018177701,0.0021449681,0.0011769,0.0016645066,0.005795258,0.0045973863,0.0037001919,0.01487504],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094302645,0.00038072636,0.002489688,0.001610617,0.0003368672,0.0008972936,0.0002802115,0.08901452,0.018048663,0.1422149,0.20502292,0.5387604],"study_design_scores_gemma":[0.00035865992,0.00023748039,0.00047759886,0.00027291354,0.00009899556,0.0007485077,0.000087093744,0.69327104,0.059822097,0.15775663,0.086763754,0.00010524184],"about_ca_topic_score_codex":0.0014370797,"about_ca_topic_score_gemma":0.0021684046,"teacher_disagreement_score":0.067558825,"about_ca_system_score_codex":0.00083137886,"about_ca_system_score_gemma":0.0016414204,"threshold_uncertainty_score":0.2260068},"labels":[],"label_agreement":null},{"id":"W4401337508","doi":"10.1109/chase60773.2024.00019","title":"Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Health care; Computer science; Healthcare system; Computer security; Risk analysis (engineering); Business","score_opus":0.04762902723506356,"score_gpt":0.378228063145709,"score_spread":0.33059903591064543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401337508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70486915,0.0013578053,0.2847849,0.0013061618,0.00007525615,0.00044263067,0.0004210746,0.00044680267,0.006296213],"genre_scores_gemma":[0.98261005,0.000211382,0.0165884,0.00006146008,0.000015634125,0.000054391134,0.00013157679,0.000021781894,0.0003052488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994177,0.0027095403,0.000323502,0.0006322727,0.0017612661,0.00039643364],"domain_scores_gemma":[0.9325334,0.054871395,0.0053427536,0.0036767141,0.002883193,0.0006925311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006928762,0.0010448691,0.0006121907,0.00221006,0.0005941508,0.0017818987,0.0008588196,0.0013101383,0.0010866915],"category_scores_gemma":[0.0464825,0.00038741942,0.0007320546,0.00065964577,0.0016492546,0.0033673916,0.0022277462,0.0014153961,0.00018700353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071015017,0.00024358556,0.08205399,0.0006254431,0.0005447196,0.0010808388,0.0007167572,0.79738003,0.008817004,0.036665127,0.0015251162,0.06963715],"study_design_scores_gemma":[0.000017742197,0.0002841628,0.009486522,0.00012849855,0.00011530406,0.00049687776,0.00031343848,0.95443964,0.0061338553,0.027483886,0.0010601182,0.00003984885],"about_ca_topic_score_codex":0.0013460937,"about_ca_topic_score_gemma":0.0009875656,"teacher_disagreement_score":0.006928762,"about_ca_system_score_codex":0.0012332588,"about_ca_system_score_gemma":0.0010869525,"threshold_uncertainty_score":0.036643207},"labels":[],"label_agreement":null},{"id":"W4401358215","doi":"10.36227/techrxiv.172296728.87985494/v1","title":"Privacy of Deep Learning Systems: A Penetration Testing Framework","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Penetration (warfare); Computer science; Computer security; Engineering; Operations research","score_opus":0.028764999826434847,"score_gpt":0.294085740466823,"score_spread":0.2653207406403882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401358215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02655334,0.0009827031,0.9595522,0.003517888,0.0000635995,0.0002878976,0.00013242233,0.0006776717,0.008232209],"genre_scores_gemma":[0.87143856,0.0011829615,0.1228696,0.0011248395,0.000125896,0.0004252383,0.00014651049,0.0001763207,0.0025100608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97236955,0.01609284,0.0011548729,0.0021021364,0.0066734883,0.0016070748],"domain_scores_gemma":[0.9233115,0.050810866,0.004214467,0.01635818,0.004510911,0.0007940468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020119732,0.0013074746,0.0011773569,0.0019928985,0.0012937654,0.0052414713,0.003548897,0.003842469,0.002961433],"category_scores_gemma":[0.072680816,0.0010513569,0.0020240364,0.0012822291,0.00758876,0.014434009,0.007323849,0.006535818,0.0005761818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036784503,0.00024746387,0.006408592,0.0004640313,0.00027934738,0.0011158615,0.0014772939,0.22462095,0.0051862816,0.61852324,0.003380481,0.13792868],"study_design_scores_gemma":[0.000033703807,0.00019485028,0.0004977455,0.00024883114,0.000069734095,0.00072831236,0.00023767047,0.6319876,0.009499523,0.35065076,0.0058019347,0.00004942082],"about_ca_topic_score_codex":0.0011960953,"about_ca_topic_score_gemma":0.0005608709,"teacher_disagreement_score":0.020119732,"about_ca_system_score_codex":0.0026478942,"about_ca_system_score_gemma":0.0025707055,"threshold_uncertainty_score":0.10640454},"labels":[],"label_agreement":null},{"id":"W4401415801","doi":"10.1109/access.2024.3440647","title":"A Systematic Literature Review on AI Safety: Identifying Trends, Challenges, and Future Directions","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Mitacs","keywords":"Interpretability; Computer science; Software deployment; Notice; Autonomy; Risk analysis (engineering); Robustness (evolution); Adversarial system; Trustworthiness; Artificial intelligence; Data science; Computer security; Software engineering; Medicine","score_opus":0.02978114980185721,"score_gpt":0.33989891432288905,"score_spread":0.31011776452103185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401415801","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005301076,0.9958406,0.0005020787,0.0020988027,0.00019616191,0.00005375159,0.00022134683,0.000014364978,0.0005426309],"genre_scores_gemma":[0.004063104,0.9931726,0.0009965781,0.0011915677,0.00016399055,0.00009946037,0.0002120504,0.00000879883,0.0000917923],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9906826,0.0032642642,0.0031695003,0.00075718825,0.0018612668,0.00026513048],"domain_scores_gemma":[0.87400377,0.103196286,0.010064902,0.0017405101,0.01006857,0.0009259299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013580688,0.0011185841,0.0033121218,0.018921027,0.0009137548,0.0034265008,0.0018359501,0.0022545306,0.0067212824],"category_scores_gemma":[0.08004707,0.0009685789,0.004184439,0.016300298,0.0015499863,0.0049335593,0.0022274114,0.0024189753,0.0010009687],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018482131,0.000048825732,0.00204714,0.6047522,0.0020021892,0.00019191088,0.0008065414,0.00046527904,0.00029370916,0.0040642857,0.022161823,0.36298126],"study_design_scores_gemma":[0.00004682607,0.0001251337,0.0032904276,0.85074,0.007083021,0.0005132615,0.00083573034,0.00019852712,0.00019635235,0.0035889102,0.13333373,0.000047982576],"about_ca_topic_score_codex":0.00514716,"about_ca_topic_score_gemma":0.017922733,"teacher_disagreement_score":0.018921027,"about_ca_system_score_codex":0.0027499644,"about_ca_system_score_gemma":0.019343488,"threshold_uncertainty_score":0.071822464},"labels":[],"label_agreement":null},{"id":"W4401536986","doi":"10.1109/lnet.2024.3442833","title":"Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance Machine Learning Robustness","year":2024,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robustness (evolution); Adversarial system; Computer science; Artificial intelligence; Training (meteorology); Machine learning; Geography; Biology","score_opus":0.015515503359315534,"score_gpt":0.26177603512276487,"score_spread":0.24626053176344934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401536986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020916479,0.00027170693,0.9758614,0.00024143975,0.00009555727,0.00005545292,0.000026050315,0.0010498477,0.0014821353],"genre_scores_gemma":[0.78003806,0.0002550942,0.21673839,0.0003730506,0.00016050675,0.0001096177,0.000115309944,0.00019762617,0.0020123841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983998,0.00052314793,0.00008456548,0.00034610034,0.0004820864,0.00016418136],"domain_scores_gemma":[0.99468154,0.0033138231,0.00039179393,0.0008944656,0.0005723532,0.0001460349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023925467,0.0011501713,0.000887511,0.0005958277,0.00040104208,0.0007741654,0.0012997672,0.0010106296,0.0016790369],"category_scores_gemma":[0.011078646,0.00037090442,0.00062947144,0.0004251184,0.0012384363,0.0015828469,0.0020970101,0.0024465735,0.00052734296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002514959,0.00021441006,0.0027508368,0.00013332156,0.00011949466,0.00025549863,0.00017749435,0.7195323,0.038644046,0.011638494,0.0029142457,0.22336832],"study_design_scores_gemma":[0.000004819422,0.000060563096,0.00021048395,0.0000054903644,0.0000058076644,0.000051925468,0.0000054616107,0.9926085,0.0046165683,0.0017728457,0.00064888643,0.000008641749],"about_ca_topic_score_codex":0.0011188202,"about_ca_topic_score_gemma":0.001217648,"teacher_disagreement_score":0.0023925467,"about_ca_system_score_codex":0.00045949247,"about_ca_system_score_gemma":0.0006037413,"threshold_uncertainty_score":0.012653112},"labels":[],"label_agreement":null},{"id":"W4401632402","doi":"10.22215/etd/2024-16076","title":"Robust Defenses Against Adversarial Machine Learning in IoT Security","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Adversarial system; Adversarial machine learning; Context (archaeology); Computer science; Artificial intelligence; Internet of Things; Vulnerability (computing); Set (abstract data type); Machine learning; Computer security; Vulnerability assessment; Data science; Geography","score_opus":0.014430101384463963,"score_gpt":0.2555331890060016,"score_spread":0.24110308762153762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401632402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03737208,0.005594947,0.92867345,0.006962822,0.00042801545,0.00009424597,0.00010093466,0.00041163887,0.0203618],"genre_scores_gemma":[0.9022359,0.0051936726,0.07935909,0.0010415551,0.00065627747,0.00020297691,0.00015822577,0.00014547224,0.011006876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811566,0.0007029122,0.00007909746,0.0003134012,0.0005900389,0.00019890713],"domain_scores_gemma":[0.9916646,0.006176693,0.00054667774,0.00079144794,0.0005802814,0.00024029208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031395212,0.00082506175,0.0009150072,0.0008398408,0.00072582666,0.0020570103,0.0009042886,0.001586345,0.002358599],"category_scores_gemma":[0.012447724,0.00058577413,0.00081147073,0.0005385456,0.002977186,0.0023348182,0.0031974139,0.0046045296,0.0006201545],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013522606,0.00007478519,0.0013899091,0.00022098397,0.00016969528,0.00018646645,0.0003201022,0.55228937,0.0054411846,0.33484077,0.008231456,0.09670007],"study_design_scores_gemma":[0.00001490254,0.000085132546,0.0005158424,0.000099923,0.000021035561,0.00011148514,0.00007008304,0.7435173,0.0025639674,0.24789509,0.0050737835,0.00003147661],"about_ca_topic_score_codex":0.0007256005,"about_ca_topic_score_gemma":0.0005164618,"teacher_disagreement_score":0.0031395212,"about_ca_system_score_codex":0.0012613906,"about_ca_system_score_gemma":0.0009363841,"threshold_uncertainty_score":0.016603589},"labels":[],"label_agreement":null},{"id":"W4401635890","doi":"10.1145/3688838","title":"History-Driven Fuzzing for Deep Learning Libraries","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Fuzz testing; Computer science; Heuristic; Artificial intelligence; Set (abstract data type); Machine learning; Natural language processing; Programming language; Software","score_opus":0.06773932218144975,"score_gpt":0.29855303110614345,"score_spread":0.23081370892469372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401635890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26617515,0.001482722,0.70717806,0.0011564462,0.00009203577,0.000371016,0.0013271321,0.019027524,0.0031899002],"genre_scores_gemma":[0.8413182,0.00022528347,0.15491876,0.00045153973,0.00002337388,0.00023795902,0.0012339938,0.00035285376,0.001237992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997214,0.0006404426,0.00025286898,0.0007310903,0.0008390958,0.00032244285],"domain_scores_gemma":[0.99003243,0.0065439506,0.00094913796,0.0013438134,0.00092983147,0.00020078816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00307524,0.0015585833,0.0008434035,0.0023724153,0.0006095274,0.0013217838,0.0027268012,0.0011685041,0.0021691541],"category_scores_gemma":[0.016925247,0.0009518524,0.0018011567,0.00073524413,0.0019246296,0.0030283346,0.002092718,0.0019173824,0.00031941],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004964342,0.00024465335,0.029519891,0.00049875467,0.0002455995,0.0004972302,0.0003298028,0.6994122,0.012864845,0.013675925,0.003121489,0.23909318],"study_design_scores_gemma":[0.000022023563,0.000052757972,0.0007615606,0.000038436745,0.000030460387,0.000051409774,0.000020263475,0.97757566,0.007284441,0.013484817,0.0006636434,0.000014445913],"about_ca_topic_score_codex":0.010153112,"about_ca_topic_score_gemma":0.015798865,"teacher_disagreement_score":0.010153112,"about_ca_system_score_codex":0.0031635494,"about_ca_system_score_gemma":0.0030977775,"threshold_uncertainty_score":0.022953272},"labels":[],"label_agreement":null},{"id":"W4401838084","doi":"10.2139/ssrn.4935952","title":"Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Trimming; Training (meteorology); Computer science; Artificial intelligence; Risk analysis (engineering); Operations management; Business; Engineering; Geography","score_opus":0.01518606958273447,"score_gpt":0.26851984394590317,"score_spread":0.2533337743631687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401838084","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068926117,0.00017459432,0.99162227,0.00014954452,0.00002277316,0.000014189051,0.00002581997,0.000445856,0.0006523087],"genre_scores_gemma":[0.80646294,0.00024535504,0.18772097,0.0002662542,0.000102855236,0.00012297019,0.00014244576,0.00032585952,0.004610395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902415,0.00028817018,0.000046368055,0.00023432652,0.0002985622,0.000108464854],"domain_scores_gemma":[0.9953253,0.0026458881,0.0004901821,0.00079646026,0.000578813,0.00016340829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029904381,0.0013123282,0.0014289389,0.000529943,0.00041655544,0.0011690997,0.0021884388,0.002250895,0.003157824],"category_scores_gemma":[0.013448377,0.00096119504,0.00067836145,0.00045575184,0.0017160918,0.0020749932,0.0042015356,0.0041198307,0.00065347354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020358397,0.00003709633,0.00046785767,0.00011679488,0.00004451976,0.00005207762,0.000086974054,0.89385855,0.004382037,0.011164027,0.0013591058,0.08822737],"study_design_scores_gemma":[0.0000032689231,0.000022815504,0.000056311026,0.0000074487207,0.000002822482,0.000009099595,0.0000024160718,0.99410015,0.00074006995,0.004897022,0.00015524267,0.000003315152],"about_ca_topic_score_codex":0.0023508708,"about_ca_topic_score_gemma":0.0018740543,"teacher_disagreement_score":0.003157824,"about_ca_system_score_codex":0.000985819,"about_ca_system_score_gemma":0.0013919253,"threshold_uncertainty_score":0.015815139},"labels":[],"label_agreement":null},{"id":"W4401878944","doi":"10.1109/tiv.2024.3449830","title":"AVATAR: Autonomous Vehicle Assessment Through Testing of Adversarial Patches in Real-Time","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Avatar; Adversarial system; Computer science; Human–computer interaction; Computer security; Artificial intelligence","score_opus":0.035103200692350976,"score_gpt":0.3060655858895797,"score_spread":0.27096238519722876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401878944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34348264,0.00022770929,0.6468602,0.00027670636,0.0000973914,0.00019504534,0.0003178255,0.0051118485,0.003430596],"genre_scores_gemma":[0.9540972,0.000045318287,0.04459497,0.000047154983,0.000009460715,0.000055411867,0.0002854687,0.00010852553,0.0007564643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868876,0.00034563473,0.00006503736,0.00028823284,0.00048843335,0.00012395144],"domain_scores_gemma":[0.99593997,0.0019782512,0.0005225233,0.0007154488,0.0005959686,0.00024789118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020849947,0.0008069244,0.0005998508,0.00074267376,0.00026777037,0.00081763515,0.001755002,0.0009566562,0.001348054],"category_scores_gemma":[0.009475597,0.0002612989,0.00036033572,0.00026514963,0.0011174051,0.0017191946,0.0015887952,0.000921244,0.00035385985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053070986,0.0002638541,0.014230508,0.00011026719,0.00010717961,0.00030141187,0.00017931087,0.8659579,0.017782392,0.007129288,0.0024517644,0.09095545],"study_design_scores_gemma":[0.0000067071405,0.000104661194,0.0007242531,0.0000043395175,0.0000037914217,0.000049590355,0.000020352501,0.992331,0.0047417795,0.0017148367,0.00029165306,0.000006977642],"about_ca_topic_score_codex":0.0027631551,"about_ca_topic_score_gemma":0.0020593512,"teacher_disagreement_score":0.0027631551,"about_ca_system_score_codex":0.0007862756,"about_ca_system_score_gemma":0.0006901313,"threshold_uncertainty_score":0.0110266805},"labels":[],"label_agreement":null},{"id":"W4401990434","doi":"10.1109/dsn-w60302.2024.00021","title":"Unlearning Backdoor Attacks Through Gradient-Based Model Pruning","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queensland University of Technology; Canadian Institute for Advanced Research","keywords":"Backdoor; Computer science; Pruning; Artificial intelligence; Computer security","score_opus":0.025816187144971775,"score_gpt":0.29940035394970493,"score_spread":0.2735841668047332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401990434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023059009,0.00040291573,0.97343135,0.0003708186,0.000043035303,0.00006882284,0.00004601646,0.00088983984,0.0016881275],"genre_scores_gemma":[0.87070996,0.0004195072,0.1255031,0.00042260802,0.00009306975,0.00013739413,0.0001864153,0.00019455278,0.0023333712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985763,0.00044298026,0.00007122155,0.00024537774,0.00047213116,0.0001920418],"domain_scores_gemma":[0.9944758,0.003527779,0.00056188006,0.00084771967,0.00040863262,0.00017817292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022556356,0.0015983279,0.0014177252,0.0008600606,0.0005092929,0.0011884968,0.0019140062,0.0014712876,0.0014182121],"category_scores_gemma":[0.012783085,0.00061711913,0.00088978285,0.00042687633,0.001646498,0.0026256284,0.0028201109,0.0032799074,0.00042974006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120868266,0.00009922318,0.00096535985,0.00008516863,0.00006317026,0.0001498211,0.000063001804,0.9113208,0.004437301,0.020699298,0.0016408254,0.0603552],"study_design_scores_gemma":[0.0000041211374,0.000021776923,0.00004372466,0.000008645136,0.0000062463664,0.00003370223,0.0000041910816,0.9913974,0.0011150412,0.0071790144,0.00018242064,0.0000037641807],"about_ca_topic_score_codex":0.001455485,"about_ca_topic_score_gemma":0.0022328089,"teacher_disagreement_score":0.0022556356,"about_ca_system_score_codex":0.0007717715,"about_ca_system_score_gemma":0.0013997803,"threshold_uncertainty_score":0.011929095},"labels":[],"label_agreement":null},{"id":"W4402002228","doi":"10.1007/s10207-024-00903-2","title":"Adversarial robustness of deep reinforcement learning-based intrusion detection","year":2024,"lang":"en","type":"article","venue":"International Journal of Information Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Mitacs","keywords":"Computer science; Adversarial system; Intrusion detection system; Reinforcement learning; Artificial intelligence; Machine learning; Robustness (evolution); Deep learning; Adversarial machine learning; Overfitting; Deep neural networks; Artificial neural network","score_opus":0.005763070019284183,"score_gpt":0.2554729891537023,"score_spread":0.24970991913441812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402002228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086471215,0.0005095874,0.9087416,0.00050426763,0.00009290521,0.000045879835,0.000064006636,0.0006601548,0.0029103875],"genre_scores_gemma":[0.9805242,0.000094373805,0.017982705,0.000094314775,0.000025797803,0.00002684497,0.000047406134,0.0000405755,0.001163699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985089,0.0005272055,0.0000628301,0.000305019,0.00036962144,0.000226417],"domain_scores_gemma":[0.99262285,0.005134677,0.00064879406,0.00054459827,0.0008157981,0.00023331317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004295954,0.0009865246,0.0013100578,0.0005973624,0.0003358345,0.0009153909,0.0014609736,0.001107941,0.0012432033],"category_scores_gemma":[0.014117715,0.00047787774,0.0005705579,0.0003136287,0.00157793,0.0015899992,0.0021955736,0.0022000326,0.00020115411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014022723,0.000039129103,0.00075704476,0.000032834414,0.00004412606,0.000033319022,0.000021025593,0.9717058,0.0019959572,0.006984361,0.00044211335,0.01780395],"study_design_scores_gemma":[0.000001907452,0.000017570017,0.00007365549,0.0000021896299,0.000002709227,0.000006262142,0.0000011946427,0.9979978,0.00036131052,0.0015000441,0.000032920074,0.000002284681],"about_ca_topic_score_codex":0.002438422,"about_ca_topic_score_gemma":0.0013129283,"teacher_disagreement_score":0.004295954,"about_ca_system_score_codex":0.0012822028,"about_ca_system_score_gemma":0.001156711,"threshold_uncertainty_score":0.022719502},"labels":[],"label_agreement":null},{"id":"W4402134976","doi":"10.1093/cid/ciae443","title":"Is Strong Artificial Intelligence Skepticism Justified or Counterproductive?","year":2024,"lang":"en","type":"letter","venue":"Clinical Infectious Diseases","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Infection and Immunity","funders":"","keywords":"Medicine; Skepticism; Intensive care medicine; Epistemology","score_opus":0.09312384238899925,"score_gpt":0.39965042601182166,"score_spread":0.3065265836228224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402134976","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029398236,0.00057927356,0.0003326783,0.9929645,0.0025785347,0.0000036867596,0.000017454417,0.0000101635715,0.0032196462],"genre_scores_gemma":[0.015452387,0.0006320503,0.0005760992,0.96388495,0.016125446,0.000028255177,0.000015389553,0.000021347947,0.003263977],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98616356,0.006097169,0.0007508413,0.001516028,0.004148647,0.0013237068],"domain_scores_gemma":[0.8817234,0.099805795,0.0038292038,0.003385638,0.0064860736,0.0047698314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02249197,0.00051366386,0.001334319,0.0012248906,0.0029798879,0.008302062,0.002545996,0.0631798,0.007956866],"category_scores_gemma":[0.11587181,0.00050342,0.00097572175,0.0006469276,0.013165277,0.005546344,0.0026001637,0.057261907,0.006645345],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001454501,0.0000763258,0.0012124317,0.00007198277,0.000075341595,0.0008463621,0.00025361287,0.00033697966,0.00025475174,0.10253384,0.86528575,0.02890713],"study_design_scores_gemma":[0.00025478707,0.00007688852,0.0012763547,0.000500531,0.000059519985,0.00087700447,0.0005315508,0.0032968246,0.0007667317,0.47424096,0.5179894,0.00012945701],"about_ca_topic_score_codex":0.0021604914,"about_ca_topic_score_gemma":0.0031388826,"teacher_disagreement_score":0.0631798,"about_ca_system_score_codex":0.00495199,"about_ca_system_score_gemma":0.0043433076,"threshold_uncertainty_score":0.11895037},"labels":[],"label_agreement":null},{"id":"W4402156097","doi":"10.1109/icc51166.2024.10622742","title":"Towards Well-trained Model Robustness in Federated Learning: An Adversarial- Example-Generation- Efficiency Perspective","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Robustness (evolution); Computer science; Perspective (graphical); Artificial intelligence; Machine learning","score_opus":0.03492455370878724,"score_gpt":0.3012251720324484,"score_spread":0.2663006183236612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402156097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037600935,0.00026324752,0.9593096,0.0005002883,0.000035977806,0.00005242054,0.000044825178,0.0008099606,0.0013826543],"genre_scores_gemma":[0.86271715,0.00019877178,0.1341063,0.0003725571,0.000047536418,0.00012925474,0.00016217865,0.00019273689,0.0020735164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864274,0.0005247244,0.000073505624,0.00031937854,0.00025869216,0.00018097504],"domain_scores_gemma":[0.9960996,0.0019243131,0.00035530486,0.0009304465,0.000507745,0.00018256015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038887945,0.0012136648,0.0012249867,0.0005539855,0.00056323496,0.0011035453,0.0018104386,0.0017283849,0.0013496823],"category_scores_gemma":[0.011784871,0.0005536046,0.0007699211,0.00036831936,0.0018225028,0.002334827,0.0025424517,0.0024844736,0.00042781862],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011648687,0.000046672732,0.0008476181,0.00005214278,0.000047188998,0.00007160237,0.00006194914,0.95056796,0.00256907,0.011909756,0.0008277533,0.032881845],"study_design_scores_gemma":[0.000005134965,0.000023565084,0.000048829897,0.0000046042524,0.000003898883,0.000018174516,0.000005282472,0.99343383,0.0009481865,0.0053514712,0.00015354835,0.0000034124848],"about_ca_topic_score_codex":0.0019199692,"about_ca_topic_score_gemma":0.0014189956,"teacher_disagreement_score":0.0038887945,"about_ca_system_score_codex":0.0009399031,"about_ca_system_score_gemma":0.0015352111,"threshold_uncertainty_score":0.020566165},"labels":[],"label_agreement":null},{"id":"W4402159627","doi":"10.1109/icc51166.2024.10622879","title":"Integrated Sensing and Communications Using Generative AI: Countering Adversarial Machine Learning Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Adversarial system; Generative grammar; Computer science; Adversarial machine learning; Artificial intelligence; Machine learning","score_opus":0.035370564658745385,"score_gpt":0.3162588762722731,"score_spread":0.28088831161352773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402159627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028562969,0.0002119942,0.9665494,0.0003413166,0.000058228066,0.000032422926,0.000031410036,0.0005296028,0.0036826285],"genre_scores_gemma":[0.9512512,0.00014366489,0.046440378,0.00030945026,0.000047672787,0.000038510072,0.000043065487,0.000055572884,0.0016705814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895585,0.0003378676,0.00003063751,0.00019210235,0.00033768814,0.00014584344],"domain_scores_gemma":[0.9962225,0.0024331638,0.00038418433,0.0005023939,0.0003418724,0.00011581006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001833367,0.0009175206,0.0006491332,0.00041888235,0.00036325402,0.00088754063,0.0010407546,0.0008460645,0.0012329697],"category_scores_gemma":[0.0062836404,0.0003393149,0.0004442404,0.0002987683,0.0017858453,0.0012444438,0.0021662402,0.0017474755,0.00032833533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008381812,0.000024059336,0.00081681536,0.000038813232,0.000041053783,0.00008028089,0.000058234513,0.94710654,0.0054404163,0.020936364,0.0006765311,0.024697047],"study_design_scores_gemma":[0.0000030694302,0.00002355083,0.00009574615,0.0000040871773,0.000003981498,0.000026804275,0.0000061176715,0.99333596,0.0012900325,0.0049888263,0.00021638341,0.0000054651914],"about_ca_topic_score_codex":0.0017746219,"about_ca_topic_score_gemma":0.001793086,"teacher_disagreement_score":0.001833367,"about_ca_system_score_codex":0.0006903882,"about_ca_system_score_gemma":0.0007160089,"threshold_uncertainty_score":0.009695888},"labels":[],"label_agreement":null},{"id":"W4402263925","doi":"10.1109/sp54263.2024.00056","title":"ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Black box; Adversarial system; Speech recognition; Natural language processing; Artificial intelligence","score_opus":0.02050883652037649,"score_gpt":0.3074111192845921,"score_spread":0.2869022827642156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402263925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06263553,0.0005246662,0.92551637,0.0005437517,0.00012432678,0.00022644646,0.00020030329,0.0054168943,0.00481185],"genre_scores_gemma":[0.8945449,0.00017796204,0.10037615,0.0004875009,0.000059015492,0.00017025768,0.00024373196,0.00020988479,0.0037307623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978364,0.0006650177,0.000105081584,0.00033032848,0.00079743535,0.0002658322],"domain_scores_gemma":[0.9964934,0.0020544806,0.00032036012,0.0007713967,0.00024268824,0.0001176429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018312564,0.0012671074,0.0008684877,0.00051973324,0.00050117547,0.000841044,0.0014498995,0.0013997935,0.0029877196],"category_scores_gemma":[0.007161527,0.00033400115,0.0007422645,0.0002360253,0.0013682259,0.0024563216,0.0031060495,0.002051328,0.0011076349],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012616679,0.00029377756,0.0024354227,0.00028416768,0.00017227777,0.0008085558,0.00029481138,0.6185421,0.06642385,0.04065336,0.009511053,0.25931895],"study_design_scores_gemma":[0.000029273624,0.00013930627,0.00020260556,0.000011981994,0.000012010411,0.00017943131,0.000020673473,0.9806116,0.010467655,0.0070091435,0.0012975179,0.000018708872],"about_ca_topic_score_codex":0.00097171677,"about_ca_topic_score_gemma":0.0010440303,"teacher_disagreement_score":0.0029877196,"about_ca_system_score_codex":0.000686592,"about_ca_system_score_gemma":0.0006358572,"threshold_uncertainty_score":0.009994924},"labels":[],"label_agreement":null},{"id":"W4402263996","doi":"10.1109/sp54263.2024.00050","title":"GrOVe: Ownership Verification of Graph Neural Networks using Embeddings","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Graph; Artificial neural network; Theoretical computer science; Artificial intelligence","score_opus":0.02442510282704261,"score_gpt":0.2900625097927003,"score_spread":0.2656374069656577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402263996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16714226,0.001277491,0.8144445,0.0013526621,0.00022096642,0.000254751,0.0009451086,0.010593322,0.0037690154],"genre_scores_gemma":[0.88269544,0.00024376296,0.11287471,0.00042042977,0.000050993556,0.00010736946,0.001277229,0.00030764902,0.0020223188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978429,0.0007721865,0.00012933082,0.0005051492,0.00056582445,0.00018457028],"domain_scores_gemma":[0.9888021,0.005602246,0.0012167685,0.003320823,0.00085582683,0.00020235254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003524326,0.0012410897,0.0011229411,0.0012891078,0.0006195776,0.0014035264,0.002717034,0.002554826,0.002165312],"category_scores_gemma":[0.027204102,0.0005672589,0.00090191886,0.00057036546,0.001828036,0.0050562625,0.0033140113,0.0024261326,0.00063295284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052464654,0.00018610599,0.009631522,0.000251612,0.00023038713,0.00037791498,0.00020022318,0.71160334,0.006539034,0.029408092,0.007847211,0.23319988],"study_design_scores_gemma":[0.000013661118,0.00003724983,0.00022704825,0.00001445032,0.0000069683456,0.000052353986,0.000017745995,0.9849036,0.0021962465,0.012122448,0.00039974292,0.000008519984],"about_ca_topic_score_codex":0.0029813559,"about_ca_topic_score_gemma":0.004015664,"teacher_disagreement_score":0.003524326,"about_ca_system_score_codex":0.0013384401,"about_ca_system_score_gemma":0.0009878776,"threshold_uncertainty_score":0.01863867},"labels":[],"label_agreement":null},{"id":"W4402264099","doi":"10.1109/icodsa62899.2024.10651973","title":"A Secure Federated Learning Approach: Preventing Model Poisoning Attacks via Optimal Clustering","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Cluster analysis; Federated learning; Computer security; Artificial intelligence","score_opus":0.01801768968782898,"score_gpt":0.2725282903524999,"score_spread":0.25451060066467096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402264099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031404,0.00018652035,0.9637823,0.00035232428,0.000044969667,0.000079237434,0.0000599581,0.002900355,0.0011902722],"genre_scores_gemma":[0.783898,0.0001191474,0.2126403,0.0003342792,0.00004900747,0.000087359265,0.00023400992,0.0001845076,0.0024535072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962282,0.0010472345,0.00021960818,0.0009540291,0.0011026062,0.0004484029],"domain_scores_gemma":[0.9935667,0.00119785,0.0006077041,0.0032752634,0.0010515096,0.00030098055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038448423,0.0011572024,0.0015322865,0.0014309803,0.0016234044,0.0017029819,0.0030655756,0.0023309616,0.0013726274],"category_scores_gemma":[0.01015108,0.00051943137,0.0011473163,0.0010858844,0.0017925082,0.0041738776,0.004525157,0.0023331128,0.0009773946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009853287,0.0005236531,0.0051596654,0.00011713132,0.00022587257,0.00027441216,0.00045039848,0.6067041,0.01611651,0.03721423,0.0076876436,0.32454097],"study_design_scores_gemma":[0.00001604237,0.00006947572,0.00020023085,0.000007866817,0.000012949512,0.00008715604,0.000039829487,0.9788776,0.005468742,0.014271339,0.00093256583,0.000016235743],"about_ca_topic_score_codex":0.0030976906,"about_ca_topic_score_gemma":0.0024216578,"teacher_disagreement_score":0.0038448423,"about_ca_system_score_codex":0.0015706051,"about_ca_system_score_gemma":0.0024484352,"threshold_uncertainty_score":0.020333707},"labels":[],"label_agreement":null},{"id":"W4402264355","doi":"10.1109/sp54263.2024.00243","title":"SoK: Unintended Interactions among Machine Learning Defenses and Risks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Government of Ontario","keywords":"Unintended consequences; Computer science; Machine learning; Human–computer interaction; Risk analysis (engineering); Business; Political science","score_opus":0.02972554940440073,"score_gpt":0.30470285002960285,"score_spread":0.2749773006252021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402264355","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078710206,0.0011807629,0.8687602,0.012199656,0.00034373612,0.00016949825,0.00027701657,0.00079194765,0.037566893],"genre_scores_gemma":[0.9615389,0.00042840678,0.032164592,0.0013188832,0.00022113565,0.00015939078,0.000077621626,0.0001570553,0.0039340253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9916128,0.003782892,0.0003315487,0.0015106242,0.001725455,0.0010365534],"domain_scores_gemma":[0.9282599,0.048040982,0.0077648773,0.011691884,0.0025717935,0.001670513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009419977,0.0011565109,0.0014260216,0.0011547155,0.0018417186,0.0042473874,0.0022471598,0.0036512043,0.00679525],"category_scores_gemma":[0.066391334,0.00074725994,0.0011536903,0.00068557466,0.008596735,0.008901156,0.005442859,0.00685957,0.0006704614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017304528,0.00007480491,0.003753163,0.0001610984,0.00012417288,0.00024455693,0.0004589599,0.067369536,0.0019219602,0.8966521,0.004551545,0.02451499],"study_design_scores_gemma":[0.00001854299,0.00006627152,0.00082707766,0.000060553095,0.000033830525,0.00022099419,0.0001115965,0.11889409,0.0008479325,0.8766449,0.0022306247,0.000043577842],"about_ca_topic_score_codex":0.0007209274,"about_ca_topic_score_gemma":0.000728277,"teacher_disagreement_score":0.009419977,"about_ca_system_score_codex":0.0022117347,"about_ca_system_score_gemma":0.0020490258,"threshold_uncertainty_score":0.049818218},"labels":[],"label_agreement":null},{"id":"W4402340220","doi":"10.1007/978-3-031-68606-1_3","title":"Strategic Resilience Evaluation of Neural Networks Within Autonomous Vehicle Software","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Resilience (materials science); Artificial neural network; Software; Artificial intelligence; Operating system","score_opus":0.02792294607070531,"score_gpt":0.28173622142855326,"score_spread":0.2538132753578479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402340220","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31605372,0.0010544686,0.66269463,0.0007649702,0.00017843742,0.0000943022,0.000083791136,0.00065175863,0.018423848],"genre_scores_gemma":[0.989578,0.00009882895,0.008673157,0.000026986141,0.000013727159,0.000019487949,0.000024016581,0.000037455935,0.001528399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999479,0.0001994436,0.000015534451,0.000074175994,0.00014464624,0.00008729239],"domain_scores_gemma":[0.9974503,0.0017356225,0.00017328696,0.00014313425,0.00037881418,0.00011889695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016221073,0.0006991329,0.00064955145,0.0005119528,0.00031025446,0.00082245923,0.00084319897,0.0009212805,0.0021518634],"category_scores_gemma":[0.005564888,0.00023759768,0.00035548912,0.0003027961,0.000973094,0.0010654153,0.0012594392,0.00091482024,0.00015843593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007671019,0.000011567564,0.00016757777,0.000021766598,0.000014781036,0.000021303884,0.000011052172,0.9843052,0.0011128626,0.0071308604,0.00019934583,0.0069269836],"study_design_scores_gemma":[0.0000012937248,0.000025168776,0.000045330722,0.000003179258,0.0000028786205,0.000003423046,0.000003883083,0.99739647,0.00037512026,0.0020931438,0.0000480166,0.000001993829],"about_ca_topic_score_codex":0.002885893,"about_ca_topic_score_gemma":0.0019099198,"teacher_disagreement_score":0.002885893,"about_ca_system_score_codex":0.0015844179,"about_ca_system_score_gemma":0.0007349121,"threshold_uncertainty_score":0.011495769},"labels":[],"label_agreement":null},{"id":"W4402406946","doi":"10.1145/3643659.3648560","title":"AmbieGenVAE at the SBFT 2024 Tool Competition - Cyber-Physical Systems Track","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Track (disk drive); Competition (biology); Cyber-physical system; Operating system","score_opus":0.011390432342016704,"score_gpt":0.2602914419415798,"score_spread":0.24890100959956313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402406946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22939296,0.0027986136,0.459187,0.004329562,0.0076903743,0.0027985256,0.039256856,0.17632647,0.07821966],"genre_scores_gemma":[0.59696865,0.00039550924,0.21844745,0.001536974,0.00044824462,0.0016529637,0.113229625,0.012520733,0.054799896],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9927085,0.0024775567,0.00028691126,0.0013652162,0.0023873253,0.00077467156],"domain_scores_gemma":[0.98994815,0.0039967964,0.0003100536,0.0019926624,0.0023560214,0.0013963314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008305577,0.0036443295,0.0014716014,0.0016466366,0.00091120135,0.0025603143,0.0034242042,0.003972674,0.022757161],"category_scores_gemma":[0.01625891,0.0006434234,0.0014489685,0.00057624775,0.0013915051,0.0023363049,0.0041191126,0.0028857286,0.015340717],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030333528,0.00258617,0.010306615,0.0011326114,0.0007612765,0.0019667256,0.0006349324,0.07663119,0.032665435,0.011503723,0.5471431,0.31163487],"study_design_scores_gemma":[0.0020734826,0.005671338,0.015518866,0.00038792207,0.00018742555,0.0025422312,0.00050060527,0.46555075,0.0923294,0.029648006,0.38521874,0.00037131397],"about_ca_topic_score_codex":0.0037816006,"about_ca_topic_score_gemma":0.0066651073,"teacher_disagreement_score":0.022757161,"about_ca_system_score_codex":0.0008905517,"about_ca_system_score_gemma":0.0017994916,"threshold_uncertainty_score":0.07613033},"labels":[],"label_agreement":null},{"id":"W4402571105","doi":"10.1109/icstw60967.2024.00015","title":"Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Metric (unit); Artificial intelligence; Adversarial system; Object detection; Computer vision; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.04999830138167369,"score_gpt":0.29491617282393784,"score_spread":0.24491787144226415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402571105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14918208,0.0008186564,0.84014666,0.000821099,0.00011550957,0.00023995206,0.00023123372,0.0027997394,0.0056450455],"genre_scores_gemma":[0.8233763,0.00014170335,0.17274933,0.0005133161,0.00003581168,0.0002193242,0.0005439831,0.00031985765,0.0021003152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988564,0.0004490622,0.000049476213,0.00024125168,0.00028480124,0.000118940174],"domain_scores_gemma":[0.9955505,0.0031837488,0.00025686785,0.00049062184,0.00034153787,0.00017670236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029591604,0.001672712,0.0010708036,0.0008697879,0.0003970191,0.0007273898,0.0023230563,0.0017981395,0.0015590213],"category_scores_gemma":[0.012546609,0.00051879947,0.0007929251,0.0003935501,0.0014614238,0.0014125636,0.002676751,0.0022099328,0.00034876665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012833285,0.0000997431,0.0020302832,0.00006145434,0.00007374014,0.00009025486,0.000043800723,0.9430304,0.0027050693,0.006212614,0.0015435988,0.043980643],"study_design_scores_gemma":[0.000008943861,0.000042231753,0.00008989305,0.000006063365,0.000004873975,0.000020316384,0.0000062228487,0.99654895,0.0008372617,0.0022437675,0.00018781226,0.0000036345148],"about_ca_topic_score_codex":0.0025833233,"about_ca_topic_score_gemma":0.0035047876,"teacher_disagreement_score":0.0029591604,"about_ca_system_score_codex":0.0011971302,"about_ca_system_score_gemma":0.001094301,"threshold_uncertainty_score":0.015649736},"labels":[],"label_agreement":null},{"id":"W4402591948","doi":"10.36227/techrxiv.172668728.85275057/v1","title":"Improving Adversarial Robustness of Conjugate Neural Networks with Guided Diversity","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adversarial system; Robustness (evolution); Conjugate; Diversity (politics); Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Mathematics; Political science; Biology; Law","score_opus":0.01809941721385828,"score_gpt":0.2484768591472456,"score_spread":0.23037744193338733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402591948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10615211,0.0004630643,0.88811326,0.00039780684,0.00009690908,0.000043655986,0.000054840228,0.00080088933,0.0038775634],"genre_scores_gemma":[0.95841426,0.00014641328,0.03932782,0.00016681627,0.000037907943,0.000035697518,0.00005943832,0.00007764049,0.0017338978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912256,0.00031870542,0.000045838573,0.00017504433,0.00022355015,0.00011426604],"domain_scores_gemma":[0.9973137,0.0014723757,0.00033256147,0.0004242256,0.00034309327,0.00011405525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018889641,0.0010791539,0.00079944,0.0005323691,0.0004121432,0.0007696588,0.0010313037,0.0010399603,0.001275449],"category_scores_gemma":[0.007421088,0.00043105648,0.00052466587,0.00031849652,0.0016471142,0.0018997896,0.002933476,0.0016563468,0.00029655758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013405233,0.00003369559,0.0009330178,0.0000377825,0.000059726266,0.0000770619,0.00005131109,0.9505899,0.0071803704,0.012797166,0.0006485176,0.027457386],"study_design_scores_gemma":[0.0000043157347,0.000030472022,0.00006806626,0.0000032472535,0.000004035395,0.000016241254,0.000003390478,0.9936081,0.0020568832,0.004041683,0.00015891503,0.000004620333],"about_ca_topic_score_codex":0.0011560043,"about_ca_topic_score_gemma":0.0012229384,"teacher_disagreement_score":0.0018889641,"about_ca_system_score_codex":0.00066856184,"about_ca_system_score_gemma":0.00070418953,"threshold_uncertainty_score":0.009989917},"labels":[],"label_agreement":null},{"id":"W4402661245","doi":"10.1109/tse.2024.3461657","title":"D<sup>3</sup>: Differential Testing of Distributed Deep Learning With Model Generation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Division of Computing and Communication Foundations","keywords":"Computer science; Artificial intelligence; Programming language","score_opus":0.014824459603576702,"score_gpt":0.21919131723217236,"score_spread":0.20436685762859566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402661245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02394977,0.00008880115,0.9395513,0.00091700297,0.0003235708,0.00022243023,0.0009126747,0.014662194,0.019372173],"genre_scores_gemma":[0.5317449,0.00011273313,0.44384646,0.0019818735,0.00016200876,0.000534597,0.0048709773,0.0029914156,0.013754971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953928,0.0010756829,0.0005095119,0.00090124767,0.0017312712,0.00038945614],"domain_scores_gemma":[0.9892,0.004475313,0.00047312133,0.0037197412,0.0018700964,0.0002617463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036788357,0.0009066171,0.0005298175,0.00082955364,0.0005591001,0.0017418566,0.0042445627,0.0014722877,0.017934714],"category_scores_gemma":[0.017039806,0.0004286156,0.0011406504,0.0005652162,0.0019238702,0.0023487369,0.002263523,0.0021184788,0.0039833444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016979149,0.00068161933,0.01321409,0.0004889234,0.00014820659,0.0014335314,0.00032253872,0.10117956,0.059908003,0.1382799,0.096103445,0.58654237],"study_design_scores_gemma":[0.0002020449,0.00038308345,0.00217632,0.000055701355,0.00004006967,0.00066698645,0.00007909438,0.7584573,0.13577542,0.068627514,0.033475265,0.00006122161],"about_ca_topic_score_codex":0.0031847185,"about_ca_topic_score_gemma":0.0042319633,"teacher_disagreement_score":0.017934714,"about_ca_system_score_codex":0.0014318492,"about_ca_system_score_gemma":0.0017295742,"threshold_uncertainty_score":0.05999756},"labels":[],"label_agreement":null},{"id":"W4402716028","doi":"10.1109/cvpr52733.2024.02686","title":"Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Nanyang Technological University; National Research Foundation Singapore","keywords":"Distillation; Adversarial system; Computer science; Process engineering; Chemistry; Artificial intelligence; Chromatography; Engineering","score_opus":0.017712275481541914,"score_gpt":0.241334167419715,"score_spread":0.22362189193817308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402716028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020324538,0.000105370535,0.9765679,0.00026786054,0.000024847783,0.000058382855,0.00007420654,0.0011550869,0.0014218024],"genre_scores_gemma":[0.7329148,0.00013671433,0.25989345,0.00053623406,0.000068679474,0.000260656,0.00051406695,0.00040480623,0.0052705505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820197,0.00058582175,0.00007779818,0.00048829766,0.00041217983,0.00023385273],"domain_scores_gemma":[0.99627256,0.0021898616,0.00032359795,0.00073973264,0.00030159537,0.00017266755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002520223,0.002139064,0.001681318,0.0007278874,0.0006951053,0.0014890515,0.0027150474,0.0019089482,0.0029544132],"category_scores_gemma":[0.009838948,0.0009104056,0.0012838929,0.00063809124,0.0024799649,0.0038331226,0.005806484,0.0037713752,0.0009863094],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020395558,0.00006756098,0.0004871809,0.00005405842,0.00004902306,0.00011148849,0.00008506416,0.91093594,0.0039228364,0.026352743,0.0013846499,0.056345496],"study_design_scores_gemma":[0.000008300606,0.00002449975,0.00003428933,0.0000043484365,0.0000040937493,0.000012307599,0.0000058793066,0.98693824,0.0018416586,0.010857665,0.00026282205,0.0000058919923],"about_ca_topic_score_codex":0.0027285472,"about_ca_topic_score_gemma":0.003058804,"teacher_disagreement_score":0.0029544132,"about_ca_system_score_codex":0.0012922229,"about_ca_system_score_gemma":0.0017820717,"threshold_uncertainty_score":0.013328373},"labels":[],"label_agreement":null},{"id":"W4402727630","doi":"10.1109/cvpr52733.2024.00345","title":"Class Tokens Infusion for Weakly Supervised Semantic Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Class (philosophy); Natural language processing; Segmentation; Artificial intelligence","score_opus":0.017030644780512375,"score_gpt":0.2878132132293406,"score_spread":0.27078256844882825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019558184,0.00026253058,0.974311,0.00022494569,0.000063788,0.00008338993,0.00018828896,0.0032666586,0.002041256],"genre_scores_gemma":[0.61700845,0.00034453976,0.36918154,0.0007379037,0.0001751395,0.0003099742,0.0017761444,0.0018869484,0.008579377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885535,0.0003108143,0.00005596309,0.00038420706,0.00025043247,0.00014321564],"domain_scores_gemma":[0.9984717,0.00058503245,0.00018120358,0.00045795192,0.00020618709,0.000098029916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015430769,0.0015879824,0.0012934334,0.0010317586,0.00060667447,0.0012959483,0.0022005518,0.0016987133,0.0036890707],"category_scores_gemma":[0.0041736,0.0005669368,0.0011504594,0.00070525904,0.0021634859,0.002082057,0.0029311448,0.0021882753,0.0018225246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012178252,0.00023156722,0.0022860467,0.00032905026,0.00019991033,0.00030572878,0.00040664242,0.37565812,0.06066665,0.04230184,0.0119669065,0.50442976],"study_design_scores_gemma":[0.000021141856,0.00007489987,0.00031687922,0.000019822675,0.000021773149,0.0001071152,0.000034030476,0.9536188,0.016774729,0.025888722,0.0031017347,0.000020273914],"about_ca_topic_score_codex":0.0023829092,"about_ca_topic_score_gemma":0.0034157091,"teacher_disagreement_score":0.0036890707,"about_ca_system_score_codex":0.0012058234,"about_ca_system_score_gemma":0.001187384,"threshold_uncertainty_score":0.012341201},"labels":[],"label_agreement":null},{"id":"W4402753419","doi":"10.1109/mwscas60917.2024.10658862","title":"Advanced SEU and MBU Vulnerability Assessment of Deep Neural Networks in Air-to-Air Collision Avoidance Systems via SAT-Based Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Concordia University","funders":"","keywords":"Collision avoidance; Collision; Computer science; Vulnerability (computing); Artificial neural network; Real-time computing; Artificial intelligence; Computer security","score_opus":0.007189436815416911,"score_gpt":0.2962022666831362,"score_spread":0.2890128298677193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402753419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46505293,0.0006703552,0.5272112,0.00066492567,0.00004290179,0.000086536646,0.00023221504,0.0007733786,0.005265617],"genre_scores_gemma":[0.9827382,0.000098409226,0.016358761,0.000047382357,0.0000062824934,0.000033111042,0.00007480884,0.000023743192,0.00061929465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995554,0.00017092426,0.000018160008,0.00006560334,0.00011935884,0.000070497714],"domain_scores_gemma":[0.9966903,0.002457382,0.00029504742,0.00017725678,0.00031884378,0.00006119728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014441754,0.00085412216,0.00045086947,0.0008338578,0.00035325653,0.0005965462,0.0008069144,0.00061945274,0.002068233],"category_scores_gemma":[0.0055219284,0.00033298787,0.0005589667,0.00034438126,0.00089586526,0.0012315847,0.0009963124,0.0010545689,0.00010991828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004974197,0.000018188619,0.0013317527,0.000041396375,0.000029574585,0.00003974977,0.000023132365,0.9842613,0.0015645754,0.004742277,0.00014499179,0.007753287],"study_design_scores_gemma":[0.0000012001575,0.000016966007,0.00013558748,0.0000038043713,0.0000040088776,0.000006305611,0.000005841266,0.99715155,0.0006869182,0.0019502898,0.000036004007,0.0000015538213],"about_ca_topic_score_codex":0.0042211963,"about_ca_topic_score_gemma":0.0049558305,"teacher_disagreement_score":0.0042211963,"about_ca_system_score_codex":0.001338142,"about_ca_system_score_gemma":0.0010091003,"threshold_uncertainty_score":0.009708941},"labels":[],"label_agreement":null},{"id":"W4402811446","doi":"10.1109/iccc62479.2024.10681828","title":"Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Adversarial system; Computer science; Layer (electronics); Computer security; Computer network; Artificial intelligence; Nanotechnology","score_opus":0.02279584422011831,"score_gpt":0.29221984950544183,"score_spread":0.2694240052853235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402811446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07912426,0.00027285152,0.9167982,0.0003820819,0.0000610831,0.000054913504,0.000039608993,0.0023009845,0.0009660838],"genre_scores_gemma":[0.9276032,0.00008081998,0.07083827,0.00024154245,0.000024713523,0.000039843653,0.00006443826,0.00006356502,0.0010436431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844617,0.00043578728,0.00009566315,0.00039623768,0.00036261967,0.00026350064],"domain_scores_gemma":[0.99429333,0.0023170086,0.00059127825,0.0016597555,0.00084075984,0.000297933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041257767,0.0010276532,0.0012263743,0.0005649654,0.00070694945,0.0012519849,0.0019180924,0.0016214573,0.0009389834],"category_scores_gemma":[0.011718247,0.00048125983,0.0006095347,0.00033281106,0.0015417396,0.0030986876,0.003228744,0.002269836,0.00034687592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034447404,0.0002367515,0.0060941274,0.00007099315,0.0001146607,0.00016445703,0.00019795408,0.81382424,0.009991457,0.0070241443,0.0015735349,0.16036317],"study_design_scores_gemma":[0.00000398822,0.00003229379,0.00016301606,0.0000049974756,0.0000055011774,0.00002458425,0.000010308591,0.99471515,0.0025759933,0.0023063556,0.00015182687,0.000005862824],"about_ca_topic_score_codex":0.0032418037,"about_ca_topic_score_gemma":0.0032209484,"teacher_disagreement_score":0.0041257767,"about_ca_system_score_codex":0.00085495634,"about_ca_system_score_gemma":0.0017047549,"threshold_uncertainty_score":0.021819413},"labels":[],"label_agreement":null},{"id":"W4402897246","doi":"10.1109/qrs62785.2024.00048","title":"cf-TDFM: A Framework for Limiting Fault Infusion Attacks on Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Limiting; Computer science; Artificial neural network; Fault (geology); Computer security; Artificial intelligence; Geology; Engineering; Seismology","score_opus":0.02105955689231267,"score_gpt":0.30905776134468144,"score_spread":0.2879982044523688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402897246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005831612,0.00033848788,0.9912219,0.00017101933,0.00005577829,0.000032236865,0.000054370645,0.0009930795,0.0013015037],"genre_scores_gemma":[0.70584273,0.0006707036,0.28618282,0.0005312046,0.0001796753,0.0002490485,0.00027078748,0.00025329136,0.0058197575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993136,0.00017578684,0.000029636489,0.00013132094,0.0002348052,0.00011484051],"domain_scores_gemma":[0.99848276,0.00067050033,0.00017519068,0.00027957134,0.00029385037,0.00009809329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001803612,0.0010758861,0.0008106155,0.0010085386,0.00040821216,0.0007590645,0.0021929825,0.0017818987,0.0024645526],"category_scores_gemma":[0.005977329,0.00030214005,0.00054405635,0.0005407221,0.0011909526,0.0016958152,0.0021908246,0.001731598,0.00041241088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001794226,0.00008052102,0.000551599,0.00011971092,0.000057190868,0.00011529958,0.00005750019,0.7752137,0.006745084,0.040426474,0.0053419108,0.17111154],"study_design_scores_gemma":[0.0000051913726,0.000044129563,0.00004674664,0.000010433162,0.0000047286694,0.000024969424,0.0000044762623,0.9891426,0.00104056,0.008867791,0.00080312364,0.0000052458686],"about_ca_topic_score_codex":0.0031231067,"about_ca_topic_score_gemma":0.0034668765,"teacher_disagreement_score":0.0031231067,"about_ca_system_score_codex":0.000975791,"about_ca_system_score_gemma":0.0013336075,"threshold_uncertainty_score":0.009538472},"labels":[],"label_agreement":null},{"id":"W4402916566","doi":"10.1109/cvprw63382.2024.00348","title":"Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Backdoor; Computer science; Action (physics); Computer security; Artificial intelligence","score_opus":0.054690972758210735,"score_gpt":0.31901976042073,"score_spread":0.26432878766251927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31887466,0.0012303254,0.6659379,0.0016801935,0.00033786325,0.00020290335,0.00034597938,0.0034538317,0.007936343],"genre_scores_gemma":[0.97641027,0.00015652737,0.021305615,0.0003489736,0.000031282463,0.000033487937,0.00009331767,0.0000572647,0.0015632233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979875,0.0005756454,0.00008783754,0.00040449467,0.0006292625,0.00031529626],"domain_scores_gemma":[0.9953099,0.0024545568,0.00063521735,0.0011185153,0.0002808735,0.00020096952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015530147,0.0009799895,0.0007622503,0.00065896555,0.00053532695,0.0009073434,0.0011343571,0.0016420784,0.0013651665],"category_scores_gemma":[0.009476811,0.00036843662,0.0008216007,0.00033030636,0.0022410867,0.0032459004,0.0026145892,0.002644042,0.00041575744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024160224,0.00050984416,0.014655245,0.0003741691,0.0006577085,0.0024463534,0.0007792031,0.47648436,0.11035702,0.056389052,0.010601177,0.32432988],"study_design_scores_gemma":[0.000028659713,0.000278824,0.0014346077,0.00003433778,0.0000483901,0.00062686845,0.00008427648,0.9415631,0.038340133,0.015910657,0.0016075233,0.000042541884],"about_ca_topic_score_codex":0.0017470514,"about_ca_topic_score_gemma":0.0016886942,"teacher_disagreement_score":0.0017470514,"about_ca_system_score_codex":0.0010462352,"about_ca_system_score_gemma":0.0005567664,"threshold_uncertainty_score":0.008213222},"labels":[],"label_agreement":null},{"id":"W4402979718","doi":"10.1109/otcon60325.2024.10687374","title":"The Role of Unsupervised Learning in Defending Against Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Adversarial system; Computer science; Unsupervised learning; Artificial intelligence; Adversarial machine learning; Machine learning; Computer security","score_opus":0.006317408571191223,"score_gpt":0.24516651200296616,"score_spread":0.23884910343177493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402979718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046175987,0.00076810335,0.9475473,0.00089659315,0.00006993694,0.00006133615,0.000052601812,0.0004673853,0.003960743],"genre_scores_gemma":[0.8589204,0.00070195115,0.13777086,0.0003089105,0.00009578083,0.00007862908,0.000106803665,0.00009980082,0.0019170002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975872,0.0011688138,0.000101130216,0.00040278587,0.00058600254,0.00015412636],"domain_scores_gemma":[0.98010033,0.01231779,0.0016781422,0.004438845,0.0011325894,0.00033235585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048637763,0.0007630112,0.0007807297,0.0008106808,0.0007089498,0.0013035913,0.0011191912,0.0012070973,0.00073720445],"category_scores_gemma":[0.016955161,0.00038528763,0.00055876083,0.00041009058,0.0029880237,0.0029778401,0.001981372,0.002267569,0.00036002413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014222783,0.00014398756,0.0068496866,0.00015350977,0.00021171726,0.00011628109,0.00022521261,0.74008274,0.012787914,0.07890621,0.0017873946,0.15859315],"study_design_scores_gemma":[0.000008743598,0.00017536749,0.0014153004,0.000036643138,0.000020786147,0.00017758635,0.000051041763,0.93428963,0.0072247055,0.054064702,0.0025009548,0.00003450919],"about_ca_topic_score_codex":0.0011580338,"about_ca_topic_score_gemma":0.001129918,"teacher_disagreement_score":0.0048637763,"about_ca_system_score_codex":0.00080919935,"about_ca_system_score_gemma":0.0009785001,"threshold_uncertainty_score":0.025722444},"labels":[],"label_agreement":null},{"id":"W4403289443","doi":"10.29007/hgfv","title":"ARCH-COMP 2024 Category Report: Falsification","year":2024,"lang":"en","type":"article","venue":"EPiC series in computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"ACT-X; JST-Mirai Program; Exploratory Research for Advanced Technology; Electronic Components and Systems for European Leadership; Defense Advanced Research Projects Agency; Universität Salzburg; Alliance de recherche numérique du Canada; Japan Society for the Promotion of Science; Austrian Science Fund; Division of Civil, Mechanical and Manufacturing Innovation; European Commission; Technische Universität Wien; Core Research for Evolutional Science and Technology; National Science Foundation","keywords":"Benchmark (surveying); Arch; Competition (biology); Computer science; Software engineering; Data science; Artificial intelligence; Engineering; Civil engineering; Geography","score_opus":0.016761438070993426,"score_gpt":0.29554506268939496,"score_spread":0.2787836246184015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403289443","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04772394,0.006665145,0.31729403,0.027925706,0.044188034,0.0046829972,0.10608173,0.07858473,0.3668536],"genre_scores_gemma":[0.2099287,0.0019559483,0.16102524,0.010220561,0.0045701372,0.0035213483,0.38322595,0.029828178,0.19572397],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.95524836,0.010420979,0.0018106563,0.0044788686,0.023663096,0.0043780813],"domain_scores_gemma":[0.90998447,0.019941045,0.0020044912,0.01880201,0.04326984,0.005998238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04038029,0.0037419293,0.0021802098,0.0040755495,0.0041164397,0.012219649,0.0070833275,0.0075402865,0.08704304],"category_scores_gemma":[0.051238634,0.0010854267,0.002862496,0.0020995212,0.0028747907,0.006864425,0.010575292,0.00728207,0.069268346],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018185101,0.0004961974,0.0012769754,0.00073157443,0.00011712761,0.00023398471,0.00015969372,0.0066896286,0.004297707,0.02168701,0.90212774,0.0603639],"study_design_scores_gemma":[0.00068152836,0.0012196032,0.0020381177,0.0004625808,0.000107188476,0.00064512057,0.00046673496,0.031216005,0.028434519,0.035789907,0.8987275,0.00021125944],"about_ca_topic_score_codex":0.008075395,"about_ca_topic_score_gemma":0.009432658,"teacher_disagreement_score":0.08704304,"about_ca_system_score_codex":0.004851815,"about_ca_system_score_gemma":0.008237901,"threshold_uncertainty_score":0.291188},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W4403321808","doi":"10.48550/arxiv.2409.05657","title":"Adversarial Attacks on Data Attribution","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Adversarial system; Attribution; Computer security; Computer science; Authorship attribution; Data science; Artificial intelligence; Psychology; Social psychology","score_opus":0.1544047434716,"score_gpt":0.24478357554781954,"score_spread":0.09037883207621955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403321808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022925198,0.00033200937,0.9682919,0.0016914369,0.00014282636,0.00016335007,0.00028015106,0.0011146292,0.0050585256],"genre_scores_gemma":[0.84313536,0.00044475214,0.14880341,0.0011333734,0.00020955985,0.00042444674,0.00057263626,0.00027219902,0.005004382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98914975,0.004891733,0.00049020373,0.0018731919,0.002858886,0.0007362855],"domain_scores_gemma":[0.9644993,0.021370577,0.0021416242,0.010012213,0.0013698646,0.00060656894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008247538,0.0015146472,0.001175167,0.0011282917,0.0011875333,0.0021297522,0.0025716287,0.0020854482,0.0028982745],"category_scores_gemma":[0.040144593,0.00066580763,0.001355425,0.001094899,0.00441678,0.0050381785,0.007778868,0.0058070696,0.0009276249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063344435,0.00016840867,0.0044457074,0.00024730898,0.00020030883,0.00039124856,0.0005592673,0.6066502,0.010014625,0.25647846,0.009608604,0.11060239],"study_design_scores_gemma":[0.000029687673,0.00007923163,0.00047392113,0.000045198758,0.000017259637,0.0001678646,0.00005138775,0.8675304,0.0051712296,0.1219749,0.0044301464,0.000028754172],"about_ca_topic_score_codex":0.0011248613,"about_ca_topic_score_gemma":0.00087746914,"teacher_disagreement_score":0.008247538,"about_ca_system_score_codex":0.0019321828,"about_ca_system_score_gemma":0.0014052311,"threshold_uncertainty_score":0.043617666},"labels":[],"label_agreement":null},{"id":"W4403511313","doi":"10.1109/tse.2024.3482984","title":"TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Ottawa","funders":"Huawei Technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland","keywords":"Computer science; Artificial neural network; Test (biology); Artificial intelligence; Machine learning; Reliability engineering; Software engineering; Engineering","score_opus":0.03694737320835111,"score_gpt":0.34241031545145006,"score_spread":0.30546294224309894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403511313","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034253918,0.00026130807,0.9498095,0.00014413746,0.000052318464,0.0003962715,0.0009006629,0.012421541,0.001760362],"genre_scores_gemma":[0.26638913,0.00012621624,0.7287574,0.00015131786,0.000032014304,0.0011630014,0.0016994524,0.0007950743,0.00088642054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927362,0.002350373,0.0009995585,0.0008892016,0.0027490782,0.00027561988],"domain_scores_gemma":[0.970574,0.017654607,0.0036254001,0.0029138632,0.004874863,0.0003572478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009175902,0.0020850424,0.0008224024,0.0055722627,0.00049713603,0.0014977589,0.0021512224,0.0013396397,0.0037371684],"category_scores_gemma":[0.054914247,0.0006274105,0.0011741683,0.0014733135,0.0010957145,0.0018097708,0.0023870992,0.0016222084,0.0006819736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063375104,0.00032113248,0.028460763,0.00095815735,0.00044611457,0.00054113794,0.0006365847,0.37902874,0.024600502,0.020620547,0.011278543,0.532474],"study_design_scores_gemma":[0.000057486843,0.00028903375,0.0034349903,0.00011030407,0.00004040356,0.00022248465,0.0001051998,0.9627237,0.018429866,0.011138729,0.0033953474,0.000052519474],"about_ca_topic_score_codex":0.0034287826,"about_ca_topic_score_gemma":0.0043553296,"teacher_disagreement_score":0.009175902,"about_ca_system_score_codex":0.001122662,"about_ca_system_score_gemma":0.0021336745,"threshold_uncertainty_score":0.04852736},"labels":[],"label_agreement":null},{"id":"W4403557996","doi":"10.1016/j.asoc.2024.112338","title":"Few-shot intent detection with mutual information and contrastive learning","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Computer science; Mutual information; Shot (pellet); Artificial intelligence; Pattern recognition (psychology); Chemistry","score_opus":0.006481579155710362,"score_gpt":0.21988171901388975,"score_spread":0.2134001398581794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403557996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010530611,0.00016943237,0.98808134,0.00011550021,0.00002846371,0.00003919849,0.00004026314,0.00031238605,0.0006828021],"genre_scores_gemma":[0.5541122,0.0002570599,0.43959326,0.0003355406,0.00020084338,0.00019519165,0.00042906552,0.00024138804,0.004635398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998173,0.000548569,0.00007948025,0.0005065148,0.0005354313,0.00015699658],"domain_scores_gemma":[0.99601126,0.002567856,0.0003610557,0.00053893676,0.00035619544,0.0001647005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003022219,0.0011760836,0.0021004544,0.0021864895,0.00067406654,0.0017416145,0.0030004722,0.002165422,0.0015339836],"category_scores_gemma":[0.008909695,0.0008983596,0.0012881812,0.00096985494,0.0017646495,0.0028362412,0.0036114121,0.002566022,0.0006278561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068158016,0.00045291818,0.0025335504,0.00034822847,0.00036124198,0.0003172795,0.00023691154,0.46443978,0.032831803,0.057599444,0.00432427,0.43587294],"study_design_scores_gemma":[0.0000046921664,0.000038969956,0.0002384544,0.0000075269118,0.000009233377,0.00005510791,0.000005891624,0.98511446,0.002893143,0.0113777425,0.00024368841,0.0000110854],"about_ca_topic_score_codex":0.0015554886,"about_ca_topic_score_gemma":0.00197971,"teacher_disagreement_score":0.003022219,"about_ca_system_score_codex":0.00088201906,"about_ca_system_score_gemma":0.0008920442,"threshold_uncertainty_score":0.015983224},"labels":[],"label_agreement":null},{"id":"W4403582747","doi":"10.1145/3627673.3679726","title":"Dynamic Neural Control Flow Execution: an Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Blackberry (Canada); McGill University; Queen's University","funders":"","keywords":"Computer science; Vulnerability (computing); Binary number; Flow (mathematics); Control (management); Control flow; Artificial neural network; Artificial intelligence; Computer security; Mathematics","score_opus":0.01563875621875029,"score_gpt":0.2803951293155705,"score_spread":0.2647563730968202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403582747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08685596,0.000844788,0.9030709,0.00082439097,0.00010136682,0.00007645391,0.0002793931,0.0029367036,0.005009993],"genre_scores_gemma":[0.8955337,0.00027052753,0.09701363,0.00034417884,0.000038182578,0.00012406387,0.00055384013,0.00015098075,0.005971015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997826,0.000037837606,0.000009520335,0.00006855511,0.000047116162,0.00005438616],"domain_scores_gemma":[0.99953043,0.00021699855,0.000063429754,0.000043800832,0.00010684766,0.0000385501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005458541,0.0009833992,0.0006824151,0.0006087472,0.00034547545,0.0006906452,0.0016814491,0.0011416908,0.002132006],"category_scores_gemma":[0.001948736,0.000501482,0.0006332781,0.00040226468,0.0007226163,0.0012867472,0.0011367757,0.0016887488,0.00032222786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006994782,0.00007218795,0.0012773479,0.000035267494,0.00003698184,0.00008225236,0.00003915,0.93466824,0.0014836555,0.005377715,0.0012134098,0.055643894],"study_design_scores_gemma":[0.0000018442198,0.0000049162963,0.000041409894,0.0000017543979,0.0000020458203,0.0000033542285,0.0000014959279,0.998139,0.0002149482,0.0014902587,0.00009771934,0.0000012490605],"about_ca_topic_score_codex":0.013258664,"about_ca_topic_score_gemma":0.014931416,"teacher_disagreement_score":0.013258664,"about_ca_system_score_codex":0.0013507971,"about_ca_system_score_gemma":0.001171837,"threshold_uncertainty_score":0.026362956},"labels":[],"label_agreement":null},{"id":"W4403792311","doi":"10.1145/3664647.3680638","title":"MetaRepair: Learning to Repair Deep Neural Networks from Repairing Experiences","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Deep learning; Artificial intelligence","score_opus":0.011533766574676911,"score_gpt":0.26264782931994496,"score_spread":0.25111406274526804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403792311","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018635813,0.00044283667,0.9755452,0.0003070457,0.00015508926,0.000054457087,0.00011010951,0.0033229447,0.0014264723],"genre_scores_gemma":[0.7798275,0.00031357197,0.21104568,0.00047159047,0.0001522065,0.00023251346,0.0005029952,0.0007647715,0.0066892384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992719,0.0001922975,0.000045426703,0.00021567171,0.0001834942,0.000091301285],"domain_scores_gemma":[0.99649304,0.0018058969,0.0003212892,0.00093488145,0.00029937024,0.00014543699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002430641,0.0014073638,0.0011736227,0.00063255994,0.00030064932,0.000890968,0.0029352559,0.0022372908,0.00465831],"category_scores_gemma":[0.011211533,0.0006665775,0.0008328687,0.0003586072,0.0017702644,0.0025742254,0.0040427,0.0028036053,0.00083625363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045431947,0.00014282686,0.0011853175,0.00021791937,0.00018076388,0.00030154435,0.00018087338,0.75025344,0.007916255,0.023211487,0.008157879,0.20779732],"study_design_scores_gemma":[0.000011046134,0.000072815965,0.00008057572,0.000016126349,0.000015446418,0.000048443668,0.0000143372745,0.97917545,0.0022995165,0.01771624,0.0005403797,0.000009495991],"about_ca_topic_score_codex":0.00089401874,"about_ca_topic_score_gemma":0.0011022724,"teacher_disagreement_score":0.00465831,"about_ca_system_score_codex":0.00051985506,"about_ca_system_score_gemma":0.00062226696,"threshold_uncertainty_score":0.015583634},"labels":[],"label_agreement":null},{"id":"W4403906474","doi":"10.1007/978-3-031-73033-7_5","title":"Improving Adversarial Transferability via Model Alignment","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Transferability; Computer science; Adversarial system; Artificial intelligence; Machine learning","score_opus":0.012804128661244363,"score_gpt":0.2440903831401785,"score_spread":0.23128625447893414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403906474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004877605,0.00020798604,0.9897968,0.00018393692,0.000071943956,0.000026001937,0.000044982582,0.0010722313,0.0037185722],"genre_scores_gemma":[0.62520695,0.0008546677,0.34796336,0.0007109642,0.00032648243,0.00027063146,0.00082295417,0.0016724598,0.022171374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845755,0.0005163394,0.000064345906,0.00033662992,0.00048050113,0.0001445552],"domain_scores_gemma":[0.9967163,0.001944037,0.00018862667,0.0008683342,0.00019012006,0.00009242791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018990528,0.0017235383,0.001395325,0.00081552257,0.00052815204,0.0012063023,0.002062451,0.0021121055,0.0068777655],"category_scores_gemma":[0.008163786,0.00078694645,0.0012149805,0.00090914307,0.0015923529,0.003491717,0.005466472,0.0043945806,0.002637173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016132699,0.00010438925,0.00025251083,0.00010496785,0.00009565966,0.00011350921,0.000062656574,0.7586004,0.011267353,0.074229665,0.0052465885,0.14976089],"study_design_scores_gemma":[0.0000060940597,0.000031720712,0.000059339796,0.000008941634,0.000010273548,0.000043314463,0.0000056940053,0.95140064,0.0031084702,0.04427881,0.001037781,0.000008861209],"about_ca_topic_score_codex":0.0008949494,"about_ca_topic_score_gemma":0.00070373766,"teacher_disagreement_score":0.0068777655,"about_ca_system_score_codex":0.0007689034,"about_ca_system_score_gemma":0.00060943764,"threshold_uncertainty_score":0.023008466},"labels":[],"label_agreement":null},{"id":"W4404102001","doi":"10.1109/tse.2024.3491496","title":"SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Machine learning; Software engineering","score_opus":0.01605624727510781,"score_gpt":0.25027956476942625,"score_spread":0.23422331749431843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404102001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00785913,0.00013183166,0.98212034,0.00028164696,0.00005892673,0.00010568331,0.00011048393,0.0071728737,0.002159027],"genre_scores_gemma":[0.4665076,0.00014883274,0.52528286,0.0004827265,0.000068374145,0.00048686532,0.0003825749,0.00051668327,0.006123519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890375,0.0003363695,0.00007464801,0.00022677198,0.0003350437,0.00012334106],"domain_scores_gemma":[0.99845695,0.00061680237,0.00025200142,0.00020551147,0.00033824192,0.00013051486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021526918,0.001146469,0.00097631646,0.0006847046,0.00055587845,0.0010275244,0.0028633974,0.0014208434,0.0039326735],"category_scores_gemma":[0.004901146,0.00069090846,0.0008282402,0.00024543126,0.0008446661,0.0014666106,0.0020221246,0.0024529148,0.0009440963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031941262,0.00020549461,0.0016192192,0.0001333757,0.00010923223,0.00014118322,0.00014558375,0.7462417,0.004991782,0.016386334,0.0073308237,0.22237585],"study_design_scores_gemma":[0.000018648174,0.000030315454,0.00005501049,0.000006475904,0.000006211652,0.000008911282,0.0000035285482,0.99413943,0.0011300453,0.0034822437,0.0011140098,0.0000052159403],"about_ca_topic_score_codex":0.005575088,"about_ca_topic_score_gemma":0.006611646,"teacher_disagreement_score":0.005575088,"about_ca_system_score_codex":0.0014211269,"about_ca_system_score_gemma":0.0023315505,"threshold_uncertainty_score":0.013156116},"labels":[],"label_agreement":null},{"id":"W4404198152","doi":"10.1007/s40747-024-01628-4","title":"Enhancing adversarial transferability with local transformation","year":2024,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Transferability; Adversarial system; Computational intelligence; Transformation (genetics); Computer science; Computer security; Artificial intelligence; Machine learning; Chemistry","score_opus":0.026274900155223265,"score_gpt":0.27328627075846434,"score_spread":0.24701137060324108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404198152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031738132,0.00028212505,0.9631927,0.00028696112,0.000054749948,0.000055093227,0.000037513608,0.0012547439,0.0030979053],"genre_scores_gemma":[0.8992997,0.0003149971,0.09554834,0.00029115027,0.00007373468,0.000104272025,0.000119334545,0.00020557195,0.0040429058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990376,0.00028130022,0.00004695317,0.000168307,0.0003513166,0.00011455069],"domain_scores_gemma":[0.9978569,0.0011698196,0.00025936076,0.0004680517,0.00017025117,0.00007564439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014959631,0.0014468713,0.0009336188,0.00059970474,0.00035183618,0.0008265726,0.001134912,0.0011552327,0.0023872457],"category_scores_gemma":[0.0053957086,0.00035392042,0.00095519156,0.0003986554,0.0015863646,0.0022166155,0.0029781612,0.0024890467,0.0006307302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011458328,0.00006404838,0.00074914924,0.000058499525,0.00007133456,0.00013951599,0.0000714985,0.8868745,0.013060519,0.022694446,0.001506512,0.07459535],"study_design_scores_gemma":[0.0000037287812,0.000028714692,0.00008133188,0.00000504724,0.000005413298,0.00004071753,0.0000045135257,0.9913645,0.003101076,0.0049209753,0.00043807045,0.0000058856917],"about_ca_topic_score_codex":0.0011591789,"about_ca_topic_score_gemma":0.0012440545,"teacher_disagreement_score":0.0023872457,"about_ca_system_score_codex":0.00071292213,"about_ca_system_score_gemma":0.00067523797,"threshold_uncertainty_score":0.007986188},"labels":[],"label_agreement":null},{"id":"W4404199042","doi":"10.1007/s10664-024-10579-w","title":"Towards enhancing the reproducibility of deep learning bugs: an empirical study","year":2024,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Dalhousie University","funders":"","keywords":"Reproducibility; Empirical research; Computer science; Data science; Artificial intelligence; Statistics; Mathematics","score_opus":0.02200891701765622,"score_gpt":0.32076736995582494,"score_spread":0.2987584529381687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404199042","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8724037,0.001311158,0.11813426,0.0014164897,0.00012540777,0.00017383767,0.00051439885,0.0017735214,0.0041471166],"genre_scores_gemma":[0.98532605,0.000098234406,0.013450378,0.00011342048,0.000030840158,0.000037005615,0.00024133707,0.00017604223,0.0005265636],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9721835,0.013411736,0.0019755305,0.0043612015,0.007370084,0.0006979575],"domain_scores_gemma":[0.40540993,0.44339937,0.031094229,0.09931839,0.018890902,0.0018872397],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.028014367,0.0007686686,0.00069220946,0.0016683978,0.0009113282,0.0018351148,0.0025473335,0.0021657867,0.0026685847],"category_scores_gemma":[0.35097265,0.0005191975,0.000932158,0.0012529722,0.003048871,0.0043176673,0.002505649,0.0034795334,0.00055023807],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033301055,0.0036839,0.32348263,0.0017120847,0.0012068333,0.0010893524,0.0032805805,0.22553591,0.019356305,0.043540873,0.012307475,0.36147392],"study_design_scores_gemma":[0.00042356562,0.0027512307,0.06616565,0.00049107795,0.00053313834,0.0020046826,0.0010210312,0.7969645,0.024881748,0.09724044,0.0073593375,0.00016361711],"about_ca_topic_score_codex":0.001399817,"about_ca_topic_score_gemma":0.0014084888,"teacher_disagreement_score":0.97198564,"about_ca_system_score_codex":0.0011796593,"about_ca_system_score_gemma":0.0017354623,"threshold_uncertainty_score":0.14815587},"labels":[],"label_agreement":null},{"id":"W4404296390","doi":"10.1007/978-3-031-71464-1_34","title":"Sophon IDS: Mitigating the Effectiveness of GAN-Based Adversarial Attacks via Tailored Misinformation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Misinformation; Adversarial system; Computer security; Artificial intelligence","score_opus":0.008853468510542944,"score_gpt":0.24774145202888892,"score_spread":0.23888798351834598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404296390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059569232,0.0012871174,0.91170996,0.0010122305,0.00065044494,0.00013523592,0.00018047827,0.0054844595,0.019970773],"genre_scores_gemma":[0.8316775,0.00053865276,0.15220065,0.0007875676,0.00017524557,0.00008447324,0.0002809793,0.0003282286,0.013926699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999014,0.00028629773,0.000028249486,0.00016212193,0.00037369123,0.00013563375],"domain_scores_gemma":[0.99843067,0.0007789244,0.00012318534,0.0004033318,0.00018782499,0.00007599861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001346883,0.0009767067,0.0008895056,0.00045252484,0.00035427487,0.00091390416,0.0011112896,0.0012785784,0.003035186],"category_scores_gemma":[0.0037195948,0.00036133674,0.00037787846,0.00020664187,0.001062935,0.0016261595,0.002283319,0.0022187626,0.0010394066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013736762,0.00032244902,0.002694197,0.0002907163,0.00023981681,0.0005298161,0.00022176883,0.44274828,0.062983856,0.09336448,0.045161888,0.35006914],"study_design_scores_gemma":[0.000017719007,0.000112098365,0.00025721142,0.000017845789,0.000020320056,0.00021905912,0.000014397731,0.97000957,0.013698516,0.012331933,0.0032835924,0.00001773703],"about_ca_topic_score_codex":0.00049932156,"about_ca_topic_score_gemma":0.0008910138,"teacher_disagreement_score":0.003035186,"about_ca_system_score_codex":0.0006281203,"about_ca_system_score_gemma":0.0006050362,"threshold_uncertainty_score":0.010153711},"labels":[],"label_agreement":null},{"id":"W4404520551","doi":"10.1109/lra.2024.3502066","title":"GraspAgent 1.0: Adversarial Continual Dexterous Grasp Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"GRASP; Adversarial system; Computer science; Artificial intelligence; Human–computer interaction","score_opus":0.00833948835200547,"score_gpt":0.23967357420486957,"score_spread":0.2313340858528641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404520551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0096881045,0.00017195837,0.98073375,0.0001707592,0.000051089108,0.000087212444,0.00012747677,0.005593746,0.003375921],"genre_scores_gemma":[0.52115065,0.00028963952,0.46442184,0.0004339208,0.00008300377,0.0005472953,0.0006632371,0.0011493568,0.011261029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967194,0.000091636924,0.000015866384,0.00007760115,0.0001013317,0.000041645104],"domain_scores_gemma":[0.99941266,0.00030363104,0.000053810287,0.00011840776,0.00006866301,0.000042840384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011927163,0.0010211159,0.0007474351,0.00037011903,0.00022688626,0.0006281269,0.0019912014,0.001427122,0.005400347],"category_scores_gemma":[0.0023955205,0.0005953835,0.00061602675,0.00024625633,0.00083838997,0.000882257,0.0020202287,0.001961569,0.001262131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011655936,0.00006001867,0.0004926986,0.000078655736,0.00005137274,0.000089142486,0.00005940282,0.9131879,0.0039960206,0.013767378,0.004075708,0.064025156],"study_design_scores_gemma":[0.000006180352,0.000019217263,0.000030351239,0.0000035143862,0.000002492734,0.000014003113,0.0000015580337,0.99632335,0.0008920713,0.0020213928,0.0006823499,0.0000034699428],"about_ca_topic_score_codex":0.0018848816,"about_ca_topic_score_gemma":0.002120524,"teacher_disagreement_score":0.005400347,"about_ca_system_score_codex":0.00083685113,"about_ca_system_score_gemma":0.0007558305,"threshold_uncertainty_score":0.018065989},"labels":[],"label_agreement":null},{"id":"W4404563191","doi":"10.1007/978-3-031-73650-6_23","title":"Cocktail Universal Adversarial Attack on Deep Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); McMaster University","funders":"","keywords":"Computer science; Adversarial system; Artificial neural network; Artificial intelligence; Deep neural networks; Computer security","score_opus":0.017317118369238847,"score_gpt":0.2599931781544103,"score_spread":0.24267605978517146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404563191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01681843,0.0007880256,0.9678422,0.00037222452,0.000130297,0.000026964472,0.000100663456,0.0005472212,0.013373956],"genre_scores_gemma":[0.8431308,0.0015690412,0.1155227,0.00051552744,0.00020327113,0.00013216527,0.00032522684,0.00035768867,0.03824359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897575,0.00029842622,0.000038800463,0.00015590101,0.0003547947,0.00017641032],"domain_scores_gemma":[0.9982748,0.001058572,0.000110764064,0.00034855705,0.00013343277,0.00007381764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013058305,0.001083357,0.0011688147,0.0005378828,0.00048397106,0.0010924513,0.0011417826,0.0015767779,0.004307254],"category_scores_gemma":[0.0040018116,0.0006993525,0.0010138042,0.0006621706,0.001714973,0.0021168555,0.004251826,0.0032947892,0.0010121473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038044224,0.00004603402,0.00036780522,0.00013895967,0.00017862253,0.00016812887,0.00007549547,0.7069414,0.014693094,0.17183845,0.0063128327,0.09885866],"study_design_scores_gemma":[0.00000676239,0.00003614843,0.00012796154,0.000018292736,0.000014804037,0.00006787327,0.0000072043367,0.92861086,0.0037076988,0.06573518,0.0016522148,0.000014911469],"about_ca_topic_score_codex":0.0006593934,"about_ca_topic_score_gemma":0.0006773162,"teacher_disagreement_score":0.004307254,"about_ca_system_score_codex":0.00084518216,"about_ca_system_score_gemma":0.0005072119,"threshold_uncertainty_score":0.014409125},"labels":[],"label_agreement":null},{"id":"W4404569063","doi":"10.48550/arxiv.2411.09776","title":"Combining Machine Learning Defenses without Conflicts","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Government of Ontario; City University of Hong Kong","keywords":"Computer science; Artificial intelligence; Psychology","score_opus":0.06609237339693284,"score_gpt":0.21120627372840353,"score_spread":0.1451139003314707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404569063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11895529,0.00160126,0.8457122,0.0022980014,0.0002761055,0.0005791951,0.00039373038,0.0031940027,0.02699032],"genre_scores_gemma":[0.827004,0.00033589013,0.16517663,0.0008765724,0.00013212988,0.00027925943,0.0005509396,0.00026540252,0.005379226],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9887933,0.0041278196,0.00068073504,0.0016647532,0.0036789116,0.0010544581],"domain_scores_gemma":[0.98496836,0.006665578,0.0013240317,0.0051510837,0.001262147,0.0006289207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00818056,0.0023075005,0.0017465625,0.0020692665,0.0015254043,0.002750464,0.0021833167,0.0023377512,0.003935437],"category_scores_gemma":[0.021423975,0.0007854309,0.0021104247,0.0012044545,0.002215526,0.005467122,0.0068373573,0.004190283,0.0018286776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007762573,0.0006015592,0.016150353,0.00047584387,0.0007899952,0.0006777963,0.0004950968,0.37510148,0.027644876,0.07499161,0.015682459,0.48661268],"study_design_scores_gemma":[0.000088175184,0.00063160877,0.0020929845,0.00009985176,0.00022503699,0.00091563916,0.00024395314,0.8652567,0.019940812,0.09829305,0.012117529,0.00009458265],"about_ca_topic_score_codex":0.0005152058,"about_ca_topic_score_gemma":0.0008407748,"teacher_disagreement_score":0.00818056,"about_ca_system_score_codex":0.00109219,"about_ca_system_score_gemma":0.0016726364,"threshold_uncertainty_score":0.043263435},"labels":[],"label_agreement":null},{"id":"W4404582517","doi":"10.1007/978-3-031-72643-9_26","title":"Efficient and Versatile Robust Fine-Tuning of Zero-Shot Models","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Shot (pellet); Zero (linguistics); Algorithm; Artificial intelligence; Materials science","score_opus":0.02636609983055857,"score_gpt":0.2511047402448113,"score_spread":0.2247386404142527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404582517","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007725041,0.0002183876,0.9890505,0.00007380588,0.00003263368,0.000027691964,0.000035133784,0.00068738475,0.0021493877],"genre_scores_gemma":[0.71044,0.00031596233,0.27769756,0.00023756699,0.00007801806,0.00017657931,0.0003490893,0.0005245921,0.0101806875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916446,0.00020378304,0.000032686545,0.00022705799,0.00024545554,0.0001266427],"domain_scores_gemma":[0.9984816,0.0008851983,0.00008609491,0.00032768367,0.00013437367,0.00008510738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014731621,0.001075552,0.0016578378,0.000452921,0.00044246178,0.0012903387,0.002599114,0.0019521102,0.0037967109],"category_scores_gemma":[0.005833406,0.00077394856,0.0009195835,0.00041753208,0.0010908045,0.0017484773,0.0036886046,0.0025877492,0.0009907858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016283822,0.00008159537,0.00024350725,0.000099840385,0.00006783182,0.00006463595,0.00007144865,0.82265306,0.012384683,0.03992938,0.00286207,0.12137908],"study_design_scores_gemma":[0.0000033872877,0.000014137032,0.000028308692,0.0000046968,0.000003506784,0.000015310345,0.0000035104144,0.98866826,0.00093901024,0.010044078,0.0002709001,0.000004959891],"about_ca_topic_score_codex":0.0019143524,"about_ca_topic_score_gemma":0.0027970804,"teacher_disagreement_score":0.0037967109,"about_ca_system_score_codex":0.00083414663,"about_ca_system_score_gemma":0.0009592125,"threshold_uncertainty_score":0.012701213},"labels":[],"label_agreement":null},{"id":"W4404780969","doi":"10.18653/v1/2024.findings-emnlp.990","title":"Improving Adversarial Robustness in Vision-Language Models with Architecture and Prompt Design","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Canada Excellence Research Chairs, Government of Canada; Canadian Institute for Advanced Research","keywords":"Robustness (evolution); Computer science; Adversarial system; Architecture; Artificial intelligence; Computer architecture","score_opus":0.008985468768117039,"score_gpt":0.24439132616375764,"score_spread":0.2354058573956406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404780969","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008188864,0.00016787866,0.98918074,0.00033281182,0.00005945135,0.000031187512,0.00003825965,0.000979441,0.0010213093],"genre_scores_gemma":[0.7688703,0.00031350474,0.2223499,0.0006848017,0.00014024798,0.00019134356,0.0002607202,0.00056942645,0.006619796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99864787,0.00047520114,0.000066539316,0.00032823873,0.00030270967,0.00017937487],"domain_scores_gemma":[0.99628925,0.002276185,0.00022716894,0.00054112874,0.00050382566,0.00016243488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027259756,0.0014775841,0.0012283635,0.00055450975,0.0005878735,0.0011368803,0.0019439397,0.0023032136,0.0036614446],"category_scores_gemma":[0.014233668,0.00090582576,0.0008811669,0.0004015748,0.0014388512,0.0027921055,0.003489297,0.0036890444,0.0011768126],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028885435,0.00010674442,0.0005485655,0.00012375509,0.000075260235,0.00011064152,0.00009753693,0.8613207,0.013458808,0.03307999,0.0030512912,0.08773783],"study_design_scores_gemma":[0.000008417933,0.000029962916,0.000042762265,0.000005846077,0.000009253424,0.000019023875,0.0000041545245,0.9874077,0.0023304205,0.009852074,0.00028367733,0.0000068032164],"about_ca_topic_score_codex":0.002454766,"about_ca_topic_score_gemma":0.0028851782,"teacher_disagreement_score":0.0036614446,"about_ca_system_score_codex":0.0009909671,"about_ca_system_score_gemma":0.0016207765,"threshold_uncertainty_score":0.014416516},"labels":[],"label_agreement":null},{"id":"W4404788571","doi":"10.1145/3652892.3700787","title":"AsyncFilter: Detecting Poisoning Attacks in Asynchronous Federated Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Asynchronous communication; Computer security; Asynchronous learning; Computer network; Psychology; Synchronous learning","score_opus":0.01075448443594169,"score_gpt":0.2719802046839382,"score_spread":0.2612257202479965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404788571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19827078,0.0006117511,0.7691634,0.0006432624,0.0001551891,0.00021181331,0.0002739252,0.028802183,0.0018677242],"genre_scores_gemma":[0.9283534,0.00007918257,0.069808014,0.0003047959,0.000023985074,0.0000817671,0.0002484724,0.00018922608,0.00091110164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726367,0.00092015625,0.00016429085,0.000666002,0.0007300096,0.00025578367],"domain_scores_gemma":[0.9933869,0.002297906,0.0006051059,0.002731324,0.0006862989,0.00029253896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056577804,0.0009818554,0.0010675951,0.000912842,0.0008440768,0.001480238,0.002413572,0.0014923171,0.0009240377],"category_scores_gemma":[0.013595534,0.00033772964,0.00054470135,0.000466291,0.0014623293,0.003315932,0.0027304655,0.0017851654,0.0003969258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023995307,0.0010140669,0.034847826,0.0002917051,0.0003498786,0.0006129561,0.0005263612,0.5075195,0.025265995,0.014884294,0.011437126,0.40085074],"study_design_scores_gemma":[0.000043505548,0.00016976417,0.0008392579,0.000013004848,0.00001660483,0.00011511547,0.000032571566,0.98006606,0.010553007,0.00714755,0.0009855912,0.000017984108],"about_ca_topic_score_codex":0.002216764,"about_ca_topic_score_gemma":0.0022616875,"teacher_disagreement_score":0.0056577804,"about_ca_system_score_codex":0.0010127926,"about_ca_system_score_gemma":0.0017798083,"threshold_uncertainty_score":0.029921532},"labels":[],"label_agreement":null},{"id":"W4404908807","doi":"10.1017/s0956792524000822","title":"On the existence of solutions to adversarial training in multiclass classification","year":2024,"lang":"en","type":"article","venue":"European Journal of Applied Mathematics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Training (meteorology); Artificial intelligence; Class (philosophy); Multiclass classification; Computer science; Combinatorics; Mathematics; Pattern recognition (psychology); Calculus (dental); Applied mathematics; Physics; Medicine; Support vector machine; Orthodontics","score_opus":0.07266749660790316,"score_gpt":0.28171292953269345,"score_spread":0.20904543292479028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404908807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023571473,0.00028353906,0.9703254,0.0012821938,0.00003310073,0.00003431855,0.000050125716,0.00010644729,0.0043133376],"genre_scores_gemma":[0.83907074,0.00077954674,0.15273371,0.00065529573,0.00013593755,0.00023401261,0.00022676044,0.00019869447,0.005965287],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976562,0.0012747378,0.00008623263,0.0003621502,0.00042881307,0.0001917489],"domain_scores_gemma":[0.9827397,0.014283989,0.0009842078,0.00056944904,0.0009363948,0.00048626633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065676738,0.0013263432,0.0013774572,0.0013027659,0.00065786496,0.0016539359,0.001646045,0.0028000467,0.0021647657],"category_scores_gemma":[0.027347917,0.0006602895,0.0009642458,0.0007736823,0.0044385814,0.0028271906,0.0043703215,0.0037705796,0.00024720552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006886076,0.000045025306,0.000529925,0.00012767935,0.00004609226,0.0000963914,0.00015229483,0.41894224,0.0021512078,0.5628052,0.0015135,0.013521596],"study_design_scores_gemma":[0.000007849639,0.000027439168,0.00009458874,0.00003312606,0.0000059815343,0.000025042806,0.000016918813,0.8439489,0.00065783324,0.15480004,0.0003726865,0.0000096628755],"about_ca_topic_score_codex":0.0012556167,"about_ca_topic_score_gemma":0.0006329212,"teacher_disagreement_score":0.0065676738,"about_ca_system_score_codex":0.0018552142,"about_ca_system_score_gemma":0.0011673016,"threshold_uncertainty_score":0.034733593},"labels":[],"label_agreement":null},{"id":"W4404915790","doi":"10.1109/icipcw64161.2024.10769168","title":"SLACK: Attacking LiDAR-Based SLAM with Adversarial Point Injections","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Adversarial system; Lidar; Point (geometry); Computer science; Simultaneous localization and mapping; Artificial intelligence; Computer vision; Point cloud; Remote sensing; Mobile robot; Robot; Mathematics; Geology","score_opus":0.009734349011117293,"score_gpt":0.2569873151186532,"score_spread":0.2472529661075359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404915790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092199676,0.0003984591,0.90010387,0.00054175063,0.00015154503,0.000085701606,0.00014454598,0.0032777444,0.003096722],"genre_scores_gemma":[0.93940943,0.00011096595,0.058025487,0.00035742004,0.000033942644,0.0000613046,0.0001891449,0.00013277339,0.0016794802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991067,0.00025875337,0.00003500938,0.0001700395,0.0002857223,0.00014370939],"domain_scores_gemma":[0.9981165,0.0010393283,0.00019478645,0.00040279076,0.00015455102,0.000092100854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013247732,0.0009647259,0.00066494965,0.00031337168,0.00037199527,0.0005523918,0.0011901044,0.0011900228,0.0015482199],"category_scores_gemma":[0.0049174847,0.00035769914,0.00061655947,0.00025268976,0.0018011214,0.0014117964,0.003157605,0.0021409497,0.0004019285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002569787,0.000059679205,0.001850275,0.0000657899,0.00006633467,0.0001959523,0.00009386699,0.9250785,0.011873553,0.00962915,0.0022719887,0.048558004],"study_design_scores_gemma":[0.000009243858,0.000053435273,0.00014300388,0.0000071312425,0.0000045695174,0.000043028842,0.000010849834,0.99229515,0.0031718768,0.0037919192,0.00046336662,0.0000064231303],"about_ca_topic_score_codex":0.001859501,"about_ca_topic_score_gemma":0.0020044043,"teacher_disagreement_score":0.001859501,"about_ca_system_score_codex":0.0006894506,"about_ca_system_score_gemma":0.00078975986,"threshold_uncertainty_score":0.0070061684},"labels":[],"label_agreement":null},{"id":"W4404988789","doi":"","title":"Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université Laval; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimum bounding box; BitTorrent tracker; Computer science; Adversarial system; Bounding overwatch; Artificial intelligence; Transformer; Benchmark (surveying); Video tracking; Computer vision; Object (grammar); Eye tracking; Image (mathematics); Engineering","score_opus":0.0792852768337041,"score_gpt":0.24160963773702215,"score_spread":0.16232436090331806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404988789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045773495,0.00063693884,0.9421812,0.0005584735,0.00017551234,0.00018048062,0.00034150443,0.005581882,0.0045703575],"genre_scores_gemma":[0.85498285,0.00029873592,0.13851076,0.0007331724,0.000093731745,0.00019780417,0.00076565455,0.00045284996,0.0039644116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976338,0.0006622394,0.0001110892,0.0005634728,0.00082191906,0.00020750574],"domain_scores_gemma":[0.9950897,0.0025234947,0.0005072181,0.0014080461,0.00030961272,0.00016191225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030815117,0.0016321603,0.00113038,0.00075225777,0.0006321158,0.0008512642,0.00161208,0.0018742817,0.0022616142],"category_scores_gemma":[0.011295085,0.00048000133,0.0010791847,0.00062300166,0.001868504,0.0019133969,0.0033922766,0.0026426392,0.0009611172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051366404,0.00009073759,0.0022227576,0.0001165815,0.00011981051,0.00033563003,0.0001335984,0.8375107,0.016500434,0.024966562,0.009796823,0.1076926],"study_design_scores_gemma":[0.000022635732,0.000059740698,0.00019090787,0.000012095576,0.000010743755,0.00010221759,0.0000071213312,0.9854069,0.006263465,0.006801959,0.0011108823,0.000011315205],"about_ca_topic_score_codex":0.001865973,"about_ca_topic_score_gemma":0.001803854,"teacher_disagreement_score":0.0030815117,"about_ca_system_score_codex":0.001078992,"about_ca_system_score_gemma":0.000949441,"threshold_uncertainty_score":0.016296804},"labels":[],"label_agreement":null},{"id":"W4405184720","doi":"10.1016/j.jii.2024.100745","title":"A blockchain-enabled horizontal federated learning system for fuzzy invasion detection in maintaining space security","year":2024,"lang":"en","type":"article","venue":"Journal of Industrial Information Integration","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Hong Kong Polytechnic University; Hang Seng University of Hong Kong","keywords":"Blockchain; Space (punctuation); Fuzzy logic; Computer science; Computer security; Artificial intelligence; Operating system","score_opus":0.018842172050531524,"score_gpt":0.2571739755365058,"score_spread":0.23833180348597427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405184720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18011218,0.00047305712,0.8006381,0.00043980108,0.00024007996,0.0003035581,0.00039206538,0.00854285,0.008858228],"genre_scores_gemma":[0.9632338,0.00006493852,0.03342397,0.000086973945,0.000017904596,0.000064139254,0.00014173085,0.000033728877,0.0029327534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995684,0.000074320014,0.00003103643,0.00010720652,0.0001271386,0.00009197193],"domain_scores_gemma":[0.9991499,0.00022302411,0.00007353791,0.00022526731,0.00024592647,0.00008235273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007364398,0.00032960897,0.0006751499,0.00052754855,0.0007984932,0.00081937795,0.0013191792,0.0009341799,0.006407011],"category_scores_gemma":[0.001327856,0.00016620288,0.00021879087,0.0004116795,0.00046721264,0.0014484402,0.0016101005,0.00052542106,0.0010400445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002860573,0.0008170689,0.0066072955,0.00035135698,0.00019232387,0.0010931297,0.0003566111,0.3125737,0.05586273,0.023641797,0.011774211,0.5838692],"study_design_scores_gemma":[0.00004906626,0.00014158108,0.0003578198,0.000013226019,0.000021063926,0.00009626342,0.000025891386,0.9828619,0.009079934,0.0052943723,0.0020437061,0.000015219688],"about_ca_topic_score_codex":0.0031940343,"about_ca_topic_score_gemma":0.003647118,"teacher_disagreement_score":0.006407011,"about_ca_system_score_codex":0.0004946506,"about_ca_system_score_gemma":0.0013848079,"threshold_uncertainty_score":0.021433651},"labels":[],"label_agreement":null},{"id":"W4405394884","doi":"10.1016/j.eswa.2024.126017","title":"Learning adversarially robust kernel ensembles with kernel average pooling","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation","keywords":"Kernel (algebra); Pooling; Computer science; Artificial intelligence; Multiple kernel learning; Machine learning; Kernel method; Radial basis function kernel; Mathematics; Support vector machine; Discrete mathematics","score_opus":0.011579720252070871,"score_gpt":0.2465815101318245,"score_spread":0.23500178987975362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405394884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036136664,0.0003127427,0.96100456,0.00022013977,0.000046802837,0.000028755196,0.00008061056,0.0010907946,0.0010789088],"genre_scores_gemma":[0.90338206,0.00026806555,0.09318336,0.00021177274,0.00006451353,0.00008010178,0.00033416782,0.00017503682,0.002300968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899036,0.00030325074,0.000055400087,0.00025014026,0.00026313233,0.00013770729],"domain_scores_gemma":[0.99808025,0.00066025107,0.00028643687,0.0005714837,0.00028090124,0.000120682496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020838124,0.0015136366,0.0014337301,0.0004966228,0.00045966284,0.0009477048,0.001585731,0.0014077284,0.0010136584],"category_scores_gemma":[0.0062740957,0.0006793069,0.0010781756,0.0004643925,0.0010990645,0.0029863578,0.0027477215,0.0026500355,0.0005354709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084699386,0.00004279942,0.0009068353,0.00003802244,0.00012090456,0.00007317995,0.000052452648,0.94000435,0.005236167,0.012301308,0.0015212406,0.039618015],"study_design_scores_gemma":[0.0000016737765,0.000017286719,0.00006696731,0.0000029129749,0.000005105472,0.000013856411,0.000003119657,0.993436,0.0010669532,0.005235533,0.00014618097,0.000004412843],"about_ca_topic_score_codex":0.0017483492,"about_ca_topic_score_gemma":0.0019259178,"teacher_disagreement_score":0.0020838124,"about_ca_system_score_codex":0.00084946334,"about_ca_system_score_gemma":0.0007522796,"threshold_uncertainty_score":0.011020362},"labels":[],"label_agreement":null},{"id":"W4405433065","doi":"10.48550/arxiv.2412.09910","title":"Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on\\n Breast Ultrasound Images","year":2024,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Breast ultrasound; Ultrasound; Diffusion; Artificial intelligence; Radiology; Medicine; Breast cancer; Mammography; Physics; Internal medicine","score_opus":0.04455234186634167,"score_gpt":0.2171425067991563,"score_spread":0.17259016493281465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405433065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04457134,0.00045682653,0.94292367,0.0011226733,0.00024489712,0.00018705778,0.00030693875,0.005180138,0.0050065373],"genre_scores_gemma":[0.84748226,0.00043401116,0.13821173,0.0011363748,0.00013077966,0.00028462414,0.000642328,0.0005410064,0.011137038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991003,0.00028287366,0.000049439423,0.00020486757,0.00026283978,0.000099646335],"domain_scores_gemma":[0.9981281,0.0011137191,0.00017043555,0.0003514228,0.00013985179,0.00009651591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011962338,0.0013828824,0.0005929128,0.00036241056,0.0003353354,0.00067259057,0.0010442024,0.0013940684,0.0033338892],"category_scores_gemma":[0.0067295963,0.00028250716,0.00062831945,0.0001964728,0.0015677387,0.002088517,0.0026595592,0.0024133092,0.0009127455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011529275,0.00025784163,0.0020260094,0.000427147,0.00011778199,0.0007939204,0.00030649296,0.58660436,0.053032942,0.072135806,0.02286958,0.26027521],"study_design_scores_gemma":[0.00003580839,0.00018670723,0.00019484982,0.000020830334,0.000010723355,0.00016693512,0.000023422743,0.9612553,0.01477223,0.020533277,0.0027815688,0.000018346958],"about_ca_topic_score_codex":0.000771784,"about_ca_topic_score_gemma":0.0011193217,"teacher_disagreement_score":0.0033338892,"about_ca_system_score_codex":0.0006387061,"about_ca_system_score_gemma":0.0007351306,"threshold_uncertainty_score":0.011152983},"labels":[],"label_agreement":null},{"id":"W4405440279","doi":"10.1109/pst62714.2024.10788064","title":"Poisoning and Evasion: Deep Learning-Based NIDS under Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of New Brunswick","funders":"","keywords":"Adversarial system; Evasion (ethics); Computer science; Computer security; Artificial intelligence; Deep learning; Machine learning; Medicine","score_opus":0.009398891778544322,"score_gpt":0.26381769075473255,"score_spread":0.25441879897618824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405440279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44788057,0.0033275909,0.5239716,0.0032231621,0.00045562937,0.00044153281,0.00063604314,0.010552969,0.009510833],"genre_scores_gemma":[0.9605963,0.00024224674,0.036977146,0.00040612396,0.000032953518,0.000056849698,0.00033984403,0.000054331995,0.0012941739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862564,0.00043944927,0.00008070073,0.00023859857,0.00037238855,0.00024326646],"domain_scores_gemma":[0.99731255,0.0012303301,0.0003742271,0.00053256867,0.00034945627,0.00020092422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037481617,0.0009410348,0.0011845919,0.0009900371,0.00047536715,0.00087882334,0.0019088109,0.0014816949,0.0007209644],"category_scores_gemma":[0.006122343,0.0002815054,0.00072360726,0.0004422094,0.0015454987,0.0020088828,0.002210183,0.0020490629,0.0002393151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063613825,0.0004640667,0.01041447,0.00013391107,0.00020516638,0.00029436382,0.00012048164,0.85978824,0.0040883794,0.011520536,0.00688754,0.1054468],"study_design_scores_gemma":[0.000013494682,0.00007418431,0.00035405587,0.000006559864,0.000011591825,0.00004894608,0.000009909549,0.9941294,0.0015651755,0.0033548584,0.00042463755,0.0000071507366],"about_ca_topic_score_codex":0.0033309497,"about_ca_topic_score_gemma":0.0022010389,"teacher_disagreement_score":0.0037481617,"about_ca_system_score_codex":0.0014621357,"about_ca_system_score_gemma":0.0010810585,"threshold_uncertainty_score":0.019822419},"labels":[],"label_agreement":null},{"id":"W4405483995","doi":"10.2139/ssrn.5062204","title":"Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Fredericton; University of New Brunswick; Saint Mary's University","funders":"","keywords":"Adversarial system; Cloud computing; Inference; Computer science; Computer security; Internet privacy; Artificial intelligence; Data science","score_opus":0.01691801415515692,"score_gpt":0.2902167308032319,"score_spread":0.273298716648075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405483995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031231495,0.00045153286,0.9626784,0.0019190842,0.000114143506,0.00007046262,0.00015204432,0.0005776361,0.0028052109],"genre_scores_gemma":[0.9012125,0.000360758,0.093799576,0.0007853504,0.00022901909,0.00010799068,0.00020349576,0.00024085832,0.0030604114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99438596,0.002203644,0.00018882907,0.0010506718,0.0013739292,0.0007970452],"domain_scores_gemma":[0.9788133,0.013324946,0.0010356668,0.005049938,0.0010927808,0.0006834367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075377394,0.0014653445,0.0022759377,0.00075699604,0.0014243862,0.0037985838,0.0032669476,0.0037647397,0.0031355615],"category_scores_gemma":[0.028151048,0.00093471276,0.0014144455,0.0011622161,0.0045231082,0.006664987,0.009966161,0.007988758,0.00086931477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014815772,0.00018516667,0.0014945623,0.00015083606,0.0001663081,0.0002752169,0.000272037,0.65786654,0.0071497886,0.2661208,0.007000728,0.0578365],"study_design_scores_gemma":[0.000025896989,0.000031730895,0.000072323564,0.000010615978,0.000009920834,0.00004801638,0.000026319638,0.881683,0.0018934134,0.1157559,0.0004340325,0.000008826145],"about_ca_topic_score_codex":0.0012333028,"about_ca_topic_score_gemma":0.0009292747,"teacher_disagreement_score":0.0075377394,"about_ca_system_score_codex":0.0017388576,"about_ca_system_score_gemma":0.0025491794,"threshold_uncertainty_score":0.039863825},"labels":[],"label_agreement":null},{"id":"W4405717542","doi":"10.1109/tdsc.2024.3521451","title":"Feature Reconstruction Attacks and Countermeasures of DNN Training in Vertical Federated Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Training (meteorology); Feature (linguistics); Computer security; Feature extraction; Artificial intelligence; Multimedia","score_opus":0.015035523475006769,"score_gpt":0.2595993449859744,"score_spread":0.24456382151096764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405717542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30285293,0.00082903163,0.67774653,0.0034622108,0.00034326885,0.00020425339,0.00027023535,0.005666632,0.008624846],"genre_scores_gemma":[0.9534712,0.00008015703,0.04460492,0.00034455652,0.000034152585,0.000049743012,0.00015695751,0.00004713708,0.0012111457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954482,0.0013909953,0.00033218422,0.0008468831,0.001356075,0.0006257198],"domain_scores_gemma":[0.9898974,0.0043559964,0.00081639655,0.003697295,0.00090230716,0.00033073887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047435695,0.0009153216,0.000899869,0.0010232438,0.00094688573,0.0012428523,0.0017868078,0.0022141659,0.0014390429],"category_scores_gemma":[0.020204332,0.0004808128,0.0006310729,0.0006927734,0.0016363957,0.0036043103,0.0031752188,0.0028188697,0.00043562392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030257273,0.00062790356,0.010657373,0.00018181426,0.00025405138,0.0006592452,0.00036300573,0.43211964,0.019601198,0.05819125,0.009389519,0.4649293],"study_design_scores_gemma":[0.000032979773,0.00012201278,0.00074629206,0.000026595972,0.000017261578,0.00014213815,0.00004624617,0.9706654,0.009585309,0.017709078,0.00089276925,0.000013944911],"about_ca_topic_score_codex":0.002064516,"about_ca_topic_score_gemma":0.0017297585,"teacher_disagreement_score":0.0047435695,"about_ca_system_score_codex":0.0016652707,"about_ca_system_score_gemma":0.0017282557,"threshold_uncertainty_score":0.0250867},"labels":[],"label_agreement":null},{"id":"W4405880184","doi":"10.1007/s10619-024-07450-8","title":"Infomod: information-theoretic machine learning model diagnostics","year":2024,"lang":"en","type":"article","venue":"Distributed and Parallel Databases","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Automatic summarization; Computer science; SPARK (programming language); Debugging; Process (computing); Machine learning; Artificial intelligence; Ranging; Test data; Data mining; Programming language","score_opus":0.01595322642870192,"score_gpt":0.25958549389043345,"score_spread":0.24363226746173153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405880184","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004871241,0.00028060365,0.98217505,0.00072840194,0.000095109266,0.00008129604,0.0011022156,0.008340497,0.0023255283],"genre_scores_gemma":[0.40786648,0.00060904643,0.57742554,0.0009063344,0.00035418582,0.00053180946,0.0037520938,0.003021488,0.005533024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972691,0.0011525647,0.00014211106,0.00038001893,0.00093453313,0.000121643454],"domain_scores_gemma":[0.9890215,0.00745992,0.0006270899,0.0018928584,0.0008359735,0.00016276746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051384945,0.0014186971,0.0013636831,0.0020811893,0.00055662636,0.0027328804,0.0028368037,0.0019631856,0.012406537],"category_scores_gemma":[0.029934924,0.00087194046,0.0017063657,0.0008952031,0.0012285379,0.0033383553,0.003251799,0.0032481735,0.0032717942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007947109,0.0003470592,0.0034789445,0.00049068837,0.00038696153,0.00043877307,0.00008515013,0.5007887,0.003502041,0.18463641,0.041871455,0.2631791],"study_design_scores_gemma":[0.000023491884,0.00003391631,0.00012975364,0.000016242724,0.000015332613,0.000066870925,0.000006629534,0.93052775,0.0023089452,0.064952396,0.0019069101,0.000011813644],"about_ca_topic_score_codex":0.0013174533,"about_ca_topic_score_gemma":0.0012231936,"teacher_disagreement_score":0.012406537,"about_ca_system_score_codex":0.001093608,"about_ca_system_score_gemma":0.0022909446,"threshold_uncertainty_score":0.041503966},"labels":[],"label_agreement":null},{"id":"W4406014826","doi":"10.1109/iccd63220.2024.00025","title":"A Semi Black-Box Adversarial Bit- Flip Attack with Limited DNN Model Information","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Black box; Computer science; Adversarial system; Bit (key); Computer security; Artificial intelligence","score_opus":0.013939751345557722,"score_gpt":0.2522502418062494,"score_spread":0.23831049046069167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406014826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11320716,0.00052255904,0.87792015,0.0011084055,0.00014345288,0.00013231074,0.00021214898,0.0011901916,0.005563555],"genre_scores_gemma":[0.95979905,0.00014578544,0.03769872,0.00032125894,0.000024304876,0.00008026076,0.00008762703,0.000044323104,0.0017987334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998355,0.00069160445,0.00006776059,0.00027285653,0.00038599383,0.00022683034],"domain_scores_gemma":[0.9952551,0.0030649959,0.00039806854,0.0008069811,0.00033432522,0.00014057473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023637535,0.0010763259,0.0009274755,0.0005999334,0.0005563829,0.00081620045,0.001347601,0.0016304456,0.0022856155],"category_scores_gemma":[0.009708045,0.00037246617,0.00070941314,0.0003388405,0.0019336224,0.002599962,0.0023366394,0.002250016,0.00046213108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059073564,0.00006005424,0.0014018269,0.000103773236,0.00010453588,0.00037505777,0.00010560987,0.86865866,0.012437655,0.06263016,0.0023144903,0.051217545],"study_design_scores_gemma":[0.000018098164,0.00006864453,0.00011529072,0.000014888379,0.000011609244,0.000082260456,0.000009635165,0.9756654,0.0040635117,0.01949934,0.00043668228,0.000014697659],"about_ca_topic_score_codex":0.0011099646,"about_ca_topic_score_gemma":0.0010210741,"teacher_disagreement_score":0.0023637535,"about_ca_system_score_codex":0.00088115566,"about_ca_system_score_gemma":0.00090626214,"threshold_uncertainty_score":0.012500882},"labels":[],"label_agreement":null},{"id":"W4406016888","doi":"10.1007/s10207-024-00976-z","title":"A new method for securing binary deep neural networks against model replication attacks using magnetic tunnel junctions","year":2025,"lang":"en","type":"article","venue":"International Journal of Information Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Computer science; Replication (statistics); Artificial neural network; Cryptography; Binary number; Artificial intelligence; Computer security; Virology; Medicine; Mathematics","score_opus":0.01576238653082289,"score_gpt":0.33758612036792435,"score_spread":0.32182373383710144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406016888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01926263,0.00027189963,0.9766625,0.00024157437,0.00013996942,0.000045917684,0.000031168052,0.001051109,0.0022930957],"genre_scores_gemma":[0.77692616,0.0002433447,0.21410708,0.000319758,0.00010466253,0.00010645886,0.000107090906,0.00014380345,0.007941615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99940467,0.00012657812,0.000030795087,0.00009256177,0.0002708563,0.000074618234],"domain_scores_gemma":[0.99891615,0.00038481667,0.00014510086,0.00029763734,0.00019497801,0.00006130114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096884,0.00061259285,0.00065813557,0.00053878664,0.00047579192,0.00073751115,0.0012555544,0.0014092744,0.00201204],"category_scores_gemma":[0.003233073,0.00033379105,0.00049730996,0.00028535383,0.0009387123,0.0016665442,0.0019783229,0.0018616803,0.00047943246],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081622234,0.00020501333,0.0011617454,0.00025822475,0.00022981784,0.00043480264,0.00017229356,0.4568446,0.09673749,0.111175776,0.007956446,0.32400757],"study_design_scores_gemma":[0.0000122234105,0.000068737514,0.00008089054,0.000008814375,0.000011893184,0.0001044432,0.000007942071,0.98136854,0.008925454,0.008126316,0.0012732319,0.000011531083],"about_ca_topic_score_codex":0.0007545843,"about_ca_topic_score_gemma":0.0010151018,"teacher_disagreement_score":0.00201204,"about_ca_system_score_codex":0.00055140967,"about_ca_system_score_gemma":0.00070195424,"threshold_uncertainty_score":0.0067309737},"labels":[],"label_agreement":null},{"id":"W4406137362","doi":"10.1109/jsait.2024.3508492","title":"JSAIT Issue on Information-Theoretic Methods for Trustworthy and Reliable Machine Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Information Theory","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Trustworthiness; Computer science; Artificial intelligence; Data science; Machine learning; Computer security","score_opus":0.007238332404877398,"score_gpt":0.2908124293006692,"score_spread":0.28357409689579177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406137362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021822972,0.05760207,0.3804434,0.12119108,0.340088,0.00028719325,0.0015486926,0.0009760637,0.095681146],"genre_scores_gemma":[0.05396351,0.04905328,0.11948566,0.036517806,0.5617094,0.00071289175,0.002414145,0.0025031741,0.17364006],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939476,0.0016242715,0.00041812798,0.0007652142,0.0029620708,0.00028262535],"domain_scores_gemma":[0.973472,0.017204199,0.00073372095,0.003340943,0.0043066456,0.00094242365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009800497,0.0016010321,0.0026829205,0.003737955,0.002110222,0.007078466,0.003329142,0.0077916756,0.041031983],"category_scores_gemma":[0.036180533,0.0010138878,0.0025889408,0.002903191,0.004797735,0.008178947,0.0037687852,0.014116898,0.012819843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012278651,0.000119472395,0.00022703358,0.00081044354,0.00014792924,0.00018771963,0.000090651665,0.0030696602,0.00083972915,0.26255375,0.62224656,0.10958425],"study_design_scores_gemma":[0.000055273216,0.00010044104,0.0006530036,0.00088242785,0.0001130416,0.00046566184,0.00004939185,0.037897814,0.0012839066,0.45414186,0.5042591,0.00009809618],"about_ca_topic_score_codex":0.00092520675,"about_ca_topic_score_gemma":0.0009210882,"teacher_disagreement_score":0.041031983,"about_ca_system_score_codex":0.0026676038,"about_ca_system_score_gemma":0.0025416731,"threshold_uncertainty_score":0.13726568},"labels":[],"label_agreement":null},{"id":"W4406257125","doi":"10.1007/978-3-030-71522-9_1635","title":"Adversarial Machine Learning (AML)","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence","score_opus":0.01133745474985854,"score_gpt":0.23408113605359862,"score_spread":0.2227436813037401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406257125","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090293476,0.011772536,0.8394779,0.0015155548,0.0013686179,0.00004332179,0.00036823662,0.0018019245,0.14274889],"genre_scores_gemma":[0.11841548,0.031894006,0.4844341,0.0027287018,0.0034508812,0.00035575658,0.0020180908,0.0022392003,0.35446385],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948895,0.000107843276,0.000020517908,0.00009789545,0.00025506096,0.000029736539],"domain_scores_gemma":[0.9994454,0.00029857934,0.000029354029,0.00012754434,0.00007974992,0.000019482482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006183603,0.000905099,0.0006478347,0.0008294311,0.0003436135,0.0018183351,0.0009608265,0.0012396943,0.019955112],"category_scores_gemma":[0.0023248375,0.00039037308,0.0005865912,0.001147958,0.001047708,0.001759234,0.0018858829,0.0029141535,0.013423644],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018306537,0.000034546265,0.00010302452,0.00025497624,0.000028085697,0.000046222896,0.00005294854,0.033623084,0.0022898312,0.42429873,0.1030605,0.43618968],"study_design_scores_gemma":[0.0000054212887,0.000028498727,0.00021103426,0.00018657609,0.000017207127,0.000253652,0.000017387543,0.12640044,0.0034577134,0.50212115,0.36726105,0.000039833787],"about_ca_topic_score_codex":0.00065259787,"about_ca_topic_score_gemma":0.0008334586,"teacher_disagreement_score":0.019955112,"about_ca_system_score_codex":0.00065055606,"about_ca_system_score_gemma":0.0004978279,"threshold_uncertainty_score":0.06675643},"labels":[],"label_agreement":null},{"id":"W4406308377","doi":"10.1177/10591478251313780","title":"Bridging Adversarial and Nonstationary Multi-Armed Bandit","year":2025,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bridging (networking); Adversarial system; Computer science; Operations research; Mathematical optimization; Operations management; Business; Computer security; Economics; Artificial intelligence; Mathematics","score_opus":0.008937369525116484,"score_gpt":0.2654878744464503,"score_spread":0.2565505049213338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406308377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011883635,0.0005546417,0.9810852,0.00076645956,0.00006282586,0.00004453376,0.000046147976,0.00014834454,0.0054081646],"genre_scores_gemma":[0.8925586,0.001134223,0.10005642,0.0007380389,0.00035931807,0.00018964768,0.00009948609,0.00013853672,0.004725654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956216,0.0023901763,0.00014668486,0.0006748875,0.00069638906,0.00047027608],"domain_scores_gemma":[0.9850305,0.011483643,0.0016927117,0.0011568792,0.0003542903,0.00028202447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006121275,0.001865702,0.0015476841,0.00079304905,0.00062888407,0.0024722985,0.0024331084,0.002711539,0.0028421977],"category_scores_gemma":[0.020167572,0.0006379836,0.0010011452,0.0010754941,0.0033755903,0.0038905442,0.0037125207,0.0044362918,0.00042383443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011705785,0.0001214432,0.00050704856,0.00010107631,0.000067639616,0.0001446176,0.00010714157,0.7228355,0.00085510727,0.25618714,0.0013263613,0.017629823],"study_design_scores_gemma":[0.000009519392,0.00003927339,0.00012875836,0.000015815325,0.000009668673,0.000029362196,0.000016287786,0.90794027,0.0004543679,0.09068394,0.0006605912,0.000012081232],"about_ca_topic_score_codex":0.0011169289,"about_ca_topic_score_gemma":0.00072482193,"teacher_disagreement_score":0.006121275,"about_ca_system_score_codex":0.0015676961,"about_ca_system_score_gemma":0.0010608227,"threshold_uncertainty_score":0.032372773},"labels":[],"label_agreement":null},{"id":"W4406321328","doi":"10.1109/tccn.2025.3528891","title":"Adversarial Attacks Against Shared Knowledge Interpretation in Semantic Communications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Adversarial system; Interpretation (philosophy); Computer security; Artificial intelligence; Natural language processing; Computer network; Programming language","score_opus":0.03246175882454529,"score_gpt":0.3297104159656708,"score_spread":0.2972486571411255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406321328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056091562,0.00017990032,0.93917155,0.00040475547,0.00006344541,0.000046408542,0.000051389266,0.00038672218,0.0036042554],"genre_scores_gemma":[0.9683385,0.00016471157,0.029633733,0.00023848386,0.000036041714,0.000045611298,0.000054284053,0.000038902504,0.0014497141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977639,0.00088395824,0.000107363805,0.00029486514,0.00063862384,0.00031133927],"domain_scores_gemma":[0.9943328,0.003550965,0.0006191573,0.0010155636,0.0003838134,0.000097809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001666841,0.00097405823,0.00067598873,0.00048169727,0.0005714431,0.0010446341,0.00097285205,0.0012108064,0.0011479236],"category_scores_gemma":[0.007607061,0.00029234617,0.00065291853,0.00031218902,0.0018018717,0.002491328,0.0027389184,0.0019738427,0.00026822623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000563907,0.000091957,0.0014915864,0.00013894273,0.00018099544,0.00069036,0.00035106292,0.7621615,0.032559164,0.1263474,0.0023553984,0.07306772],"study_design_scores_gemma":[0.000011622422,0.00007611939,0.00021992878,0.000015318,0.000020392421,0.00018759212,0.000036789068,0.95958096,0.011723876,0.027254561,0.000852463,0.00002035892],"about_ca_topic_score_codex":0.0007991098,"about_ca_topic_score_gemma":0.0005720919,"teacher_disagreement_score":0.001666841,"about_ca_system_score_codex":0.0007370657,"about_ca_system_score_gemma":0.00062354753,"threshold_uncertainty_score":0.008815229},"labels":[],"label_agreement":null},{"id":"W4406460297","doi":"10.1109/tps-isa62245.2024.00032","title":"Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"SWORD; Reinforcement learning; Exploit; Computer science; Noise (video); Artificial neural network; Artificial intelligence; Computer security; World Wide Web; Image (mathematics)","score_opus":0.01468093571233063,"score_gpt":0.2739371671525557,"score_spread":0.2592562314402251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406460297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18833138,0.00045011207,0.80449426,0.00101155,0.00010140559,0.00008974755,0.0000401609,0.00057765475,0.0049037477],"genre_scores_gemma":[0.9773258,0.000076149416,0.021522865,0.0001722024,0.000017416025,0.000043316973,0.000016222037,0.000030439824,0.00079558603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980597,0.000891662,0.000083499224,0.0003577771,0.00038024658,0.00022712402],"domain_scores_gemma":[0.9877864,0.00901169,0.001157589,0.0011868101,0.0005585625,0.0002989711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039586173,0.0010041862,0.0008461581,0.00050444313,0.00042926983,0.0011262901,0.0011315051,0.001181816,0.0012519032],"category_scores_gemma":[0.019151246,0.00035218266,0.00051121524,0.00022164725,0.0018141606,0.0019819932,0.0017013459,0.0020994523,0.00022112462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023188279,0.0000988875,0.0030564775,0.00008802235,0.00009487725,0.00014605137,0.000113326656,0.9169198,0.009485721,0.031875715,0.0005932237,0.03729597],"study_design_scores_gemma":[0.000010148284,0.000113775284,0.00025130316,0.000014668583,0.000010968171,0.000040135405,0.00001142605,0.98318034,0.001794376,0.014281822,0.00028081585,0.00001020977],"about_ca_topic_score_codex":0.0012021398,"about_ca_topic_score_gemma":0.0010974522,"teacher_disagreement_score":0.0039586173,"about_ca_system_score_codex":0.00090808165,"about_ca_system_score_gemma":0.0008445692,"threshold_uncertainty_score":0.020935416},"labels":[],"label_agreement":null},{"id":"W4406567805","doi":"10.1016/b978-0-44-323761-4.00012-2","title":"Machine learning robustness: a primer","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Primer (cosmetics); Robustness (evolution); Computer science; Artificial intelligence; Machine learning; Chemistry","score_opus":0.01263565973342707,"score_gpt":0.24320087960693784,"score_spread":0.23056521987351078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406567805","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081055635,0.31092215,0.46807444,0.011152067,0.005702022,0.000076600925,0.00038368753,0.0014139764,0.20146461],"genre_scores_gemma":[0.06055361,0.35200316,0.27553374,0.012122166,0.028403863,0.00074718456,0.0011579741,0.0020248124,0.2674535],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989215,0.00030801032,0.000071272814,0.00019357228,0.00046381468,0.000041803498],"domain_scores_gemma":[0.9972187,0.0020607265,0.00010003466,0.0002815042,0.00028950154,0.00004953359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016912821,0.0013998436,0.0012054725,0.002552466,0.00041251176,0.00356092,0.0017650789,0.0034739873,0.02215638],"category_scores_gemma":[0.0038154055,0.0009732299,0.0010078633,0.0027402916,0.0023121906,0.0055227084,0.0021870192,0.0046602627,0.012933646],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002095329,0.00007808156,0.0001264055,0.00090567395,0.000049399427,0.00009078885,0.000109601606,0.009375558,0.0009884975,0.38916647,0.16906632,0.43002227],"study_design_scores_gemma":[0.000008308138,0.00003506126,0.00021059801,0.0007316439,0.000016068465,0.0003175963,0.000035713045,0.014008076,0.0009241927,0.51801974,0.46565586,0.000037140435],"about_ca_topic_score_codex":0.0005214889,"about_ca_topic_score_gemma":0.0005329867,"teacher_disagreement_score":0.02215638,"about_ca_system_score_codex":0.0011265083,"about_ca_system_score_gemma":0.0006039381,"threshold_uncertainty_score":0.07412052},"labels":[],"label_agreement":null},{"id":"W4406753720","doi":"10.1109/tdsc.2025.3533029","title":"A Proactive Defense Against Model Poisoning Attacks in Federated Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Computer security","score_opus":0.010931011662338857,"score_gpt":0.26052253823047294,"score_spread":0.24959152656813408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406753720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15615675,0.0010619711,0.8220938,0.001399008,0.00018524824,0.00023140686,0.00028226408,0.016038477,0.0025510958],"genre_scores_gemma":[0.902976,0.00016717851,0.09408798,0.00062302384,0.00005143333,0.00010165248,0.00041567298,0.00019185709,0.0013851499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970952,0.0007940878,0.00020314218,0.0007849673,0.0007725699,0.00034996937],"domain_scores_gemma":[0.9929021,0.0014685318,0.00064886204,0.0036527205,0.0009983744,0.00032933915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044675763,0.0016413939,0.0015376543,0.00075903453,0.0011052571,0.001998556,0.0028975166,0.001979277,0.00090866326],"category_scores_gemma":[0.0132490415,0.0004888952,0.0010880086,0.0007437826,0.0012997158,0.004368776,0.003740767,0.002684429,0.0007312542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010519288,0.0007064505,0.023319945,0.00025749678,0.00037857864,0.0005336292,0.0005335595,0.5959362,0.017686335,0.01628243,0.012646745,0.3306667],"study_design_scores_gemma":[0.000018766708,0.000097225544,0.0004266502,0.000011539451,0.000018521292,0.00011294269,0.000038669816,0.98799604,0.0045748814,0.0057027354,0.0009880867,0.00001390602],"about_ca_topic_score_codex":0.0031288704,"about_ca_topic_score_gemma":0.0026735768,"teacher_disagreement_score":0.0044675763,"about_ca_system_score_codex":0.0010333341,"about_ca_system_score_gemma":0.0023294007,"threshold_uncertainty_score":0.023627102},"labels":[],"label_agreement":null},{"id":"W4406892457","doi":"10.1109/fllm63129.2024.10852461","title":"Comparative Analysis of Loop-Free Function Evaluation Using ChatGPT and Copilot with C Bounded Model Checking","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Bounded function; Loop (graph theory); Function (biology); Model checking; Computer science; Control theory (sociology); Mathematical optimization; Algorithm; Mathematics; Artificial intelligence; Mathematical analysis; Control (management); Combinatorics","score_opus":0.06673743189225984,"score_gpt":0.3420002828752244,"score_spread":0.27526285098296455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406892457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6808564,0.0010148924,0.2939951,0.00047976186,0.000092876,0.00021487146,0.0005117342,0.012022877,0.010811477],"genre_scores_gemma":[0.91474223,0.00016425834,0.082695365,0.00010211986,0.0000098265555,0.000109406705,0.00049261854,0.00075100927,0.0009331676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991714,0.002840774,0.000461811,0.00062025996,0.0036212646,0.0007419777],"domain_scores_gemma":[0.9282434,0.054035597,0.00305415,0.008151754,0.0060395375,0.00047551823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072427513,0.00077911397,0.0006577997,0.0017891199,0.00046402347,0.0011017632,0.0019821066,0.00077515125,0.0017131135],"category_scores_gemma":[0.04511604,0.00041113707,0.00091968075,0.0009895595,0.0017150784,0.0016947848,0.0012027245,0.0012722501,0.0002918897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020330015,0.00031212953,0.018481312,0.0009184182,0.00020697949,0.00043020525,0.0006543845,0.80249816,0.017985491,0.033991087,0.0030203639,0.119468465],"study_design_scores_gemma":[0.000046986646,0.00038763692,0.0020722111,0.000062887135,0.000055459503,0.00009402461,0.00007321443,0.9677367,0.024162978,0.003923647,0.0013580482,0.000026233442],"about_ca_topic_score_codex":0.0101287,"about_ca_topic_score_gemma":0.008171178,"teacher_disagreement_score":0.0101287,"about_ca_system_score_codex":0.00213094,"about_ca_system_score_gemma":0.0033649313,"threshold_uncertainty_score":0.038303792},"labels":[],"label_agreement":null},{"id":"W4406946785","doi":"10.1109/lgrs.2025.3536005","title":"SHAP-Assisted Resilience Enhancement Against Adversarial Perturbations in Optical and SAR Image Classification","year":2025,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Resilience (materials science); Computer science; Image (mathematics); Contextual image classification; Remote sensing; Optical imaging; Synthetic aperture radar; Artificial intelligence; Computer vision; Pattern recognition (psychology); Geology; Optics; Physics","score_opus":0.012150648297162588,"score_gpt":0.2638341849958837,"score_spread":0.25168353669872107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406946785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10548926,0.00036869894,0.8902789,0.0005326615,0.00006683866,0.000044315682,0.00008180993,0.00066827604,0.002469272],"genre_scores_gemma":[0.9517621,0.00017592807,0.046184894,0.0001519473,0.000056051813,0.000028590594,0.00011314099,0.000042509117,0.0014849392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941635,0.00017909994,0.000027752361,0.00013820211,0.00016506102,0.0000736065],"domain_scores_gemma":[0.99803084,0.0009610584,0.00034351653,0.00034087445,0.00023105867,0.00009273898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013960786,0.0008467016,0.0006189163,0.00054605654,0.0003257074,0.00063458073,0.0009341741,0.0008287932,0.0011245726],"category_scores_gemma":[0.0052580032,0.00022575683,0.00049924204,0.00033567607,0.0011623307,0.0014677403,0.0016232907,0.0013014562,0.00024897174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016474252,0.000056929795,0.0025622298,0.00007078875,0.000065623855,0.0001954639,0.00009952937,0.83187836,0.015227499,0.031712204,0.002143025,0.115823664],"study_design_scores_gemma":[0.0000031533655,0.0000332523,0.00023125255,0.000004355215,0.0000056235085,0.000032692,0.0000075412645,0.9889037,0.0022921017,0.008211698,0.00026934867,0.0000053238396],"about_ca_topic_score_codex":0.0009546757,"about_ca_topic_score_gemma":0.0011083347,"teacher_disagreement_score":0.0013960786,"about_ca_system_score_codex":0.0007291985,"about_ca_system_score_gemma":0.0005183168,"threshold_uncertainty_score":0.0073832273},"labels":[],"label_agreement":null},{"id":"W4406957873","doi":"10.1145/3715693","title":"Assessing the Robustness of Test Selection Methods for Deep Neural Networks","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Artificial neural network; Machine learning; Robustness testing; Biology","score_opus":0.05595642973612091,"score_gpt":0.38734945323747966,"score_spread":0.33139302350135874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406957873","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55061644,0.004046886,0.43720192,0.0014371708,0.00028398403,0.00043835456,0.00071338954,0.0022399917,0.0030218773],"genre_scores_gemma":[0.9368037,0.0002823818,0.061106656,0.00021970934,0.00007277293,0.00021280585,0.0007267164,0.00016390112,0.00041127024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9754701,0.013873562,0.0019206365,0.002141975,0.005849483,0.0007441508],"domain_scores_gemma":[0.70974886,0.24563453,0.014235136,0.018684816,0.010218598,0.001478001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0404065,0.0014216339,0.00077393855,0.0030639528,0.0006309539,0.0011556074,0.0019073617,0.0018994577,0.0007317656],"category_scores_gemma":[0.19915907,0.00051131076,0.0009153992,0.0013145249,0.0018986333,0.0017297618,0.0020001354,0.0019603223,0.00032748436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025465358,0.0005330928,0.14040475,0.0007260109,0.0013422352,0.00037600237,0.0004973376,0.5728644,0.011087346,0.010903579,0.004804438,0.25391427],"study_design_scores_gemma":[0.0000992087,0.00077446515,0.01163297,0.0001247513,0.00009790899,0.0002447726,0.00012317814,0.96169966,0.015727516,0.008327031,0.0011075354,0.00004095096],"about_ca_topic_score_codex":0.0020861588,"about_ca_topic_score_gemma":0.0016963055,"teacher_disagreement_score":0.0404065,"about_ca_system_score_codex":0.0013099586,"about_ca_system_score_gemma":0.0012672583,"threshold_uncertainty_score":0.21369255},"labels":[],"label_agreement":null},{"id":"W4407129488","doi":"10.1109/dsc63484.2024.00098","title":"Security and Privacy of Artificial Intelligence with Ethical Concerns","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information privacy; Internet privacy; Computer security","score_opus":0.029439307514169403,"score_gpt":0.3233924823186344,"score_spread":0.293953174804465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407129488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034023833,0.018016601,0.4692243,0.27838472,0.0024757525,0.00022344217,0.0002492421,0.00012256078,0.19727956],"genre_scores_gemma":[0.8868758,0.0070481556,0.072546504,0.018342428,0.004147947,0.0003829091,0.00010928791,0.00007881433,0.010468148],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96600926,0.021497162,0.0013776483,0.0031529467,0.0068021505,0.0011608687],"domain_scores_gemma":[0.9348717,0.045922708,0.004856645,0.009401188,0.0038759531,0.0010718734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025686612,0.00096436974,0.0011962063,0.0014585666,0.0048273318,0.011197238,0.0016356325,0.009421584,0.0031134824],"category_scores_gemma":[0.052332185,0.00072644977,0.0014120787,0.0015018235,0.036205944,0.011616125,0.006438618,0.0122465,0.0005940877],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011766395,0.000013255164,0.00026410705,0.000055322475,0.000018499277,0.00008982139,0.00041790892,0.0014375219,0.00014422987,0.9900295,0.0013395837,0.006178556],"study_design_scores_gemma":[0.000006593137,0.0000119117885,0.00012446032,0.00008818525,0.0000092269875,0.000119242286,0.00015974409,0.002481637,0.0001517893,0.9884913,0.008343259,0.000012647545],"about_ca_topic_score_codex":0.0011570448,"about_ca_topic_score_gemma":0.0007390692,"teacher_disagreement_score":0.025686612,"about_ca_system_score_codex":0.0038095508,"about_ca_system_score_gemma":0.004580328,"threshold_uncertainty_score":0.13584536},"labels":[],"label_agreement":null},{"id":"W4407185097","doi":"10.48550/arxiv.2502.01925","title":"PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Shot (pellet); Sampling (signal processing); Computer science; Psychology; Chemistry; Telecommunications","score_opus":0.041286191726323904,"score_gpt":0.29389463947520733,"score_spread":0.2526084477488834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407185097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20497508,0.0023439433,0.7462398,0.0013909148,0.000674364,0.0005099841,0.0006304524,0.03553677,0.007698717],"genre_scores_gemma":[0.847811,0.00018077886,0.14258245,0.0009412316,0.00019617223,0.00020995049,0.0011999283,0.0008001556,0.006078373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962716,0.0016072904,0.00017641262,0.0009135193,0.00073342177,0.00029785294],"domain_scores_gemma":[0.9898531,0.005556966,0.0006129381,0.0027933747,0.0006825297,0.00050120574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051015434,0.0020482712,0.001915364,0.0009816824,0.0012966187,0.0016028427,0.0033409281,0.0025149481,0.004262934],"category_scores_gemma":[0.017758567,0.00064064446,0.0011321375,0.00041740408,0.001967666,0.0033460762,0.0043546227,0.004180715,0.00205689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024297617,0.0012256384,0.007887311,0.0005945441,0.00050658017,0.0005422687,0.0009120687,0.3056393,0.043008808,0.016227994,0.028285982,0.5927397],"study_design_scores_gemma":[0.00006571287,0.0003447618,0.0005184587,0.000023674012,0.00003671703,0.00016832202,0.00008142494,0.9778556,0.010165392,0.008450625,0.0022481035,0.000041116964],"about_ca_topic_score_codex":0.0024156957,"about_ca_topic_score_gemma":0.00389206,"teacher_disagreement_score":0.0051015434,"about_ca_system_score_codex":0.0009137287,"about_ca_system_score_gemma":0.0014158498,"threshold_uncertainty_score":0.026979864},"labels":[],"label_agreement":null},{"id":"W4407218648","doi":"10.1145/3669940.3707282","title":"Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Isolation (microbiology); Deep learning; Computer architecture; Artificial intelligence; Distributed computing","score_opus":0.006374065827886231,"score_gpt":0.2641319619808613,"score_spread":0.25775789615297506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407218648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.621705,0.0018052021,0.2794319,0.0008156829,0.0008409501,0.0005621654,0.0006215709,0.08404422,0.010173285],"genre_scores_gemma":[0.9574643,0.00014605501,0.037964262,0.00036417547,0.00009648217,0.0001763064,0.00052305864,0.0011569484,0.002108445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975222,0.00035808727,0.00020025247,0.00059444876,0.0007260238,0.00059907517],"domain_scores_gemma":[0.9952122,0.0012252559,0.00045774074,0.0018849719,0.0005865251,0.0006333072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020541237,0.0018689007,0.0009264757,0.0010061058,0.0012994498,0.0018747529,0.0038047868,0.00073543965,0.0042530033],"category_scores_gemma":[0.008178606,0.0008531603,0.0006187503,0.00073790306,0.0015696716,0.003216894,0.0042109503,0.0023131787,0.0013233445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0072673894,0.002064865,0.037744295,0.00090404245,0.0007809768,0.0013443832,0.0019526144,0.21435678,0.2620772,0.015456796,0.063770175,0.39228046],"study_design_scores_gemma":[0.00034180706,0.0012549192,0.005486837,0.00004754547,0.0001309775,0.00033003886,0.00028847676,0.8773061,0.09700562,0.0062918956,0.011354352,0.00016140158],"about_ca_topic_score_codex":0.0032712235,"about_ca_topic_score_gemma":0.0042118304,"teacher_disagreement_score":0.0042530033,"about_ca_system_score_codex":0.0012987123,"about_ca_system_score_gemma":0.0031849444,"threshold_uncertainty_score":0.014227688},"labels":[],"label_agreement":null},{"id":"W4407271028","doi":"10.1007/s00366-025-02114-2","title":"Feed-forward neural networks as a mixed-integer program","year":2025,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Integer (computer science); Integer programming; Computer science; Algorithm; Artificial intelligence; Programming language","score_opus":0.002862062500412858,"score_gpt":0.21690013753231635,"score_spread":0.2140380750319035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407271028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02081752,0.00039830976,0.963931,0.0012768339,0.00013626259,0.000084717096,0.00013466996,0.00017966775,0.013040914],"genre_scores_gemma":[0.68616444,0.0005427838,0.2749156,0.0006312672,0.00023309248,0.0007027475,0.00028277285,0.00026181212,0.03626555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99914134,0.00047666085,0.000027993547,0.00012642668,0.0001478447,0.000079777834],"domain_scores_gemma":[0.9954756,0.0039608227,0.00016803207,0.00008853249,0.00020110038,0.00010591506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024604427,0.001405573,0.0012650177,0.0005414165,0.00047293963,0.0021612635,0.001366099,0.0033120762,0.0072196415],"category_scores_gemma":[0.0075672325,0.0008856408,0.0008489256,0.0006218019,0.0019481491,0.0018641611,0.0022033625,0.0027346236,0.00052166934],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004653536,0.00004135567,0.00015732428,0.00006615711,0.000023386396,0.000051387087,0.000038869992,0.9173146,0.00023945225,0.074847825,0.0008691168,0.006304069],"study_design_scores_gemma":[0.0000065939985,0.0000087737,0.000009273655,0.000005610415,0.0000029865307,0.0000032390205,0.0000032094897,0.98930895,0.00006149111,0.010399318,0.00018890447,0.0000017361169],"about_ca_topic_score_codex":0.0032888143,"about_ca_topic_score_gemma":0.0038721913,"teacher_disagreement_score":0.0072196415,"about_ca_system_score_codex":0.0013581974,"about_ca_system_score_gemma":0.0015045446,"threshold_uncertainty_score":0.0241521},"labels":[],"label_agreement":null},{"id":"W4407320790","doi":"10.1016/j.asoc.2025.112828","title":"Black-box adversarial examples via frequency distortion against fault diagnosis systems","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry; Ministry of Science and ICT, South Korea; Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries; National Research Foundation of Korea; Information Technology Research Centre; Ministry of Agriculture, Food and Rural Affairs; Ministry of Education","keywords":"Adversarial system; Black box; Distortion (music); Computer science; Fault (geology); Artificial intelligence; Telecommunications; Seismology; Geology","score_opus":0.010497269046769278,"score_gpt":0.24555390969421573,"score_spread":0.23505664064744644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407320790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034980565,0.00023463841,0.95967406,0.0005718298,0.00006369899,0.000036879333,0.000055281034,0.0004284715,0.0039546364],"genre_scores_gemma":[0.9567138,0.00011898514,0.04020787,0.00012485431,0.00004023156,0.000037653568,0.00005190452,0.00004724707,0.0026574251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877626,0.00039554416,0.00004312195,0.00023672511,0.0004201184,0.00012818913],"domain_scores_gemma":[0.9948979,0.0037770774,0.000331361,0.0005228679,0.0003820356,0.000088770255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021157728,0.0009425226,0.00092766195,0.00048286928,0.0003285276,0.00085014396,0.0010919876,0.001643203,0.002930968],"category_scores_gemma":[0.011365459,0.00035139607,0.00043357257,0.00031644144,0.0016448033,0.0014617515,0.00202326,0.0017464603,0.00040997702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002686019,0.000026264372,0.00028895205,0.000070863105,0.000030235668,0.00009960369,0.000046112596,0.91813594,0.0036098054,0.039138507,0.0007931411,0.037492014],"study_design_scores_gemma":[0.000005860893,0.000030549778,0.00005986366,0.000007606043,0.000004748696,0.00002553535,0.0000034025643,0.9865984,0.001462534,0.011598977,0.00019802118,0.0000044664607],"about_ca_topic_score_codex":0.0010658548,"about_ca_topic_score_gemma":0.0007450517,"teacher_disagreement_score":0.002930968,"about_ca_system_score_codex":0.0008576788,"about_ca_system_score_gemma":0.0005567582,"threshold_uncertainty_score":0.011189401},"labels":[],"label_agreement":null},{"id":"W4407391299","doi":"10.1016/j.compbiomed.2025.109788","title":"EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent","year":2025,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Artificial Intelligence in Medicine (Canada)","funders":"Specific Research Project of Guangxi for Research Bases and Talents; National Natural Science Foundation of China","keywords":"Gradient descent; Computer science; Adversarial system; Descent (aeronautics); Artificial intelligence; Algorithm; Pattern recognition (psychology); Meteorology; Artificial neural network; Physics","score_opus":0.012508886109210844,"score_gpt":0.31504941736289704,"score_spread":0.3025405312536862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407391299","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001455651,0.00009254287,0.99534214,0.00014256609,0.000041291456,0.000044318786,0.000112045775,0.0019818773,0.00078759657],"genre_scores_gemma":[0.24791163,0.0003551285,0.736789,0.0006714107,0.00014875596,0.0006962275,0.0012381214,0.002472445,0.009717306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993181,0.00028177287,0.000021770138,0.00011157812,0.00021649526,0.00005026904],"domain_scores_gemma":[0.9988796,0.0006889018,0.00006550299,0.00018108703,0.00012844178,0.00005646969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014025664,0.0016918684,0.0015057442,0.00070954155,0.0004739582,0.0010642111,0.002268933,0.0027126896,0.007231326],"category_scores_gemma":[0.0054073026,0.00082408317,0.0014318165,0.00047599833,0.0011409742,0.0013041346,0.0032157705,0.0035896385,0.0023422642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006074999,0.000044423177,0.00021052427,0.00008486009,0.0000822384,0.00010995838,0.000040207127,0.8913982,0.0012407856,0.026620815,0.008446238,0.07166098],"study_design_scores_gemma":[0.0000039040865,0.000007888638,0.000016818936,0.0000036955887,0.0000027575559,0.000009716747,0.0000013936867,0.9891179,0.00020223716,0.01008863,0.0005420618,0.0000029682772],"about_ca_topic_score_codex":0.003129266,"about_ca_topic_score_gemma":0.0034866682,"teacher_disagreement_score":0.007231326,"about_ca_system_score_codex":0.00068413647,"about_ca_system_score_gemma":0.0013092171,"threshold_uncertainty_score":0.0241912},"labels":[],"label_agreement":null},{"id":"W4407394336","doi":"10.15514/ispras-2024-36(5)-9","title":"Is AI Interpretability Safe: the Relationship between Interpretability and Security of Machine Learning Models","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Institute for System Programming of RAS","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Air Canada","funders":"","keywords":"Interpretability; Artificial intelligence; Computer science; Machine learning","score_opus":0.03439145558746889,"score_gpt":0.29261617923025607,"score_spread":0.2582247236427872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407394336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16640814,0.0018041858,0.79906833,0.019027904,0.00023570828,0.00021804213,0.00074537017,0.00093087036,0.0115614645],"genre_scores_gemma":[0.95487875,0.0004902216,0.041763328,0.0010007742,0.000323967,0.000111128655,0.00046350042,0.00027412327,0.00069416873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94541216,0.032747425,0.0034956837,0.007691516,0.008740497,0.0019126965],"domain_scores_gemma":[0.4702732,0.3870649,0.035590053,0.09234164,0.011935957,0.0027943284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037469834,0.0013461431,0.0020307282,0.0031678728,0.0019795294,0.00962448,0.0025469093,0.0050156,0.0037524358],"category_scores_gemma":[0.2856463,0.0012050052,0.0024610665,0.001663899,0.016118672,0.021310696,0.0070909844,0.010694292,0.0008034379],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014941648,0.00041117106,0.05506507,0.000959723,0.0013091806,0.00088704436,0.0051906235,0.2080302,0.0058865,0.6008292,0.0059964987,0.11394051],"study_design_scores_gemma":[0.000053799464,0.00015347777,0.002167303,0.00018443786,0.000085764776,0.0003853797,0.0004140242,0.18854633,0.0027263048,0.8031878,0.0020354453,0.000059931222],"about_ca_topic_score_codex":0.0011138009,"about_ca_topic_score_gemma":0.0007925886,"teacher_disagreement_score":0.037469834,"about_ca_system_score_codex":0.002449014,"about_ca_system_score_gemma":0.0022244586,"threshold_uncertainty_score":0.19816178},"labels":[],"label_agreement":null},{"id":"W4407920343","doi":"10.1007/978-3-031-80817-3_2","title":"Feasibility of Adversarial Attacks Against Machine Learning Models","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research and Productivity Council; National Research Council Canada; University of Windsor","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Machine learning; Computer security","score_opus":0.06820204632191545,"score_gpt":0.28435507333378324,"score_spread":0.21615302701186778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03685872,0.00065585074,0.9210299,0.0040870607,0.00026009016,0.000093390605,0.00023086881,0.00040146333,0.03638258],"genre_scores_gemma":[0.9264919,0.000859644,0.05625029,0.00058844435,0.00053451414,0.00024716248,0.0003968713,0.00021453851,0.014416491],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99274194,0.0035847544,0.00022193659,0.0007953409,0.0020721962,0.0005838185],"domain_scores_gemma":[0.9248845,0.06638947,0.0017778146,0.0044942973,0.001744553,0.0007093644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007701184,0.0014525675,0.0015858964,0.001021184,0.0009538747,0.0032493519,0.0020073815,0.0035184612,0.006611183],"category_scores_gemma":[0.054260056,0.001260523,0.0013720478,0.0008480982,0.0041899327,0.005724099,0.0054767784,0.0069882986,0.0010498201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007183,0.000083685445,0.000566566,0.00017659523,0.00010370005,0.00019565856,0.000090349306,0.46105283,0.003946139,0.49991465,0.0045740623,0.028577412],"study_design_scores_gemma":[0.000026267056,0.000063596264,0.00011637104,0.000025026555,0.00001272652,0.00008820672,0.000016614855,0.76626205,0.0013525565,0.23098804,0.0010359271,0.000012530062],"about_ca_topic_score_codex":0.0005732958,"about_ca_topic_score_gemma":0.00030541734,"teacher_disagreement_score":0.007701184,"about_ca_system_score_codex":0.0013561718,"about_ca_system_score_gemma":0.0012516303,"threshold_uncertainty_score":0.04072827},"labels":[],"label_agreement":null},{"id":"W4407937966","doi":"10.5220/0013240700003899","title":"HybridMTD: Enhancing Robustness Against Adversarial Attacks with Ensemble Neural Networks and Moving Target Defense","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Windsor","funders":"","keywords":"Robustness (evolution); Adversarial system; Computer science; Artificial neural network; Artificial intelligence; Deep neural networks; Machine learning; Chemistry","score_opus":0.005789607676731389,"score_gpt":0.22864795145221145,"score_spread":0.22285834377548006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407937966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026708134,0.00047849826,0.9669504,0.00021731952,0.00018698059,0.000043792556,0.0001117067,0.0018423847,0.0034607484],"genre_scores_gemma":[0.803026,0.0002935879,0.18917899,0.0002979051,0.00014923657,0.00008030228,0.0003672147,0.00022634503,0.006380447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990251,0.00018379961,0.00003764853,0.00019895703,0.00041197892,0.00014251767],"domain_scores_gemma":[0.9989674,0.00033463494,0.00008912308,0.00032562207,0.00022045911,0.00006285692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013956897,0.0009705824,0.0010174225,0.00070754584,0.0003949241,0.00095092785,0.0014002395,0.0013758275,0.0026658513],"category_scores_gemma":[0.0030484914,0.00034497716,0.00075499725,0.00046020368,0.000711447,0.0020622665,0.002903376,0.0017837528,0.00077033025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003030254,0.00012640076,0.0010510643,0.00007460184,0.00016763451,0.00012850104,0.00003964342,0.75775224,0.029749002,0.01570896,0.004120265,0.1907787],"study_design_scores_gemma":[0.0000045605716,0.00004428749,0.0001544228,0.0000028797967,0.000009480115,0.000033542237,0.0000035091518,0.99204457,0.004094145,0.0029319297,0.00067008793,0.00000661743],"about_ca_topic_score_codex":0.00097162195,"about_ca_topic_score_gemma":0.0012427965,"teacher_disagreement_score":0.0026658513,"about_ca_system_score_codex":0.0005464056,"about_ca_system_score_gemma":0.0005071336,"threshold_uncertainty_score":0.008918166},"labels":[],"label_agreement":null},{"id":"W4408107658","doi":"10.1016/j.iot.2025.101558","title":"Towards privacy-preserving split learning: Destabilizing adversarial inference and reconstruction attacks in the cloud","year":2025,"lang":"en","type":"article","venue":"Internet of Things","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Saint Mary's University; University of New Brunswick","funders":"","keywords":"Adversarial system; Cloud computing; Inference; Computer science; Computer security; Internet privacy; Artificial intelligence","score_opus":0.014672407683235017,"score_gpt":0.2877162755950569,"score_spread":0.2730438679118219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408107658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027465649,0.00043036847,0.9676148,0.0013726862,0.000097739176,0.000052740208,0.00011394134,0.0004892499,0.0023628196],"genre_scores_gemma":[0.9122029,0.0003154592,0.08393286,0.00063484604,0.00016596477,0.0000774785,0.00017436835,0.0001700031,0.002326078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955005,0.0018043475,0.00014234053,0.0008068233,0.0011427207,0.000603278],"domain_scores_gemma":[0.9838792,0.01035642,0.00077299785,0.0036337348,0.0008970884,0.00046049868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064848675,0.0013075144,0.0019350115,0.0006678489,0.001162844,0.0026869643,0.0027688728,0.0028987553,0.0024525858],"category_scores_gemma":[0.022110289,0.0007689782,0.001175561,0.0010256364,0.003910406,0.005718221,0.008017616,0.006077992,0.0006120419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010219882,0.00015867813,0.0013606447,0.000121701036,0.00015280023,0.00025169217,0.00020288353,0.7359682,0.0058041494,0.1894917,0.0062755514,0.05918997],"study_design_scores_gemma":[0.000014857564,0.000028378681,0.00006668116,0.000008393939,0.000007511882,0.00004104802,0.000022351285,0.9241463,0.0014116463,0.07388806,0.00035761943,0.0000071597105],"about_ca_topic_score_codex":0.0010819522,"about_ca_topic_score_gemma":0.00090197724,"teacher_disagreement_score":0.0064848675,"about_ca_system_score_codex":0.0013531897,"about_ca_system_score_gemma":0.0018326149,"threshold_uncertainty_score":0.03429568},"labels":[],"label_agreement":null},{"id":"W4408146479","doi":"10.1109/icairc64177.2024.10900215","title":"Robustness of Large Language Models Against Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robustness (evolution); Computer science; Adversarial system; Language model; Computer security; Artificial intelligence","score_opus":0.01302120973298575,"score_gpt":0.28682848067675587,"score_spread":0.2738072709437701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408146479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22689122,0.002106625,0.75049376,0.0048665297,0.000449044,0.000503755,0.0012852786,0.0055328854,0.007870952],"genre_scores_gemma":[0.9471224,0.0005677879,0.04786206,0.00088261423,0.00015073922,0.00023992204,0.00089389074,0.00037352712,0.0019070685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9926629,0.0038629433,0.0003928853,0.0011245671,0.0014232417,0.00053348107],"domain_scores_gemma":[0.9517787,0.035388917,0.0025879315,0.008039885,0.001580377,0.00062415027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014147438,0.0018143506,0.001264602,0.0011966445,0.0011307993,0.0020565896,0.0017198928,0.0019142922,0.0018421586],"category_scores_gemma":[0.061812595,0.00073889876,0.0012890281,0.0005294828,0.0032457537,0.003961442,0.0044818036,0.0045984886,0.0011077715],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036932924,0.00009032866,0.0042143846,0.00016132445,0.00020891773,0.0001864657,0.00018996905,0.932042,0.004827235,0.015849493,0.002738343,0.03912211],"study_design_scores_gemma":[0.000012059045,0.00011089703,0.00034325925,0.000029685922,0.00001808134,0.000077177625,0.000037818572,0.98143435,0.002809319,0.014411724,0.00069428544,0.000021273718],"about_ca_topic_score_codex":0.003111418,"about_ca_topic_score_gemma":0.0022963327,"teacher_disagreement_score":0.014147438,"about_ca_system_score_codex":0.0018728871,"about_ca_system_score_gemma":0.0016052534,"threshold_uncertainty_score":0.07481974},"labels":[],"label_agreement":null},{"id":"W4408146758","doi":"10.1109/icmla61862.2024.00234","title":"Securing 3D Deep Learning Models: Simple and Effective Defense Against Adversarial Attacks","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Adversarial system; Computer science; Simple (philosophy); Deep learning; Computer security; Artificial intelligence; Epistemology","score_opus":0.011926654034327503,"score_gpt":0.2518388314573767,"score_spread":0.23991217742304918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408146758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08117725,0.00094531575,0.90945005,0.0014750549,0.00016814494,0.00007089418,0.000096644944,0.0014655035,0.0051511587],"genre_scores_gemma":[0.9376471,0.0005412282,0.05897247,0.00051059,0.000050355848,0.00007088228,0.000115469666,0.000080977356,0.00201081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869835,0.00032817767,0.000055980658,0.00017179635,0.0005471019,0.00019866713],"domain_scores_gemma":[0.9973648,0.0011044105,0.00032781108,0.00084030064,0.00025029544,0.00011247001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001770689,0.0011416662,0.00073656155,0.0004118626,0.00050646527,0.001030377,0.0013949841,0.001782467,0.0012091766],"category_scores_gemma":[0.0068703177,0.0005688445,0.0006866745,0.00024426612,0.0017935596,0.0022665241,0.004139852,0.0029378445,0.000503994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021308573,0.00006867543,0.0023156758,0.00006996384,0.00007155806,0.00019656682,0.00012220241,0.866702,0.016479176,0.030182527,0.00302796,0.08055053],"study_design_scores_gemma":[0.0000045661172,0.000046032404,0.0001664953,0.000011833153,0.000005663062,0.000066899185,0.000012734396,0.98565674,0.004144912,0.008958054,0.0009169202,0.000009148295],"about_ca_topic_score_codex":0.0015771316,"about_ca_topic_score_gemma":0.001537043,"teacher_disagreement_score":0.001782467,"about_ca_system_score_codex":0.00084166654,"about_ca_system_score_gemma":0.0007979164,"threshold_uncertainty_score":0.009364426},"labels":[],"label_agreement":null},{"id":"W4408155387","doi":"10.1145/3721479","title":"Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey","year":2025,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Generative grammar; Diffusion; Data science; Generative model; Artificial intelligence","score_opus":0.13415497711911462,"score_gpt":0.3855403862754967,"score_spread":0.2513854091563821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408155387","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003788542,0.81566364,0.15061806,0.004473527,0.00087479217,0.00009707643,0.0001317878,0.00036703475,0.023985604],"genre_scores_gemma":[0.09594608,0.8585854,0.032532383,0.0017967583,0.0026868072,0.00015652597,0.00033595084,0.00013520768,0.007824867],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983664,0.0003939302,0.00014111078,0.00030560608,0.00067379663,0.00011917844],"domain_scores_gemma":[0.9941446,0.00456632,0.0002985961,0.00041383653,0.0004912268,0.000085423824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026031618,0.0018310734,0.0017817756,0.0022407721,0.0006898035,0.0021462617,0.0017588381,0.0029185363,0.0035081848],"category_scores_gemma":[0.0071640504,0.0009391986,0.0015266002,0.0019007311,0.0015423248,0.00492415,0.0018358032,0.0033809687,0.0016363428],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064467575,0.00016018173,0.0017972417,0.0048504635,0.00023690175,0.00020643773,0.0001949407,0.03826417,0.0013513409,0.17319898,0.023946403,0.75572854],"study_design_scores_gemma":[0.000043797038,0.00047357855,0.002470006,0.0053245537,0.00039857905,0.0032351632,0.00040328814,0.16986416,0.005656645,0.26859534,0.5433058,0.00022904444],"about_ca_topic_score_codex":0.001237351,"about_ca_topic_score_gemma":0.0008872566,"teacher_disagreement_score":0.0035081848,"about_ca_system_score_codex":0.0011224423,"about_ca_system_score_gemma":0.0011698874,"threshold_uncertainty_score":0.013767004},"labels":[],"label_agreement":null},{"id":"W4408280932","doi":"10.1109/jiot.2025.3550048","title":"IPRPAS: A Dataset of Physical Adversarial Samples for Assessing Object Detection in Intelligent Vehicles","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Adversarial system; Object detection; Object (grammar); Artificial intelligence; Pattern recognition (psychology); Data mining; Computer vision","score_opus":0.024072535853947846,"score_gpt":0.32705823064594586,"score_spread":0.302985694791998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408280932","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51899046,0.002592749,0.32647693,0.0014341677,0.0013822444,0.0013342204,0.11527135,0.019680753,0.012837065],"genre_scores_gemma":[0.7379186,0.00070278335,0.07293277,0.00046555052,0.00016517456,0.00059703976,0.1807081,0.0004367911,0.0060731308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99904007,0.00016194969,0.000056996418,0.00023310124,0.0004016907,0.000106159074],"domain_scores_gemma":[0.9986407,0.00033181804,0.00012769226,0.00047534236,0.00031629045,0.000108034204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009894812,0.0013466193,0.00083382404,0.0014669683,0.00039970755,0.00064629415,0.0019829462,0.0020729594,0.0019198111],"category_scores_gemma":[0.003602444,0.00039184207,0.0009588289,0.0009986126,0.00075164833,0.00081161293,0.0015104071,0.0012734522,0.002571192],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00155698,0.0019081322,0.041207917,0.001188523,0.0008419584,0.0012943529,0.00020622523,0.51272935,0.022499781,0.0060275923,0.18501239,0.2255269],"study_design_scores_gemma":[0.00016106528,0.000617195,0.038692325,0.00011235165,0.00013889237,0.0014719473,0.00022034634,0.9016749,0.017595882,0.0066787633,0.032506496,0.00012979534],"about_ca_topic_score_codex":0.005392938,"about_ca_topic_score_gemma":0.0076509113,"teacher_disagreement_score":0.005392938,"about_ca_system_score_codex":0.000613607,"about_ca_system_score_gemma":0.00078188226,"threshold_uncertainty_score":0.010723114},"labels":[],"label_agreement":null},{"id":"W4408352934","doi":"10.1109/icassp49660.2025.10890043","title":"PGD-Imp: Rethinking and Unleashing Potential of Classic PGD with Dual Strategies for Imperceptible Adversarial Attacks","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Guangdong Province","keywords":"Adversarial system; Dual (grammatical number); Computer science; Computer security; Artificial intelligence; Art; Literature","score_opus":0.01036345587312591,"score_gpt":0.2692732663854111,"score_spread":0.2589098105122852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408352934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022491034,0.0004381629,0.97268665,0.00029530973,0.000060162827,0.000041069525,0.000022321385,0.00043039038,0.0035347976],"genre_scores_gemma":[0.8604126,0.00037488915,0.13546816,0.00026382107,0.000047614198,0.00006813943,0.000053879034,0.00010094836,0.0032098908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989844,0.00033420496,0.00005047766,0.0001602365,0.0003565242,0.00011414318],"domain_scores_gemma":[0.99822384,0.0009497329,0.00016718583,0.0004112718,0.00015603197,0.000091911395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015880527,0.0011536309,0.0007563402,0.0005589139,0.00024677365,0.00087456725,0.0010941133,0.001074537,0.0015895118],"category_scores_gemma":[0.0037818342,0.0003145759,0.0005684691,0.0002316736,0.0013975825,0.0015590176,0.0022526819,0.0018523117,0.00034135982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004664551,0.00017152012,0.0015480723,0.00023594523,0.000112009824,0.00028807516,0.00013953733,0.66589266,0.044122748,0.084197685,0.002914203,0.1999111],"study_design_scores_gemma":[0.000016515352,0.00015620913,0.00014722641,0.000014051704,0.000014112245,0.00012349857,0.000013029755,0.97760195,0.008656679,0.011331061,0.0019107611,0.000014839435],"about_ca_topic_score_codex":0.00039558197,"about_ca_topic_score_gemma":0.00038676203,"teacher_disagreement_score":0.0015895118,"about_ca_system_score_codex":0.00059886224,"about_ca_system_score_gemma":0.0005939382,"threshold_uncertainty_score":0.008398473},"labels":[],"label_agreement":null},{"id":"W4408401425","doi":"10.1109/icaiccit64383.2024.10912255","title":"Assessing the Applicability of Adversarial Machine Learning Approaches for Cybersecurity","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Hydro (Canada)","funders":"","keywords":"Adversarial system; Computer science; Adversarial machine learning; Computer security; Artificial intelligence; Machine learning","score_opus":0.050931855088759824,"score_gpt":0.3178269163429652,"score_spread":0.2668950612542054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408401425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5452539,0.0028323268,0.41898412,0.0036130873,0.00018791047,0.00072708406,0.0002274233,0.00045615132,0.02771804],"genre_scores_gemma":[0.9644607,0.00040038844,0.034196697,0.00014719847,0.000041033723,0.00010436701,0.00007351075,0.00002270084,0.0005533557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98672473,0.008495372,0.0004957155,0.0009454023,0.002884465,0.00045431076],"domain_scores_gemma":[0.8909269,0.09226955,0.005694915,0.006605833,0.0032463104,0.0012564124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022918904,0.0012058767,0.00049333286,0.0026409244,0.000707953,0.0022892198,0.0012523893,0.0017009621,0.0017626708],"category_scores_gemma":[0.081615455,0.0002802329,0.0006932169,0.00090570276,0.0029727747,0.004458481,0.0030458928,0.00262295,0.00030834012],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078837987,0.0007919744,0.06460326,0.0006353978,0.0004530997,0.00011219299,0.0010366646,0.6866749,0.0034337766,0.05723982,0.0014811516,0.18274935],"study_design_scores_gemma":[0.000034242443,0.0013291339,0.012328444,0.00019787026,0.00009619315,0.00014475807,0.0004868367,0.9315612,0.004030931,0.04744026,0.002293414,0.00005661838],"about_ca_topic_score_codex":0.0012216362,"about_ca_topic_score_gemma":0.0013186755,"teacher_disagreement_score":0.022918904,"about_ca_system_score_codex":0.0018396238,"about_ca_system_score_gemma":0.00109007,"threshold_uncertainty_score":0.12120825},"labels":[],"label_agreement":null},{"id":"W4408423544","doi":"10.1109/tmc.2025.3551537","title":"A Joint Secure Mechanism of Multi-Task Learning for a UAV Team Under FDI Attacks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Task (project management); Mechanism (biology); Computer security; Human–computer interaction; Engineering","score_opus":0.020074393173923146,"score_gpt":0.29529807487008,"score_spread":0.27522368169615685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408423544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027273793,0.00009503154,0.97026104,0.00021948147,0.00003701808,0.000046830504,0.000022158387,0.00054876995,0.0014957769],"genre_scores_gemma":[0.93572897,0.00007114631,0.061270006,0.00012455549,0.00002702308,0.0001116193,0.000042560856,0.000035005363,0.0025891762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916816,0.00014139633,0.000044706525,0.0002472507,0.00020039783,0.00019825058],"domain_scores_gemma":[0.99898714,0.00022708092,0.00019639081,0.0002264814,0.00024028895,0.00012258464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015434221,0.00094186905,0.0007799634,0.0003949286,0.00062808086,0.00083125336,0.0017751474,0.0014124641,0.0019280482],"category_scores_gemma":[0.0026719589,0.00035773308,0.0006475143,0.00025807924,0.0012785126,0.0017506708,0.0028674216,0.0013687636,0.0005017225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003332231,0.00012971384,0.0019067827,0.000088202505,0.00006460484,0.00025201862,0.00020938038,0.86411774,0.017038804,0.021945857,0.0017311565,0.09218241],"study_design_scores_gemma":[0.0000095268715,0.000060330432,0.00010241746,0.0000035334476,0.0000071636473,0.000021375974,0.000009069804,0.9942427,0.0017952427,0.003511743,0.00023062508,0.000006186845],"about_ca_topic_score_codex":0.0019891888,"about_ca_topic_score_gemma":0.0017623863,"teacher_disagreement_score":0.0019891888,"about_ca_system_score_codex":0.0007677437,"about_ca_system_score_gemma":0.0015327921,"threshold_uncertainty_score":0.0081624985},"labels":[],"label_agreement":null},{"id":"W4408565638","doi":"10.1109/acsac63791.2024.00031","title":"You Only Perturb Once: Bypassing (Robust) Ad-Blockers Using Universal Adversarial Perturbations","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Adversarial system; Computer science; Control theory (sociology); Artificial intelligence; Control (management)","score_opus":0.022888395626744783,"score_gpt":0.2739138286725958,"score_spread":0.251025433045851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408565638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12955163,0.0006785167,0.85426885,0.0012228137,0.00019196334,0.0002791061,0.00019515636,0.0063343495,0.007277687],"genre_scores_gemma":[0.9477274,0.00020027482,0.049314946,0.00048415072,0.000049521313,0.000089908885,0.00015907858,0.00017396313,0.0018007829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998221,0.0007542058,0.00006283127,0.0003054337,0.00040072645,0.00025570954],"domain_scores_gemma":[0.99479574,0.00271742,0.0004767388,0.0015494574,0.000273101,0.00018755875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019717903,0.0011932232,0.00082933507,0.00043794516,0.0005054395,0.00089095696,0.0012641678,0.001123259,0.0015463723],"category_scores_gemma":[0.008301677,0.0004343567,0.000643394,0.0002897741,0.0018538348,0.0026035989,0.0023777448,0.0029904994,0.0006553062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075701607,0.00037237816,0.0058397916,0.00024626593,0.00016470187,0.0004103217,0.00026574978,0.71260613,0.042360924,0.06689196,0.012316692,0.15776807],"study_design_scores_gemma":[0.000023254166,0.00010804393,0.0003071991,0.00001659604,0.000016957309,0.00012221296,0.000025740826,0.9756361,0.008783953,0.013196459,0.0017487119,0.000014757992],"about_ca_topic_score_codex":0.0012790394,"about_ca_topic_score_gemma":0.0012489365,"teacher_disagreement_score":0.0019717903,"about_ca_system_score_codex":0.00079132564,"about_ca_system_score_gemma":0.0009162037,"threshold_uncertainty_score":0.010427952},"labels":[],"label_agreement":null},{"id":"W4408611426","doi":"10.1109/icaiic64266.2025.10920873","title":"Defending Against High-Intensity Adversarial Perturbations in Deep Neural Networks: A Robust Swin Transformer Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Royal Military College of Canada; University of Ottawa","funders":"","keywords":"Adversarial system; Transformer; Computer science; Artificial neural network; Artificial intelligence; Deep neural networks; Electrical engineering; Engineering; Voltage","score_opus":0.011706170883093885,"score_gpt":0.23081503854402916,"score_spread":0.21910886766093526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408611426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036504008,0.0002521047,0.9589921,0.00034499535,0.000045764715,0.000062062245,0.000068480724,0.0019980697,0.0017323624],"genre_scores_gemma":[0.9231407,0.0001914666,0.07311136,0.00033566615,0.00004741997,0.00007237622,0.000154028,0.00020388667,0.0027430903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915206,0.00022816611,0.00003182946,0.0001832632,0.00027298665,0.00013170755],"domain_scores_gemma":[0.9982931,0.00065724796,0.00023452738,0.00054785685,0.00017597397,0.000091259535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019412911,0.001604937,0.00093384925,0.0005384006,0.0003967389,0.0008499844,0.0017820556,0.0013247096,0.0015847184],"category_scores_gemma":[0.0050538587,0.00059331727,0.000758463,0.00028325396,0.0018194112,0.002715254,0.0035806852,0.0029174846,0.00058962253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003467269,0.00010169085,0.0012630691,0.00006503344,0.00009141199,0.00014235433,0.000077009434,0.869906,0.019166853,0.018549649,0.0022746997,0.08801551],"study_design_scores_gemma":[0.0000054489615,0.000049601746,0.0000808334,0.000004494907,0.0000067537508,0.000033433924,0.0000048810184,0.9895864,0.0042077918,0.005785929,0.00022930105,0.0000050310905],"about_ca_topic_score_codex":0.0010910732,"about_ca_topic_score_gemma":0.0014778522,"teacher_disagreement_score":0.0019412911,"about_ca_system_score_codex":0.00084751565,"about_ca_system_score_gemma":0.0008518805,"threshold_uncertainty_score":0.010266662},"labels":[],"label_agreement":null},{"id":"W4408696996","doi":"10.1109/itsc58415.2024.10920187","title":"Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adversarial system; Covert; Perception; Computer science; Perturbation (astronomy); Artificial intelligence; Psychology; Physics","score_opus":0.007680816905827977,"score_gpt":0.23976595656593813,"score_spread":0.23208513966011016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408696996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02843451,0.0002854694,0.96786934,0.00017940297,0.00007425257,0.000065900924,0.000059396898,0.0010993653,0.0019322173],"genre_scores_gemma":[0.8350713,0.0003237453,0.16047674,0.00027458934,0.00004938431,0.00011583349,0.00020550596,0.00016509434,0.0033177359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932396,0.00014847441,0.000026666621,0.00013748815,0.00028488127,0.000078545156],"domain_scores_gemma":[0.9991228,0.00035669844,0.00010169599,0.0002478325,0.00011905111,0.00005179871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000797551,0.0009241878,0.00056912244,0.0004957516,0.00036222438,0.0004928854,0.0010003386,0.00088705454,0.0015180049],"category_scores_gemma":[0.0021388677,0.00027493917,0.000702062,0.00029990528,0.0012636905,0.0014961389,0.0020325533,0.0017024591,0.00038431113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034463013,0.00014511096,0.0014478948,0.00017397545,0.00016006747,0.00045770968,0.00019060218,0.6364508,0.07066738,0.029187677,0.004613763,0.25616032],"study_design_scores_gemma":[0.000011249628,0.00011455563,0.00023721112,0.000009125713,0.000012181823,0.00014229675,0.000019305837,0.974501,0.013685707,0.009521517,0.0017310742,0.0000148162535],"about_ca_topic_score_codex":0.00085352093,"about_ca_topic_score_gemma":0.0008548802,"teacher_disagreement_score":0.0015180049,"about_ca_system_score_codex":0.0004465705,"about_ca_system_score_gemma":0.00055370276,"threshold_uncertainty_score":0.005078256},"labels":[],"label_agreement":null},{"id":"W4408746891","doi":"10.22399/ijcesen.1358","title":"AI-Driven Cybersecurity: Enhancing Threat Detection and Mitigation with Deep Learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational and Experimental Science and Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer security; Deep learning; Computer science; Artificial intelligence","score_opus":0.0025501522262092735,"score_gpt":0.24197596578263966,"score_spread":0.2394258135564304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408746891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07311369,0.00072666636,0.91700613,0.0009800885,0.00013767413,0.00010542823,0.000112479465,0.0023309009,0.00548696],"genre_scores_gemma":[0.90965676,0.00035418483,0.08678726,0.0004270195,0.000052396237,0.000062316976,0.00017998547,0.00006254405,0.0024176594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996207,0.00007495227,0.000018825494,0.00007993967,0.00013541874,0.00007019766],"domain_scores_gemma":[0.99911505,0.00031661423,0.00014340323,0.00012733316,0.0002217206,0.00007585961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008581701,0.0009643597,0.00041726368,0.00066909817,0.0002689676,0.00081114325,0.0011280698,0.000743129,0.0011727979],"category_scores_gemma":[0.0023285854,0.00021761579,0.00041001654,0.00032371134,0.00070352276,0.0018609336,0.0014084143,0.0014214078,0.0003389602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013315026,0.0003074288,0.003655617,0.00015843454,0.00012359541,0.00012957593,0.00012051341,0.6937216,0.029565662,0.010668734,0.00308843,0.25832725],"study_design_scores_gemma":[0.0000034467337,0.000044948974,0.00029839552,0.000008322412,0.000010041579,0.000024943114,0.000011619465,0.9889753,0.004932754,0.004936317,0.0007461396,0.000007886891],"about_ca_topic_score_codex":0.0024544578,"about_ca_topic_score_gemma":0.0031781013,"teacher_disagreement_score":0.0024544578,"about_ca_system_score_codex":0.000727895,"about_ca_system_score_gemma":0.0012938534,"threshold_uncertainty_score":0.0052812696},"labels":[],"label_agreement":null},{"id":"W4408750188","doi":"10.14722/ndss.2025.230041","title":"A Method to Facilitate Membership Inference Attacks in Deep Learning Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Inference; Artificial intelligence; Deep learning; Machine learning","score_opus":0.085488134606355,"score_gpt":0.3513555767616686,"score_spread":0.2658674421553136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408750188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0126222195,0.00026329636,0.97820103,0.0016507746,0.00013446528,0.00010935445,0.00015035935,0.0039229514,0.00294565],"genre_scores_gemma":[0.71661174,0.0003564514,0.2738727,0.0016154645,0.00034694257,0.0002906691,0.00041363738,0.0006900761,0.005802252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9883288,0.005069635,0.0006239531,0.0016676376,0.0034370124,0.0008729505],"domain_scores_gemma":[0.9642874,0.015460352,0.0018698928,0.016281173,0.0014675529,0.00063359964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009326603,0.0012771696,0.0012927307,0.0014701316,0.0015478971,0.0036876935,0.00403023,0.0035020704,0.0055185403],"category_scores_gemma":[0.05272624,0.0011286751,0.0022529736,0.0010496441,0.0043015503,0.00900402,0.011503394,0.009121873,0.0019078183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008720741,0.0004315176,0.0048264307,0.0003054683,0.00029014715,0.0006632011,0.0010834745,0.30490312,0.017037258,0.38205528,0.017654471,0.2698775],"study_design_scores_gemma":[0.0000351642,0.00007183478,0.00016059636,0.00004400588,0.000031407202,0.00021115571,0.000035911722,0.8490329,0.007732887,0.13747416,0.0051382654,0.000031748492],"about_ca_topic_score_codex":0.0012247611,"about_ca_topic_score_gemma":0.0012392275,"teacher_disagreement_score":0.009326603,"about_ca_system_score_codex":0.0018103479,"about_ca_system_score_gemma":0.0024424396,"threshold_uncertainty_score":0.049324393},"labels":[],"label_agreement":null},{"id":"W4408834070","doi":"10.1145/3725739","title":"Panda or Not Panda? Understanding Adversarial Attacks with Interactive Visualization","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Visualization; Computer science; Computer security; Computer graphics (images); Human–computer interaction; Artificial intelligence","score_opus":0.04934597232391691,"score_gpt":0.3398007247743506,"score_spread":0.2904547524504337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408834070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11888257,0.0010831773,0.8459852,0.0034381198,0.00018634234,0.00022067649,0.00030940358,0.013739979,0.016154556],"genre_scores_gemma":[0.81474036,0.00062910013,0.1781789,0.0005844078,0.0000573797,0.00022609519,0.00020543425,0.00073717936,0.004641223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988727,0.00063949573,0.00004229952,0.00014627729,0.00020318569,0.00009606851],"domain_scores_gemma":[0.99258703,0.0055193435,0.0003821918,0.0009567547,0.00031830248,0.00023637543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00258674,0.0010712148,0.0004112153,0.00046580125,0.0004978632,0.0021098645,0.0011640232,0.001280582,0.007509881],"category_scores_gemma":[0.012530056,0.00032995667,0.0005133318,0.00015600689,0.0016050724,0.0036786522,0.0024071638,0.0019670732,0.0009382077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014960561,0.00057960616,0.015854482,0.0015904504,0.00024341492,0.0021180438,0.014524466,0.20907843,0.10504317,0.19145018,0.03945751,0.41856426],"study_design_scores_gemma":[0.00013536365,0.00077104504,0.004934097,0.0003929896,0.000086050255,0.0017067663,0.001963487,0.6581929,0.052829016,0.16872075,0.11010431,0.0001633006],"about_ca_topic_score_codex":0.0005608684,"about_ca_topic_score_gemma":0.000673357,"teacher_disagreement_score":0.007509881,"about_ca_system_score_codex":0.00045773786,"about_ca_system_score_gemma":0.00045384257,"threshold_uncertainty_score":0.02512306},"labels":[],"label_agreement":null},{"id":"W4409129305","doi":"10.1109/tmm.2025.3557613","title":"Adversarial Geometric Attacks for 3D Point Cloud Object Tracking","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Point cloud; Adversarial system; Object (grammar); Computer vision; Artificial intelligence; Point (geometry); Cloud computing; Video tracking; Tracking (education); Computer security; Geometry; Mathematics","score_opus":0.017827179790808584,"score_gpt":0.2931662891273019,"score_spread":0.2753391093364933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409129305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020000812,0.00030076402,0.976033,0.00018528702,0.0000616816,0.00005634898,0.000079841426,0.0012753259,0.0020069357],"genre_scores_gemma":[0.8149169,0.0005434745,0.17896602,0.00031302383,0.00007061865,0.00015027571,0.0004744839,0.00031320806,0.0042519365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983907,0.00034429476,0.00007065949,0.00027381335,0.00077079446,0.00014973573],"domain_scores_gemma":[0.99832433,0.00074866664,0.00020465573,0.00048351227,0.00016296814,0.000075863936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012128922,0.0011914336,0.00089645357,0.00068369653,0.00052219396,0.00077440194,0.0011212486,0.0012254268,0.0016086133],"category_scores_gemma":[0.004333707,0.00044265902,0.0011576919,0.0006817043,0.0013102151,0.0015133994,0.0027647493,0.0018961753,0.00066784635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016429844,0.000034136556,0.0010714513,0.000047019887,0.000061485836,0.00015677094,0.00006317269,0.9014756,0.012003653,0.0130773345,0.0019334706,0.069911584],"study_design_scores_gemma":[0.0000044253243,0.000026637392,0.00016583875,0.000005548324,0.000005299511,0.000056370547,0.000005330908,0.9922482,0.0029952468,0.0035945137,0.00088553893,0.000007167312],"about_ca_topic_score_codex":0.0026264826,"about_ca_topic_score_gemma":0.0019030157,"teacher_disagreement_score":0.0026264826,"about_ca_system_score_codex":0.0009951048,"about_ca_system_score_gemma":0.00068718026,"threshold_uncertainty_score":0.00721997},"labels":[],"label_agreement":null},{"id":"W4409347223","doi":"10.1609/aaai.v39i15.33760","title":"Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Computer security; Graph; Computer science; Detector; Theoretical computer science; Telecommunications","score_opus":0.039585509154680384,"score_gpt":0.3006739021294139,"score_spread":0.2610883929747335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409347223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5642533,0.000718316,0.426384,0.0015432946,0.00014014123,0.00028638434,0.0001827124,0.0019258242,0.0045661945],"genre_scores_gemma":[0.9695794,0.00012967859,0.02932277,0.00019695275,0.000016944039,0.000045999197,0.00009126265,0.000035397326,0.00058161217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982377,0.0008859562,0.000058357706,0.00023528634,0.00041093523,0.00017176427],"domain_scores_gemma":[0.99185395,0.0048258947,0.0009347647,0.0014426907,0.0006454962,0.00029720375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023442237,0.00087818713,0.0006832598,0.001070796,0.0006333297,0.0007929527,0.0012002393,0.001183968,0.00063810276],"category_scores_gemma":[0.011314122,0.00032470166,0.00084145647,0.00061483256,0.0014330967,0.0025265336,0.0015998419,0.0017078698,0.00017331775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004367069,0.0003335415,0.010773096,0.00012170911,0.00017118112,0.00040395468,0.0002437238,0.87025905,0.009105265,0.020619689,0.003090597,0.08444155],"study_design_scores_gemma":[0.000010100709,0.00004380971,0.00028464096,0.00000412263,0.00001151137,0.00006373625,0.00001464222,0.99379563,0.0019355556,0.0035532482,0.00027798046,0.0000050581125],"about_ca_topic_score_codex":0.0027738437,"about_ca_topic_score_gemma":0.0029670296,"teacher_disagreement_score":0.0027738437,"about_ca_system_score_codex":0.0013405752,"about_ca_system_score_gemma":0.00067656947,"threshold_uncertainty_score":0.012397587},"labels":[],"label_agreement":null},{"id":"W4409361542","doi":"10.1609/aaai.v39i26.34980","title":"Can Go AIs Be Adversarially Robust?","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science","score_opus":0.05512165996248178,"score_gpt":0.296670524556318,"score_spread":0.2415488645938362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409361542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3058251,0.0014142538,0.6319591,0.0064743804,0.0003067701,0.0002646355,0.00034460842,0.004252555,0.049158532],"genre_scores_gemma":[0.9695313,0.00022160429,0.026115458,0.0006525586,0.000034494675,0.00007683078,0.0001487832,0.00013799989,0.003081027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989048,0.000306807,0.00004067966,0.00024184203,0.00028146812,0.00022433468],"domain_scores_gemma":[0.991888,0.004161737,0.0009911446,0.0021262022,0.00047920828,0.00035367202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025794376,0.00074870937,0.0007305915,0.0005956273,0.00095170236,0.0015097484,0.0014150444,0.002702605,0.004621656],"category_scores_gemma":[0.017969416,0.00041641828,0.0005712022,0.00027383078,0.0044266777,0.0043694125,0.002724581,0.0023486118,0.0014741858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028449038,0.000111219706,0.005861625,0.0002899709,0.00021366049,0.00029947073,0.00045491447,0.7680901,0.023795296,0.13296658,0.0056270068,0.06200561],"study_design_scores_gemma":[0.00003627511,0.00031858034,0.001766428,0.0000818164,0.000044759705,0.00040528658,0.00030902567,0.7543991,0.009906402,0.22320949,0.009467757,0.00005503081],"about_ca_topic_score_codex":0.001305501,"about_ca_topic_score_gemma":0.0013464752,"teacher_disagreement_score":0.004621656,"about_ca_system_score_codex":0.0006239928,"about_ca_system_score_gemma":0.000799861,"threshold_uncertainty_score":0.015460968},"labels":[],"label_agreement":null},{"id":"W4409361716","doi":"10.1609/aaai.v39i26.34954","title":"Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Safeguarding; Computer science; Linguistics; Natural language processing; Philosophy; Medicine","score_opus":0.039922484497689106,"score_gpt":0.32915447166851264,"score_spread":0.28923198717082355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409361716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083094664,0.0002535483,0.9027569,0.0006684785,0.00013165432,0.00020437902,0.000126168,0.009302385,0.0034618056],"genre_scores_gemma":[0.8992461,0.00008540506,0.096078545,0.0005109033,0.00006625339,0.00014461331,0.00018504563,0.0006582353,0.0030249038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807525,0.0006644094,0.00010060805,0.00039297267,0.00053030194,0.00023642209],"domain_scores_gemma":[0.993159,0.002846095,0.00064509414,0.0023369158,0.0008112434,0.00020165675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030679537,0.0014468327,0.0008800921,0.00049041404,0.0004891784,0.001416425,0.0029320312,0.0014306654,0.002777761],"category_scores_gemma":[0.019448789,0.0005792219,0.0008719039,0.00021807794,0.0016544407,0.0029554458,0.002947614,0.0029318277,0.0011214991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010858998,0.00021219472,0.005355862,0.00022677856,0.00015009067,0.0006213098,0.0010240396,0.51227826,0.052157667,0.030761266,0.008852791,0.38727388],"study_design_scores_gemma":[0.000023995071,0.00009936427,0.00017331023,0.000014842887,0.000021759866,0.00007973431,0.000041327912,0.9720129,0.0155871045,0.010640264,0.0012871778,0.000018146247],"about_ca_topic_score_codex":0.0023378157,"about_ca_topic_score_gemma":0.0020112072,"teacher_disagreement_score":0.0030679537,"about_ca_system_score_codex":0.0007870369,"about_ca_system_score_gemma":0.0013443097,"threshold_uncertainty_score":0.0162251},"labels":[],"label_agreement":null},{"id":"W4409362363","doi":"10.1609/aaai.v39i25.34821","title":"Perception-Guided Jailbreak Against Text-to-Image Models","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; National Research Foundation Singapore; National Research Foundation","keywords":"Perception; Image (mathematics); Computer science; Psychology; Artificial intelligence; Cognitive psychology; Natural language processing; Neuroscience","score_opus":0.05902485939270401,"score_gpt":0.31977056404222415,"score_spread":0.2607457046495201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409362363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19400392,0.00063738285,0.7818087,0.0014116918,0.00035411847,0.00037230493,0.00022752066,0.01426685,0.006917581],"genre_scores_gemma":[0.8928362,0.000085103406,0.10265816,0.00057403697,0.0000680911,0.00011561519,0.0002490826,0.0005082079,0.002905417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979965,0.00068343,0.000080269914,0.00041296816,0.0005698526,0.0002569017],"domain_scores_gemma":[0.9925282,0.003897703,0.0009337112,0.0016661114,0.00059444126,0.00037976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029356482,0.0012030182,0.0009772377,0.0005881388,0.00069524103,0.0012284091,0.0017163705,0.0015041485,0.003264123],"category_scores_gemma":[0.013157554,0.00036189923,0.00073329295,0.00022884985,0.0020495483,0.0029026016,0.0032059925,0.0030963877,0.0011845671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037626498,0.0010521205,0.0076338104,0.0006974345,0.00025820686,0.00097128615,0.0019237344,0.4133468,0.09383372,0.05509788,0.028423687,0.3929987],"study_design_scores_gemma":[0.00006212306,0.00027504982,0.00042369438,0.000021070435,0.000016063224,0.00017868787,0.00015653731,0.9631468,0.017897261,0.015664529,0.0021264276,0.000031751817],"about_ca_topic_score_codex":0.00095341,"about_ca_topic_score_gemma":0.0010875019,"teacher_disagreement_score":0.003264123,"about_ca_system_score_codex":0.0007664505,"about_ca_system_score_gemma":0.0009044912,"threshold_uncertainty_score":0.015525401},"labels":[],"label_agreement":null},{"id":"W4409362689","doi":"10.1609/aaai.v39i24.34764","title":"MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Artificial intelligence","score_opus":0.10742692767320933,"score_gpt":0.342294024298861,"score_spread":0.2348670966256517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409362689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011530467,0.0003485363,0.9839483,0.00039945496,0.000095828465,0.00007271928,0.0001280076,0.0018467925,0.0016299324],"genre_scores_gemma":[0.63597083,0.0005154493,0.3506719,0.001488124,0.00034541878,0.00037345296,0.0008547248,0.0006746088,0.009105534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986125,0.00056884054,0.000055606804,0.00026161858,0.00035268965,0.00014863849],"domain_scores_gemma":[0.9969938,0.0014826662,0.00031774017,0.00076448015,0.00031854358,0.00012285095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033383807,0.0017724204,0.0012442945,0.0007002962,0.00050145213,0.0007887363,0.0024096463,0.0014775314,0.0025407146],"category_scores_gemma":[0.008867457,0.00066202733,0.0011529388,0.00041437693,0.0012474955,0.001579003,0.003352792,0.0029219505,0.0015565687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024688558,0.00011579448,0.0032491009,0.0001659195,0.0002511606,0.00021753619,0.00016990812,0.73498666,0.012320968,0.021586243,0.012198616,0.21449119],"study_design_scores_gemma":[0.000010612882,0.000048720467,0.00019447139,0.000017514612,0.000016350907,0.000056168014,0.000008824968,0.98636067,0.002476562,0.009504018,0.0012951654,0.000010945115],"about_ca_topic_score_codex":0.0020443227,"about_ca_topic_score_gemma":0.0023437978,"teacher_disagreement_score":0.0033383807,"about_ca_system_score_codex":0.0007018409,"about_ca_system_score_gemma":0.0010961901,"threshold_uncertainty_score":0.017655253},"labels":[],"label_agreement":null},{"id":"W4409363782","doi":"10.1609/aaai.v39i18.34131","title":"Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche; Aix-Marseille Université","keywords":"Counterfactual thinking; Probabilistic logic; Econometrics; Computer science; Mathematics; Artificial intelligence; Psychology; Social psychology","score_opus":0.048220289045339586,"score_gpt":0.31205897222308016,"score_spread":0.26383868317774056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409363782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038860452,0.00010269545,0.99357754,0.00031475106,0.000036794034,0.00005257841,0.0000713733,0.000097179574,0.0018609822],"genre_scores_gemma":[0.5236061,0.00061525975,0.46903375,0.0006929297,0.0002720535,0.0007555103,0.0005147776,0.00048237812,0.004027162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9883272,0.0075010587,0.0006270155,0.0016298216,0.0014311865,0.00048375124],"domain_scores_gemma":[0.9426078,0.04567082,0.003209916,0.0062627527,0.0017277813,0.0005209477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022470651,0.0015186382,0.0019661346,0.0031739378,0.0014484918,0.0037627977,0.0032956677,0.002910209,0.00991197],"category_scores_gemma":[0.08893368,0.0010636725,0.0033255336,0.0026714695,0.0065884264,0.010826892,0.0045780083,0.005452801,0.0007615903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003818531,0.0000187767,0.00028618998,0.00005632718,0.000038503593,0.000066617446,0.00010078571,0.11653951,0.00014418656,0.8736498,0.0004350805,0.008626054],"study_design_scores_gemma":[0.000009132101,0.000010983999,0.000054510227,0.000019105239,0.000011228499,0.000020165919,0.000012300123,0.21860611,0.00018438709,0.78037345,0.0006870665,0.000011526434],"about_ca_topic_score_codex":0.0030484363,"about_ca_topic_score_gemma":0.002059311,"teacher_disagreement_score":0.022470651,"about_ca_system_score_codex":0.004334409,"about_ca_system_score_gemma":0.0037475098,"threshold_uncertainty_score":0.118837595},"labels":[],"label_agreement":null},{"id":"W4409364009","doi":"10.1609/aaai.v39i17.33927","title":"COMMIT: Certifying Robustness of Multi-Sensor Fusion Systems Against Semantic Attacks","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Defense Advanced Research Projects Agency; Nuclear Safety and Security Commission; National Aeronautics and Space Administration; National Science Foundation","keywords":"Commit; Robustness (evolution); Computer science; Fusion; Sensor fusion; Computer security; Artificial intelligence; Chemistry; Database; Philosophy","score_opus":0.08277057206455064,"score_gpt":0.32410618425320586,"score_spread":0.24133561218865524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409364009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06696615,0.000363569,0.92369854,0.000463149,0.00013081546,0.00018843374,0.00024055986,0.0045555937,0.0033932033],"genre_scores_gemma":[0.9437275,0.0001192511,0.054452658,0.00015946302,0.000039616454,0.0001116455,0.00040265656,0.00021623487,0.00077093305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953702,0.0010641629,0.00028673885,0.0007706187,0.001856195,0.00065205194],"domain_scores_gemma":[0.98325366,0.0067013483,0.0015658367,0.0052525876,0.0026919264,0.00053465803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055704615,0.0012371453,0.0012556734,0.0010969933,0.001010957,0.0019340853,0.0021820976,0.0019658727,0.002417856],"category_scores_gemma":[0.03402281,0.0004388351,0.0012006052,0.00057263527,0.0026858528,0.0031209858,0.004429934,0.0024821728,0.00056639797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041339363,0.0000878033,0.0041858843,0.0002149531,0.00010738912,0.0002534949,0.00020951228,0.8635282,0.015533023,0.043728348,0.0039146994,0.06782336],"study_design_scores_gemma":[0.000031318057,0.00012776504,0.00046544723,0.000019747473,0.000013578389,0.00008071824,0.00005356608,0.9731495,0.009856341,0.014954567,0.0012231389,0.000024341101],"about_ca_topic_score_codex":0.0034100928,"about_ca_topic_score_gemma":0.0020036974,"teacher_disagreement_score":0.0055704615,"about_ca_system_score_codex":0.0021086903,"about_ca_system_score_gemma":0.003024447,"threshold_uncertainty_score":0.029459715},"labels":[],"label_agreement":null},{"id":"W4409365735","doi":"10.1609/aaai.v39i9.32978","title":"Towards Adversarially Robust Dataset Distillation by Curvature Regularization","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Regularization (linguistics); Curvature; Mathematics; Econometrics; Computer science; Artificial intelligence; Geometry","score_opus":0.03516254684882889,"score_gpt":0.2926540415758772,"score_spread":0.2574914947270483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409365735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027302695,0.00033772076,0.9678429,0.0006268239,0.00006856791,0.00008366081,0.00022325426,0.0019052519,0.001609098],"genre_scores_gemma":[0.65656424,0.0003171594,0.33464563,0.0010554563,0.00014605948,0.00029081196,0.0014782798,0.000728262,0.004774135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99789053,0.0009182942,0.000102315265,0.0004883621,0.00040848585,0.00019204998],"domain_scores_gemma":[0.994028,0.003175674,0.0005738523,0.0015993895,0.00039072838,0.00023240164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038536792,0.0017884559,0.0015349159,0.0009167325,0.0006054871,0.0012727343,0.0022414236,0.0018963334,0.0018134079],"category_scores_gemma":[0.012871306,0.0008813709,0.0012725827,0.00095054344,0.0025401695,0.0037601085,0.0050622984,0.0043928265,0.0007687272],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031360876,0.00012865764,0.0014241647,0.00012289273,0.00011861273,0.00015142368,0.00014274172,0.8610494,0.009745357,0.037904866,0.005190876,0.083707385],"study_design_scores_gemma":[0.000014528701,0.000039244707,0.000098907534,0.00000937447,0.00000759353,0.000034518514,0.000008197161,0.9815414,0.0024292094,0.015089339,0.00071584224,0.0000119659235],"about_ca_topic_score_codex":0.0022872428,"about_ca_topic_score_gemma":0.002754346,"teacher_disagreement_score":0.0038536792,"about_ca_system_score_codex":0.0011321963,"about_ca_system_score_gemma":0.001519229,"threshold_uncertainty_score":0.020380437},"labels":[],"label_agreement":null},{"id":"W4409370123","doi":"10.1609/aaai.v39i3.32263","title":"AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"HORIZON EUROPE Framework Programme","keywords":"Adversarial system; Computer science; Computer security; Artificial intelligence","score_opus":0.12169054576986986,"score_gpt":0.373906943296021,"score_spread":0.2522163975261511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409370123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3141421,0.0070299017,0.62788016,0.0018912759,0.001194755,0.0010153534,0.00253068,0.017418768,0.02689713],"genre_scores_gemma":[0.7789901,0.0011260494,0.2098341,0.00060520496,0.00013759625,0.00046503128,0.0036363606,0.0010709156,0.0041346545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959174,0.0015968651,0.0002509803,0.00058066525,0.0012961138,0.00035802263],"domain_scores_gemma":[0.99103796,0.00536888,0.00059488363,0.001915196,0.0007657168,0.00031744252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005326367,0.0025461584,0.0012377287,0.0016732966,0.0005904644,0.0015977914,0.0019778768,0.0021225116,0.0034515387],"category_scores_gemma":[0.017610107,0.0004773303,0.00088350667,0.00072873035,0.001708326,0.0029581953,0.0022002922,0.0025332742,0.0011543663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068782415,0.00039818234,0.004300515,0.0005923458,0.00038551257,0.0001183936,0.00006594702,0.86506355,0.0072426554,0.013907548,0.0134994,0.093738124],"study_design_scores_gemma":[0.000058758385,0.0004435474,0.00060477905,0.00004087875,0.00003065127,0.00010872842,0.000021283575,0.98633295,0.005833577,0.0043898467,0.0021111756,0.000023813729],"about_ca_topic_score_codex":0.0023950052,"about_ca_topic_score_gemma":0.003085453,"teacher_disagreement_score":0.005326367,"about_ca_system_score_codex":0.0014663426,"about_ca_system_score_gemma":0.0013105746,"threshold_uncertainty_score":0.028168797},"labels":[],"label_agreement":null},{"id":"W4409405452","doi":"10.1007/s10664-025-10656-8","title":"Logging requirement for continuous auditing of responsible machine learning-based applications","year":2025,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Audit; Logging; Computer science; Business; Accounting; Forestry; Geography","score_opus":0.015888302818632512,"score_gpt":0.29317120968724425,"score_spread":0.27728290686861173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409405452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18990885,0.00039672,0.76855415,0.003924226,0.0006581001,0.00074272364,0.00068337866,0.025185812,0.009946037],"genre_scores_gemma":[0.95828974,0.00006778966,0.037793417,0.000508827,0.00012053632,0.00019264682,0.00028618955,0.0005954651,0.002145411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9766692,0.0048592994,0.0029653155,0.0029279778,0.010097073,0.0024810531],"domain_scores_gemma":[0.7311523,0.094423585,0.019051258,0.12346285,0.026731413,0.005178599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014858161,0.0008848664,0.0015019673,0.0015283201,0.0016684089,0.004363629,0.0035425373,0.0032333098,0.007536263],"category_scores_gemma":[0.15776864,0.0010795406,0.0009190487,0.0007676292,0.0028100917,0.0077693127,0.005125735,0.005919969,0.0029683458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005407839,0.0020758547,0.061693266,0.0014585252,0.000305903,0.0045361165,0.0018838714,0.11213192,0.14049277,0.23316224,0.028572597,0.40827915],"study_design_scores_gemma":[0.0002157181,0.0007173778,0.012571949,0.00037020197,0.00014540838,0.0026281325,0.00047983002,0.7283096,0.09796417,0.14224945,0.014174184,0.00017389284],"about_ca_topic_score_codex":0.0011020298,"about_ca_topic_score_gemma":0.00079902995,"teacher_disagreement_score":0.014858161,"about_ca_system_score_codex":0.0012779988,"about_ca_system_score_gemma":0.0060882494,"threshold_uncertainty_score":0.07857835},"labels":[],"label_agreement":null},{"id":"W4409506071","doi":"10.1007/978-3-031-85356-2_7","title":"Formal Security Analysis of Deep Neural Network Architecture","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Architecture; Artificial neural network; Artificial intelligence","score_opus":0.007615077404231362,"score_gpt":0.24450752785780794,"score_spread":0.23689245045357657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409506071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025500996,0.0005472101,0.94301754,0.0028609675,0.00022991268,0.00009002059,0.00028143177,0.00048721652,0.026984774],"genre_scores_gemma":[0.869774,0.00095167564,0.110441975,0.0008825516,0.00052657287,0.00033575087,0.00047106284,0.00032863574,0.016287837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997598,0.000661621,0.0001356822,0.00033903655,0.0009222089,0.00034336507],"domain_scores_gemma":[0.99219006,0.0052141002,0.0003733759,0.0011057752,0.00097637676,0.00014023967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003213347,0.00077082444,0.00061418477,0.0011319817,0.00070610107,0.0028078584,0.0017456432,0.0016030715,0.007760419],"category_scores_gemma":[0.011190624,0.00069735124,0.0017381014,0.0006486736,0.0041695395,0.0053567397,0.0025554674,0.0050147143,0.0008611411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003003056,0.000022565348,0.00013384753,0.000055258042,0.000017961795,0.000049243747,0.00006263319,0.031380598,0.00093999476,0.9596675,0.0011746037,0.0064658397],"study_design_scores_gemma":[0.000010333676,0.000012674325,0.000057642945,0.000024477627,0.000012445384,0.00002910374,0.000015245932,0.11656533,0.0007791596,0.880891,0.0015938327,0.000008637091],"about_ca_topic_score_codex":0.0017325991,"about_ca_topic_score_gemma":0.0016119413,"teacher_disagreement_score":0.007760419,"about_ca_system_score_codex":0.003181697,"about_ca_system_score_gemma":0.0018187359,"threshold_uncertainty_score":0.02596116},"labels":[],"label_agreement":null},{"id":"W4409506451","doi":"10.1007/978-3-031-85356-2_5","title":"A High Parallelization Method for Automated Formal Verification of Deep Neural Networks","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial neural network; Deep neural networks; Formal methods; Parallel computing; Formal verification; Artificial intelligence; Programming language","score_opus":0.011528144961753521,"score_gpt":0.28066513109541125,"score_spread":0.2691369861336577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409506451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017932083,0.00005615516,0.98947453,0.00013311964,0.00007095185,0.00010228288,0.00013896203,0.006744676,0.0014861252],"genre_scores_gemma":[0.15241995,0.00014584484,0.8354744,0.00039994673,0.00016226468,0.00066537276,0.0010570296,0.004109267,0.0055659837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99475324,0.0012941754,0.00049018965,0.0011677938,0.0017003034,0.0005943025],"domain_scores_gemma":[0.9874199,0.0065027904,0.00041837044,0.0040706564,0.0014205859,0.00016772533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029752972,0.001971114,0.0013070742,0.0014372646,0.0011181607,0.002613004,0.0035990977,0.0012901587,0.025360065],"category_scores_gemma":[0.014943857,0.0015188667,0.004186009,0.00092167815,0.0025568826,0.0047526234,0.0059070773,0.004788211,0.0055891355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067883485,0.00032759976,0.0017749628,0.0010553731,0.00036025222,0.0006140153,0.00048770435,0.12676342,0.041431326,0.32599047,0.024525912,0.47599012],"study_design_scores_gemma":[0.00017743521,0.00012461955,0.00029746053,0.0001042681,0.00012568718,0.00020755379,0.000076900295,0.5665171,0.039413147,0.37557364,0.01730494,0.00007726507],"about_ca_topic_score_codex":0.0025207526,"about_ca_topic_score_gemma":0.0046880995,"teacher_disagreement_score":0.025360065,"about_ca_system_score_codex":0.001732688,"about_ca_system_score_gemma":0.0030796388,"threshold_uncertainty_score":0.08483791},"labels":[],"label_agreement":null},{"id":"W4409784761","doi":"10.1117/12.3061115","title":"ArmorCLIP: a hybrid defense strategy for boosting adversarial robustness in vision-language models","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Yuhan","keywords":"Boosting (machine learning); Adversarial system; Robustness (evolution); Computer science; Artificial intelligence; Machine learning","score_opus":0.019195697407000917,"score_gpt":0.308642731084378,"score_spread":0.2894470336773771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409784761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026283508,0.0007968342,0.9645085,0.0005591707,0.00013066358,0.000094659656,0.00009421241,0.003733396,0.0037991144],"genre_scores_gemma":[0.8036369,0.00054463116,0.18314375,0.0013982718,0.00021398334,0.00026772695,0.00047464424,0.0006123698,0.009707671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991714,0.00021368549,0.00002982182,0.00018991937,0.00024281447,0.00015243156],"domain_scores_gemma":[0.99875164,0.00054212357,0.00015744861,0.00023865212,0.00022253588,0.00008761137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018023092,0.0019866782,0.0013122332,0.0007829772,0.00051983306,0.0008283273,0.0025312074,0.0016170108,0.0024180526],"category_scores_gemma":[0.003662974,0.0005333732,0.0008485197,0.0003711209,0.0013215182,0.0016320135,0.0027566429,0.0028479276,0.0010353951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002283986,0.00012136996,0.0007813358,0.00010291206,0.00013586426,0.0001531037,0.0000662727,0.8261864,0.014498992,0.012445838,0.0073978286,0.13788173],"study_design_scores_gemma":[0.000007987122,0.000053513857,0.000055332963,0.000005522913,0.000007602433,0.000028904456,0.0000034835432,0.99516946,0.0017383458,0.0022950938,0.00062740094,0.0000072622593],"about_ca_topic_score_codex":0.002958716,"about_ca_topic_score_gemma":0.003360845,"teacher_disagreement_score":0.002958716,"about_ca_system_score_codex":0.00084521563,"about_ca_system_score_gemma":0.0012074836,"threshold_uncertainty_score":0.009531617},"labels":[],"label_agreement":null},{"id":"W4410217185","doi":"10.2139/ssrn.5246321","title":"Representation-Based Fairness Evaluation and Bias Correction Robustness Assessment in Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Robustness (evolution); Artificial neural network; Computer science; Deep neural networks; Representation (politics); Artificial intelligence; Machine learning; Econometrics; Mathematics; Political science","score_opus":0.02693718715934507,"score_gpt":0.3384819265578299,"score_spread":0.3115447393984848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410217185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024318384,0.0004645686,0.97228473,0.0005121078,0.000091730326,0.00007276035,0.00006290858,0.00015938844,0.0020334132],"genre_scores_gemma":[0.8895044,0.00042106034,0.106279336,0.00022622019,0.00026257022,0.0001653141,0.00016939426,0.0001600654,0.002811712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9846734,0.0095004905,0.00057208416,0.0018466013,0.0025804935,0.000826878],"domain_scores_gemma":[0.9513725,0.03507015,0.0029915327,0.0048795254,0.004774603,0.00091176433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02590813,0.001616102,0.0024001708,0.0020401438,0.0011408041,0.0039360933,0.003161167,0.0033212944,0.0027363163],"category_scores_gemma":[0.0924996,0.0006543925,0.0011414074,0.0015419605,0.004087053,0.004820808,0.005095792,0.0032719013,0.00035649602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005308651,0.00011376674,0.0021288425,0.00023052856,0.00022699285,0.00009945802,0.00017659871,0.75052243,0.0026554572,0.16857786,0.0017733038,0.07296389],"study_design_scores_gemma":[0.000012582027,0.000059891627,0.0002739795,0.000026958432,0.000025216075,0.00002462563,0.000014584665,0.9213576,0.0013986327,0.07654314,0.00024404218,0.00001876269],"about_ca_topic_score_codex":0.0015818041,"about_ca_topic_score_gemma":0.000886116,"teacher_disagreement_score":0.02590813,"about_ca_system_score_codex":0.0032353296,"about_ca_system_score_gemma":0.002511921,"threshold_uncertainty_score":0.13701689},"labels":[],"label_agreement":null},{"id":"W4410563838","doi":"10.1016/j.diii.2025.05.006","title":"Adversarial artificial intelligence in radiology: Attacks, defenses, and future considerations","year":2025,"lang":"en","type":"review","venue":"Diagnostic and Interventional Imaging","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Adversarial system; Medical physics; Radiology; Artificial intelligence","score_opus":0.028092574988756315,"score_gpt":0.3440829846031796,"score_spread":0.31599040961442326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410563838","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016273237,0.9940036,0.0015737237,0.0014982955,0.0003020865,0.0000073363594,0.000012431366,0.000015660096,0.0024241284],"genre_scores_gemma":[0.0025496157,0.9945135,0.0008847743,0.0008155094,0.0004765781,0.000013817875,0.00002203164,0.0000059554877,0.0007181844],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99919516,0.00025429222,0.000092650625,0.000105071245,0.00029534585,0.000057465626],"domain_scores_gemma":[0.996349,0.00285505,0.00021512536,0.00010051165,0.00040416946,0.00007619684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018878371,0.0008271147,0.0011764909,0.0018161909,0.00047104948,0.0019607898,0.0011542509,0.002484918,0.0033903085],"category_scores_gemma":[0.0037975449,0.00037247982,0.00068585697,0.001558196,0.0014036301,0.0029727293,0.00096241425,0.0034244284,0.0016723322],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004742719,0.000073344534,0.00028872315,0.015278554,0.00011080599,0.0002513988,0.00020419582,0.0030915197,0.0010132405,0.060378384,0.027862657,0.8913998],"study_design_scores_gemma":[0.000010380295,0.00014650861,0.00066430215,0.011353193,0.00010172734,0.0015456488,0.00020267974,0.0012211205,0.0008480703,0.041173253,0.94267803,0.000055122735],"about_ca_topic_score_codex":0.0011157506,"about_ca_topic_score_gemma":0.0014866684,"teacher_disagreement_score":0.0033903085,"about_ca_system_score_codex":0.0009737395,"about_ca_system_score_gemma":0.0016791415,"threshold_uncertainty_score":0.011341751},"labels":[],"label_agreement":null},{"id":"W4410742664","doi":"10.1007/978-3-031-91524-6_9","title":"Defense Methods for Adversarial Attacks and Privacy Issues in Secure AI","year":2025,"lang":"en","type":"book-chapter","venue":"Progress in IS","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; McGill University; MacEwan University; York University; University of Toronto","funders":"","keywords":"Adversarial system; Computer security; Internet privacy; Computer science; Artificial intelligence","score_opus":0.021852775456319647,"score_gpt":0.3854299382630978,"score_spread":0.36357716280677815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410742664","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029801608,0.01627243,0.85903364,0.007399126,0.0010252397,0.000058457208,0.0000944127,0.00038981752,0.1127467],"genre_scores_gemma":[0.38814417,0.035146687,0.36330563,0.0029918088,0.0057920953,0.00047401467,0.00036181594,0.0007422987,0.20304151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985039,0.00052865397,0.000052604002,0.00020214543,0.0006151124,0.000097525066],"domain_scores_gemma":[0.99608743,0.0029026135,0.000114900475,0.0006229768,0.00021390886,0.000058196634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019780141,0.0011904513,0.00094265013,0.0010532246,0.00095490273,0.003397899,0.0015334921,0.0020231812,0.0091621475],"category_scores_gemma":[0.0053305947,0.0006128848,0.00083789957,0.0014016972,0.005039156,0.006437584,0.0019576661,0.0077852127,0.002295483],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000992182,0.000014345146,0.000033463897,0.0000790102,0.0000099471545,0.00001715966,0.00007148291,0.0074965013,0.00040549765,0.9478387,0.0090943305,0.03492962],"study_design_scores_gemma":[0.0000044258104,0.0000128601,0.000050056606,0.00005584836,0.000006328946,0.000056420846,0.0000271806,0.04083362,0.0005461273,0.9299495,0.028447667,0.000009942246],"about_ca_topic_score_codex":0.0006124857,"about_ca_topic_score_gemma":0.00044353373,"teacher_disagreement_score":0.0091621475,"about_ca_system_score_codex":0.00192174,"about_ca_system_score_gemma":0.00077320694,"threshold_uncertainty_score":0.030650496},"labels":[],"label_agreement":null},{"id":"W4410742820","doi":"10.1007/978-3-031-91524-6_10","title":"General Framework for AI Security and Privacy","year":2025,"lang":"en","type":"book-chapter","venue":"Progress in IS","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; McGill University; MacEwan University; York University; University of Toronto","funders":"","keywords":"Computer science; Computer security; Internet privacy","score_opus":0.015934385073917954,"score_gpt":0.3151750682795311,"score_spread":0.29924068320561314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410742820","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013225141,0.004929088,0.7558776,0.0076020528,0.0009103283,0.00007961418,0.00041734023,0.0006867489,0.22817463],"genre_scores_gemma":[0.21948406,0.016985523,0.4089204,0.0044130115,0.004855683,0.0007040075,0.0012103074,0.0008082455,0.34261873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900705,0.00026582065,0.000043307373,0.00018385064,0.0004093284,0.00009066191],"domain_scores_gemma":[0.99932075,0.00024968042,0.000032481203,0.00024855268,0.00010807159,0.000040476203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012147721,0.0012537935,0.00085429923,0.001389045,0.0014209023,0.004509298,0.0024088991,0.00259015,0.022145132],"category_scores_gemma":[0.001786506,0.0005907419,0.0012848777,0.0020388113,0.004904137,0.007916072,0.0022771822,0.0056305937,0.0059447004],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017876964,0.00000432747,0.0000055400565,0.000018151502,0.000002152626,0.0000069710763,0.000016919397,0.00082053826,0.00009185089,0.98930055,0.004852038,0.0048791273],"study_design_scores_gemma":[0.0000023253933,0.0000032180592,0.00001722535,0.00001725743,0.0000028379397,0.000028444309,0.000011253273,0.005694461,0.00012564096,0.94974744,0.044345424,0.0000046293803],"about_ca_topic_score_codex":0.002365118,"about_ca_topic_score_gemma":0.0014770363,"teacher_disagreement_score":0.022145132,"about_ca_system_score_codex":0.0029485996,"about_ca_system_score_gemma":0.0012291559,"threshold_uncertainty_score":0.07408279},"labels":[],"label_agreement":null},{"id":"W4410786510","doi":"10.36548/jiip.2025.2.003","title":"An Interpretability Pipeline for Image Forgery Localization using GAN-Generated Forgeries and Grad-CAM","year":2025,"lang":"en","type":"article","venue":"Journal of Innovative Image Processing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Interpretability; Pipeline (software); Image (mathematics); Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision","score_opus":0.016031870947269124,"score_gpt":0.3390190493781709,"score_spread":0.3229871784309018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410786510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06645021,0.0003514728,0.92063063,0.00034854893,0.000091890935,0.00023234611,0.00028228603,0.008168131,0.0034444176],"genre_scores_gemma":[0.7226902,0.00020608857,0.2715451,0.00030744498,0.00004782657,0.00013374307,0.0008692124,0.00052594376,0.0036743467],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993968,0.00012635086,0.000027217178,0.0001358557,0.00023678027,0.00007707126],"domain_scores_gemma":[0.9987336,0.00040094237,0.00017182878,0.0004016812,0.00023423244,0.00005779707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013175235,0.0013205543,0.0005865177,0.000793953,0.000253753,0.0009765709,0.0014759705,0.001014211,0.0037435563],"category_scores_gemma":[0.0045998204,0.0003206903,0.0008712465,0.00021067624,0.00082257914,0.0010689849,0.0015087755,0.0013788588,0.0010585503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008946224,0.00021496866,0.0061238,0.00038420875,0.00015294184,0.0010281738,0.00048797467,0.40898442,0.08323507,0.015612193,0.008132377,0.47474933],"study_design_scores_gemma":[0.000015627164,0.00016984648,0.0011669317,0.000034441608,0.000019116824,0.00042056534,0.000045378583,0.9569661,0.032559514,0.006264168,0.0023142283,0.000024066185],"about_ca_topic_score_codex":0.0011386117,"about_ca_topic_score_gemma":0.0015224701,"teacher_disagreement_score":0.0037435563,"about_ca_system_score_codex":0.0006756223,"about_ca_system_score_gemma":0.00052777136,"threshold_uncertainty_score":0.012523472},"labels":[],"label_agreement":null},{"id":"W4410787710","doi":"10.1007/978-3-031-91524-6_12","title":"AI Security Challenges, Opportunities and Future Work","year":2025,"lang":"en","type":"book-chapter","venue":"Progress in IS","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; McGill University; MacEwan University; York University; University of Toronto","funders":"","keywords":"Work (physics); Computer science; Engineering ethics; Engineering; Mechanical engineering","score_opus":0.03263729389178309,"score_gpt":0.2764023799913483,"score_spread":0.2437650860995652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410787710","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004992306,0.4941802,0.12681048,0.11963757,0.00984599,0.000084330495,0.00027546837,0.00095863885,0.24321502],"genre_scores_gemma":[0.16027673,0.60140735,0.0848182,0.013961591,0.019174363,0.00023100292,0.00091035577,0.00048377272,0.11873671],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983298,0.0005175067,0.000068928406,0.00025317198,0.0006454358,0.00018519808],"domain_scores_gemma":[0.9934877,0.0043021394,0.0001614014,0.0006917591,0.0010026239,0.0003542953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006146155,0.0008937419,0.001067039,0.0016916341,0.0013923541,0.007766806,0.002044025,0.0032791032,0.021761036],"category_scores_gemma":[0.006716847,0.0004976388,0.0005906873,0.0025930763,0.0045560272,0.016415762,0.0025301967,0.006419505,0.0074667013],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056336572,0.000102296144,0.00021802809,0.000871774,0.000022175414,0.00006599635,0.00020068458,0.0039967666,0.0006417382,0.6111792,0.07623484,0.30641007],"study_design_scores_gemma":[0.00000936909,0.00005679224,0.00017409361,0.0009037808,0.000014133525,0.00022180832,0.0003731384,0.010294717,0.0007154562,0.6287193,0.35848427,0.000033075543],"about_ca_topic_score_codex":0.001280512,"about_ca_topic_score_gemma":0.0010331273,"teacher_disagreement_score":0.021761036,"about_ca_system_score_codex":0.002121403,"about_ca_system_score_gemma":0.0019286338,"threshold_uncertainty_score":0.072797894},"labels":[],"label_agreement":null},{"id":"W4410985938","doi":"10.1109/jiot.2025.3576225","title":"Investigation of the Robustness of XAI-Based Federated Learning Against Adversarial Attacks for Smart Grid False Data Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Robustness (evolution); Adversarial system; Grid; Data mining; Smart grid; Computer security; Artificial intelligence; Data modeling; Database","score_opus":0.029971377866896992,"score_gpt":0.28123129481451026,"score_spread":0.2512599169476133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410985938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3577647,0.0007274954,0.6328706,0.0012422577,0.000114272814,0.00018715799,0.00023033672,0.0026931094,0.0041701277],"genre_scores_gemma":[0.98188007,0.000073408795,0.017424306,0.00011618121,0.000010552609,0.00003533955,0.0000720287,0.000021551992,0.0003666378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968066,0.0009630989,0.00016776066,0.00066565233,0.0009366823,0.00046017457],"domain_scores_gemma":[0.9851206,0.009169714,0.0015012091,0.0024973212,0.0013320654,0.0003791657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004597441,0.000920158,0.00089821144,0.00073237217,0.0005280043,0.0013656847,0.0015999372,0.0011994981,0.0009963675],"category_scores_gemma":[0.022527695,0.0003458401,0.0006500485,0.00041236845,0.0016336497,0.0021042859,0.002103541,0.0020071235,0.00022438298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006485175,0.00016838573,0.010689254,0.00012435052,0.00016819475,0.00024807153,0.00011477852,0.9056743,0.007951409,0.018564554,0.0012068753,0.054441366],"study_design_scores_gemma":[0.000008910866,0.000059933314,0.00047946448,0.000008209224,0.000008731984,0.000049655057,0.000011125568,0.9937821,0.003098779,0.0023403952,0.00014542304,0.0000072180155],"about_ca_topic_score_codex":0.001869905,"about_ca_topic_score_gemma":0.0010995279,"teacher_disagreement_score":0.004597441,"about_ca_system_score_codex":0.0013971798,"about_ca_system_score_gemma":0.0013746389,"threshold_uncertainty_score":0.024313867},"labels":[],"label_agreement":null},{"id":"W4411196631","doi":"10.1016/j.jnca.2025.104236","title":"Poisoning behavioral-based worker selection in mobile crowdsensing using generative adversarial networks","year":2025,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Crowdsensing; Adversarial system; Selection (genetic algorithm); Generative grammar; Generative adversarial network; Pedestrian; Artificial intelligence; Machine learning; Computer security; Human–computer interaction; Deep learning; Transport engineering","score_opus":0.011906244348397457,"score_gpt":0.2936087360083873,"score_spread":0.2817024916599899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411196631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052366707,0.00029497556,0.94359565,0.00045269134,0.00012370535,0.00010534904,0.00007402398,0.00057839556,0.0024085606],"genre_scores_gemma":[0.9653415,0.00009397562,0.030296456,0.00019735658,0.00006536574,0.00008193667,0.00009507957,0.000055598273,0.0037726958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987437,0.00036303402,0.000042248263,0.00036394483,0.00028960602,0.00019750187],"domain_scores_gemma":[0.9963959,0.0024853123,0.00029435894,0.00034359345,0.00028971268,0.00019123453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022180018,0.0011080444,0.002012152,0.0006903487,0.0008648509,0.0011011258,0.0026295485,0.0020728498,0.0017335941],"category_scores_gemma":[0.006582026,0.0008340676,0.00088932214,0.00054785307,0.0018089612,0.0014959822,0.003579297,0.0016377027,0.0005198281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025836093,0.000090812995,0.0015978052,0.00007170738,0.000059452035,0.00021599796,0.00011909297,0.9562594,0.0038048145,0.008646921,0.0010049796,0.027870584],"study_design_scores_gemma":[0.000005047258,0.00001532078,0.000096990196,0.0000030259168,0.0000036656606,0.00001783051,0.000007694286,0.99699235,0.00037912387,0.0023921742,0.00008226091,0.000004545022],"about_ca_topic_score_codex":0.0041634496,"about_ca_topic_score_gemma":0.0036442063,"teacher_disagreement_score":0.0041634496,"about_ca_system_score_codex":0.0010915124,"about_ca_system_score_gemma":0.00110926,"threshold_uncertainty_score":0.011730075},"labels":[],"label_agreement":null},{"id":"W4411233190","doi":"10.1109/sustech63138.2025.11025610","title":"Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Adversarial system; Vulnerability (computing); Computer science; Anomaly detection; Energy consumption; Computer security; Consumption (sociology); Artificial intelligence; Energy (signal processing); Data mining; Machine learning; Statistics; Engineering; Mathematics","score_opus":0.039159704660990266,"score_gpt":0.3618185106294455,"score_spread":0.3226588059684552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411233190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60875547,0.002024869,0.3806924,0.0016785088,0.00029779415,0.00013100012,0.00042257892,0.0029329094,0.0030644026],"genre_scores_gemma":[0.98191696,0.00016561654,0.017101284,0.00012634843,0.000029908753,0.00002374141,0.00024080042,0.000029201934,0.00036602063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980159,0.00073404127,0.00013707975,0.00041563308,0.00048762458,0.00020974732],"domain_scores_gemma":[0.9907644,0.005897697,0.00086670107,0.0013597081,0.00085592637,0.0002555262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037557187,0.0009931541,0.0010093302,0.0010195315,0.0004811005,0.001151314,0.0011152634,0.0014928494,0.0005201666],"category_scores_gemma":[0.017596327,0.00021573414,0.0005833694,0.00064136594,0.001194544,0.0024401254,0.0016688632,0.0020196731,0.00025571103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072046416,0.00034448967,0.023509761,0.00017208795,0.00019282497,0.00021882358,0.00012039228,0.82389694,0.005094064,0.0050931554,0.0026131424,0.13802384],"study_design_scores_gemma":[0.0000052782143,0.0000818468,0.0011889632,0.000011385938,0.000007738439,0.000057188652,0.000024225017,0.99343514,0.0026642615,0.0023115024,0.00020426852,0.000008308201],"about_ca_topic_score_codex":0.0026898945,"about_ca_topic_score_gemma":0.001781607,"teacher_disagreement_score":0.0037557187,"about_ca_system_score_codex":0.0009411846,"about_ca_system_score_gemma":0.00077685504,"threshold_uncertainty_score":0.019862354},"labels":[],"label_agreement":null},{"id":"W4411449759","doi":"10.1145/3715736","title":"One-for-All Does Not Work! Enhancing Vulnerability Detection by Mixture-of-Experts (MoE)","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on software engineering.","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Manitoba","funders":"","keywords":"Vulnerability (computing); Computer science; Task (project management); Artificial intelligence; Baseline (sea); Deep learning; Machine learning; Code (set theory); Computer security; Engineering; Biology","score_opus":0.008965197819905181,"score_gpt":0.24284575727169933,"score_spread":0.23388055945179415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411449759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086546294,0.009282838,0.8327342,0.005954359,0.0010490662,0.00031320986,0.0022792642,0.05105182,0.010788998],"genre_scores_gemma":[0.526357,0.0024955154,0.44086093,0.005691069,0.00035056158,0.00025782268,0.0063377423,0.0028315757,0.014817716],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995644,0.0011401688,0.00024382144,0.0014312487,0.0010189015,0.0005218375],"domain_scores_gemma":[0.99482083,0.0020488582,0.0003503031,0.0016626786,0.000766433,0.00035081228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005310628,0.003987368,0.0023652045,0.0027693403,0.0009540166,0.0025124014,0.0032847903,0.003991501,0.0045119748],"category_scores_gemma":[0.013659122,0.001232172,0.003011136,0.0012458379,0.0014887467,0.009675007,0.0051804013,0.0053859805,0.0057078665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009883525,0.0006475639,0.021451877,0.00077311683,0.001016319,0.00059035607,0.00036712454,0.0931206,0.015458069,0.008363274,0.07641772,0.7808056],"study_design_scores_gemma":[0.00008788864,0.00040561048,0.0027071624,0.00023014235,0.00024045749,0.0012734765,0.0002185164,0.90688676,0.020958865,0.03650761,0.030328048,0.00015553525],"about_ca_topic_score_codex":0.004856099,"about_ca_topic_score_gemma":0.010030233,"teacher_disagreement_score":0.005310628,"about_ca_system_score_codex":0.0012488324,"about_ca_system_score_gemma":0.0018461937,"threshold_uncertainty_score":0.02808559},"labels":[],"label_agreement":null},{"id":"W4411522960","doi":"10.1007/978-981-96-8728-2_10","title":"CAG: A Constraint-Driven Adversarial Traffic Generation Scheme Based on Feature Correlations","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Constraint (computer-aided design); Scheme (mathematics); Adversarial system; Feature (linguistics); Artificial intelligence; Algorithm; Theoretical computer science; Mathematics","score_opus":0.015600356446619357,"score_gpt":0.25229138256706257,"score_spread":0.2366910261204432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411522960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041962354,0.00006790909,0.9926392,0.000103743725,0.000076677985,0.00007682848,0.00008428974,0.0011490615,0.0016061348],"genre_scores_gemma":[0.47328362,0.00018879825,0.51637673,0.0003472889,0.00012179715,0.00033146693,0.0006276128,0.00047278494,0.008249858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999416,0.00015377818,0.000020469779,0.00010869785,0.00023348269,0.000067509354],"domain_scores_gemma":[0.9991198,0.00032346757,0.000070154325,0.00022864142,0.0001821031,0.00007585723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010338249,0.00092165265,0.0010583154,0.00053813896,0.00050706277,0.00074974005,0.0020171576,0.0013696267,0.0041682767],"category_scores_gemma":[0.002388534,0.000399808,0.0006249299,0.0007106419,0.00085889775,0.001112309,0.00220073,0.002271585,0.001140651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025144403,0.000086061285,0.00021070174,0.00006028709,0.000050463008,0.00009658383,0.000032766366,0.80836016,0.012765296,0.043975223,0.010436373,0.12367457],"study_design_scores_gemma":[0.000007423023,0.00002243563,0.000016859094,0.0000022118031,0.0000024804967,0.000017744826,0.0000010815972,0.9937364,0.0009987722,0.004609366,0.00058045634,0.000004764555],"about_ca_topic_score_codex":0.0019729421,"about_ca_topic_score_gemma":0.0018695643,"teacher_disagreement_score":0.0041682767,"about_ca_system_score_codex":0.0006895984,"about_ca_system_score_gemma":0.0012354336,"threshold_uncertainty_score":0.013944268},"labels":[],"label_agreement":null},{"id":"W4411534915","doi":"10.1007/978-3-031-96590-6_19","title":"Diffusion-Based Adversarial Purification for Intrusion Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Adversarial system; Intrusion detection system; Diffusion; Intrusion; Artificial intelligence; Computer security; Geology; Geochemistry","score_opus":0.010590973510448758,"score_gpt":0.24923831141652497,"score_spread":0.2386473379060762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411534915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018861213,0.00033307707,0.99604225,0.00009971911,0.000047592042,0.000018954455,0.000023380418,0.0004047427,0.0011441874],"genre_scores_gemma":[0.49607164,0.0020471169,0.47475246,0.00043066384,0.00024364995,0.00020154938,0.0003850744,0.0005058053,0.025362087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999132,0.00023084176,0.0000378937,0.00018120879,0.00033683062,0.000081302765],"domain_scores_gemma":[0.9980369,0.0011861438,0.000106329455,0.00035291185,0.00026776383,0.000049811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011809045,0.0011709783,0.0013545087,0.00072229485,0.00044866872,0.0009026892,0.0013834481,0.001170292,0.0030649956],"category_scores_gemma":[0.004071145,0.00059497164,0.0008323062,0.00085541763,0.00117425,0.0019337954,0.002620572,0.0028354093,0.0011706065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015714014,0.00009120943,0.00039152987,0.00020080653,0.00010964836,0.00009134471,0.000094803014,0.5957819,0.019739652,0.083195366,0.00759662,0.29254997],"study_design_scores_gemma":[0.0000026626867,0.000016949789,0.000056591565,0.0000061900228,0.0000062916283,0.000044189797,0.0000031494687,0.9796236,0.0030570612,0.016108176,0.001067245,0.0000079835245],"about_ca_topic_score_codex":0.0010428498,"about_ca_topic_score_gemma":0.0010462243,"teacher_disagreement_score":0.0030649956,"about_ca_system_score_codex":0.0008475318,"about_ca_system_score_gemma":0.0006113957,"threshold_uncertainty_score":0.010253429},"labels":[],"label_agreement":null},{"id":"W4411726174","doi":"10.1109/tvt.2025.3583924","title":"Generative AI-Empowered Resilient Adaptive ISAC Against Adversarial Machine Learning Attacks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Adversarial system; Computer science; Generative grammar; Artificial intelligence; Adversarial machine learning; Machine learning","score_opus":0.008808521010667574,"score_gpt":0.2593226855303517,"score_spread":0.2505141645196841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411726174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022151195,0.00028201911,0.9714049,0.00024522282,0.00006775421,0.0000524552,0.000056469064,0.0010847965,0.0046551493],"genre_scores_gemma":[0.9157405,0.00015781296,0.07890432,0.0003979967,0.0000515954,0.00009460857,0.00013706309,0.00014526756,0.004370778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939716,0.000116713374,0.000014550052,0.00016350523,0.0001951629,0.000112883994],"domain_scores_gemma":[0.9989492,0.00047417573,0.00011410108,0.0001693197,0.00022196327,0.00007125725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010389369,0.0010026098,0.0006224325,0.00039976567,0.000343148,0.0006781672,0.0014831298,0.0008459303,0.0017357771],"category_scores_gemma":[0.0025282982,0.00033101754,0.00054871326,0.00026476997,0.0012846966,0.0010563547,0.001795991,0.0018014765,0.0004896837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005014821,0.000022266231,0.0005918595,0.000028964798,0.000030339566,0.00007000113,0.00005071119,0.9483989,0.004602943,0.0110678105,0.0013016199,0.033784505],"study_design_scores_gemma":[0.0000018776549,0.0000129857535,0.000069214264,0.0000022738525,0.0000030094939,0.00001814507,0.0000035410892,0.9966666,0.000823899,0.0020724267,0.00032188374,0.0000041535054],"about_ca_topic_score_codex":0.0033697737,"about_ca_topic_score_gemma":0.0033919443,"teacher_disagreement_score":0.0033697737,"about_ca_system_score_codex":0.0009258082,"about_ca_system_score_gemma":0.0010066192,"threshold_uncertainty_score":0.0067172647},"labels":[],"label_agreement":null},{"id":"W4411969761","doi":"10.5220/0013640600003979","title":"Robust Peer-to-Peer Machine Learning Against Poisoning Attacks","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Peer-to-peer; Computer security; Artificial intelligence; Computer network","score_opus":0.019438336859679876,"score_gpt":0.2776429571010392,"score_spread":0.2582046202413593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411969761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08423015,0.0005521779,0.9010457,0.0015387547,0.00029893938,0.0001897565,0.00016133401,0.0037771505,0.008206078],"genre_scores_gemma":[0.9630505,0.00014931014,0.033605322,0.0001814731,0.00010774774,0.000089958296,0.00012807021,0.00016202952,0.002525551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964545,0.001110675,0.00016813395,0.00064197375,0.0011907602,0.000434059],"domain_scores_gemma":[0.9854306,0.0074474164,0.0010070464,0.0039200443,0.0017888813,0.0004059967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003959326,0.0010601367,0.0020508773,0.0009032294,0.0010265694,0.0013850175,0.002267459,0.0028497714,0.0019796763],"category_scores_gemma":[0.026310021,0.0004704872,0.0005487608,0.0006263655,0.001982599,0.0031512952,0.0041132136,0.002460974,0.0013082649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005309936,0.00014567358,0.001196647,0.00018437166,0.00016440744,0.0003609681,0.0001943592,0.8353342,0.01655926,0.04748516,0.007469157,0.09037486],"study_design_scores_gemma":[0.000015817766,0.000067688976,0.00015700157,0.0000085790725,0.000011501086,0.00010617373,0.00002796573,0.9676288,0.003898292,0.027344866,0.00072233187,0.0000109672565],"about_ca_topic_score_codex":0.0006545063,"about_ca_topic_score_gemma":0.00040727094,"teacher_disagreement_score":0.003959326,"about_ca_system_score_codex":0.0008161048,"about_ca_system_score_gemma":0.0010833719,"threshold_uncertainty_score":0.020939171},"labels":[],"label_agreement":null},{"id":"W4412030442","doi":"10.1109/ticps.2025.3586211","title":"An Integrated Trustworthy Detection and Classification of Cyber-Physical Attacks in the Presence of Disturbances Using Morphological Image Processing and Explainable AI","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Cyber-Physical Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trustworthiness; Computer science; Cyber-physical system; Image (mathematics); Computer security; Image processing; Artificial intelligence; Pattern recognition (psychology); Operating system","score_opus":0.035011644076668805,"score_gpt":0.3085257070440984,"score_spread":0.2735140629674296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412030442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12961552,0.00029029878,0.86548245,0.00034969545,0.000078783974,0.000078672696,0.00006393387,0.0008559748,0.003184674],"genre_scores_gemma":[0.8997372,0.00024480908,0.098297864,0.00006709669,0.00004450951,0.000041960175,0.00011381314,0.00002898132,0.0014238174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993678,0.00009460613,0.000037037127,0.00014386613,0.0002832296,0.000073540614],"domain_scores_gemma":[0.9992132,0.00022029666,0.00021790397,0.0001268164,0.00018808481,0.000033647062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000689863,0.00054142426,0.00055317103,0.0012098046,0.00033449856,0.0010924725,0.0006513483,0.0007177876,0.0006284422],"category_scores_gemma":[0.0021972896,0.0002243584,0.0005542324,0.00056901394,0.0006434099,0.001138225,0.0007909996,0.00070410565,0.00024270164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050579105,0.00025961114,0.020336345,0.00020954154,0.00015997673,0.0011729609,0.0005560694,0.17837766,0.1084798,0.019523649,0.0019036209,0.668515],"study_design_scores_gemma":[0.000008002481,0.00011444648,0.0050052404,0.000012414503,0.000029771049,0.00018552314,0.000061811,0.97272056,0.016626896,0.004119875,0.0010980106,0.000017482977],"about_ca_topic_score_codex":0.0012628242,"about_ca_topic_score_gemma":0.0010816746,"teacher_disagreement_score":0.0012628242,"about_ca_system_score_codex":0.0004094681,"about_ca_system_score_gemma":0.0005098571,"threshold_uncertainty_score":0.0036484003},"labels":[],"label_agreement":null},{"id":"W4412079529","doi":"10.1016/j.neucom.2025.130703","title":"Adversarial defenses via vector quantization","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Vector quantization; Computer science; Artificial intelligence; Quantization (signal processing); Vector (molecular biology); Machine learning; Pattern recognition (psychology); Algorithm; Biology; Genetics","score_opus":0.006379592196067275,"score_gpt":0.25439758687640845,"score_spread":0.2480179946803412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412079529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013328365,0.0007128712,0.9747258,0.0013570882,0.00023762566,0.00004412866,0.00007292202,0.00049897283,0.009022238],"genre_scores_gemma":[0.91117877,0.0005926922,0.07478318,0.00080603134,0.00038277122,0.00010933632,0.00014175115,0.00015468335,0.011850786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838865,0.0005530601,0.000059957456,0.0002709515,0.0005536126,0.00017375228],"domain_scores_gemma":[0.99607974,0.0024886806,0.00029722584,0.0006673464,0.00033559193,0.00013147546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002187987,0.000951804,0.0011166906,0.00079742377,0.00062732527,0.0013714083,0.0013576593,0.00194196,0.0043732086],"category_scores_gemma":[0.009769173,0.00040245953,0.00052208896,0.0006598422,0.002439842,0.0028535246,0.0034152886,0.0030486565,0.00069580844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014167129,0.00006149909,0.00030895378,0.00008130766,0.00005973621,0.00007838702,0.00007239395,0.5029954,0.0050643077,0.40788886,0.007553965,0.07569345],"study_design_scores_gemma":[0.0000095070845,0.000037029058,0.00007985168,0.000012647234,0.000006919143,0.000037830905,0.000010472104,0.813889,0.00091057137,0.183904,0.0010907823,0.000011299507],"about_ca_topic_score_codex":0.00072126754,"about_ca_topic_score_gemma":0.0006317936,"teacher_disagreement_score":0.0043732086,"about_ca_system_score_codex":0.00086871325,"about_ca_system_score_gemma":0.0007060213,"threshold_uncertainty_score":0.0146299},"labels":[],"label_agreement":null},{"id":"W4413024210","doi":"10.1007/s11219-025-09727-2","title":"Investigating adversarial attacks in software analytics via machine learning explainability","year":2025,"lang":"en","type":"article","venue":"Software Quality Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software analytics; Analytics; Computer science; Adversarial system; Software; Artificial intelligence; Software engineering; Machine learning; Data science; Software development; Software construction; Operating system","score_opus":0.026464954080557494,"score_gpt":0.32756341787785526,"score_spread":0.30109846379729777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413024210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13152589,0.00059758243,0.86014384,0.0029456438,0.000081744656,0.00006268376,0.0000905702,0.000383833,0.004168288],"genre_scores_gemma":[0.98524946,0.00022640568,0.013294132,0.0001512074,0.00008493039,0.000030361807,0.000054990087,0.00005360346,0.0008549686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945893,0.0029983623,0.00013635973,0.0007044217,0.0011646565,0.00040686785],"domain_scores_gemma":[0.8699252,0.1156108,0.0053896885,0.006437523,0.0019300838,0.00070664636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008069549,0.0009777383,0.0012192519,0.0012745204,0.0005872029,0.0020604988,0.001406371,0.0021141889,0.0019949367],"category_scores_gemma":[0.07300747,0.0005732108,0.0009361084,0.00081187714,0.0038707494,0.0050041364,0.0035574192,0.004106654,0.00014306874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019279218,0.00009549213,0.0035502953,0.00011373123,0.00014650359,0.0001581612,0.00018821484,0.7579965,0.0018947272,0.2184549,0.0010816477,0.016127039],"study_design_scores_gemma":[0.000007247924,0.000028750668,0.00035799522,0.000011371928,0.0000113154465,0.000021124055,0.0000164554,0.9275478,0.0004924031,0.07131509,0.00018278776,0.000007604123],"about_ca_topic_score_codex":0.0014849607,"about_ca_topic_score_gemma":0.00084096874,"teacher_disagreement_score":0.008069549,"about_ca_system_score_codex":0.001652225,"about_ca_system_score_gemma":0.0011175084,"threshold_uncertainty_score":0.04267633},"labels":[],"label_agreement":null},{"id":"W4413024567","doi":"10.1111/coin.70113","title":"Taming the Triangle: On the Interplays Between Fairness, Interpretability, and Privacy in Machine Learning","year":2025,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Polytechnique Montréal","funders":"Canada Research Chairs; Centre International de Mathématiques et Informatique de Toulouse; Agence Nationale de la Recherche; Polytechnique Montréal","keywords":"Interpretability; Computer science; Artificial intelligence; Machine learning; Natural language processing","score_opus":0.02465387779662896,"score_gpt":0.303107553909016,"score_spread":0.278453676112387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413024567","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048997976,0.009887285,0.8919513,0.023622435,0.00034074925,0.00010750543,0.00009292912,0.00020331937,0.024796413],"genre_scores_gemma":[0.9341117,0.0044792905,0.05617416,0.0023539944,0.0007390364,0.00016911236,0.00004663745,0.000115240604,0.0018107845],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96100205,0.028145496,0.0011898614,0.002998069,0.005292454,0.0013720591],"domain_scores_gemma":[0.8214391,0.15313809,0.0059583914,0.014258435,0.0038394765,0.0013664522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027713846,0.0010439925,0.0015297943,0.0018305315,0.002008791,0.008329655,0.002762631,0.0039457385,0.0040710075],"category_scores_gemma":[0.09137078,0.00093373575,0.0011676931,0.0017596605,0.017181434,0.015056956,0.008361683,0.008586407,0.0006168622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009398151,0.00004027222,0.0009232688,0.00018744559,0.000063732834,0.00013517072,0.0005032732,0.011887481,0.00057754153,0.9630359,0.00125333,0.021298645],"study_design_scores_gemma":[0.000015477479,0.00006271986,0.0003512596,0.00021451837,0.00004085083,0.00013477875,0.00015633176,0.04379688,0.0009503471,0.9501873,0.0040538437,0.000035634566],"about_ca_topic_score_codex":0.00052801665,"about_ca_topic_score_gemma":0.0003474748,"teacher_disagreement_score":0.027713846,"about_ca_system_score_codex":0.0023370052,"about_ca_system_score_gemma":0.0017945641,"threshold_uncertainty_score":0.14656663},"labels":[],"label_agreement":null},{"id":"W4413145210","doi":"10.2139/ssrn.5377845","title":"The Case for Inadmissibility of Surreptitious Interceptions","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Computational biology; Pharmacology; Biology","score_opus":0.015081308957884284,"score_gpt":0.313265948427207,"score_spread":0.2981846394693227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413145210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15033077,0.0018561194,0.7042836,0.029747054,0.0014764104,0.0002887522,0.0010562268,0.0037204523,0.10724057],"genre_scores_gemma":[0.95646113,0.00033827784,0.027773859,0.0019729927,0.00044878104,0.00012281799,0.00019425465,0.00040930527,0.01227862],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98165685,0.005466828,0.0012559885,0.0044574947,0.004479153,0.002683704],"domain_scores_gemma":[0.78980374,0.110638805,0.012971946,0.07715443,0.007088466,0.0023425925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019402947,0.0011905958,0.0025280123,0.0016120856,0.0028257729,0.0054033957,0.005355833,0.01599856,0.015737265],"category_scores_gemma":[0.21006131,0.0015294083,0.0018370548,0.001356573,0.0073798653,0.013595119,0.007006615,0.015636887,0.00466163],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012217051,0.00016310246,0.009513289,0.00037707738,0.00036337643,0.004477555,0.0016022319,0.042019553,0.0055826223,0.83521295,0.020827767,0.07863887],"study_design_scores_gemma":[0.00011440433,0.0001780337,0.0013357867,0.00018628668,0.00012360858,0.003467866,0.00030323715,0.09867844,0.006556622,0.8750981,0.0138587635,0.000098779514],"about_ca_topic_score_codex":0.0006714803,"about_ca_topic_score_gemma":0.00045276879,"teacher_disagreement_score":0.019402947,"about_ca_system_score_codex":0.0014540574,"about_ca_system_score_gemma":0.001818337,"threshold_uncertainty_score":0.10261387},"labels":[],"label_agreement":null},{"id":"W4413157643","doi":"10.1109/cvpr52734.2025.02660","title":"Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on Breast Ultrasound Images","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Artificial intelligence; Diffusion; Breast ultrasound; Computer vision; Pattern recognition (psychology); Mammography; Breast cancer; Medicine; Physics; Internal medicine","score_opus":0.0085022753043582,"score_gpt":0.2711750793036014,"score_spread":0.2626728039992432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413157643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068147235,0.0007117157,0.92022586,0.00078676024,0.00020289526,0.00014598745,0.00030932596,0.0062659127,0.0032041797],"genre_scores_gemma":[0.82427615,0.0005485547,0.16474707,0.00087740424,0.00011275439,0.00020998341,0.00079441676,0.0005691199,0.007864528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923444,0.0002547107,0.00003852009,0.00017048458,0.00022960665,0.0000722001],"domain_scores_gemma":[0.99828076,0.0010274379,0.00018242397,0.0002967292,0.00013679091,0.00007595423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010348916,0.001286377,0.00058025593,0.00036212476,0.00024326441,0.0005582721,0.0008951332,0.0011618846,0.002025579],"category_scores_gemma":[0.00556554,0.00025589857,0.00060442387,0.00019695633,0.0011733024,0.0015675306,0.0018583986,0.0020372218,0.0007058477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008283066,0.00023212183,0.0021155563,0.00038592823,0.00010448117,0.00067783037,0.0002492177,0.62839407,0.060380675,0.024294745,0.0154296635,0.26690748],"study_design_scores_gemma":[0.000022491096,0.0001364199,0.00024045154,0.000018007848,0.000009109221,0.00017044733,0.000017328748,0.9736623,0.016530251,0.0073905047,0.0017871629,0.000015509055],"about_ca_topic_score_codex":0.0007383093,"about_ca_topic_score_gemma":0.0010669999,"teacher_disagreement_score":0.002025579,"about_ca_system_score_codex":0.0005410897,"about_ca_system_score_gemma":0.0005265378,"threshold_uncertainty_score":0.0067762136},"labels":[],"label_agreement":null},{"id":"W4413219439","doi":"10.1007/s10664-025-10693-3","title":"Adversarial attack classification and robustness testing for large language models for code","year":2025,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Robustness (evolution); Adversarial system; Artificial intelligence; Programming language; Biology","score_opus":0.04674644636670435,"score_gpt":0.3271197308610341,"score_spread":0.28037328449432974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413219439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06454762,0.0004132369,0.93037933,0.0010252743,0.00007119097,0.00008522037,0.00023181607,0.0017185814,0.0015276694],"genre_scores_gemma":[0.88829494,0.00022099905,0.10639226,0.0003618463,0.00021599329,0.00017171992,0.0010794984,0.0004454501,0.002817257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9926466,0.0042414917,0.00030625417,0.00108961,0.0012752245,0.00044076698],"domain_scores_gemma":[0.9211485,0.067526765,0.0025359413,0.006486217,0.0015719859,0.000730628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010190294,0.001596007,0.0013724373,0.0023218421,0.00083303504,0.0018252487,0.0021473793,0.002283921,0.0030585798],"category_scores_gemma":[0.066360235,0.0007081736,0.001791338,0.00091205485,0.0028915657,0.0034683181,0.004320963,0.0044041863,0.0007008571],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036416345,0.0002279033,0.0047665965,0.0001381107,0.00019016101,0.00015352022,0.00017473519,0.8483362,0.003438636,0.04318233,0.004530644,0.09449698],"study_design_scores_gemma":[0.0000063716643,0.000018571916,0.00021601972,0.0000065055615,0.00000578145,0.000014619364,0.0000074226446,0.9843593,0.00059731165,0.014628591,0.00013384283,0.0000056210492],"about_ca_topic_score_codex":0.0027018995,"about_ca_topic_score_gemma":0.002597441,"teacher_disagreement_score":0.010190294,"about_ca_system_score_codex":0.0017258336,"about_ca_system_score_gemma":0.0014210044,"threshold_uncertainty_score":0.053892076},"labels":[],"label_agreement":null},{"id":"W4413318932","doi":"10.1109/jiot.2025.3599106","title":"Securing Smart Grid Federated Learning Against Advanced Evasion Attacks Using Ensemble-Based Adversarial Training","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Science Foundation","keywords":"Adversarial system; Computer science; Training (meteorology); Evasion (ethics); Smart grid; Ensemble learning; Artificial intelligence; Grid; Computer security; Machine learning; Training set; Computer network; Engineering","score_opus":0.023716067879406905,"score_gpt":0.2903333922325174,"score_spread":0.26661732435311053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413318932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09851502,0.00028443293,0.895734,0.00041357783,0.00015321927,0.000055321565,0.000054243992,0.0011907634,0.0035993916],"genre_scores_gemma":[0.98468626,0.000045568933,0.014154174,0.0000882395,0.000020913254,0.000023141756,0.00003353134,0.000019672209,0.0009285491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897146,0.00029629155,0.00004448264,0.00020473627,0.00028043677,0.00020257519],"domain_scores_gemma":[0.99735427,0.0014523815,0.00023450801,0.00049325335,0.0003822516,0.000083464474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019322609,0.0009300374,0.0011809011,0.00040350674,0.00043262244,0.0007308571,0.0009070431,0.0010434024,0.00096419244],"category_scores_gemma":[0.0065385187,0.00028581073,0.00046480837,0.00032232184,0.00089457637,0.0016175082,0.0021242585,0.0016311171,0.00027622478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018636018,0.000057238296,0.0010803819,0.000023653327,0.00005625956,0.00007804887,0.000037269336,0.94942516,0.0026526267,0.0055569373,0.00087876525,0.03996732],"study_design_scores_gemma":[0.0000021311214,0.000023914949,0.000095863725,0.00000258195,0.0000037002928,0.000018443452,0.0000041118583,0.9976307,0.00056888093,0.0015623092,0.00008478561,0.0000025302556],"about_ca_topic_score_codex":0.001214162,"about_ca_topic_score_gemma":0.0011823429,"teacher_disagreement_score":0.0019322609,"about_ca_system_score_codex":0.00047628244,"about_ca_system_score_gemma":0.00070596003,"threshold_uncertainty_score":0.010218918},"labels":[],"label_agreement":null},{"id":"W4413476775","doi":"10.1016/j.eswa.2025.129481","title":"Clean-label backdoor attack via sample-customized feature alignment","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China-Shandong Joint Fund; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Backdoor; Computer science; Sample (material); Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Data mining; Computer security; Chromatography","score_opus":0.015064438993322509,"score_gpt":0.2946220818503113,"score_spread":0.2795576428569888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413476775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02527059,0.0003089643,0.9675656,0.00079027645,0.00020807136,0.00008246072,0.0002974181,0.0028758724,0.0026007555],"genre_scores_gemma":[0.7472788,0.00027516286,0.24353655,0.00088706973,0.00020619511,0.0001905346,0.0009124818,0.0005386986,0.00617447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638414,0.00085741235,0.00012920311,0.0007711955,0.0013957722,0.00046225262],"domain_scores_gemma":[0.99550205,0.0018875017,0.0002977946,0.0018712865,0.0003135491,0.00012792593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020113105,0.0016637389,0.0017145436,0.00077939726,0.00092416065,0.001417593,0.0013802266,0.0024941012,0.0033634575],"category_scores_gemma":[0.010550121,0.0006315344,0.0013156546,0.0008970244,0.0018363293,0.0028626116,0.0052594226,0.004137778,0.0016768649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026694073,0.0005631396,0.0036322845,0.0005033541,0.000528034,0.001001678,0.00036761136,0.26275197,0.08943509,0.15139572,0.028116994,0.45903468],"study_design_scores_gemma":[0.000072954106,0.00014238147,0.00076452084,0.000031097385,0.000049382244,0.00043834693,0.00004352211,0.88125616,0.027598476,0.08657973,0.0029739768,0.000049340488],"about_ca_topic_score_codex":0.000789627,"about_ca_topic_score_gemma":0.0012147756,"teacher_disagreement_score":0.0033634575,"about_ca_system_score_codex":0.0006914167,"about_ca_system_score_gemma":0.001584114,"threshold_uncertainty_score":0.011251867},"labels":[],"label_agreement":null},{"id":"W4413491346","doi":"10.64628/aam.rxn9s4aks","title":"Meta’s AI-powered smart glasses raise concerns about privacy and user data","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Internet privacy; Information privacy; Computer science; Computer security","score_opus":0.09638403523568237,"score_gpt":0.3524392849812942,"score_spread":0.2560552497456118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413491346","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039480668,0.0011737944,0.8558583,0.01798669,0.0015111592,0.0002114846,0.0007977236,0.007910293,0.07506987],"genre_scores_gemma":[0.8045576,0.000515795,0.15813823,0.0054742317,0.00040795607,0.0002298494,0.00040282236,0.0012083661,0.029065145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965855,0.001074708,0.00012691668,0.0005579934,0.0013544372,0.0003004486],"domain_scores_gemma":[0.9881132,0.004164549,0.00041080266,0.0060303775,0.00092306617,0.0003579645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004667005,0.00078095065,0.0007335981,0.00058862084,0.0012097557,0.004040909,0.0022207133,0.0034279665,0.011656872],"category_scores_gemma":[0.017959867,0.0007860397,0.00090148195,0.0005749733,0.0029974708,0.006000731,0.0045004277,0.007314752,0.0046431087],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013515158,0.00029684373,0.0033537697,0.00038552517,0.00025530552,0.0005104455,0.001132826,0.03953222,0.049675222,0.61944485,0.050758574,0.23330279],"study_design_scores_gemma":[0.00015259284,0.0003345787,0.0016919337,0.00021231067,0.000099360834,0.0008599813,0.0003860608,0.31916472,0.08233942,0.4643543,0.13025846,0.00014624317],"about_ca_topic_score_codex":0.0012236463,"about_ca_topic_score_gemma":0.0012743088,"teacher_disagreement_score":0.011656872,"about_ca_system_score_codex":0.00095136283,"about_ca_system_score_gemma":0.0010495561,"threshold_uncertainty_score":0.03899616},"labels":[],"label_agreement":null},{"id":"W4413679387","doi":"10.1109/compsac65507.2025.00338","title":"Spatio-temporal Explanation for Adversarial-Aware Cloud Vision AI Services","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Adversarial system; Cloud computing; Computer science; Artificial intelligence; Computer security; Computer vision; Operating system","score_opus":0.007076239481570051,"score_gpt":0.29584086658015896,"score_spread":0.2887646270985889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413679387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022128694,0.0002809369,0.97298133,0.0009825828,0.000051067545,0.00011163499,0.00016905596,0.0008851287,0.002409531],"genre_scores_gemma":[0.83018833,0.0003945417,0.1661511,0.00030444298,0.00008268317,0.00008510158,0.000389111,0.00016356427,0.0022411693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985134,0.0004731291,0.00009237045,0.00023804835,0.0005134842,0.00016951056],"domain_scores_gemma":[0.99466115,0.0024112498,0.0007154729,0.0012324776,0.00076744513,0.00021230342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019752844,0.0007022454,0.000546194,0.000848206,0.000558596,0.0016480357,0.001485721,0.0011873235,0.0029710536],"category_scores_gemma":[0.011366209,0.0003129522,0.00096850685,0.0005059058,0.0013709115,0.0027561332,0.0029124205,0.0022611045,0.00042853248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044554958,0.00013650885,0.007944026,0.00033397038,0.00021317745,0.0011246596,0.0010829282,0.6123084,0.015072878,0.19063146,0.0063307607,0.16437574],"study_design_scores_gemma":[0.0000065824383,0.000025133995,0.0004906221,0.000017126253,0.000014788287,0.00011055964,0.00007689792,0.9578705,0.0020205744,0.03754858,0.0018025334,0.000016263686],"about_ca_topic_score_codex":0.0075041284,"about_ca_topic_score_gemma":0.006537085,"teacher_disagreement_score":0.0075041284,"about_ca_system_score_codex":0.0014956611,"about_ca_system_score_gemma":0.0017923035,"threshold_uncertainty_score":0.01492089},"labels":[],"label_agreement":null},{"id":"W4413682498","doi":"10.1287/ijoo.2024.0048","title":"Wasserstein Distributionally Robust Shallow Convex Neural Networks","year":2025,"lang":"en","type":"article","venue":"INFORMS Journal on Optimization","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Regular polygon; Artificial neural network; Mathematical optimization; Computer science; Mathematics; Artificial intelligence; Geometry","score_opus":0.009001984855003472,"score_gpt":0.24249124139660466,"score_spread":0.2334892565416012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413682498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01488746,0.00019743152,0.9828288,0.00025763485,0.00003410997,0.000031147145,0.00009112479,0.0004891779,0.0011830977],"genre_scores_gemma":[0.80706966,0.0003600264,0.18371916,0.000584017,0.00007753498,0.00022704633,0.00069915986,0.00030334122,0.0069599957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926156,0.00022392864,0.00004084123,0.00019863476,0.00017154313,0.00010351981],"domain_scores_gemma":[0.9983967,0.0007746927,0.00020167895,0.00017390122,0.00037790395,0.0000750661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017132725,0.001650256,0.0012444525,0.0004553758,0.00037142175,0.0010761572,0.0022813897,0.0015191631,0.0018051285],"category_scores_gemma":[0.0067579467,0.0007984058,0.00078404223,0.00045323497,0.0013784689,0.0017233833,0.0020377424,0.0024403227,0.00052362593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046395642,0.000014873595,0.0004778352,0.00003461458,0.000028526556,0.00005434618,0.000028986591,0.96681535,0.0011841842,0.006543228,0.0007462203,0.024025429],"study_design_scores_gemma":[0.0000014065713,0.000007782346,0.000021595048,0.0000022805052,0.0000015777448,0.0000036768881,0.0000016232236,0.997931,0.00019880284,0.0017563449,0.0000719631,0.0000020599073],"about_ca_topic_score_codex":0.0075712265,"about_ca_topic_score_gemma":0.008095675,"teacher_disagreement_score":0.0075712265,"about_ca_system_score_codex":0.0011429786,"about_ca_system_score_gemma":0.0015928592,"threshold_uncertainty_score":0.0150542855},"labels":[],"label_agreement":null},{"id":"W4413786017","doi":"10.1109/tdsc.2025.3603639","title":"I2I Backdoor: Backdoor Attacks Against Image-to-Image Tasks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Backdoor; Computer science; Image (mathematics); Computer security; Artificial intelligence; Computer vision","score_opus":0.008572549446998498,"score_gpt":0.2709500114351712,"score_spread":0.26237746198817274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413786017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08430095,0.0010947003,0.90499973,0.00089930056,0.00023620136,0.00024170634,0.00021017181,0.00303394,0.0049833306],"genre_scores_gemma":[0.92583513,0.00036567476,0.070194304,0.0005605737,0.000061959,0.00015758011,0.00018683304,0.00018997933,0.002448086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976394,0.0005653089,0.000107113156,0.00047206625,0.0007712898,0.0004447697],"domain_scores_gemma":[0.9951715,0.0022807806,0.00060708064,0.0013291126,0.00040424927,0.00020729443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021599291,0.0017004496,0.0011389859,0.0006076,0.00076725363,0.001052142,0.0017855775,0.0021154084,0.0019331237],"category_scores_gemma":[0.010047169,0.00053923443,0.0012493173,0.00041424544,0.002378014,0.0031842554,0.004157432,0.0034433275,0.00063657056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016975441,0.00032284722,0.0039082533,0.00050295243,0.00041505616,0.0015922606,0.00051083363,0.6125049,0.097883165,0.069259696,0.011669851,0.1997326],"study_design_scores_gemma":[0.00002370463,0.00016174861,0.0003828135,0.000026721871,0.000028174305,0.00041797032,0.000042670898,0.95168775,0.029751584,0.016082328,0.0013609531,0.000033568016],"about_ca_topic_score_codex":0.0009631009,"about_ca_topic_score_gemma":0.0008380325,"teacher_disagreement_score":0.0021599291,"about_ca_system_score_codex":0.0009858091,"about_ca_system_score_gemma":0.0007791629,"threshold_uncertainty_score":0.011422932},"labels":[],"label_agreement":null},{"id":"W4413846568","doi":"10.1016/j.infsof.2025.107876","title":"Representation-based fairness evaluation and bias correction robustness assessment in neural networks","year":2025,"lang":"en","type":"article","venue":"Information and Software Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Robustness (evolution); Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Machine learning; Representation (politics); Political science; Chemistry","score_opus":0.015202705191202088,"score_gpt":0.30853651052135794,"score_spread":0.29333380533015585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413846568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1669335,0.00051506655,0.82852906,0.00074471306,0.0000791161,0.00015279463,0.00014181131,0.0003330661,0.0025709132],"genre_scores_gemma":[0.9660306,0.00009692149,0.033151668,0.00010715461,0.000038865866,0.00007877927,0.00010753276,0.000029264365,0.00035931976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98982763,0.0059359893,0.0005275078,0.0012454109,0.0019786612,0.0004847598],"domain_scores_gemma":[0.96459544,0.023767125,0.004263245,0.0036427632,0.0030455678,0.0006858555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018687887,0.0012386669,0.0010695935,0.0017900203,0.0008021115,0.0021978992,0.0015362836,0.0014999728,0.0011354992],"category_scores_gemma":[0.068677194,0.00029805023,0.0008326847,0.00086585147,0.0020336215,0.0028058619,0.0030494318,0.0019043322,0.00015832085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063709205,0.00017623123,0.019057859,0.00015313075,0.00028656077,0.00012256338,0.00035981886,0.84869015,0.0029466057,0.028284816,0.00089921866,0.098385915],"study_design_scores_gemma":[0.00001343641,0.000094939416,0.0017225409,0.000029561159,0.000031324333,0.0000339965,0.000045296667,0.9763948,0.0025521899,0.018824138,0.00023750533,0.000020280131],"about_ca_topic_score_codex":0.0021011871,"about_ca_topic_score_gemma":0.0011818317,"teacher_disagreement_score":0.018687887,"about_ca_system_score_codex":0.0024833467,"about_ca_system_score_gemma":0.0017215689,"threshold_uncertainty_score":0.09883219},"labels":[],"label_agreement":null},{"id":"W4413925366","doi":"10.1109/icra55743.2025.11128007","title":"Prepared for the Worst: Resilience Analysis of the ICP Algorithm via Learning-Based Worst-Case Adversarial Attacks","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies","funders":"","keywords":"Adversarial system; Resilience (materials science); Computer science; Algorithm; Algorithm design; Artificial intelligence; Materials science","score_opus":0.008131493173203323,"score_gpt":0.28299531419542723,"score_spread":0.2748638210222239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413925366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055446185,0.0003798129,0.93883973,0.00062912493,0.00011860859,0.000115037146,0.0001499473,0.0005312683,0.0037902934],"genre_scores_gemma":[0.9352839,0.00025142194,0.06210603,0.00018698425,0.000060634826,0.00014767217,0.00019723103,0.00016583162,0.0016002817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658585,0.0011222024,0.00014426,0.000597182,0.0010896012,0.0004608231],"domain_scores_gemma":[0.980853,0.012691466,0.002108231,0.0023718989,0.0013590452,0.00061643095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047276844,0.0012131736,0.0010051563,0.0012404674,0.00090879924,0.001362101,0.0015898054,0.0013102164,0.0019267567],"category_scores_gemma":[0.03072161,0.00048942096,0.0009106537,0.0006981176,0.002807107,0.0028472536,0.0033133153,0.00261247,0.0003723344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110358764,0.000021351769,0.0013729191,0.00004340894,0.00005011858,0.00007890115,0.000053053478,0.9705842,0.0015456569,0.015479065,0.00092985877,0.009730984],"study_design_scores_gemma":[0.00000349364,0.000030684674,0.0002236339,0.000008435332,0.000006561548,0.000038406426,0.000019440984,0.99116087,0.0010104773,0.007200808,0.00028799963,0.000009198118],"about_ca_topic_score_codex":0.0026239455,"about_ca_topic_score_gemma":0.0016377261,"teacher_disagreement_score":0.0047276844,"about_ca_system_score_codex":0.0018251191,"about_ca_system_score_gemma":0.0014763206,"threshold_uncertainty_score":0.025002718},"labels":[],"label_agreement":null},{"id":"W4413943652","doi":"10.1007/s10586-025-05326-9","title":"L-xaids: A LIME-based eXplainable AI framework for intrusion detection systems","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Mitacs","keywords":"Computer science; Intrusion detection system; Lime; Artificial intelligence; Geology","score_opus":0.010833975656361544,"score_gpt":0.28906552911999034,"score_spread":0.2782315534636288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413943652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021878236,0.0003420932,0.9904392,0.0013672399,0.000045890705,0.00019384643,0.000751124,0.0025793496,0.0020933675],"genre_scores_gemma":[0.12508537,0.0005325603,0.86809933,0.0006520568,0.00008624121,0.0006658481,0.0020396353,0.0002741451,0.002564763],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797934,0.0009787587,0.00018336278,0.00033896827,0.0004089631,0.00011065137],"domain_scores_gemma":[0.99610317,0.0027989747,0.00029208238,0.0002937344,0.00039228602,0.00011970415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002963391,0.0013254938,0.000550482,0.0023722202,0.0007375168,0.002877374,0.0024565705,0.0016739347,0.0076550795],"category_scores_gemma":[0.009861457,0.0005830153,0.002745607,0.00083147077,0.001845633,0.0028217647,0.0033773521,0.0037044887,0.0010613682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018862223,0.00020766887,0.003887115,0.0010765169,0.0002543648,0.0007986071,0.001588891,0.27581507,0.0030418565,0.5185552,0.011746254,0.18283983],"study_design_scores_gemma":[0.000043995366,0.000062927,0.00040530835,0.0001702932,0.00006120682,0.00013269861,0.00013573798,0.7098716,0.0013904414,0.2623806,0.02530828,0.0000368541],"about_ca_topic_score_codex":0.0076534827,"about_ca_topic_score_gemma":0.012113075,"teacher_disagreement_score":0.0076550795,"about_ca_system_score_codex":0.0019759368,"about_ca_system_score_gemma":0.002244593,"threshold_uncertainty_score":0.025608778},"labels":[],"label_agreement":null},{"id":"W4414014113","doi":"10.1145/3766071","title":"Joint Spatiotemporal Adversarial Attacks on Video Transformer Models Through XAI-guided Perturbation","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Adversarial system; Transformer; Joint (building); Computer network; Computer security; Artificial intelligence","score_opus":0.05164157767213799,"score_gpt":0.3270187243269382,"score_spread":0.2753771466548002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414014113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10904484,0.0005297232,0.88288105,0.00060640264,0.00009360479,0.0001528079,0.00026004805,0.0024757856,0.003955658],"genre_scores_gemma":[0.94217384,0.00026992094,0.054111063,0.00024267726,0.00004073965,0.00007371511,0.00036260346,0.00012358918,0.002601848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933773,0.00017126487,0.000033055785,0.00014724108,0.00020280258,0.00010801335],"domain_scores_gemma":[0.99870336,0.00062096136,0.00016671034,0.00027034275,0.00015816893,0.000080372585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009622267,0.0009710458,0.0005479935,0.00046542863,0.00026490024,0.00067139266,0.00092732004,0.00059937645,0.001347053],"category_scores_gemma":[0.00466348,0.00022263509,0.00059273536,0.0003080284,0.0008727978,0.0013530098,0.0015666136,0.0013371112,0.00037777025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002321743,0.000080329846,0.0033037097,0.00008628406,0.000075996984,0.00026965069,0.00012824609,0.8477682,0.018281873,0.020161226,0.0036998347,0.10591239],"study_design_scores_gemma":[0.0000031743482,0.000031395168,0.00017650951,0.0000035278963,0.0000054169545,0.000042237913,0.00001119047,0.9941596,0.0024515328,0.0027602098,0.00035142046,0.0000038162357],"about_ca_topic_score_codex":0.0039073927,"about_ca_topic_score_gemma":0.003850095,"teacher_disagreement_score":0.0039073927,"about_ca_system_score_codex":0.0010463973,"about_ca_system_score_gemma":0.0008281814,"threshold_uncertainty_score":0.0077692866},"labels":[],"label_agreement":null},{"id":"W4414091836","doi":"10.1145/3766890","title":"FairFLRep: Fairness-Aware Fault Localization and Repair of Deep Neural Networks","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Deep neural networks; Artificial neural network; Quality (philosophy); Baseline (sea); Fault (geology); Pattern recognition (psychology)","score_opus":0.02717373461444464,"score_gpt":0.29582057895049046,"score_spread":0.2686468443360458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414091836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.146862,0.0015947511,0.8353573,0.0008310262,0.0003287053,0.00024487363,0.00027164328,0.011791867,0.002717758],"genre_scores_gemma":[0.8927033,0.00019353113,0.10315473,0.00053257286,0.00006304806,0.00013559882,0.00028771497,0.00030380473,0.0026257855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99836963,0.0003888007,0.000106243846,0.0003770526,0.00050958537,0.0002486315],"domain_scores_gemma":[0.9946407,0.002435313,0.0006663809,0.0012170807,0.0008410539,0.00019945514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005365148,0.0013270855,0.0009370231,0.0009494206,0.00095032476,0.001178695,0.003578068,0.0015324681,0.0018668595],"category_scores_gemma":[0.017260676,0.00047133185,0.0006714557,0.00039506206,0.0016769095,0.0029698831,0.0023962976,0.0017481053,0.00036450682],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072948367,0.0002855487,0.0070143933,0.00020562929,0.00015570698,0.00028689497,0.00024784723,0.7034642,0.009068737,0.007711978,0.0069388305,0.26389077],"study_design_scores_gemma":[0.000029717507,0.00012232213,0.0004249659,0.00001628573,0.000018086103,0.000056120727,0.000022563623,0.98296124,0.007938561,0.0075434446,0.0008511424,0.000015490947],"about_ca_topic_score_codex":0.0075141643,"about_ca_topic_score_gemma":0.010398501,"teacher_disagreement_score":0.0075141643,"about_ca_system_score_codex":0.0022878982,"about_ca_system_score_gemma":0.0023722155,"threshold_uncertainty_score":0.028373957},"labels":[],"label_agreement":null},{"id":"W4414105744","doi":"10.1111/coin.70124","title":"Fairness Evaluation of Neural Networks Through Computational Profile Likelihood","year":2025,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Outcome (game theory); Artificial neural network; Representation (politics); Pattern recognition (psychology); Conditional probability; Value (mathematics); Conditional probability distribution","score_opus":0.03684910314458238,"score_gpt":0.34775491727067254,"score_spread":0.31090581412609014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414105744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2228815,0.0005602448,0.7709971,0.0014204917,0.00009745199,0.00014131556,0.0002209018,0.00048735592,0.0031936748],"genre_scores_gemma":[0.9772151,0.000090146306,0.021552384,0.00016839379,0.000047949816,0.00006224768,0.00013077777,0.00004485046,0.0006879538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951702,0.0029171798,0.00018268297,0.00055946026,0.0008906973,0.00027977445],"domain_scores_gemma":[0.96667093,0.025803873,0.002539326,0.0022688208,0.0019634427,0.00075362157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01464267,0.0011481352,0.001352548,0.0012415467,0.0007460965,0.0020578904,0.0020783313,0.0016566545,0.0016981792],"category_scores_gemma":[0.048356272,0.00045322438,0.00066825125,0.0006115906,0.0021186338,0.0030548673,0.003203176,0.0023764034,0.00024580455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031923645,0.000058805974,0.0033499897,0.00005061074,0.000065396925,0.000060967737,0.000053733846,0.9653176,0.00065195444,0.009571625,0.000427221,0.020072922],"study_design_scores_gemma":[0.000005001767,0.000034126613,0.00017976628,0.000008433325,0.0000041521666,0.000007851762,0.000006243492,0.9935168,0.00046371194,0.005720432,0.000048619193,0.0000048102074],"about_ca_topic_score_codex":0.0025142315,"about_ca_topic_score_gemma":0.0014661128,"teacher_disagreement_score":0.01464267,"about_ca_system_score_codex":0.002511965,"about_ca_system_score_gemma":0.001355079,"threshold_uncertainty_score":0.07743877},"labels":[],"label_agreement":null},{"id":"W4414230387","doi":"10.1109/tpami.2025.3610113","title":"ACLI: A CNN Pruning Framework Leveraging Adjacent Convolutional Layer Interdependence and $\\gamma$γ-Weakly Submodularity","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pruning; Benchmark (surveying); Submodular set function; Convolutional neural network; Metric (unit); Reduction (mathematics); Heuristic; Layer (electronics)","score_opus":0.022807508126121992,"score_gpt":0.2887793482567814,"score_spread":0.26597184013065944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414230387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009846209,0.0006377587,0.985185,0.00021906363,0.000054762822,0.00008253615,0.000102182494,0.0010991764,0.0027732714],"genre_scores_gemma":[0.36465427,0.0012528874,0.6195425,0.0009980444,0.00027165425,0.00042808682,0.0014347011,0.0007848396,0.010633052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988127,0.00020291943,0.000056087698,0.00021105877,0.0005656397,0.00015157636],"domain_scores_gemma":[0.99855655,0.00053401716,0.00019375971,0.00028798642,0.00032789772,0.000099644545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019871623,0.0019081606,0.0014239176,0.001411379,0.0006897318,0.0012931506,0.0031217989,0.0015047782,0.0022056974],"category_scores_gemma":[0.004634841,0.0006049561,0.0009760211,0.0010175263,0.0011206052,0.0018500094,0.0022927283,0.00266925,0.0009897028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021688639,0.00018822195,0.0019500377,0.0002735913,0.00015109469,0.00036014774,0.00014938154,0.45695698,0.022190062,0.047922015,0.018205067,0.45143655],"study_design_scores_gemma":[0.000013441868,0.000055157245,0.00024367776,0.000021272355,0.00002559757,0.00012734634,0.000012972143,0.9812516,0.0047194534,0.01057055,0.0029454953,0.000013405511],"about_ca_topic_score_codex":0.0053743697,"about_ca_topic_score_gemma":0.008961207,"teacher_disagreement_score":0.0053743697,"about_ca_system_score_codex":0.0014675047,"about_ca_system_score_gemma":0.0027322916,"threshold_uncertainty_score":0.010686159},"labels":[],"label_agreement":null},{"id":"W4414359142","doi":"10.24963/ijcai.2025/1240","title":"Ensuring Reliable and Transparent Algorithmic Fairness Through Optimal Transport and Uncertainty Quantification","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Transparency (behavior); Trustworthiness; Calibration; Uncertainty quantification; Attribution; Measurement uncertainty","score_opus":0.020784796558822274,"score_gpt":0.28227418089198314,"score_spread":0.2614893843331609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414359142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009096101,0.00013199706,0.9849058,0.001736345,0.00006898869,0.00008677763,0.000050760038,0.00026892088,0.0036543028],"genre_scores_gemma":[0.78060305,0.00030603065,0.21477938,0.00092374615,0.00020938565,0.0003066728,0.00010787329,0.00032769263,0.002436176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9670892,0.019695707,0.0014734067,0.0035233672,0.0067476295,0.0014707632],"domain_scores_gemma":[0.8849676,0.071141444,0.00954918,0.026485234,0.0063069747,0.0015494551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02989453,0.0015459985,0.0015217946,0.0014342627,0.0019068151,0.0064084833,0.0032815977,0.0036736329,0.0036011161],"category_scores_gemma":[0.16363926,0.0009997807,0.0012418655,0.001070999,0.008430203,0.009833523,0.010924574,0.007128901,0.0008707718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020248136,0.000099880854,0.0020010015,0.00018978113,0.000113822796,0.000148807,0.0007276233,0.3116789,0.0028059136,0.62316966,0.0024955098,0.05636664],"study_design_scores_gemma":[0.000030120502,0.000056986726,0.00023212486,0.000093950126,0.000020005366,0.000053128104,0.00009943544,0.33729088,0.0022875855,0.65745646,0.00234045,0.000038805203],"about_ca_topic_score_codex":0.0015398322,"about_ca_topic_score_gemma":0.0010356476,"teacher_disagreement_score":0.02989453,"about_ca_system_score_codex":0.0031576965,"about_ca_system_score_gemma":0.0058970004,"threshold_uncertainty_score":0.1580993},"labels":[],"label_agreement":null},{"id":"W4414360277","doi":"10.24963/ijcai.2025/774","title":"Accelerating Adversarial Training on Under-Utilized GPU","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Simon Fraser University","funders":"","keywords":"Adversarial system; Training (meteorology); Training set; Code (set theory); Artificial neural network; Key (lock); Acceleration","score_opus":0.049117371684648976,"score_gpt":0.312347199548724,"score_spread":0.263229827864075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414360277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11510182,0.000919528,0.8613362,0.0009147633,0.0002609544,0.00012531887,0.00028720923,0.010857025,0.010197228],"genre_scores_gemma":[0.8230976,0.000341208,0.17081411,0.00043626642,0.000055541484,0.00014140838,0.00050992647,0.0005792534,0.004024746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931514,0.00019734932,0.000036295376,0.00013475007,0.00017842208,0.0001379782],"domain_scores_gemma":[0.99804294,0.0009849736,0.00012740958,0.000593452,0.00016989459,0.00008129072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010610714,0.0011103469,0.0008247776,0.0003557873,0.00041360816,0.00076401816,0.0012145853,0.0008405073,0.0048307623],"category_scores_gemma":[0.005167378,0.00042140513,0.0006367465,0.00033784367,0.0008948181,0.0016636099,0.0017689326,0.00253386,0.0012782544],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033884222,0.000100751146,0.0019255485,0.000113162336,0.00006747418,0.00013911322,0.00007933549,0.87928116,0.012371189,0.011940438,0.0063084904,0.0873345],"study_design_scores_gemma":[0.000009008064,0.000025311781,0.00010925928,0.000005958443,0.0000039211823,0.000024112347,0.000005915582,0.9932152,0.0028000388,0.003025751,0.00077177765,0.0000037501168],"about_ca_topic_score_codex":0.0026285518,"about_ca_topic_score_gemma":0.0036123334,"teacher_disagreement_score":0.0048307623,"about_ca_system_score_codex":0.0007533288,"about_ca_system_score_gemma":0.00090059376,"threshold_uncertainty_score":0.016160488},"labels":[],"label_agreement":null},{"id":"W4414360820","doi":"10.24963/ijcai.2024/1240","title":"Ensuring Reliable and Transparent Algorithmic Fairness Through Optimal Transport and Uncertainty Quantification","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Transparency (behavior); Trustworthiness; Calibration; Uncertainty quantification; Attribution; Measurement uncertainty","score_opus":0.027719588818182324,"score_gpt":0.2836940897194794,"score_spread":0.2559745009012971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414360820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009106531,0.00013253828,0.98488,0.0017414967,0.0000691037,0.000086827014,0.000050955037,0.00026953386,0.0036630537],"genre_scores_gemma":[0.7808689,0.0003059603,0.2145099,0.00092430355,0.00020931577,0.0003064243,0.00010819753,0.00032767624,0.0024393564],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96707916,0.019688414,0.0014730794,0.0035306504,0.0067556663,0.0014729489],"domain_scores_gemma":[0.88521796,0.07100162,0.009533144,0.026395697,0.006302135,0.0015494641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029865224,0.0015445659,0.0015204179,0.0014353326,0.0019056828,0.0064024017,0.0032808958,0.0036706338,0.0036024954],"category_scores_gemma":[0.16317321,0.0009982084,0.001241221,0.0010703293,0.008417024,0.009817212,0.010897228,0.0071188593,0.00087061414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002026103,0.00009996238,0.0020079925,0.00018992879,0.00011390799,0.00014898642,0.000727358,0.31195033,0.0028093425,0.6227072,0.0025042128,0.05653823],"study_design_scores_gemma":[0.00003009504,0.000056998648,0.00023255215,0.00009393608,0.000020032807,0.000053129188,0.00009941224,0.33763418,0.0022884689,0.6571069,0.002345507,0.000038790342],"about_ca_topic_score_codex":0.0015457172,"about_ca_topic_score_gemma":0.0010395086,"teacher_disagreement_score":0.029865224,"about_ca_system_score_codex":0.0031636264,"about_ca_system_score_gemma":0.005912235,"threshold_uncertainty_score":0.15794426},"labels":[],"label_agreement":null},{"id":"W4414594223","doi":"10.2196/80987","title":"Data Contamination in AI Evaluation","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"","score_opus":0.031852463887514726,"score_gpt":0.386149169344468,"score_spread":0.3542967054569533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414594223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06165273,0.006631005,0.89590025,0.0034521667,0.0008664051,0.00094889285,0.0017434112,0.0055230344,0.023282073],"genre_scores_gemma":[0.7630352,0.0008459163,0.2242026,0.0014568085,0.00042870303,0.0006075245,0.003080278,0.0010781201,0.005264929],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9397628,0.042346913,0.0025207282,0.003082825,0.011077694,0.0012091021],"domain_scores_gemma":[0.8628801,0.104991004,0.0037216172,0.01641748,0.010516767,0.0014730957],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.043912854,0.0017494772,0.0015923285,0.003961559,0.0013655949,0.0050688735,0.0025904176,0.0030129347,0.0059012477],"category_scores_gemma":[0.18067719,0.00081438804,0.0011303669,0.0023499047,0.0030230412,0.0048219357,0.005054281,0.003424484,0.00222526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00378895,0.00073021173,0.018320981,0.001568632,0.0011346736,0.0004581426,0.00080436823,0.19009759,0.012011853,0.07771174,0.04045291,0.6529199],"study_design_scores_gemma":[0.00019505405,0.0007084123,0.0050364505,0.00032184256,0.00019997692,0.00054687617,0.00023641979,0.84915745,0.028233185,0.09229342,0.02296691,0.0001040426],"about_ca_topic_score_codex":0.002738719,"about_ca_topic_score_gemma":0.0021486282,"teacher_disagreement_score":0.9560872,"about_ca_system_score_codex":0.0021147784,"about_ca_system_score_gemma":0.0021545598,"threshold_uncertainty_score":0.23223615},"labels":[],"label_agreement":null},{"id":"W4414648486","doi":"10.1109/eeeic/icpseurope64998.2025.11169022","title":"Grid Mirror: Harnessing Adversarial PINNs to Model Power Grid Dynamics","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Grid; Generator (circuit theory); Power grid; Control theory (sociology); Adversarial system; Power (physics); Perturbation (astronomy); Electric power system; Mean squared error","score_opus":0.00792859304939908,"score_gpt":0.27489619013919775,"score_spread":0.26696759708979867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414648486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014205028,0.00010165632,0.98304856,0.00015932941,0.000031523658,0.000029812094,0.000048885744,0.0004958486,0.0018793422],"genre_scores_gemma":[0.86318606,0.00017646076,0.13229302,0.0003216209,0.00006472514,0.00011810093,0.00023103532,0.00015517001,0.0034537974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952483,0.00017231953,0.000017161998,0.000104976694,0.0001354799,0.00004529348],"domain_scores_gemma":[0.99861443,0.000828103,0.0001993568,0.00018230615,0.00013341237,0.000042321062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014498194,0.0011236875,0.0006631318,0.00043011957,0.00028447292,0.00062803744,0.0013563837,0.00086571166,0.0016179958],"category_scores_gemma":[0.004823952,0.00052237615,0.00047588584,0.00027236642,0.000994297,0.0014958441,0.0018078956,0.001631698,0.00032360954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026467296,0.000007973934,0.00024786507,0.000010793508,0.00001797691,0.000029858702,0.000013811771,0.9853491,0.0005304289,0.0047421474,0.0002966885,0.008726786],"study_design_scores_gemma":[0.0000011520905,0.000005973072,0.000025895908,0.0000015866418,0.0000013596826,0.0000056138633,0.0000011210019,0.99717414,0.00022798778,0.0024431383,0.00011033436,0.0000015685488],"about_ca_topic_score_codex":0.0034463399,"about_ca_topic_score_gemma":0.002773937,"teacher_disagreement_score":0.0034463399,"about_ca_system_score_codex":0.0007055578,"about_ca_system_score_gemma":0.00065038126,"threshold_uncertainty_score":0.007667482},"labels":[],"label_agreement":null},{"id":"W4414704986","doi":"10.3390/s25196026","title":"ConvNet-Generated Adversarial Perturbations for Evaluating 3D Object Detection Robustness","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia, Okanagan Campus","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Object detection; Convolutional neural network; Adversarial system; Inference; Novelty detection; Pattern recognition (psychology); Deep learning; Detector","score_opus":0.02296424581799517,"score_gpt":0.31094145924258837,"score_spread":0.2879772134245932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414704986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14772066,0.0009567507,0.83392215,0.00042225915,0.00025689986,0.00030571426,0.001453707,0.00902308,0.0059387656],"genre_scores_gemma":[0.8576132,0.00028952642,0.13577348,0.00031645724,0.00004133799,0.00023046808,0.0025397541,0.0006179415,0.0025779058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990752,0.00015365539,0.000036673733,0.0002501238,0.00038998184,0.000094403964],"domain_scores_gemma":[0.99877375,0.00059903646,0.00014660048,0.00020692879,0.0002155426,0.000058198915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015944458,0.0017502701,0.00063534745,0.0008132069,0.00029542317,0.0006721241,0.0014246174,0.0010418667,0.0020368388],"category_scores_gemma":[0.006565263,0.00048520786,0.0007514692,0.00040077514,0.0011043056,0.0009410451,0.0014799436,0.0014142306,0.00065142964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013673256,0.000031784886,0.0012881809,0.000094067975,0.000071420516,0.000072350245,0.00002217775,0.9583189,0.007221958,0.0016806552,0.0017335071,0.02932823],"study_design_scores_gemma":[0.000004250325,0.00004092926,0.0003399539,0.000009278794,0.0000065561176,0.000035402678,0.000004943916,0.9926998,0.0056409976,0.00079816603,0.0004139978,0.000005789402],"about_ca_topic_score_codex":0.0068074903,"about_ca_topic_score_gemma":0.006033817,"teacher_disagreement_score":0.0068074903,"about_ca_system_score_codex":0.0014291308,"about_ca_system_score_gemma":0.00094538747,"threshold_uncertainty_score":0.013535678},"labels":[],"label_agreement":null},{"id":"W4414829247","doi":"10.4230/lipics.csl.2026.25","title":"Constructing Witnesses for Lower Bounds on Behavioural Distances","year":2025,"lang":"en","type":"article","venue":"Leibniz-Zentrum für Informatik (Schloss Dagstuhl)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Leverhulme Trust","keywords":"Soundness; Mathematical proof; Bounding overwatch; Completeness (order theory); Probabilistic logic; Equivalence (formal languages); Constructive; Context (archaeology); Modal logic","score_opus":0.013612370468160712,"score_gpt":0.3008382864708418,"score_spread":0.2872259160026811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414829247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026888624,0.00016300438,0.965528,0.0012711107,0.00008282685,0.00009490424,0.00027504147,0.00071927503,0.004977158],"genre_scores_gemma":[0.5538665,0.0005381182,0.43899256,0.00087544846,0.00017141846,0.00044493424,0.00097040273,0.00063752336,0.0035030548],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98874646,0.0029587464,0.00095118367,0.002742547,0.0037671975,0.0008338797],"domain_scores_gemma":[0.91609186,0.06528445,0.003075179,0.009409643,0.0051287473,0.0010101231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009099292,0.0010798218,0.0012827318,0.0029627976,0.0016870293,0.004163268,0.0035891437,0.003122605,0.007387158],"category_scores_gemma":[0.07431809,0.0016812154,0.0028504247,0.0018142988,0.0059665474,0.013539368,0.010079438,0.009008871,0.001206403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016173403,0.00008500712,0.0018361785,0.0003723384,0.00008244479,0.00036645398,0.00092471525,0.01757461,0.009280774,0.9337838,0.0013248239,0.03420702],"study_design_scores_gemma":[0.000043404085,0.00006644464,0.00033109658,0.000112673886,0.000058452886,0.00019226046,0.0001664428,0.0690224,0.013186753,0.9125291,0.004234883,0.000056081222],"about_ca_topic_score_codex":0.00079882704,"about_ca_topic_score_gemma":0.0010612933,"teacher_disagreement_score":0.009099292,"about_ca_system_score_codex":0.0023642438,"about_ca_system_score_gemma":0.0017710844,"threshold_uncertainty_score":0.048122227},"labels":[],"label_agreement":null},{"id":"W4414934603","doi":"10.1145/3770920","title":"Runtime Fault Localization in Deep Neural Network Accelerators","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Design Automation of Electronic Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Semiconductor Research Corporation","keywords":"Dataflow; Robustness (evolution); Checksum; Fault detection and isolation; Fault tolerance; Artificial neural network; Fault coverage; Overhead (engineering); Systolic array","score_opus":0.01294349680915495,"score_gpt":0.26092095627620343,"score_spread":0.24797745946704847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414934603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26067248,0.0015053492,0.7157761,0.00048351983,0.00026530112,0.00007783536,0.0002129176,0.017663624,0.0033428832],"genre_scores_gemma":[0.90143645,0.00017899337,0.09569277,0.00014174172,0.000032097927,0.000051608476,0.0002144214,0.00017998875,0.0020719704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993069,0.00012274347,0.000058824306,0.00014140693,0.00027154802,0.0000985382],"domain_scores_gemma":[0.9981621,0.0006839864,0.0002982477,0.00043690842,0.00034296702,0.00007593087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009828199,0.0007726504,0.0005083546,0.0007333045,0.0003859455,0.00066424155,0.0017052217,0.00048726966,0.0025684566],"category_scores_gemma":[0.0033696264,0.00037063277,0.00039505324,0.00042369365,0.0006028644,0.0013365268,0.00088589726,0.0008452318,0.00046170835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011561407,0.00022179322,0.007106963,0.00040165504,0.00013678038,0.0006327156,0.00022466201,0.5564315,0.05930057,0.010272083,0.008761428,0.3553538],"study_design_scores_gemma":[0.000023982297,0.00017613295,0.00056881306,0.000019701147,0.000026797034,0.00009326442,0.000026980535,0.9588631,0.03473813,0.0038295016,0.0016208049,0.000012898561],"about_ca_topic_score_codex":0.0024877377,"about_ca_topic_score_gemma":0.0035421688,"teacher_disagreement_score":0.0025684566,"about_ca_system_score_codex":0.0011168672,"about_ca_system_score_gemma":0.0011693303,"threshold_uncertainty_score":0.008592308},"labels":[],"label_agreement":null},{"id":"W4415036044","doi":"10.48550/arxiv.2505.20628","title":"Position: Adopt Constraints Over Fixed Penalties in Deep Learning","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Samsung; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Constraint (computer-aided design); Task (project management); Deep learning; Constrained optimization; Constraint satisfaction; Trustworthiness; Constraint satisfaction problem; Key (lock)","score_opus":0.023306651132376344,"score_gpt":0.2865426702924097,"score_spread":0.26323601916003336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415036044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004885811,0.0005972711,0.97874033,0.0033558733,0.0002591345,0.00005524637,0.000048293565,0.0002292162,0.011828823],"genre_scores_gemma":[0.4066561,0.0019297266,0.56089556,0.003518136,0.0012586884,0.00061098795,0.0001860951,0.0013198226,0.023624903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956867,0.0021713693,0.00014406485,0.00074187556,0.0010908879,0.00016495188],"domain_scores_gemma":[0.99003196,0.006703457,0.0007594439,0.0013743441,0.00077180297,0.00035897104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007006529,0.0022952603,0.00085536455,0.0009088906,0.00097324053,0.0029308703,0.0026212402,0.003421668,0.00747777],"category_scores_gemma":[0.027222129,0.0009790414,0.0008886597,0.00081264647,0.0056605283,0.00605244,0.005127977,0.008049902,0.001608049],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014600319,0.00006676849,0.00042572236,0.00016330014,0.00006519095,0.00010390696,0.00018331548,0.12932613,0.002891237,0.80575985,0.007986372,0.052882105],"study_design_scores_gemma":[0.00007471321,0.00015942862,0.00016792688,0.00018604321,0.0000290245,0.00008205077,0.000031694995,0.46591055,0.0040764203,0.51188153,0.017347498,0.00005309052],"about_ca_topic_score_codex":0.0013244798,"about_ca_topic_score_gemma":0.0015605281,"teacher_disagreement_score":0.00747777,"about_ca_system_score_codex":0.0021930668,"about_ca_system_score_gemma":0.0018699076,"threshold_uncertainty_score":0.03705448},"labels":[],"label_agreement":null},{"id":"W4415100143","doi":"10.1016/j.sysarc.2025.103595","title":"Eidos revisited: Expanding Efficient, imperceptible adversarial attacks on 3D point clouds","year":2025,"lang":"en","type":"article","venue":"Journal of Systems Architecture","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"H2020 Marie Skłodowska-Curie Actions; UK Research and Innovation; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; Institute of Software, Chinese Academy of Sciences; European Commission; Canadian Anesthesiologists' Society","keywords":"Point cloud; Adversarial system; Generalizability theory; Set (abstract data type); Process (computing); Point (geometry); Segmentation; Cloud computing","score_opus":0.008511477248732158,"score_gpt":0.2799934282760977,"score_spread":0.27148195102736555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415100143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022279182,0.0009499704,0.9691188,0.0012784646,0.00022721972,0.0000395034,0.00008665461,0.000555292,0.0054649385],"genre_scores_gemma":[0.8716454,0.0015689315,0.11800307,0.0007291698,0.00028766665,0.0000839092,0.00015947134,0.00024498336,0.007277443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982743,0.0005085501,0.00006588386,0.0002379276,0.00069880276,0.00021448403],"domain_scores_gemma":[0.9945779,0.003503427,0.0002744088,0.001194848,0.00030834845,0.00014108836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027837893,0.0011231487,0.0014173595,0.00064055534,0.000499303,0.0011997124,0.001987188,0.0022551666,0.0028111315],"category_scores_gemma":[0.0112711,0.00065036723,0.00096247613,0.0006924054,0.0024464733,0.0039847842,0.0064015095,0.0035604497,0.0006478766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003117806,0.000039050574,0.0005257799,0.00012133439,0.00008986554,0.00015137724,0.00009618206,0.8002205,0.008275416,0.12913714,0.0036513142,0.057380296],"study_design_scores_gemma":[0.000014294284,0.000045713103,0.00009643808,0.00001648793,0.000014523526,0.000088084984,0.000018272907,0.93440264,0.002409688,0.0606396,0.0022420494,0.000012192943],"about_ca_topic_score_codex":0.0008289088,"about_ca_topic_score_gemma":0.0006727624,"teacher_disagreement_score":0.0028111315,"about_ca_system_score_codex":0.00086022826,"about_ca_system_score_gemma":0.00066673313,"threshold_uncertainty_score":0.014722288},"labels":[],"label_agreement":null},{"id":"W4415230017","doi":"10.1609/aies.v8i2.36631","title":"Towards Interactive Evaluations for Interaction Harms in Human-AI Systems","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI/ACM Conference on AI Ethics and Society","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Construct (python library); Corporate governance; Cognition; Natural (archaeology); Work (physics); Social relation","score_opus":0.08023321290939993,"score_gpt":0.4229158206196218,"score_spread":0.34268260771022185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415230017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05835068,0.0013118614,0.8743413,0.008517993,0.0002728755,0.001790911,0.00022865512,0.0011816332,0.054004036],"genre_scores_gemma":[0.7276004,0.00035833352,0.26613632,0.0011383042,0.00010812569,0.0018664823,0.00020183092,0.0002737164,0.0023164395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7782889,0.18767604,0.005637048,0.005007482,0.021486301,0.0019041708],"domain_scores_gemma":[0.5748905,0.3516509,0.019242378,0.02269968,0.028105378,0.0034112441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12657757,0.002095151,0.0014255864,0.005294552,0.0022021888,0.011634967,0.0032998524,0.0035757413,0.008976852],"category_scores_gemma":[0.37254325,0.0007734645,0.0013057913,0.0021380181,0.011762762,0.016560692,0.009151886,0.0046551516,0.0010327696],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009784772,0.0009565202,0.019367252,0.0025832008,0.00055607466,0.00027421757,0.017515376,0.056791067,0.0039254073,0.5975152,0.010356885,0.28918034],"study_design_scores_gemma":[0.0003101412,0.001243424,0.008869506,0.002045406,0.0003046665,0.0002650123,0.0074308245,0.17434491,0.007107979,0.76055586,0.03727893,0.00024329172],"about_ca_topic_score_codex":0.0017201082,"about_ca_topic_score_gemma":0.0016272677,"teacher_disagreement_score":0.12657757,"about_ca_system_score_codex":0.005607813,"about_ca_system_score_gemma":0.004921684,"threshold_uncertainty_score":0.66941416},"labels":[],"label_agreement":null},{"id":"W4415332398","doi":"10.48550/arxiv.2509.23325","title":"Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Robustness (evolution); Transfer of learning; Metric (unit); Workflow; Task (project management); Schedule","score_opus":0.06255534979700363,"score_gpt":0.2593754850901944,"score_spread":0.1968201352931908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415332398","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1506511,0.0013410068,0.8344787,0.0015182367,0.00026927536,0.00021793507,0.0002749343,0.0053039524,0.0059448965],"genre_scores_gemma":[0.92096084,0.00025473515,0.074240066,0.00070191966,0.00006902862,0.00023322391,0.0004089368,0.00058013044,0.0025510858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876934,0.00044598486,0.00006946316,0.00030206473,0.00019884578,0.00021430672],"domain_scores_gemma":[0.9948513,0.003115915,0.00046160267,0.00090845954,0.00040176092,0.00026097448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032161174,0.002450355,0.001439413,0.00060867,0.00063657906,0.0012883001,0.0023998625,0.0019839176,0.0024459776],"category_scores_gemma":[0.013492589,0.0009156041,0.0010427848,0.00036412652,0.0018399882,0.0021549258,0.0032266423,0.0038200608,0.0009598544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001325572,0.000087062894,0.0010570877,0.000078933175,0.000050901454,0.000087146625,0.000065183965,0.96530646,0.0029172725,0.0026059253,0.0017103072,0.025901053],"study_design_scores_gemma":[0.000011621849,0.000054300366,0.000119683,0.000013324706,0.000008329852,0.00001768956,0.000011063723,0.994457,0.0013872135,0.0036465018,0.00026435193,0.000008874094],"about_ca_topic_score_codex":0.004984511,"about_ca_topic_score_gemma":0.0062709334,"teacher_disagreement_score":0.004984511,"about_ca_system_score_codex":0.0012391614,"about_ca_system_score_gemma":0.0021761577,"threshold_uncertainty_score":0.017008662},"labels":[],"label_agreement":null},{"id":"W4415366712","doi":"10.1109/tvt.2025.3623875","title":"A Vision-Based Covert Attack and Hybrid Adversary Detection for Autonomous Vehicles Using Generative Adversarial Network","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Ministère de la Défense Nationale; Innovation for Defence Excellence and Security","keywords":"Covert; Adversary; Adversarial system; Artificial neural network; Global Positioning System; Remotely operated underwater vehicle; Battlefield","score_opus":0.013676279381555315,"score_gpt":0.2828904057170907,"score_spread":0.2692141263355354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415366712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058906242,0.00030148446,0.93736595,0.0003030191,0.00007935936,0.000056986504,0.000030112742,0.00041054277,0.0025463093],"genre_scores_gemma":[0.9663738,0.00013416832,0.03154007,0.000108554945,0.00002902468,0.0000389994,0.000039668594,0.000021415566,0.0017143011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990508,0.00021988497,0.000029809347,0.00023321804,0.00031078112,0.00015555508],"domain_scores_gemma":[0.9988165,0.00056225463,0.00018251318,0.00016474545,0.00019471868,0.00007932492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085277954,0.0008944771,0.0006683993,0.0005820298,0.00042608439,0.00066443364,0.00099384,0.0010352604,0.00080637477],"category_scores_gemma":[0.0021433972,0.00032532198,0.00094241847,0.00027535367,0.0012277091,0.0014418879,0.0017079487,0.0011129545,0.00015768806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019287349,0.00007521536,0.00224195,0.00006692881,0.00012380982,0.00030859667,0.0001332682,0.8855846,0.017357321,0.019213162,0.001013926,0.07368839],"study_design_scores_gemma":[0.0000024287997,0.00003843099,0.00015123148,0.0000019651864,0.0000064317114,0.000046256508,0.0000053115114,0.99594337,0.0021309643,0.0014932089,0.00017463817,0.0000057161856],"about_ca_topic_score_codex":0.0020472675,"about_ca_topic_score_gemma":0.0013004408,"teacher_disagreement_score":0.0020472675,"about_ca_system_score_codex":0.0009308135,"about_ca_system_score_gemma":0.00067805994,"threshold_uncertainty_score":0.006753564},"labels":[],"label_agreement":null},{"id":"W4415406050","doi":"10.1007/978-981-95-1050-4_16","title":"Securing AI with AI: Novel Framework for Drone Communication Security","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Drone; Software deployment; Resilience (materials science); Wireless; Data integrity; Secure communication; Intrusion detection system; Telecommunications network","score_opus":0.014001055890533742,"score_gpt":0.28049493967372047,"score_spread":0.2664938837831867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415406050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030336725,0.002108739,0.967772,0.0006089006,0.00024456938,0.00006897416,0.000040159855,0.0012056573,0.02491732],"genre_scores_gemma":[0.23334579,0.0054122033,0.6974365,0.00064451253,0.0004987523,0.00025991548,0.00020748697,0.00037916968,0.061815593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995478,0.000078484576,0.000017909537,0.00009449313,0.00021912223,0.000042207503],"domain_scores_gemma":[0.99957377,0.00016323512,0.00003383822,0.00012646244,0.00006336653,0.00003927283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007030024,0.00068981334,0.0003219791,0.0005471973,0.00056609686,0.0020383175,0.0016635212,0.0010922145,0.004657134],"category_scores_gemma":[0.00093757635,0.00034961736,0.0005408528,0.00039818024,0.0022440485,0.002165143,0.0019817348,0.002671852,0.0016139168],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049700804,0.00005327066,0.00024559154,0.00022678224,0.000038768907,0.00021097867,0.00034870428,0.055264737,0.017501105,0.74541485,0.011212957,0.16943268],"study_design_scores_gemma":[0.000015129991,0.000098808894,0.00024040177,0.00017024942,0.000032189273,0.00052179507,0.0000927124,0.44533312,0.0153538175,0.32318217,0.21491176,0.000047845748],"about_ca_topic_score_codex":0.00083303225,"about_ca_topic_score_gemma":0.00083870674,"teacher_disagreement_score":0.004657134,"about_ca_system_score_codex":0.00083680684,"about_ca_system_score_gemma":0.0007290929,"threshold_uncertainty_score":0.015579641},"labels":[],"label_agreement":null},{"id":"W4415524633","doi":"10.1109/mlsp62443.2025.11204323","title":"Learning or Cheating? Assessing Data Contamination in Large Vision-Language Models","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Overfitting; Generalization; Question answering; Image (mathematics); Chart; Data modeling; Visual reasoning","score_opus":0.03124462337557485,"score_gpt":0.37517098897103685,"score_spread":0.34392636559546197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415524633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47355124,0.0024664062,0.50383115,0.0034261786,0.0003432703,0.0004662883,0.001963396,0.010113276,0.0038387063],"genre_scores_gemma":[0.9247719,0.00020741964,0.070647076,0.0007113248,0.00006216029,0.00018363993,0.002349845,0.00039682287,0.0006697842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.98236656,0.010104053,0.00088549254,0.0028174825,0.003226554,0.0005999235],"domain_scores_gemma":[0.9048331,0.06723178,0.005465012,0.016908277,0.004359812,0.0012020579],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.024260966,0.0016772306,0.0013489935,0.0013890662,0.0008418903,0.0023152903,0.002924669,0.002456246,0.0011080538],"category_scores_gemma":[0.13437296,0.00067733205,0.001251436,0.0010043135,0.0033266796,0.004731896,0.004761925,0.0041837455,0.0006610898],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016841147,0.00048318526,0.043909535,0.0007771627,0.00086206937,0.00043182686,0.0009543038,0.7689501,0.01033603,0.010220129,0.010212038,0.15117955],"study_design_scores_gemma":[0.000059345813,0.00034011205,0.003607298,0.0000791826,0.000054306827,0.00014537806,0.00015516469,0.9664599,0.011837173,0.015484806,0.0017375897,0.000039748284],"about_ca_topic_score_codex":0.0046598758,"about_ca_topic_score_gemma":0.0037232526,"teacher_disagreement_score":0.97573906,"about_ca_system_score_codex":0.0019764362,"about_ca_system_score_gemma":0.0018932142,"threshold_uncertainty_score":0.1283058},"labels":[],"label_agreement":null},{"id":"W4415549799","doi":"10.1007/978-3-032-08707-2_14","title":"Solution-Aware Vs Global ReLU Selection: Partial MILP Strikes Back for DNN Verification","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Agence Nationale de la Recherche","keywords":"Binary number; Set (abstract data type); Lipschitz continuity; Binary decision diagram; Upper and lower bounds; Branching (polymer chemistry); Branch and bound; Deep neural networks","score_opus":0.017369423961441664,"score_gpt":0.27471014161536356,"score_spread":0.2573407176539219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415549799","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020390399,0.0005518978,0.96177596,0.0013286936,0.0003559855,0.00011410091,0.0001441536,0.00219267,0.013146249],"genre_scores_gemma":[0.64457965,0.0002448797,0.34548575,0.0010024817,0.000167255,0.00017355448,0.0003500281,0.00087820506,0.0071181157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979672,0.00086420565,0.000078257406,0.00033548573,0.0004951465,0.00025961787],"domain_scores_gemma":[0.99569654,0.002556933,0.0001628835,0.0010114654,0.00041064058,0.00016149975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035760202,0.0016870525,0.0014057384,0.0005218606,0.0008611181,0.001846076,0.0020601922,0.0021070503,0.011147146],"category_scores_gemma":[0.01155345,0.0007481224,0.001092484,0.00042093938,0.001836532,0.0033013362,0.003578954,0.0037475864,0.0014720192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050107134,0.00013490231,0.0006132321,0.0002741228,0.00011748437,0.00018672172,0.000100140416,0.692955,0.0053226347,0.10132263,0.011667465,0.1868046],"study_design_scores_gemma":[0.000021118349,0.000051971725,0.00004010464,0.000028408549,0.000012691018,0.000023238936,0.000024564857,0.94514924,0.0015752371,0.05217534,0.0008916525,0.000006439762],"about_ca_topic_score_codex":0.0018318327,"about_ca_topic_score_gemma":0.0044777635,"teacher_disagreement_score":0.011147146,"about_ca_system_score_codex":0.001099111,"about_ca_system_score_gemma":0.002807761,"threshold_uncertainty_score":0.03729093},"labels":[],"label_agreement":null},{"id":"W4415595958","doi":"10.1016/j.future.2025.108220","title":"Robust DCNN: The impact of approximate multipliers in defending against adversarial attacks","year":2025,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Instituto Tecnológico de Costa Rica","keywords":"Robustness (evolution); Adversarial system; Computation; Optimization problem; Ambiguity; Lagrange multiplier; Metric (unit); Pareto principle","score_opus":0.02295755602172824,"score_gpt":0.276162746961469,"score_spread":0.25320519093974075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415595958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034737576,0.0010646011,0.9570267,0.00058408064,0.00020045994,0.000047982772,0.000114256334,0.0006334913,0.005590915],"genre_scores_gemma":[0.8600818,0.0006798367,0.13207124,0.00033208198,0.00017178984,0.00007819095,0.0001999677,0.00015622978,0.00622878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989785,0.00029789898,0.000037791746,0.00024055102,0.00033343415,0.000111835034],"domain_scores_gemma":[0.9952943,0.0029845384,0.0003647404,0.00070832984,0.00051488075,0.00013313757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002515223,0.0011568796,0.0010587662,0.0005844237,0.00041532904,0.0012192427,0.0013770924,0.0016009818,0.002774746],"category_scores_gemma":[0.012118993,0.00043800066,0.0003279258,0.0004970301,0.0013369285,0.0026093929,0.0019305906,0.0022548942,0.00054794626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003335064,0.00005350924,0.0005413744,0.00008428982,0.000052488795,0.000048478967,0.000025176809,0.8904989,0.0037260386,0.032208376,0.0024358677,0.069991946],"study_design_scores_gemma":[0.000007446939,0.000043080654,0.000053854208,0.000007374041,0.0000047131525,0.000021409367,0.0000035030318,0.9908793,0.00085960067,0.007761044,0.00035402065,0.0000045889365],"about_ca_topic_score_codex":0.00173083,"about_ca_topic_score_gemma":0.001624407,"teacher_disagreement_score":0.002774746,"about_ca_system_score_codex":0.00072962214,"about_ca_system_score_gemma":0.0010007947,"threshold_uncertainty_score":0.013301969},"labels":[],"label_agreement":null},{"id":"W4415779021","doi":"10.1111/coin.70149","title":"Survey on <scp>AI</scp> Ethics: A Socio‐Technical Perspective","year":2025,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Canadian Institute for International Peace and Security; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Transparency (behavior); Software deployment; Perspective (graphical); Ethical issues; Stakeholder; Point (geometry); Social responsibility","score_opus":0.055825932555241875,"score_gpt":0.38760302188025,"score_spread":0.3317770893250081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415779021","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015365566,0.36947522,0.048728995,0.32222828,0.0022670955,0.00009145007,0.00031194586,0.00016927932,0.24136223],"genre_scores_gemma":[0.40741232,0.49534944,0.01691128,0.057697173,0.0057117655,0.00023584245,0.00040169872,0.00020488922,0.016075606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9883278,0.0065477714,0.00074461894,0.0005849856,0.0032209947,0.0005738023],"domain_scores_gemma":[0.9487856,0.040448625,0.0025587091,0.0015814117,0.0057669375,0.00085867225],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.013496765,0.0004650477,0.0005958882,0.004229601,0.0021227642,0.005984101,0.0010788727,0.004452269,0.0056057763],"category_scores_gemma":[0.02628086,0.00036095033,0.00044842932,0.0051493607,0.011423347,0.006921096,0.002805914,0.0042936415,0.0013423727],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021228605,0.00003770215,0.0020269048,0.0016641521,0.000024069435,0.00019586393,0.0035646318,0.0014997388,0.00032522602,0.7323441,0.056609455,0.20168689],"study_design_scores_gemma":[0.0000037299405,0.000036916485,0.0028856772,0.005423131,0.000010427689,0.0007221352,0.0040226965,0.0016235765,0.0004244746,0.22776082,0.7570403,0.000046025267],"about_ca_topic_score_codex":0.0028741714,"about_ca_topic_score_gemma":0.002394986,"teacher_disagreement_score":0.99787724,"about_ca_system_score_codex":0.0038340734,"about_ca_system_score_gemma":0.00386508,"threshold_uncertainty_score":0.07137859},"labels":[],"label_agreement":null},{"id":"W4415947615","doi":"10.1007/978-3-032-10489-2_36","title":"Evaluating AI Agents for Cyber Defense: A Comparison of Deep Reinforcement Learning and LLM Approaches","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reinforcement learning; Adaptability; Curiosity; Robustness (evolution); Transparency (behavior); Autonomous agent; Intelligent agent; Deep learning","score_opus":0.06335727882812493,"score_gpt":0.3479322205925678,"score_spread":0.28457494176444287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415947615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3612482,0.006593235,0.5933836,0.003151843,0.00041755635,0.0004535753,0.00040445526,0.0027975268,0.03154999],"genre_scores_gemma":[0.93572426,0.00044183343,0.060728878,0.00019831791,0.000060349936,0.00008236987,0.00018404645,0.00010372437,0.0024762792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979031,0.0011888,0.00008372116,0.00020551837,0.000446333,0.00017247364],"domain_scores_gemma":[0.98555875,0.01132251,0.00073754374,0.0008523017,0.0010723699,0.00045644035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005873733,0.0011095549,0.0012501201,0.0009755791,0.0003527097,0.0014394466,0.0019897032,0.0019932333,0.0030108087],"category_scores_gemma":[0.0141279055,0.00036220814,0.0003765092,0.00056346477,0.0009798589,0.0023219471,0.0017632524,0.0017874364,0.00041997188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005834443,0.00026476945,0.0027319458,0.00019708501,0.00010997631,0.00002669472,0.000042226653,0.880007,0.0007058492,0.0075872266,0.001804969,0.10593877],"study_design_scores_gemma":[0.000013259979,0.00006287621,0.00014922171,0.000008598885,0.000006375953,0.000003794572,0.0000068111513,0.99719393,0.00021035028,0.002178631,0.00016350225,0.0000025595682],"about_ca_topic_score_codex":0.005133568,"about_ca_topic_score_gemma":0.0053277663,"teacher_disagreement_score":0.005873733,"about_ca_system_score_codex":0.0023262813,"about_ca_system_score_gemma":0.0017425831,"threshold_uncertainty_score":0.031063676},"labels":[],"label_agreement":null},{"id":"W4415965747","doi":"10.2139/ssrn.5705186","title":"Open Technical Problems in Open-Weight AI Model Risk Management","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Mila - Quebec Artificial Intelligence Institute; University of Toronto; Institute on Governance","funders":"","keywords":"Key (lock); Risk management; Openness to experience; Risk assessment; Training (meteorology); Best practice","score_opus":0.016512475115472863,"score_gpt":0.313643105026537,"score_spread":0.2971306299110641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415965747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013015488,0.0017495597,0.95869124,0.009213116,0.00043312323,0.000038937134,0.00013197928,0.00016114894,0.016565368],"genre_scores_gemma":[0.79104847,0.0047912495,0.16965245,0.0025149882,0.0040146285,0.0003816145,0.0005135067,0.00043891612,0.026644113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949523,0.0022383572,0.00025847292,0.0008266473,0.0014119082,0.00031230605],"domain_scores_gemma":[0.96333253,0.030311862,0.0014123701,0.0022162201,0.0019186792,0.00080833747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009207043,0.0014309686,0.0021016793,0.0011853065,0.0014506653,0.005616416,0.003915006,0.004565706,0.010709235],"category_scores_gemma":[0.047957852,0.00085086765,0.0012816864,0.0015460448,0.005248223,0.0108073,0.0061772647,0.009676292,0.0010055159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002552925,0.000050338804,0.00023463773,0.000114033326,0.00004270724,0.000055124514,0.00008217164,0.051318876,0.00023439575,0.9279374,0.0031833297,0.016721485],"study_design_scores_gemma":[0.000004579988,0.00000836463,0.00004026772,0.000014763951,0.000004766673,0.000013466134,0.0000141105165,0.10042005,0.000078276455,0.8986158,0.0007780301,0.0000074456825],"about_ca_topic_score_codex":0.0011868435,"about_ca_topic_score_gemma":0.00069771585,"teacher_disagreement_score":0.010709235,"about_ca_system_score_codex":0.0018402411,"about_ca_system_score_gemma":0.0015310316,"threshold_uncertainty_score":0.048692048},"labels":[],"label_agreement":null},{"id":"W4416013676","doi":"10.1609/aiide.v21i1.36819","title":"Generic Guard AI in Stealth Game with Composite Potential Fields","year":2025,"lang":"","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Guard (computer science); Baseline (sea); Potential field; Game theory; Composite number; Decision system","score_opus":0.022604064913327847,"score_gpt":0.28706732807287977,"score_spread":0.26446326315955193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416013676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043493137,0.000074165226,0.95136744,0.00016814945,0.000019287356,0.000038662314,0.00004930604,0.0005092406,0.0042805797],"genre_scores_gemma":[0.8558531,0.0000678878,0.1400042,0.0001190086,0.000016572709,0.00008321945,0.00007361692,0.00016168029,0.0036207882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971837,0.000083597,0.000010064378,0.00006221823,0.00007314024,0.000052595602],"domain_scores_gemma":[0.9993125,0.00040615522,0.000066116554,0.00009155254,0.000053338754,0.00007031778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084413186,0.0006414885,0.00059585297,0.00050690066,0.0003828866,0.0008735578,0.0013204174,0.0010835216,0.0026920522],"category_scores_gemma":[0.0028010362,0.00040588976,0.00063046516,0.00022551931,0.0017381585,0.0015062426,0.001942646,0.0012364285,0.00032394228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050764775,0.000024053516,0.0007104669,0.000032946155,0.000014656508,0.00008088611,0.000096235264,0.9430465,0.0023815823,0.036317542,0.0004743889,0.016770002],"study_design_scores_gemma":[0.0000039166607,0.000011765787,0.00004831989,0.0000033768813,0.0000018756499,0.000013099024,0.0000070823835,0.99067545,0.0002478783,0.008769626,0.00021393708,0.0000036015106],"about_ca_topic_score_codex":0.0031662574,"about_ca_topic_score_gemma":0.0034183073,"teacher_disagreement_score":0.0031662574,"about_ca_system_score_codex":0.0008635461,"about_ca_system_score_gemma":0.00076726935,"threshold_uncertainty_score":0.009005785},"labels":[],"label_agreement":null},{"id":"W4416017187","doi":"10.1145/3746252.3761626","title":"Datasets for Supervised Adversarial Attacks on Neural Rankers","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo; University of Toronto","funders":"","keywords":"Adversarial system; Artificial neural network; Key (lock); Feature (linguistics); Supervised learning","score_opus":0.02193130906810358,"score_gpt":0.32109183872490554,"score_spread":0.29916052965680195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416017187","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13078216,0.0039577303,0.063563414,0.0038821655,0.0017378285,0.00201191,0.7418276,0.021594794,0.03064244],"genre_scores_gemma":[0.15658991,0.0010676155,0.048663866,0.00074112223,0.00018937784,0.0013619771,0.7784559,0.0009787829,0.011951454],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99716115,0.0007013907,0.00023789698,0.0004509384,0.0010988539,0.00034989842],"domain_scores_gemma":[0.9935789,0.0017257744,0.00035956202,0.002946951,0.00115995,0.00022888591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028159302,0.0018326341,0.00085529534,0.002464478,0.0011063267,0.0012388021,0.0024231062,0.0032752715,0.013182268],"category_scores_gemma":[0.012570564,0.00055180455,0.0015711877,0.0019718204,0.0010154949,0.0016448795,0.001650923,0.0027826605,0.009353761],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012910184,0.001436013,0.004244678,0.00152816,0.00035483934,0.00031853857,0.00010562388,0.07043313,0.0075906175,0.012489988,0.8110431,0.08916431],"study_design_scores_gemma":[0.0028645603,0.0014961113,0.022462068,0.00063171354,0.00027590521,0.002161088,0.0004903354,0.3540159,0.055480387,0.042820767,0.51693356,0.00036750457],"about_ca_topic_score_codex":0.005387963,"about_ca_topic_score_gemma":0.012418889,"teacher_disagreement_score":0.013182268,"about_ca_system_score_codex":0.0014097462,"about_ca_system_score_gemma":0.0015798978,"threshold_uncertainty_score":0.044099033},"labels":[],"label_agreement":null},{"id":"W4416017657","doi":"10.1145/3746252.3760929","title":"Uncovering the Persuasive Fingerprint of LLMs in Jailbreaking Attacks","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Persuasion; Adversarial system; Readability; Fingerprint (computing); Interrogative; Sophistication","score_opus":0.010331237277410792,"score_gpt":0.28936924557820853,"score_spread":0.2790380083007977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416017657","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8349394,0.0009458427,0.14703308,0.0025049504,0.00028807065,0.00018043445,0.0005348187,0.0040096045,0.0095638065],"genre_scores_gemma":[0.9821093,0.00010467007,0.015755605,0.00038327216,0.000051734463,0.00006423163,0.00029581707,0.00027212704,0.00096318516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942749,0.0037834377,0.00022725556,0.0006627629,0.0007952586,0.00025635384],"domain_scores_gemma":[0.9394082,0.045456566,0.004522278,0.008025519,0.0016377161,0.0009497561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059660124,0.0007418063,0.0005635695,0.0006731456,0.00067618367,0.0021103204,0.0005858799,0.0012762054,0.0024774252],"category_scores_gemma":[0.06219842,0.00037634806,0.00039361892,0.00028029995,0.0016873997,0.0027830284,0.0023392264,0.0030435435,0.0009355008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040442217,0.0015504182,0.12596776,0.0026786653,0.0007294701,0.002367657,0.017475126,0.17548843,0.16469201,0.0648002,0.027697721,0.41250834],"study_design_scores_gemma":[0.00018186218,0.0017609985,0.035471845,0.00049384707,0.00022448674,0.0015759954,0.0032578383,0.7569066,0.080223076,0.09621396,0.023411138,0.0002783306],"about_ca_topic_score_codex":0.00035138032,"about_ca_topic_score_gemma":0.00062912866,"teacher_disagreement_score":0.0059660124,"about_ca_system_score_codex":0.00040648144,"about_ca_system_score_gemma":0.00045907698,"threshold_uncertainty_score":0.03155166},"labels":[],"label_agreement":null},{"id":"W4416017819","doi":"10.1109/iccd65941.2025.00068","title":"LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models","year":2025,"lang":"","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Science Foundation","keywords":"Robustness (evolution); Overhead (engineering); Security token; Reliability (semiconductor); Speedup; Offset (computer science)","score_opus":0.021034008406349773,"score_gpt":0.28858442629279607,"score_spread":0.2675504178864463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416017819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037851392,0.0001516827,0.95350045,0.000171587,0.000037559217,0.00012165531,0.00023297872,0.041512527,0.00048642498],"genre_scores_gemma":[0.2137788,0.00028723566,0.7753523,0.0004902055,0.000080457954,0.00068694033,0.0013042408,0.004159241,0.0038605274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99708253,0.00068032567,0.00024233965,0.00045331498,0.0012867046,0.00025480942],"domain_scores_gemma":[0.9957463,0.0014353115,0.0005816098,0.001522901,0.0005873667,0.00012655815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029269713,0.0019451474,0.0012409277,0.0018570307,0.0007535262,0.0019340401,0.005444876,0.002324665,0.00556],"category_scores_gemma":[0.011079258,0.0010961994,0.0031058767,0.0005723641,0.0017782536,0.005337562,0.0041796253,0.0030916827,0.0022604074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009070861,0.00031061552,0.0048453854,0.0009259586,0.00041495956,0.0009367704,0.0007976217,0.33041102,0.042450715,0.078914285,0.03342775,0.50565785],"study_design_scores_gemma":[0.00003698435,0.00010219667,0.00014728928,0.000037170805,0.000038627055,0.0001739402,0.000040505904,0.9487953,0.017296536,0.026684472,0.0065933526,0.00005368464],"about_ca_topic_score_codex":0.0064034993,"about_ca_topic_score_gemma":0.008328604,"teacher_disagreement_score":0.0064034993,"about_ca_system_score_codex":0.0018351304,"about_ca_system_score_gemma":0.0032004898,"threshold_uncertainty_score":0.018600106},"labels":[],"label_agreement":null},{"id":"W4416035462","doi":"10.18653/v1/2025.emnlp-main.1430","title":"Improving Large Language Model Safety with Contrastive Representation Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Supercomputing Centre Singapore; National Science Foundation; Centro Svizzero di Calcolo Scientifico; Bundesministerium für Bildung und Forschung; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Representation (politics); Feature (linguistics); Natural language; Language identification; Language model; Quality (philosophy)","score_opus":0.005280031098914127,"score_gpt":0.26793963221203015,"score_spread":0.26265960111311604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416035462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037088186,0.00033791037,0.9569304,0.00087507005,0.000069158574,0.0000907894,0.00009955246,0.0026606948,0.0018481796],"genre_scores_gemma":[0.8367224,0.00025729756,0.15727721,0.0009011702,0.00012072079,0.00017401774,0.0004896576,0.0005055406,0.0035519754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997026,0.0012975362,0.00011967094,0.00052093633,0.0008202226,0.00021565455],"domain_scores_gemma":[0.9886264,0.007084599,0.0009239883,0.0026060003,0.0005470971,0.00021195563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003991143,0.0014473295,0.0010413512,0.00065788213,0.00069783983,0.0016803882,0.0018358215,0.0018647681,0.0024983934],"category_scores_gemma":[0.019188212,0.00048765558,0.0012737797,0.00048161094,0.0018425329,0.004394656,0.004707349,0.004883109,0.0012787051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041014428,0.0003403174,0.0030526372,0.00017575746,0.00014612761,0.00036985258,0.00032529,0.68791795,0.024051886,0.075721286,0.0072083273,0.20028044],"study_design_scores_gemma":[0.000013565893,0.00006934574,0.0000606154,0.000007910682,0.000008922944,0.00006189694,0.000016686621,0.9698295,0.0035834922,0.025725437,0.00061382836,0.00000885255],"about_ca_topic_score_codex":0.0011656748,"about_ca_topic_score_gemma":0.0014433078,"teacher_disagreement_score":0.003991143,"about_ca_system_score_codex":0.0010800522,"about_ca_system_score_gemma":0.001393505,"threshold_uncertainty_score":0.021107435},"labels":[],"label_agreement":null},{"id":"W4416048252","doi":"10.48550/arxiv.2505.22356","title":"Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Classifier (UML); Ground truth; Covariate; Modular design; Filter (signal processing); Software deployment; Statistical hypothesis testing; Test data; Statistical model","score_opus":0.08535396798824715,"score_gpt":0.3860397162206754,"score_spread":0.3006857482324282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416048252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008390685,0.00013580664,0.9887912,0.00027572183,0.000040288305,0.00013255457,0.00015519593,0.001441401,0.0006370853],"genre_scores_gemma":[0.603839,0.00029832032,0.39101866,0.00059377495,0.00026827774,0.00095156406,0.0009707179,0.0007027912,0.0013568887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98513424,0.008042216,0.0008221693,0.001785019,0.0036548541,0.0005615288],"domain_scores_gemma":[0.9529247,0.030931152,0.0046599377,0.0057233437,0.0047950754,0.0009657567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027092269,0.0024263253,0.0016668561,0.0033498183,0.0010332336,0.003397962,0.0027494703,0.0030732083,0.0022622116],"category_scores_gemma":[0.09835494,0.0007765682,0.0012865829,0.0014279997,0.0029274945,0.004027616,0.004176447,0.0036067478,0.0010777885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053555804,0.00030785968,0.01867777,0.00030439493,0.00043646453,0.00030098532,0.00031594836,0.7539774,0.008399689,0.042771395,0.009030737,0.16494176],"study_design_scores_gemma":[0.000017093307,0.00019837181,0.0011673161,0.000030099214,0.000017419525,0.00007761635,0.00003461512,0.9732656,0.0029490006,0.021291332,0.00092001725,0.000031666383],"about_ca_topic_score_codex":0.0022096226,"about_ca_topic_score_gemma":0.0022571103,"teacher_disagreement_score":0.027092269,"about_ca_system_score_codex":0.001824501,"about_ca_system_score_gemma":0.0027705783,"threshold_uncertainty_score":0.14327931},"labels":[],"label_agreement":null},{"id":"W4416157244","doi":"10.1109/trustcom66490.2025.00125","title":"Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Guelph; York University","funders":"National Research Council","keywords":"Adversarial system; Robustness (evolution); Intrusion detection system; Internet of Things; Evasion (ethics); Transparency (behavior); Attack model; Attribution","score_opus":0.007174917290799207,"score_gpt":0.24915104037864833,"score_spread":0.2419761230878491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416157244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13790572,0.00034896168,0.85767657,0.00074619864,0.000051876534,0.00006478788,0.00016235841,0.0009588181,0.0020846121],"genre_scores_gemma":[0.9692272,0.00011325966,0.029488014,0.00013007593,0.00003230558,0.000033513803,0.00012930257,0.000035359062,0.0008109892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989806,0.00040054848,0.000049074257,0.00026553796,0.00020049782,0.00010366109],"domain_scores_gemma":[0.99267274,0.0047241515,0.0009474949,0.000939956,0.00051359757,0.00020198115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002066589,0.0008130663,0.000882963,0.00084208447,0.00039531844,0.0010637403,0.0014218756,0.0011368732,0.0012273209],"category_scores_gemma":[0.011938544,0.00038081003,0.00081358326,0.0005091667,0.0014854118,0.002369309,0.0018548515,0.0019172765,0.0001783116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012459434,0.000062201085,0.003525779,0.000054322696,0.000053492997,0.00011294667,0.00010235088,0.9396772,0.0021163342,0.020089734,0.00076585263,0.033315245],"study_design_scores_gemma":[0.0000038750272,0.000017356766,0.00015223066,0.0000042373163,0.0000055091373,0.000018636318,0.0000041353874,0.9897681,0.000492596,0.0094227865,0.000105506646,0.0000050596454],"about_ca_topic_score_codex":0.0014477252,"about_ca_topic_score_gemma":0.0013252761,"teacher_disagreement_score":0.002066589,"about_ca_system_score_codex":0.0010488259,"about_ca_system_score_gemma":0.000860906,"threshold_uncertainty_score":0.0109292865},"labels":[],"label_agreement":null},{"id":"W4416214887","doi":"10.1109/tmm.2025.3632696","title":"Fast and Effective Overwrite Attack Against DNN-Based Image Watermarking Models","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Digital watermarking; Robustness (evolution); Watermark; Noise (video); Image (mathematics); Watermarking attack; Vulnerability (computing); Artificial neural network","score_opus":0.012829775666558433,"score_gpt":0.2746773812058975,"score_spread":0.2618476055393391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416214887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22058673,0.00063570443,0.7727712,0.0003830053,0.00015192875,0.000080025864,0.0000617783,0.0016801133,0.0036494941],"genre_scores_gemma":[0.9234125,0.00025646307,0.073829204,0.00012591854,0.000037110294,0.000037197744,0.000079966594,0.00006654547,0.0021551875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917656,0.00016223492,0.0000734678,0.00016907275,0.0002891808,0.00012944615],"domain_scores_gemma":[0.9985422,0.0006252689,0.00024734522,0.00029376475,0.00023060036,0.000060866187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010370249,0.0010824064,0.000695597,0.0005129945,0.0002846642,0.0005450285,0.0008412151,0.00094326056,0.00079587405],"category_scores_gemma":[0.004430626,0.0003120587,0.00073824066,0.00024394049,0.00082137564,0.002078553,0.0013427301,0.0015365647,0.00024680683],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006398557,0.00012921455,0.0024243882,0.00014334699,0.00016636758,0.00041336566,0.00014387057,0.6874285,0.07682173,0.021621138,0.0017865173,0.20828174],"study_design_scores_gemma":[0.0000045829706,0.000043711436,0.000095052004,0.0000039630468,0.0000074859013,0.00004443622,0.0000040527625,0.98770535,0.0107430965,0.0011162191,0.0002257977,0.0000062867407],"about_ca_topic_score_codex":0.001894055,"about_ca_topic_score_gemma":0.0018896412,"teacher_disagreement_score":0.001894055,"about_ca_system_score_codex":0.0006511984,"about_ca_system_score_gemma":0.0006007004,"threshold_uncertainty_score":0.005484402},"labels":[],"label_agreement":null},{"id":"W4416222651","doi":"10.1109/issre66568.2025.00057","title":"Effective, Efficient, and Environmentally Friendly Out-of-Model-Scope Detection Methodology","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Science and Engineering Research Council","keywords":"Reliability (semiconductor); Focus (optics); Noise (video); Artificial neural network; Environmentally friendly; Quality (philosophy); Deep neural networks","score_opus":0.0232253755933041,"score_gpt":0.3137636722198638,"score_spread":0.29053829662655967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416222651","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008595879,0.00009938763,0.98916376,0.00009355304,0.000026960803,0.000049466173,0.000038728595,0.0010958664,0.00083646143],"genre_scores_gemma":[0.36953393,0.00027206694,0.62464565,0.00034129314,0.00005304656,0.00020372208,0.00041795796,0.00035346957,0.0041788844],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999041,0.00014923618,0.000051691535,0.00023332924,0.00042164908,0.00010313556],"domain_scores_gemma":[0.998784,0.0003936919,0.00018023499,0.0002791457,0.00031224932,0.000050596882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001427099,0.0017347141,0.00088498107,0.0010901546,0.00043232762,0.00085227715,0.0020795423,0.0014335485,0.001726686],"category_scores_gemma":[0.0031862615,0.0005590658,0.0010436564,0.0004197021,0.000877646,0.0016150867,0.002384954,0.0016689243,0.0005716779],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002237942,0.0002452927,0.004057407,0.00024345878,0.00016904298,0.0005043708,0.00015665038,0.34290454,0.069953434,0.023116054,0.006144799,0.5522812],"study_design_scores_gemma":[0.000012906437,0.00009058891,0.0006540958,0.000021250586,0.000022484963,0.00018805217,0.00002808959,0.95537823,0.030748012,0.010375259,0.002457085,0.000023812376],"about_ca_topic_score_codex":0.0016778032,"about_ca_topic_score_gemma":0.0028740568,"teacher_disagreement_score":0.0020795423,"about_ca_system_score_codex":0.00058457913,"about_ca_system_score_gemma":0.0014536703,"threshold_uncertainty_score":0.007547319},"labels":[],"label_agreement":null},{"id":"W4416223010","doi":"10.48550/arxiv.2511.08985","title":"DeepTracer: Tracing Stolen Model via Deep Coupled Watermarks","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Indian Council of Medical Research; Institute for Catastrophic Loss Reduction","keywords":"Digital watermarking; Watermark; Task (project management); Key (lock); Reliability (semiconductor); Tracing","score_opus":0.025295311758631554,"score_gpt":0.2790813172186406,"score_spread":0.25378600546000907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416223010","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038948406,0.00042006682,0.9557446,0.0003327178,0.000060329912,0.00007591766,0.00018633605,0.002973984,0.0012576425],"genre_scores_gemma":[0.7753661,0.0004553343,0.21842775,0.00033380132,0.00007694905,0.00013180215,0.0007148478,0.00060739525,0.0038858878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977738,0.0005505843,0.00014455958,0.0005793533,0.00070061657,0.00025113826],"domain_scores_gemma":[0.9928744,0.0023062208,0.0010994807,0.0031474945,0.00040431687,0.00016815893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034530673,0.0014417905,0.0012207842,0.0012948378,0.00062533206,0.001895846,0.002740367,0.0022762862,0.0027492621],"category_scores_gemma":[0.015066916,0.0008964681,0.001624281,0.00089654326,0.0029697334,0.0074683274,0.0059990273,0.002902966,0.00083305046],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007914533,0.0002032692,0.0044026924,0.00031890927,0.00024716504,0.000516966,0.00039037934,0.6244731,0.02412546,0.07732038,0.0039445576,0.26326567],"study_design_scores_gemma":[0.000018875991,0.00005722791,0.0001462636,0.000017990344,0.000017669658,0.000063826395,0.00001856503,0.9605477,0.008296096,0.029799696,0.0010014625,0.000014644496],"about_ca_topic_score_codex":0.0021796476,"about_ca_topic_score_gemma":0.0021514865,"teacher_disagreement_score":0.0034530673,"about_ca_system_score_codex":0.001245651,"about_ca_system_score_gemma":0.0014375547,"threshold_uncertainty_score":0.01826173},"labels":[],"label_agreement":null},{"id":"W4416229447","doi":"10.1145/3776739","title":"An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Documentation; License; Software; Face (sociological concept); Artificial neural network; Facial recognition system; Work (physics); Convolutional neural network","score_opus":0.06670334465444491,"score_gpt":0.3676025793805503,"score_spread":0.30089923472610536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416229447","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9438133,0.0031587621,0.020369228,0.0033061963,0.00025663356,0.00017843288,0.019217039,0.0016756153,0.008024722],"genre_scores_gemma":[0.9308812,0.00061818823,0.013051281,0.0005018708,0.0001085292,0.00016016557,0.051992495,0.00034171776,0.0023445627],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98833734,0.0062164925,0.00077719206,0.0016657653,0.002502224,0.00050105766],"domain_scores_gemma":[0.87235785,0.090268105,0.006236547,0.022727834,0.0072016716,0.0012080243],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.022000683,0.000926572,0.0008632117,0.0029285622,0.0016381457,0.0027123413,0.0022206753,0.0020683096,0.0039019752],"category_scores_gemma":[0.099203065,0.00039984495,0.001004014,0.0043028737,0.0019622413,0.0061177867,0.0023502212,0.003338382,0.0018958197],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016220867,0.0017927383,0.44949323,0.0014535598,0.00069288444,0.0013419086,0.0020839719,0.1727332,0.0016625657,0.023490654,0.15383878,0.1897945],"study_design_scores_gemma":[0.0002914403,0.00093665725,0.17789447,0.0009054098,0.000307321,0.0026081188,0.004312743,0.67455226,0.006165662,0.03982922,0.09191566,0.0002809862],"about_ca_topic_score_codex":0.0071057477,"about_ca_topic_score_gemma":0.009163543,"teacher_disagreement_score":0.9977793,"about_ca_system_score_codex":0.0016966455,"about_ca_system_score_gemma":0.0014472764,"threshold_uncertainty_score":0.11635214},"labels":[],"label_agreement":null},{"id":"W4416248540","doi":"10.48550/arxiv.2510.19761","title":"Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Robustness (evolution); Artificial neural network; Deep neural networks; Intrusion detection system; Intrusion","score_opus":0.06254933044981503,"score_gpt":0.29166525761497475,"score_spread":0.22911592716515972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416248540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86858237,0.0016655051,0.11968535,0.0014697841,0.00015044954,0.00009443804,0.00025512694,0.0007377599,0.007359203],"genre_scores_gemma":[0.9915085,0.00017526608,0.0077059404,0.00009410763,0.000013734382,0.000011510952,0.000059656304,0.00002886538,0.00040240848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838185,0.00069119514,0.000086030755,0.0002282554,0.00036615392,0.00024650208],"domain_scores_gemma":[0.98275536,0.013023154,0.0014243346,0.0015170915,0.0008759686,0.00040415302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034888529,0.0009287949,0.0004945146,0.0006264571,0.00043100555,0.0009310783,0.0008233018,0.0010685831,0.0015834015],"category_scores_gemma":[0.023453424,0.00038162956,0.00035993286,0.0002815859,0.0013818088,0.0028459055,0.0018892877,0.0016189389,0.00023924164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000671552,0.00016041052,0.0050737667,0.00012892728,0.00010401047,0.00013430668,0.00007993178,0.9383052,0.012712501,0.005971424,0.0009352001,0.03572287],"study_design_scores_gemma":[0.00001820576,0.00033776788,0.0011488511,0.000023301693,0.000025785686,0.00006993252,0.000039130427,0.9808435,0.011604695,0.005526096,0.00034892804,0.000013835847],"about_ca_topic_score_codex":0.0018043683,"about_ca_topic_score_gemma":0.0019354332,"teacher_disagreement_score":0.0034888529,"about_ca_system_score_codex":0.0010847367,"about_ca_system_score_gemma":0.0005802706,"threshold_uncertainty_score":0.018451035},"labels":[],"label_agreement":null},{"id":"W4416250911","doi":"10.1109/ijcnn64981.2025.11228300","title":"Cascade Adversarial Attack Search","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adversarial system; Robustness (evolution); Hyperparameter; Attack model; Cascade; Transferability","score_opus":0.02714895413917641,"score_gpt":0.3322156332473623,"score_spread":0.3050666791081859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416250911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030587392,0.0005349843,0.96210027,0.00024260425,0.00006946766,0.00014411133,0.000064135165,0.00065417436,0.005602872],"genre_scores_gemma":[0.86058974,0.0003318006,0.13357803,0.00027521828,0.00005044961,0.0002609432,0.00017180837,0.00010670714,0.004635211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988512,0.00035457083,0.00006167884,0.00023567241,0.0003193919,0.00017751948],"domain_scores_gemma":[0.99748385,0.0016557215,0.000232759,0.00027584622,0.00024237917,0.00010947632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001968035,0.0019348116,0.0015269846,0.0010467716,0.0005150143,0.0009062199,0.0016703416,0.0014984695,0.0032999453],"category_scores_gemma":[0.004811166,0.0005557638,0.0012077983,0.0005363298,0.0012331998,0.0014914321,0.0022663127,0.0017300905,0.0005432635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085289685,0.0000485126,0.00071332604,0.000059913746,0.00008003327,0.00009624118,0.00003398129,0.9482071,0.0028160524,0.012953909,0.0012793198,0.033626355],"study_design_scores_gemma":[0.000005000437,0.0000403756,0.000051974777,0.0000045759202,0.0000090627245,0.000025739713,0.000003418347,0.9956169,0.00069259864,0.0032391527,0.0003063205,0.000004788175],"about_ca_topic_score_codex":0.0017050739,"about_ca_topic_score_gemma":0.0017163596,"teacher_disagreement_score":0.0032999453,"about_ca_system_score_codex":0.0007987619,"about_ca_system_score_gemma":0.0009496726,"threshold_uncertainty_score":0.011039436},"labels":[],"label_agreement":null},{"id":"W4416260937","doi":"10.1007/978-981-95-4109-6_33","title":"Semantic Consistency Guided Backdoor Trigger Inversion and InitDistillNet for Backdoor Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"","keywords":"Backdoor; Inversion (geology); Outlier; Anomaly detection; Consistency (knowledge bases)","score_opus":0.034931989835471036,"score_gpt":0.3011699993093499,"score_spread":0.2662380094738789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416260937","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011732007,0.00048200064,0.9755258,0.0002438064,0.00024247658,0.0001158967,0.00024744184,0.0048473934,0.0065630716],"genre_scores_gemma":[0.4975391,0.00046818875,0.48317134,0.0005715958,0.00021513546,0.00018665961,0.0015153852,0.0014001621,0.014932304],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891627,0.00017105656,0.00004761099,0.00029956037,0.00040068827,0.00016482775],"domain_scores_gemma":[0.9991297,0.00033957412,0.000055587767,0.00029542312,0.00013843535,0.000041300813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008125706,0.0013171466,0.0013256529,0.00082885753,0.0007942538,0.0019481676,0.002170079,0.0016972125,0.008338278],"category_scores_gemma":[0.0030484174,0.0005322838,0.0010227223,0.00063212845,0.001457639,0.0031939994,0.0031363205,0.002675429,0.001961696],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008932135,0.0003078072,0.0009817265,0.00033740318,0.00014416558,0.00035163143,0.00021033881,0.16813205,0.029831216,0.1309495,0.022826467,0.6450345],"study_design_scores_gemma":[0.000025766138,0.00009510339,0.00017413861,0.000033479286,0.000036504538,0.00014498176,0.0000464524,0.89867866,0.016875105,0.07826371,0.00559839,0.000027797418],"about_ca_topic_score_codex":0.0033997209,"about_ca_topic_score_gemma":0.006912949,"teacher_disagreement_score":0.008338278,"about_ca_system_score_codex":0.0010498046,"about_ca_system_score_gemma":0.0016926129,"threshold_uncertainty_score":0.027894318},"labels":[],"label_agreement":null},{"id":"W4416306598","doi":"10.1049/cdt2/5384331","title":"A Systematic Literature Review on the Applications, Models, Limitations, and Future Directions of Generative Adversarial Networks","year":2025,"lang":"en","type":"article","venue":"IET Computers & Digital Techniques","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Adversarial system; Key (lock); Domain (mathematical analysis); Systematic review; Similarity (geometry); Generative grammar; Taxonomy (biology); Architecture","score_opus":0.01106865353179942,"score_gpt":0.2506989948505807,"score_spread":0.2396303413187813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416306598","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011687397,0.9863491,0.0074181366,0.0019901865,0.00025347134,0.00008317796,0.00028765787,0.00005960992,0.0023899232],"genre_scores_gemma":[0.00884216,0.98411554,0.0050477074,0.00096588145,0.00020264035,0.00009905454,0.00026473656,0.000021015963,0.00044131285],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9981668,0.00059382885,0.00041478456,0.0002553477,0.00051335053,0.00005586166],"domain_scores_gemma":[0.97689307,0.020166742,0.0010284835,0.00041026674,0.001379708,0.00012161976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004635305,0.0012146842,0.0016065263,0.004893013,0.00040406836,0.0019810344,0.0013277277,0.0013994543,0.004199964],"category_scores_gemma":[0.02008803,0.0007067837,0.001986419,0.004639529,0.0008336012,0.0026859997,0.0010413463,0.001293684,0.0008456704],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011439673,0.00008213351,0.001583152,0.13329268,0.00077980745,0.00017802819,0.00028127563,0.0055978685,0.00094669196,0.019230498,0.02059213,0.81732136],"study_design_scores_gemma":[0.000060951497,0.0005434885,0.0047463235,0.26088405,0.0047572153,0.0014252054,0.0006711712,0.007818473,0.0024632774,0.035211004,0.6812523,0.00016654561],"about_ca_topic_score_codex":0.0034613046,"about_ca_topic_score_gemma":0.006974874,"teacher_disagreement_score":0.004893013,"about_ca_system_score_codex":0.0011549988,"about_ca_system_score_gemma":0.0052415854,"threshold_uncertainty_score":0.024514139},"labels":[],"label_agreement":null},{"id":"W4416384622","doi":"10.1016/j.engappai.2025.113111","title":"Trustworthy requirements for foundation models—A comprehensive survey and roadmap","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Research Ireland; Canada Research Chairs; Science Foundation Ireland","keywords":"Trustworthiness; Foundation (evidence); Process (computing); Adversarial system; Generalization","score_opus":0.05329427709778476,"score_gpt":0.3276717843211859,"score_spread":0.2743775072234011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416384622","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030606441,0.2014824,0.6559556,0.026913349,0.0012847971,0.0010263248,0.0010692648,0.0010375101,0.08062431],"genre_scores_gemma":[0.39881772,0.2577745,0.3244196,0.0036030759,0.0024438864,0.0014703478,0.0027234675,0.0008884337,0.007858988],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97870207,0.005693337,0.0029753868,0.0019044327,0.009773169,0.0009516481],"domain_scores_gemma":[0.92826104,0.049529232,0.0039957846,0.0062181395,0.010834776,0.0011608845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015763514,0.0015935769,0.0017343138,0.0056445873,0.0022498094,0.008749817,0.0032950758,0.0040878253,0.003999733],"category_scores_gemma":[0.055931464,0.0019416219,0.0023865008,0.004086515,0.0052202027,0.017036546,0.0045881215,0.005588292,0.0017162724],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010793787,0.0001529356,0.0035083357,0.0048442907,0.00011620188,0.00090383063,0.0020876415,0.014851167,0.0015867829,0.7810487,0.009876411,0.18091567],"study_design_scores_gemma":[0.000025217316,0.00017300618,0.0017818055,0.0074400315,0.00014652868,0.0016879904,0.0019401071,0.04841552,0.0022065202,0.70211107,0.23389079,0.00018138901],"about_ca_topic_score_codex":0.0049557886,"about_ca_topic_score_gemma":0.002246998,"teacher_disagreement_score":0.015763514,"about_ca_system_score_codex":0.0047980193,"about_ca_system_score_gemma":0.0064890105,"threshold_uncertainty_score":0.083366394},"labels":[],"label_agreement":null},{"id":"W4416553071","doi":"10.1609/aaaiss.v7i1.36928","title":"Filtered-ViT: A Robust Defense Against Multiple AdversarialPatch Attacks","year":2025,"lang":"","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Adversarial system; Robustness (evolution); Transformer; Noise (video); Motion planning; Exploit","score_opus":0.010541494932510626,"score_gpt":0.22739906790148495,"score_spread":0.21685757296897432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416553071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034676418,0.0005860204,0.9552341,0.00037468874,0.00013554515,0.000092147886,0.0001237298,0.0050198627,0.0037574419],"genre_scores_gemma":[0.84743035,0.00024617976,0.1465469,0.0005153876,0.000070449874,0.000079321326,0.00033083887,0.00035849446,0.004421989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893016,0.00020055751,0.000046173293,0.00024110203,0.00041136343,0.0001707027],"domain_scores_gemma":[0.9982834,0.0005341265,0.00021511056,0.0005763969,0.00027873155,0.00011226825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018160666,0.0009816183,0.0009775371,0.00076495035,0.00051361124,0.0012187272,0.0021942891,0.0016168916,0.0022337076],"category_scores_gemma":[0.0051825834,0.00039237546,0.00070609327,0.0003480804,0.0016584947,0.0019651158,0.002685162,0.0019110998,0.0008438239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000563079,0.00015692908,0.0026189815,0.00018094045,0.0002501719,0.00026033443,0.00016983168,0.5539496,0.07560347,0.043728728,0.014309782,0.30820814],"study_design_scores_gemma":[0.000025789459,0.00022277609,0.00035080453,0.000016431437,0.000025277044,0.00022008452,0.00002401752,0.95773256,0.02417555,0.013367229,0.0038103177,0.000029109131],"about_ca_topic_score_codex":0.0016011891,"about_ca_topic_score_gemma":0.001913099,"teacher_disagreement_score":0.0022337076,"about_ca_system_score_codex":0.0008866705,"about_ca_system_score_gemma":0.0010022856,"threshold_uncertainty_score":0.009604394},"labels":[],"label_agreement":null},{"id":"W4416707339","doi":"10.1109/lcomm.2025.3637721","title":"Backdoor Attacks on Semantic Communications Systems With Joint Source and Channel Coding","year":2025,"lang":"","type":"article","venue":"IEEE Communications Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Backdoor; Joint (building); Focus (optics); Coding (social sciences); Channel (broadcasting); Encoder; Communications system","score_opus":0.044237613860515515,"score_gpt":0.2937851249402814,"score_spread":0.24954751107976592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416707339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18088733,0.00047885638,0.80979687,0.0007551458,0.00013914294,0.00010843678,0.00020851684,0.0017915131,0.0058342214],"genre_scores_gemma":[0.97919697,0.000093328024,0.019338418,0.0001333202,0.00002822683,0.000039255166,0.000062014136,0.000055930876,0.0010525103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961706,0.0011298142,0.00016144017,0.0004951339,0.0011871784,0.0008558668],"domain_scores_gemma":[0.9919538,0.004762769,0.00077980454,0.0017749183,0.0005680077,0.00016072644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024572997,0.0011514022,0.0009341728,0.0008462793,0.00084248814,0.0015603884,0.00084383076,0.001294103,0.0020075152],"category_scores_gemma":[0.012581951,0.0003528797,0.0007642199,0.00059802434,0.0030591267,0.0035342888,0.0036072037,0.0022096583,0.00039131255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010866834,0.00014982455,0.002727322,0.00024563787,0.0001776347,0.00060726184,0.00029971072,0.6531266,0.027708085,0.23424233,0.0035240585,0.07610485],"study_design_scores_gemma":[0.00003670386,0.00014269711,0.0002379718,0.000028509092,0.000024313762,0.0001920817,0.000050518902,0.9149713,0.01790389,0.06543222,0.0009455123,0.000034165365],"about_ca_topic_score_codex":0.00092137663,"about_ca_topic_score_gemma":0.0007375839,"teacher_disagreement_score":0.0024572997,"about_ca_system_score_codex":0.0010245802,"about_ca_system_score_gemma":0.0014323238,"threshold_uncertainty_score":0.012995601},"labels":[],"label_agreement":null},{"id":"W4416780419","doi":"10.1109/bigdata66926.2025.11401828","title":"Beyond Accuracy: An Empirical Study of Uncertainty Estimation in Imputation","year":2025,"lang":"","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Missing data; Imputation (statistics); Calibration; Reliability (semiconductor); Empirical likelihood; Empirical research; Observational error; Measurement uncertainty","score_opus":0.032459003359278704,"score_gpt":0.3851021096578575,"score_spread":0.3526431062985788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416780419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19900477,0.010900343,0.7755241,0.0048141577,0.00025565957,0.00025793468,0.0017243195,0.0014995927,0.0060191182],"genre_scores_gemma":[0.8861587,0.0020247041,0.106501415,0.0007956412,0.00030739495,0.0002566949,0.0022894675,0.0008921072,0.0007738372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.93672997,0.04314752,0.0027428854,0.006923635,0.009373263,0.001082752],"domain_scores_gemma":[0.28296185,0.63646775,0.017680002,0.05080331,0.011006446,0.001080678],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09115881,0.001768654,0.001501243,0.0035747427,0.0015892575,0.0036560649,0.0031266997,0.0033202448,0.0019499739],"category_scores_gemma":[0.4626545,0.0010266756,0.001786226,0.004678293,0.006962813,0.010614862,0.004915413,0.005979398,0.0005246701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011989631,0.0003092947,0.15021673,0.0014289931,0.0020186817,0.0003887741,0.0020255847,0.5815942,0.0020947845,0.09124174,0.008593306,0.15888904],"study_design_scores_gemma":[0.000115408526,0.0006025876,0.033788566,0.0010339621,0.0004306877,0.0012693262,0.0008234295,0.7464978,0.010436051,0.1945905,0.010162374,0.0002492817],"about_ca_topic_score_codex":0.0026997405,"about_ca_topic_score_gemma":0.0017194387,"teacher_disagreement_score":0.9088412,"about_ca_system_score_codex":0.0024893486,"about_ca_system_score_gemma":0.0017670407,"threshold_uncertainty_score":0.4820996},"labels":[],"label_agreement":null},{"id":"W4416851502","doi":"10.1145/3733800.3763269","title":"When Vision Fails: Text Attacks Against ViT and OCR","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Unicode; Adversarial system; Comprehension; Character (mathematics); Class (philosophy); Key (lock); Optical character recognition; Language model","score_opus":0.009273556421475487,"score_gpt":0.28796328700353646,"score_spread":0.278689730582061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416851502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49607298,0.00070951256,0.47068578,0.0034469685,0.00031403088,0.0002122412,0.00043759975,0.0062809535,0.021839917],"genre_scores_gemma":[0.9751652,0.000095120995,0.02184414,0.00040060576,0.000022687682,0.000030252331,0.00009842597,0.00015275889,0.0021908167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977615,0.0007980244,0.00007734169,0.00038039335,0.00079709955,0.00018567343],"domain_scores_gemma":[0.99349654,0.0036955646,0.0006095092,0.0016797612,0.0003655125,0.0001531896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017790908,0.0007828668,0.0005256612,0.0004943504,0.0005229977,0.0009489178,0.000946149,0.0019275829,0.0017699779],"category_scores_gemma":[0.01451097,0.00031725553,0.00069521886,0.00023763732,0.0017863213,0.001989435,0.0018034402,0.0017164937,0.00068357924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010838528,0.00020032979,0.0068258694,0.00028317975,0.00028159074,0.002177937,0.0008825399,0.6896025,0.08832863,0.07005209,0.01222208,0.1280594],"study_design_scores_gemma":[0.000026991156,0.00016889692,0.0007828271,0.000040879255,0.000025583315,0.00049784937,0.000062457424,0.93719673,0.041131746,0.0168156,0.003219457,0.000030913805],"about_ca_topic_score_codex":0.0019996834,"about_ca_topic_score_gemma":0.0011440085,"teacher_disagreement_score":0.0019996834,"about_ca_system_score_codex":0.0010212332,"about_ca_system_score_gemma":0.0004481654,"threshold_uncertainty_score":0.009408891},"labels":[],"label_agreement":null},{"id":"W4416862091","doi":"10.48550/arxiv.2508.08521","title":"VISOR: Visual Input-based Steering for Output Redirection in Vision-Language Models","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Weyerhauser (Canada)","funders":"","keywords":"Software deployment; Control (management); Overhead (engineering); Modalities; Work (physics); Driving simulator; Range (aeronautics)","score_opus":0.03610660869457845,"score_gpt":0.3329578615989784,"score_spread":0.29685125290439995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416862091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02955756,0.0004142989,0.67395896,0.000592669,0.0002920207,0.00023102218,0.0017669277,0.2806531,0.012533373],"genre_scores_gemma":[0.5630678,0.00040211843,0.38312775,0.0013920683,0.00007461684,0.0006687376,0.0050449218,0.027329609,0.018892368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999064,0.00022569887,0.00006643886,0.00020705664,0.0003128472,0.00012386197],"domain_scores_gemma":[0.9987159,0.00040793905,0.00009025915,0.00054579874,0.0001679048,0.000072307506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010088215,0.0013076968,0.00047527178,0.00038409667,0.00036663463,0.0015440042,0.0023099785,0.0011162004,0.011170165],"category_scores_gemma":[0.005344645,0.00058683445,0.0010373845,0.00019775327,0.0010611159,0.0030028597,0.0029592742,0.002052794,0.0048121773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002323032,0.0005531467,0.00713354,0.0013694927,0.00025330795,0.0008473599,0.0018875077,0.12924203,0.15229294,0.097745724,0.13482681,0.47152504],"study_design_scores_gemma":[0.00018760047,0.0003539945,0.0005188467,0.00008167093,0.00004685863,0.00019067689,0.00021251127,0.7844068,0.0977684,0.043072972,0.073033765,0.00012591381],"about_ca_topic_score_codex":0.004036172,"about_ca_topic_score_gemma":0.0066456725,"teacher_disagreement_score":0.011170165,"about_ca_system_score_codex":0.00077898597,"about_ca_system_score_gemma":0.0013310107,"threshold_uncertainty_score":0.037368},"labels":[],"label_agreement":null},{"id":"W4416941112","doi":"10.65521/ijacte.v12i1.106","title":"Adversarial Machine Learning: Attacks and Defenses in Deep Neural Networks","year":2025,"lang":"","type":"article","venue":"International Journal on Advanced Computer Theory and Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northern College","funders":"","keywords":"Adversarial system; Categorization; Trustworthiness; Software deployment; Vulnerability (computing); Threat model; Deep neural networks; Artificial neural network","score_opus":0.004638457784238451,"score_gpt":0.24282606424764344,"score_spread":0.23818760646340498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416941112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021987054,0.0048396452,0.959813,0.0033408904,0.00023272938,0.00007295182,0.00006761065,0.00044329982,0.009202923],"genre_scores_gemma":[0.9055693,0.005562403,0.08219562,0.0012290472,0.0004052975,0.00017767827,0.00010207838,0.00012826864,0.0046302835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970798,0.001249485,0.00012457138,0.00038613088,0.0008972484,0.00026270622],"domain_scores_gemma":[0.99157,0.006202716,0.0005995735,0.0010756062,0.00039197566,0.00016019319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042361813,0.0011174914,0.000977729,0.0009123738,0.0009443613,0.002177327,0.001568023,0.0025817943,0.0015156507],"category_scores_gemma":[0.012965033,0.0006137026,0.0007676416,0.0005849021,0.004256268,0.0043632584,0.004150284,0.004723473,0.00034453886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017468134,0.0000662332,0.0014145965,0.00021736138,0.00012102171,0.00018310419,0.00021607624,0.51848763,0.0057792584,0.39922625,0.004320563,0.06979319],"study_design_scores_gemma":[0.000011012318,0.00006147944,0.00018797483,0.00008673515,0.000014094805,0.00011907933,0.000032580007,0.8008101,0.0026244705,0.19252232,0.0035064912,0.00002370063],"about_ca_topic_score_codex":0.0008564991,"about_ca_topic_score_gemma":0.00065731345,"teacher_disagreement_score":0.0042361813,"about_ca_system_score_codex":0.0014182283,"about_ca_system_score_gemma":0.0007427768,"threshold_uncertainty_score":0.0224033},"labels":[],"label_agreement":null},{"id":"W4416961862","doi":"10.1109/pst65910.2025.11268874","title":"LAID: Lightweight AI-Generated Image Detection in Spatial and Spectral Domains","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Software deployment; Object detection; Image (mathematics); Computation; Trustworthiness; Artificial neural network; Adversarial system; Code (set theory)","score_opus":0.005454317170498574,"score_gpt":0.25139103792405243,"score_spread":0.24593672075355386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416961862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059036024,0.0013892415,0.8903608,0.0009458647,0.0005061929,0.000533896,0.0024800154,0.033413284,0.0113346055],"genre_scores_gemma":[0.44699875,0.0006620396,0.5263972,0.0010329632,0.00021597557,0.00040504246,0.0097337,0.00144954,0.013104757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991691,0.00013551374,0.0000267233,0.00023925264,0.0003165287,0.000113043185],"domain_scores_gemma":[0.9988136,0.00039143764,0.000118258955,0.00040087532,0.00020407743,0.00007179803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015549511,0.0015531671,0.0008042902,0.001304026,0.00047728958,0.0014494115,0.002430433,0.0014666449,0.003902678],"category_scores_gemma":[0.005371098,0.00048061964,0.00085290626,0.0005463996,0.0010209491,0.002184911,0.0028530848,0.0022784458,0.0028107143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005512222,0.00036819692,0.006236116,0.00048836094,0.00034329153,0.00033884327,0.00019571731,0.2501568,0.04744913,0.019056262,0.06556172,0.60925436],"study_design_scores_gemma":[0.000026629152,0.000088712644,0.0010957161,0.00003536561,0.000018371442,0.00021803597,0.000038479397,0.96644264,0.015114149,0.009342819,0.0075538037,0.000025328785],"about_ca_topic_score_codex":0.0056456495,"about_ca_topic_score_gemma":0.008508036,"teacher_disagreement_score":0.0056456495,"about_ca_system_score_codex":0.0009160902,"about_ca_system_score_gemma":0.0010399771,"threshold_uncertainty_score":0.013055742},"labels":[],"label_agreement":null},{"id":"W4416961865","doi":"10.1109/pst65910.2025.11268824","title":"G-STAR: A Threat Modeling Framework for General-Purpose AI Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Key (lock); Software deployment; Threat model; Work (physics); Focus (optics); Taxonomy (biology)","score_opus":0.02117561319398679,"score_gpt":0.32160725514236127,"score_spread":0.30043164194837446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416961865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046500335,0.0001734501,0.9854638,0.0009015039,0.00005601519,0.00031339301,0.00043743593,0.002227939,0.005776355],"genre_scores_gemma":[0.14029323,0.0006907231,0.85254025,0.00041158087,0.00008610329,0.0006664371,0.0010162927,0.00053624646,0.0037591269],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99778616,0.0010287677,0.00019994858,0.0002698975,0.00054375554,0.00017154812],"domain_scores_gemma":[0.9967483,0.0013449492,0.00044003932,0.0006560888,0.00060579577,0.00020489993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005000536,0.0016249206,0.00065433857,0.003121063,0.0011605219,0.004210399,0.0028824292,0.0018032633,0.0038935964],"category_scores_gemma":[0.006292245,0.000730816,0.002786719,0.0013543809,0.0024826557,0.004980492,0.0033230486,0.003336696,0.0012276968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055980436,0.000074163094,0.0021476243,0.00033898765,0.00009925405,0.00024522637,0.0010453333,0.26768586,0.0017475754,0.6863841,0.0069093364,0.033266623],"study_design_scores_gemma":[0.000016035763,0.00007622982,0.00038928422,0.00018785479,0.000039394006,0.00022871423,0.00033559313,0.7316592,0.0010094849,0.21897383,0.047040887,0.000043438256],"about_ca_topic_score_codex":0.009420836,"about_ca_topic_score_gemma":0.01077423,"teacher_disagreement_score":0.009420836,"about_ca_system_score_codex":0.002223025,"about_ca_system_score_gemma":0.0036276423,"threshold_uncertainty_score":0.026445627},"labels":[],"label_agreement":null},{"id":"W4416961978","doi":"10.1109/pst65910.2025.11268888","title":"A Generic Framework for Privacy Risk Assessment of Machine Learning Models","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Canada; York University; University of Guelph","funders":"National Research Council","keywords":"Information privacy; Testbed; Set (abstract data type); Risk assessment; Privacy by Design; Safeguard; Focus (optics); Privacy software","score_opus":0.03684883092377972,"score_gpt":0.3360739578851268,"score_spread":0.29922512696134707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416961978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013056123,0.00024735217,0.99390054,0.000530657,0.000040484938,0.00027005136,0.00021364451,0.0005772029,0.0029145111],"genre_scores_gemma":[0.1276671,0.0009553409,0.86637986,0.00061785436,0.00027291934,0.001161078,0.0007108064,0.00026167266,0.0019733177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.966795,0.014443327,0.002907968,0.0029157307,0.01159911,0.0013387966],"domain_scores_gemma":[0.9677816,0.012028812,0.0034968287,0.011350062,0.004556182,0.00078654825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025404908,0.0028900828,0.0020449162,0.0067866207,0.0020564036,0.0080820015,0.005239416,0.0053732665,0.0037921313],"category_scores_gemma":[0.047140677,0.0012886506,0.0045307046,0.003707885,0.0051734583,0.009175265,0.008015242,0.008630059,0.0013590171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059230282,0.00017698754,0.0021015494,0.00030920235,0.00023166713,0.0002815588,0.00034691725,0.15652685,0.0020539933,0.78730035,0.005583825,0.04502789],"study_design_scores_gemma":[0.000027778116,0.00014592358,0.0006228474,0.00027515186,0.000104197105,0.0005275205,0.000108043045,0.51953065,0.002600656,0.45870656,0.01726311,0.00008757573],"about_ca_topic_score_codex":0.003308379,"about_ca_topic_score_gemma":0.002475372,"teacher_disagreement_score":0.025404908,"about_ca_system_score_codex":0.0039609643,"about_ca_system_score_gemma":0.0052517797,"threshold_uncertainty_score":0.13435566},"labels":[],"label_agreement":null},{"id":"W4416962018","doi":"10.1109/pst65910.2025.11268845","title":"Evaluating Efficient Patch-Based Backdoor Attacks in Satellite Image Classification Systems","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Backdoor; Aerial image; Image (mathematics); Orientation (vector space); Satellite; Satellite image; Contextual image classification","score_opus":0.056365421335660845,"score_gpt":0.38662566362936834,"score_spread":0.3302602422937075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416962018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92810786,0.001982042,0.06439433,0.00032104886,0.00023377035,0.00035361445,0.00042165496,0.0019815338,0.0022041907],"genre_scores_gemma":[0.9777258,0.00026837026,0.020059286,0.00008287496,0.000042781914,0.00007767391,0.0008638744,0.000066957815,0.00081239094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827564,0.00036153573,0.00017594387,0.00053420383,0.00041150264,0.00024118638],"domain_scores_gemma":[0.9959306,0.002030034,0.00055437355,0.000604731,0.00061876216,0.00026147443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023038227,0.0015916737,0.001475501,0.0008539236,0.00046550002,0.0011910729,0.0014656291,0.0018132308,0.0014564735],"category_scores_gemma":[0.009813713,0.00040293383,0.0007888622,0.00053346704,0.0008890523,0.001600841,0.001413542,0.0010969689,0.00072701083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022356398,0.00086348737,0.012447133,0.00041424495,0.00034325526,0.00015264332,0.000110529174,0.8033543,0.013380372,0.0006576064,0.002427249,0.16361362],"study_design_scores_gemma":[0.00003268921,0.00070220395,0.0021784364,0.000010625769,0.00003890891,0.00004997771,0.000033169403,0.99077237,0.0056800223,0.00026914364,0.00022214362,0.000010441736],"about_ca_topic_score_codex":0.008342541,"about_ca_topic_score_gemma":0.004782856,"teacher_disagreement_score":0.008342541,"about_ca_system_score_codex":0.001461096,"about_ca_system_score_gemma":0.0010374686,"threshold_uncertainty_score":0.016587973},"labels":[],"label_agreement":null},{"id":"W4416962290","doi":"10.1109/embc58623.2025.11253101","title":"Impact of Adversarial Attack on Pediatric Hip Ultrasound Deep Learning Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates","keywords":"Adversarial system; Deep learning; Robustness (evolution); Convolutional neural network; Pattern recognition (psychology); Deep neural networks; Artificial neural network; Contextual image classification","score_opus":0.02771103881776131,"score_gpt":0.3126090415229891,"score_spread":0.28489800270522775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416962290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8632681,0.0010511702,0.12658288,0.0009706491,0.00021825281,0.00013217329,0.000775731,0.0016401792,0.0053608473],"genre_scores_gemma":[0.98894775,0.00020075054,0.009153692,0.00014115516,0.000012298011,0.00002553569,0.00037708727,0.000061066086,0.0010807466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992416,0.00025707038,0.000046159155,0.00013615937,0.00019775832,0.00012123405],"domain_scores_gemma":[0.9967535,0.0020967398,0.00026756438,0.00045023247,0.00031321595,0.000118665776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001275486,0.0009054129,0.0004490314,0.0003861847,0.00021222443,0.00048189703,0.00054865336,0.00072075974,0.0013944183],"category_scores_gemma":[0.007232034,0.00023040148,0.0005304837,0.0001807299,0.000732893,0.000780347,0.00091580354,0.0011409514,0.00037541834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006168593,0.00013123776,0.009851604,0.0001320974,0.0001405357,0.00037755133,0.00009708738,0.92980975,0.01358035,0.0021764217,0.002148135,0.04093832],"study_design_scores_gemma":[0.0000075498256,0.00018801806,0.0023681757,0.000036652113,0.00002409056,0.00015804407,0.000032986536,0.97634083,0.01920237,0.001003851,0.000623551,0.000014022661],"about_ca_topic_score_codex":0.0042924327,"about_ca_topic_score_gemma":0.003022472,"teacher_disagreement_score":0.0042924327,"about_ca_system_score_codex":0.0007831656,"about_ca_system_score_gemma":0.0004272077,"threshold_uncertainty_score":0.008534908},"labels":[],"label_agreement":null},{"id":"W4416962382","doi":"10.1109/pst65910.2025.11268872","title":"A Per-Bag Suspicion-Based Bagging Strategy for Fighting Poisoning Attacks in Classification","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Robustness (evolution); Resampling; Outlier; Ensemble learning; Weighting; Adversarial system; Boosting (machine learning); Artificial neural network; MNIST database","score_opus":0.0415817767880461,"score_gpt":0.3434218178192638,"score_spread":0.3018400410312177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416962382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1049769,0.00081062864,0.88821745,0.0006122151,0.00022897213,0.00028413386,0.00011604396,0.0030220547,0.001731644],"genre_scores_gemma":[0.8568181,0.0002385781,0.13867885,0.0007154903,0.00016906124,0.00015970656,0.00030989345,0.00015956955,0.0027507076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997174,0.00055877765,0.00019823303,0.00064367766,0.0011087637,0.00031654717],"domain_scores_gemma":[0.99156415,0.0024321417,0.0012498619,0.0021712459,0.0020339403,0.0005486195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003913818,0.0014256183,0.0017384787,0.0015777758,0.0013136419,0.0014916607,0.0029125744,0.0017705712,0.0014779853],"category_scores_gemma":[0.014760824,0.00050813035,0.0007924795,0.001340577,0.0015636233,0.004645604,0.0030578517,0.0021186792,0.0009016232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010496139,0.00072822947,0.017455464,0.00021067327,0.00026776106,0.00038250693,0.00068409526,0.18385777,0.03334387,0.014275016,0.008587927,0.7391571],"study_design_scores_gemma":[0.000031415668,0.00032503525,0.0016909036,0.000024823079,0.000064155225,0.00029176037,0.00008818845,0.97350967,0.01102728,0.010407399,0.0024936155,0.000045780936],"about_ca_topic_score_codex":0.0014611767,"about_ca_topic_score_gemma":0.002019188,"teacher_disagreement_score":0.003913818,"about_ca_system_score_codex":0.0009755986,"about_ca_system_score_gemma":0.001409106,"threshold_uncertainty_score":0.020698488},"labels":[],"label_agreement":null},{"id":"W4417003494","doi":"10.1109/icce-asia67487.2025.11263774","title":"Action Elimination through Hallucination Detection: A Reinforcement Learning Approach for LLM Fine-Tuning","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Reinforcement learning; Action (physics); Focus (optics); Filter (signal processing); Noise (video); Reinforcement","score_opus":0.03738149689377482,"score_gpt":0.31735000531879093,"score_spread":0.2799685084250161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417003494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014190619,0.00013726427,0.98362744,0.00014406425,0.000032781772,0.00005755499,0.00001549754,0.00094765524,0.0008471286],"genre_scores_gemma":[0.81470394,0.00008023009,0.18223804,0.00035458035,0.00006577769,0.00018768206,0.000058989506,0.00017479916,0.0021360207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926585,0.00024747374,0.00004064937,0.00019977851,0.00015347569,0.000092830625],"domain_scores_gemma":[0.99758756,0.0013669137,0.00031221926,0.00031757093,0.00027770468,0.00013809348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020091666,0.0010354375,0.0009907386,0.00046140503,0.00036882883,0.0007436898,0.0021180988,0.00107939,0.002154687],"category_scores_gemma":[0.007259306,0.00044876497,0.00050112605,0.00021553904,0.0012847541,0.0010223999,0.0017899104,0.0019249631,0.0005353183],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025067566,0.00023741371,0.0013947658,0.00012240665,0.00008638261,0.00015942928,0.00017521539,0.7725498,0.022326635,0.008993043,0.0013163588,0.1923878],"study_design_scores_gemma":[0.00001304688,0.000046986217,0.0000721326,0.000005744365,0.000005191923,0.00001945771,0.000005385422,0.9950723,0.0016976094,0.0028000087,0.00025479653,0.0000073059027],"about_ca_topic_score_codex":0.0018757802,"about_ca_topic_score_gemma":0.0021271978,"teacher_disagreement_score":0.002154687,"about_ca_system_score_codex":0.0006092439,"about_ca_system_score_gemma":0.0010582499,"threshold_uncertainty_score":0.010625601},"labels":[],"label_agreement":null},{"id":"W4417041447","doi":"10.48550/arxiv.2504.19989","title":"HJRNO: Hamilton-Jacobi Reachability with Neural Operators","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Reachability; Generalization; Obstacle; Artificial neural network; Inference; Hybrid system","score_opus":0.025921894542206735,"score_gpt":0.2802650962406976,"score_spread":0.2543432016984909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417041447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008287037,0.00011329422,0.98766685,0.00019901038,0.00003777009,0.000038104532,0.00008975183,0.0006642352,0.002903992],"genre_scores_gemma":[0.6939654,0.00030244727,0.29733732,0.0003654789,0.00008854781,0.00033972002,0.0004322063,0.0005016426,0.006667253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943894,0.00015444618,0.000028149172,0.00011922856,0.00018910677,0.00007011913],"domain_scores_gemma":[0.9987753,0.0007655381,0.00013621947,0.00013258259,0.00012128129,0.00006907435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011768473,0.0010346816,0.00080311834,0.0005532267,0.00046984476,0.00073367596,0.0016715976,0.0012144541,0.002973415],"category_scores_gemma":[0.0038395205,0.0004516181,0.0010563738,0.000328014,0.0017678274,0.0013491645,0.0026268347,0.0024596353,0.00048403358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051269995,0.000021821543,0.00027421542,0.000065811444,0.000026002472,0.00005288467,0.000041311636,0.9157019,0.0018011043,0.06352658,0.0011639189,0.01727304],"study_design_scores_gemma":[0.0000030748968,0.000008631923,0.000015477624,0.0000033746537,0.0000019036196,0.000004742632,0.0000020448663,0.9775569,0.0002502633,0.021960715,0.00019001428,0.0000028699562],"about_ca_topic_score_codex":0.0056782993,"about_ca_topic_score_gemma":0.0071827318,"teacher_disagreement_score":0.0056782993,"about_ca_system_score_codex":0.0011077652,"about_ca_system_score_gemma":0.0019922242,"threshold_uncertainty_score":0.011290491},"labels":[],"label_agreement":null},{"id":"W4417064347","doi":"10.48550/arxiv.2509.08089","title":"Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Royal Bank of Canada","keywords":"Backdoor; Adversary; Set (abstract data type); Inference; Federated learning; Adversarial system","score_opus":0.03834422189501694,"score_gpt":0.2868456534938301,"score_spread":0.2485014315988132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417064347","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061068404,0.00044345748,0.9898915,0.0014217255,0.000054596032,0.00006633538,0.00007115166,0.00067718374,0.0012671641],"genre_scores_gemma":[0.6616931,0.0011862563,0.3293118,0.0023679452,0.00036303714,0.000681272,0.00036493177,0.0004161757,0.003615528],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98078406,0.00853767,0.000997363,0.0035857712,0.004632659,0.0014624853],"domain_scores_gemma":[0.9508151,0.028359221,0.0030558985,0.014429056,0.0023606655,0.0009800742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021134788,0.0020316308,0.0025300172,0.0031331566,0.0025423297,0.006156171,0.0056491573,0.005423426,0.0024243526],"category_scores_gemma":[0.055126272,0.0016977588,0.002951218,0.0027210733,0.012352018,0.013444893,0.012902198,0.012662547,0.00066958845],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028319628,0.00014701797,0.0028260828,0.0002399316,0.00022264203,0.00016825325,0.0005313015,0.35028243,0.0013666184,0.569964,0.0053197225,0.06864881],"study_design_scores_gemma":[0.00003087312,0.000072448514,0.00014489806,0.000053923846,0.00002472204,0.00007678039,0.00003994159,0.66065806,0.0010511077,0.33580336,0.0020129608,0.000030958923],"about_ca_topic_score_codex":0.003344126,"about_ca_topic_score_gemma":0.0024511348,"teacher_disagreement_score":0.021134788,"about_ca_system_score_codex":0.004738966,"about_ca_system_score_gemma":0.005527663,"threshold_uncertainty_score":0.111772835},"labels":[],"label_agreement":null},{"id":"W4417064600","doi":"10.1145/3773028","title":"Securing Large Language Models: A Survey of Watermarking and Fingerprinting Techniques","year":2025,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Digital watermarking; Fingerprint (computing); Intellectual property; Sophistication; Ingenuity; Adversarial system; Digital Watermarking Alliance; Security token","score_opus":0.04331983216852198,"score_gpt":0.3479031577032387,"score_spread":0.30458332553471673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417064600","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023909044,0.9073457,0.07648014,0.0022848179,0.0004864611,0.000082558516,0.000106643,0.0003232793,0.010499552],"genre_scores_gemma":[0.03480951,0.9263493,0.032140862,0.0009020474,0.0010997809,0.00009142262,0.00022855295,0.00008396271,0.0042945165],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865794,0.000273236,0.00013709236,0.00023303276,0.0006101538,0.00008846318],"domain_scores_gemma":[0.9939307,0.004350196,0.00035043887,0.0005944113,0.0006944231,0.00007973655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002290684,0.0012575226,0.00132693,0.0026981093,0.00044309572,0.0018092662,0.001443419,0.0015819082,0.0032256255],"category_scores_gemma":[0.0075356783,0.00073081703,0.00085330335,0.0025833654,0.001273181,0.00494688,0.0013635831,0.002164976,0.0025002037],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047689915,0.00006634565,0.0005821557,0.003395283,0.00008094144,0.00011375571,0.00011665876,0.005196326,0.0018114297,0.029184686,0.011225167,0.94817966],"study_design_scores_gemma":[0.000044215976,0.00059938367,0.00251279,0.007014532,0.0003035989,0.004623229,0.00045044845,0.04756039,0.015448474,0.078303084,0.84293854,0.00020136578],"about_ca_topic_score_codex":0.00088525855,"about_ca_topic_score_gemma":0.00065616076,"teacher_disagreement_score":0.0032256255,"about_ca_system_score_codex":0.00077050825,"about_ca_system_score_gemma":0.0012720847,"threshold_uncertainty_score":0.012114406},"labels":[],"label_agreement":null},{"id":"W4417101019","doi":"10.70777/si.v2i4.16671","title":"International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management","year":2025,"lang":"","type":"article","venue":"SuperIntelligence - Robotics - Safety & Alignment","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Key (lock); Risk management; SAFER; Risk assessment; Safeguard; Corporate governance; Risk governance","score_opus":0.009785592565935903,"score_gpt":0.2868525432628055,"score_spread":0.2770669506968696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417101019","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014514569,0.032630216,0.03177185,0.25315997,0.17220804,0.0024871177,0.031417985,0.010219905,0.46465346],"genre_scores_gemma":[0.028641703,0.051721826,0.048739336,0.12351611,0.048373133,0.004863664,0.079452865,0.005034108,0.6096572],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.96212584,0.00398751,0.0028145872,0.0012918757,0.026187174,0.0035930243],"domain_scores_gemma":[0.890845,0.009949615,0.0047399374,0.0038734472,0.086443566,0.0041484316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026029127,0.0031307242,0.0022873092,0.0068098214,0.0032296297,0.0140802385,0.007126012,0.016672004,0.0630223],"category_scores_gemma":[0.06618345,0.0012455543,0.0023641584,0.003268813,0.0028243032,0.008087251,0.0049198135,0.012269675,0.075797446],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002937623,0.000026359166,0.00012680134,0.00025422874,0.000010709362,0.000037554786,0.000040995474,0.00023366103,0.00019446385,0.0033888747,0.9713866,0.02427045],"study_design_scores_gemma":[0.000009011786,0.000025676201,0.00026669825,0.00037784647,0.000011457741,0.00003520956,0.000044593475,0.00012461189,0.00023290225,0.0012306479,0.99761814,0.000023013696],"about_ca_topic_score_codex":0.065508336,"about_ca_topic_score_gemma":0.045308802,"teacher_disagreement_score":0.065508336,"about_ca_system_score_codex":0.013071843,"about_ca_system_score_gemma":0.039274633,"threshold_uncertainty_score":0.21083057},"labels":[],"label_agreement":null},{"id":"W4417107654","doi":"10.6000/1929-6029.2025.14.71","title":"Adversarial Machine Learning in Healthcare: Risks to AI-Driven Diagnostics and Treatment Plans","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Adversarial machine learning; Smoothing; Software deployment; Deep learning; Randomized experiment; Resilience (materials science)","score_opus":0.06891208228734991,"score_gpt":0.47407467883792326,"score_spread":0.40516259655057335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417107654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15379818,0.002054193,0.8157909,0.015274836,0.000337057,0.00019307474,0.000278187,0.0010456211,0.011227928],"genre_scores_gemma":[0.9590568,0.00046940343,0.03850032,0.00088446686,0.000077586,0.00006697798,0.00008063461,0.00005150815,0.00081230106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942947,0.0034237632,0.00020631733,0.0005149863,0.0011929526,0.00036719866],"domain_scores_gemma":[0.9740538,0.018823927,0.0018930222,0.003546446,0.0012747167,0.00040805625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009925335,0.0007107909,0.0005039808,0.0004624433,0.00048106583,0.0016370245,0.00097102777,0.0012709209,0.0015127568],"category_scores_gemma":[0.036005154,0.00032037188,0.0004320996,0.00028585023,0.002397001,0.002125584,0.0020748144,0.002798842,0.00034263774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005574289,0.00014519993,0.015111279,0.00023212489,0.00020819965,0.0003198314,0.0003848797,0.72512484,0.010307698,0.09872782,0.004607536,0.14427324],"study_design_scores_gemma":[0.000030103014,0.00029369903,0.0029073432,0.00012501913,0.000037629557,0.0002695779,0.00013109004,0.9110894,0.009732333,0.06961903,0.005719761,0.00004492712],"about_ca_topic_score_codex":0.0016377947,"about_ca_topic_score_gemma":0.0012281652,"teacher_disagreement_score":0.009925335,"about_ca_system_score_codex":0.0012881125,"about_ca_system_score_gemma":0.0016951164,"threshold_uncertainty_score":0.05249083},"labels":[],"label_agreement":null},{"id":"W4417124880","doi":"10.1016/j.eswa.2025.130721","title":"A robust dual-pronged proactive defense framework against deepfakes via adversarial semi-fragile watermarking","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Social Science Fund Youth Project; National Natural Science Foundation of China","keywords":"Adversarial system; Robustness (evolution); Lossy compression; Digital watermarking; Identification (biology); Focus (optics); Generator (circuit theory); Backdoor","score_opus":0.01282736345966247,"score_gpt":0.2520024130151556,"score_spread":0.23917504955549312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417124880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073378333,0.00033973067,0.98962796,0.0001630959,0.00008709339,0.000027152337,0.000025996931,0.0006982301,0.0016929526],"genre_scores_gemma":[0.75913155,0.0003804518,0.23055035,0.000407145,0.00014330064,0.00008688241,0.000095669246,0.00012350154,0.009081057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994081,0.00008547704,0.000026199725,0.0001296892,0.00023960516,0.000110861154],"domain_scores_gemma":[0.99942434,0.00021891974,0.00007344314,0.00012830374,0.000120683886,0.0000343714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090406724,0.0010164769,0.00088302227,0.00045503103,0.0004262477,0.0009859045,0.0013927772,0.0019255988,0.0027314813],"category_scores_gemma":[0.0017103635,0.0003631337,0.00060790207,0.0002536473,0.00087589823,0.0017719867,0.0022572714,0.0019536447,0.00096525496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049795554,0.00013837685,0.00049191766,0.00026538965,0.00014258001,0.00037444025,0.00014110892,0.4400148,0.10933378,0.08885593,0.0058192005,0.3539244],"study_design_scores_gemma":[0.000011774083,0.00009253918,0.00006742836,0.000015318767,0.000017818376,0.000111941124,0.000009601684,0.97617537,0.010142827,0.011895418,0.0014424017,0.000017465774],"about_ca_topic_score_codex":0.0005566213,"about_ca_topic_score_gemma":0.0010254312,"teacher_disagreement_score":0.0027314813,"about_ca_system_score_codex":0.00035153513,"about_ca_system_score_gemma":0.0007993421,"threshold_uncertainty_score":0.00913769},"labels":[],"label_agreement":null},{"id":"W4417266690","doi":"10.48550/arxiv.2505.16789","title":"Accidental Vulnerability: Factors in Fine-Tuning that Shift Model Safeguards","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; University of Toronto; Government of Canada; Canadian Institute for Advanced Research","keywords":"Adversarial system; Interpretability; Robustness (evolution); Vulnerability (computing); Threat model; Key (lock); Accidental","score_opus":0.07862829881679946,"score_gpt":0.23378055315652552,"score_spread":0.15515225433972607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417266690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38246125,0.0011761865,0.5990185,0.0035645852,0.00031592872,0.00032568653,0.001448825,0.004980023,0.006709027],"genre_scores_gemma":[0.954893,0.00014825002,0.04199537,0.00066274294,0.00004954788,0.00014715665,0.00080617506,0.00050085556,0.000796901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98775,0.00671953,0.0007995568,0.00222999,0.001846075,0.00065491814],"domain_scores_gemma":[0.9426637,0.026950903,0.0029550386,0.025657348,0.00123748,0.0005355547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013187752,0.0010028497,0.0008032068,0.000783154,0.0010648851,0.0021503486,0.0016025474,0.0015044041,0.0020717336],"category_scores_gemma":[0.08404984,0.0005278793,0.00095196225,0.00077882933,0.0024025491,0.0040375562,0.0036655208,0.004539492,0.0009060468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012270021,0.00048668703,0.07244522,0.0005919759,0.0008861911,0.0010749744,0.0018080935,0.5980984,0.040629268,0.08515937,0.015078685,0.18251415],"study_design_scores_gemma":[0.00007996099,0.0004291785,0.0075136996,0.00016219201,0.000113908114,0.0009847679,0.00045350564,0.80149734,0.030843247,0.14874339,0.009080184,0.000098676144],"about_ca_topic_score_codex":0.0013733009,"about_ca_topic_score_gemma":0.0019584864,"teacher_disagreement_score":0.013187752,"about_ca_system_score_codex":0.0010753205,"about_ca_system_score_gemma":0.0012027296,"threshold_uncertainty_score":0.06974435},"labels":[],"label_agreement":null},{"id":"W4417465483","doi":"10.48550/arxiv.2512.14000","title":"On the Hardness of Conditional Independence Testing In Practice","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Conditional independence; Independence (probability theory); Test (biology); Measure (data warehouse); Covariance; Conditional variance","score_opus":0.0728923376602707,"score_gpt":0.333310105440735,"score_spread":0.2604177677804643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417465483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041596282,0.002707895,0.9111813,0.022033922,0.00044013732,0.00027042837,0.0008845652,0.00076853234,0.020116918],"genre_scores_gemma":[0.77520126,0.0018431335,0.20867406,0.005677662,0.0019645144,0.0013673147,0.001220622,0.0006560479,0.003395503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9186925,0.054143377,0.003074648,0.0109455185,0.011096849,0.0020470012],"domain_scores_gemma":[0.32625502,0.62123054,0.0070327967,0.03665508,0.0066483244,0.002178236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09697803,0.0015879347,0.0042067477,0.002469929,0.002996106,0.0058755293,0.005259435,0.0055634766,0.0076838653],"category_scores_gemma":[0.45022884,0.0016548242,0.002030503,0.0024623687,0.021109775,0.015863352,0.009843901,0.012464659,0.0015433009],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005222734,0.00013119883,0.008740776,0.00048636686,0.00029458568,0.00061747455,0.0010437367,0.07508664,0.0007819889,0.85119075,0.010413199,0.05069113],"study_design_scores_gemma":[0.00006842235,0.000054566764,0.0006356827,0.00009858618,0.000016023365,0.00015120835,0.000060867,0.06433031,0.0003445711,0.932446,0.0017653325,0.000028419772],"about_ca_topic_score_codex":0.0025047876,"about_ca_topic_score_gemma":0.0012567479,"teacher_disagreement_score":0.09697803,"about_ca_system_score_codex":0.0034001397,"about_ca_system_score_gemma":0.003929076,"threshold_uncertainty_score":0.51287496},"labels":[],"label_agreement":null},{"id":"W4417506422","doi":"10.1001/jamanetworkopen.2025.49963","title":"Vulnerability of Large Language Models to Prompt Injection When Providing Medical Advice","year":2025,"lang":"en","type":"article","venue":"JAMA Network Open","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Vulnerability (computing); Adversarial system; Vulnerability assessment; Robustness (evolution); Quality (philosophy); Medical advice; Risk assessment; Language model","score_opus":0.0176346959177542,"score_gpt":0.32562449533782106,"score_spread":0.3079897994200669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417506422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90923434,0.00056064845,0.080094725,0.0014568433,0.000116594434,0.001208209,0.00037942504,0.00388368,0.00306541],"genre_scores_gemma":[0.9537217,0.00018222806,0.043521106,0.0006364313,0.000024856094,0.00044278338,0.00035405532,0.00021449957,0.0009022668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9866029,0.008952836,0.0007886497,0.00091018213,0.002285475,0.00045989425],"domain_scores_gemma":[0.87555486,0.10182678,0.008036864,0.009343499,0.0038278748,0.0014101119],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.015340976,0.00096135354,0.00056842196,0.0004577992,0.00044087306,0.0020223344,0.0019260288,0.0017232726,0.002227406],"category_scores_gemma":[0.10359992,0.00067016686,0.00078726595,0.00023533721,0.0013866158,0.0022401891,0.0020101853,0.001986613,0.00057496945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017943688,0.0067679673,0.14480859,0.0042629256,0.0015904449,0.0020759602,0.014442258,0.3869715,0.0974763,0.014000713,0.01021847,0.2994412],"study_design_scores_gemma":[0.0013022823,0.00955251,0.017322287,0.000599265,0.0008841515,0.0009457694,0.0014546836,0.90117776,0.04022541,0.0104095135,0.015805343,0.00032093353],"about_ca_topic_score_codex":0.0034551222,"about_ca_topic_score_gemma":0.0027135308,"teacher_disagreement_score":0.984659,"about_ca_system_score_codex":0.0013127064,"about_ca_system_score_gemma":0.0023396316,"threshold_uncertainty_score":0.08113176},"labels":[],"label_agreement":null},{"id":"W6891502772","doi":"10.3932/ethz-a-001236014","title":"Montréal, Collégiale Notre-Dame, Chor von Südwesten","year":2001,"lang":"de","type":"other","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Relation (database); Product (mathematics); Term (time)","score_opus":0.011739710059711006,"score_gpt":0.24406403958053663,"score_spread":0.23232432952082563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891502772","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006538842,0.039093167,0.022529379,0.01333953,0.0038914173,0.00011368619,0.00913347,0.0033460532,0.90201443],"genre_scores_gemma":[0.01103283,0.007959282,0.005303815,0.00021722403,0.00021896051,0.000039072085,0.0010624921,0.0005024295,0.973664],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99925953,0.00006894932,0.000014584319,0.0002919143,0.00024987,0.0001151722],"domain_scores_gemma":[0.99897194,0.00027157506,0.00004725023,0.00012106375,0.00040613607,0.00018210012],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007408504,0.0016219134,0.0009707009,0.001490114,0.0027315381,0.0040433393,0.0012995648,0.0012646646,0.37383774],"category_scores_gemma":[0.0019394913,0.0006205639,0.0005096736,0.0023341752,0.0009877166,0.0016363513,0.0012828173,0.0019567916,0.10817202],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016512004,0.000055967288,0.0005932565,0.00020177865,0.000029139499,0.00025549548,0.00015013116,0.003689031,0.001646904,0.056088053,0.70815855,0.2289666],"study_design_scores_gemma":[0.000016857035,0.000016803075,0.0014712703,0.00008805886,0.000009140466,0.00007848964,0.0000841919,0.0013429243,0.0011293193,0.0050077625,0.9907347,0.000020593761],"about_ca_topic_score_codex":0.26301882,"about_ca_topic_score_gemma":0.48880753,"teacher_disagreement_score":0.73698115,"about_ca_system_score_codex":0.0058156517,"about_ca_system_score_gemma":0.0051306295,"threshold_uncertainty_score":0.8931445},"labels":[],"label_agreement":null},{"id":"W6892591105","doi":"10.5281/zenodo.10566526","title":"FIGURE 12 in A problematic species complex for Lasioglossum subgeneric diagnostics in North America (Hymenoptera: Halictidae)","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Species complex; Scale (ratio); Taxonomy (biology); Ecological succession","score_opus":0.038808998182407774,"score_gpt":0.2559297622291591,"score_spread":0.21712076404675132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892591105","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55071115,0.00883841,0.031341475,0.00613322,0.0024137625,0.0013696005,0.029300112,0.0037702182,0.36612207],"genre_scores_gemma":[0.9157185,0.0012776932,0.027586628,0.0014538202,0.00016899884,0.00050797977,0.01356856,0.00042608115,0.039291695],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962366,0.000048984053,0.000024294859,0.0001278628,0.000097701115,0.000077409],"domain_scores_gemma":[0.9995993,0.0000590262,0.00010773809,0.00004307479,0.00012628744,0.00006462575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036316662,0.0005059562,0.00026793333,0.0023514647,0.0026127396,0.00096606417,0.00071442995,0.00076430436,0.036847696],"category_scores_gemma":[0.0009857649,0.00019614748,0.00026049447,0.0017661202,0.0010184563,0.0008416297,0.0011285681,0.000682538,0.0070685893],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006117574,0.00014225785,0.23016793,0.0009850141,0.00014946164,0.005267707,0.006580526,0.0008153876,0.023986468,0.015951404,0.31521672,0.40012532],"study_design_scores_gemma":[0.000050731214,0.0000975757,0.3684429,0.0006119928,0.00019563093,0.012852173,0.008149234,0.0015727509,0.0029493356,0.0065853805,0.59842443,0.00006777932],"about_ca_topic_score_codex":0.016182704,"about_ca_topic_score_gemma":0.07196371,"teacher_disagreement_score":0.036847696,"about_ca_system_score_codex":0.0007112141,"about_ca_system_score_gemma":0.00072509487,"threshold_uncertainty_score":0.12326777},"labels":[],"label_agreement":null},{"id":"W6892669129","doi":"10.5281/zenodo.11722191","title":"Der untergang script german pdf","year":2024,"lang":"de","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"German; Battle; Nazism; Victory; Curse; Nazi Germany; World War II","score_opus":0.025455180963870533,"score_gpt":0.2702003406432473,"score_spread":0.2447451596793768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892669129","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003069782,0.0006337303,0.0021343532,0.0013703583,0.0035974008,0.00032676474,0.0118075395,0.007527205,0.97229564],"genre_scores_gemma":[0.0013694628,0.00047630287,0.0008511167,0.0008278766,0.0003932447,0.00012412111,0.004504875,0.0033357588,0.9881173],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992167,0.00006718723,0.00005225685,0.00011527177,0.00044167624,0.00010685855],"domain_scores_gemma":[0.99793565,0.00028929993,0.000079708014,0.00031714956,0.00105892,0.0003193445],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00077566114,0.0015347375,0.0010121776,0.0019452687,0.001596226,0.0066852886,0.0020519365,0.0020395156,0.9348601],"category_scores_gemma":[0.005167824,0.0009016477,0.00083756197,0.001941014,0.00066336786,0.006944167,0.0028913636,0.0022572866,0.9111216],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026389573,0.0000134184775,0.000021716665,0.00010619097,0.0000010490896,0.000036321777,0.000025679547,0.00003200141,0.00024040623,0.0012369406,0.9731993,0.02506061],"study_design_scores_gemma":[0.0000078216935,0.000009279354,0.00009917452,0.000048615006,0.0000012627692,0.000052730247,0.000037058457,0.000028004286,0.00025631787,0.0005085424,0.9989453,0.000005839153],"about_ca_topic_score_codex":0.0030166253,"about_ca_topic_score_gemma":0.004244599,"teacher_disagreement_score":0.06513989,"about_ca_system_score_codex":0.0017663336,"about_ca_system_score_gemma":0.0015127171,"threshold_uncertainty_score":0.092914045},"labels":[],"label_agreement":null},{"id":"W6893502067","doi":"10.5281/zenodo.15605217","title":"Performance Dataset for Hardware Model Checking on Btor2 Benchmarks (Technical Report, May 2025)","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Benchmark (surveying); Set (abstract data type); Construct (python library); Scripting language; Selection (genetic algorithm)","score_opus":0.039644193336415515,"score_gpt":0.29927446023680876,"score_spread":0.25963026690039326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893502067","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12048043,0.0033177764,0.026444444,0.0014977254,0.0008420408,0.00070072507,0.73138225,0.087304845,0.028029691],"genre_scores_gemma":[0.08996532,0.00036371395,0.01874037,0.00039779462,0.00006363961,0.0005128657,0.88332623,0.0030135913,0.003616575],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958728,0.0007741531,0.0003804266,0.00076514925,0.0017336049,0.00047387253],"domain_scores_gemma":[0.9942971,0.0015343582,0.00039165502,0.0016958469,0.0017983542,0.00028281263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00255982,0.003310438,0.001012273,0.0020389801,0.00078628457,0.0013406683,0.003573287,0.0016967342,0.010238547],"category_scores_gemma":[0.008978052,0.00059788843,0.0014846708,0.0033293874,0.00077186304,0.0017185909,0.0014043547,0.0021346533,0.008120517],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020687156,0.0007330661,0.0077800564,0.0021168976,0.00034686836,0.00025227846,0.000084290536,0.07135491,0.00881334,0.004345307,0.8573789,0.044725318],"study_design_scores_gemma":[0.002376878,0.0020234063,0.04029656,0.0005232808,0.0003402385,0.0007871446,0.00024255553,0.44976363,0.050026957,0.02167542,0.43164685,0.00029714414],"about_ca_topic_score_codex":0.014943715,"about_ca_topic_score_gemma":0.02208032,"teacher_disagreement_score":0.014943715,"about_ca_system_score_codex":0.0018397692,"about_ca_system_score_gemma":0.0025141684,"threshold_uncertainty_score":0.034251392},"labels":[],"label_agreement":null},{"id":"W6893889155","doi":"10.5281/zenodo.6464098","title":"FalsifAI: Falsification of AI-Enabled Hybrid Control Systems Guided by Time-Aware Coverage Criteria","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robustness (evolution); Artificial neural network; Hybrid system; Exploit; Context (archaeology); Control system; Controller (irrigation)","score_opus":0.01905003428521876,"score_gpt":0.2506281898350346,"score_spread":0.23157815554981584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893889155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04703277,0.0003598101,0.94599617,0.00044087754,0.00007782219,0.0001206472,0.00019551028,0.0014356296,0.0043407925],"genre_scores_gemma":[0.8993075,0.00023791181,0.0977923,0.0002384688,0.00005880239,0.0002206696,0.00032054857,0.00028419704,0.0015395903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967468,0.0008100116,0.00020924678,0.00058554707,0.0011876437,0.0004608228],"domain_scores_gemma":[0.98623925,0.009961108,0.0011007529,0.0010337273,0.0013210257,0.00034412558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031866615,0.0016280295,0.0010926161,0.0014152931,0.0007466577,0.0021451642,0.0016037624,0.0015370863,0.0031323764],"category_scores_gemma":[0.020685952,0.00051508006,0.0017545075,0.0004853214,0.0032213188,0.002143094,0.0027512514,0.0018148472,0.00029952437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043473096,0.000078400444,0.003990275,0.00036749808,0.00014371134,0.0009795595,0.00049879315,0.81745774,0.014345513,0.124317855,0.001479528,0.035906386],"study_design_scores_gemma":[0.000025083264,0.00007523935,0.0002097272,0.00004647579,0.000022659242,0.00009425841,0.00004047359,0.946556,0.004567338,0.047546264,0.0007964143,0.00002014805],"about_ca_topic_score_codex":0.003737598,"about_ca_topic_score_gemma":0.0025170613,"teacher_disagreement_score":0.003737598,"about_ca_system_score_codex":0.0016003882,"about_ca_system_score_gemma":0.0018164156,"threshold_uncertainty_score":0.016852856},"labels":[],"label_agreement":null},{"id":"W6902454079","doi":"10.6084/m9.figshare.c.7308065","title":"Will a lack of fabric durability be their downfall? Impact of textile durability on the efficacy of three types of dual-active-ingredient long-lasting insecticidal nets: a secondary analysis on malaria prevalence and incidence from a cluster-randomized trial in north-west Tanzania","year":2024,"lang":"en","type":"other","venue":"Figshare","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Malaria; Piperonyl butoxide; Incidence (geometry); Tanzania; Pyrethroid; Permethrin; Chlorfenapyr","score_opus":0.034348193451582965,"score_gpt":0.29708901465077575,"score_spread":0.2627408211991928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902454079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98564523,0.008586604,0.000803535,0.0010860149,0.00031596547,0.002222955,0.00036162708,0.000025053574,0.0009530482],"genre_scores_gemma":[0.9976211,0.0005124327,0.00071119954,0.00027106123,0.00006440989,0.00055842625,0.00005991948,0.0000026381524,0.00019878356],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9927423,0.0053847744,0.0006289954,0.00041939263,0.00043954948,0.00038497197],"domain_scores_gemma":[0.9890915,0.0050583645,0.0037788732,0.00057172717,0.0006873175,0.0008122597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014110236,0.0005407319,0.0013629373,0.00024325814,0.00033347076,0.00080230186,0.00067653693,0.0013376023,0.0034320136],"category_scores_gemma":[0.012239695,0.00032130236,0.0044357586,0.00031666478,0.0009622203,0.00078847964,0.00040252684,0.0013928516,0.00014520521],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.8201072,0.009610553,0.036683578,0.008790975,0.02194783,0.00017207494,0.0004431187,0.0021050656,0.0071206084,0.0006695466,0.0017575762,0.090591885],"study_design_scores_gemma":[0.31916258,0.47595263,0.16255176,0.0017000579,0.028055504,0.00016037897,0.000485354,0.003258977,0.0035121962,0.00087207975,0.00420219,0.00008623789],"about_ca_topic_score_codex":0.0017480919,"about_ca_topic_score_gemma":0.0033177037,"teacher_disagreement_score":0.014110236,"about_ca_system_score_codex":0.0010474579,"about_ca_system_score_gemma":0.0014309639,"threshold_uncertainty_score":0.07462299},"labels":[],"label_agreement":null},{"id":"W6912068948","doi":"10.5281/zenodo.15605218","title":"Performance Dataset for Hardware Model Checking on Btor2 Benchmarks (Technical Report, May 2025)","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Benchmark (surveying); Set (abstract data type); Construct (python library); Scripting language; Selection (genetic algorithm)","score_opus":0.039644193336415515,"score_gpt":0.29927446023680876,"score_spread":0.25963026690039326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912068948","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12048043,0.0033177764,0.026444444,0.0014977254,0.0008420408,0.00070072507,0.73138225,0.087304845,0.028029691],"genre_scores_gemma":[0.08996532,0.00036371395,0.01874037,0.00039779462,0.00006363961,0.0005128657,0.88332623,0.0030135913,0.003616575],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958728,0.0007741531,0.0003804266,0.00076514925,0.0017336049,0.00047387253],"domain_scores_gemma":[0.9942971,0.0015343582,0.00039165502,0.0016958469,0.0017983542,0.00028281263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00255982,0.003310438,0.001012273,0.0020389801,0.00078628457,0.0013406683,0.003573287,0.0016967342,0.010238547],"category_scores_gemma":[0.008978052,0.00059788843,0.0014846708,0.0033293874,0.00077186304,0.0017185909,0.0014043547,0.0021346533,0.008120517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020687156,0.0007330661,0.0077800564,0.0021168976,0.00034686836,0.00025227846,0.000084290536,0.07135491,0.00881334,0.004345307,0.8573789,0.044725318],"study_design_scores_gemma":[0.002376878,0.0020234063,0.04029656,0.0005232808,0.0003402385,0.0007871446,0.00024255553,0.44976363,0.050026957,0.02167542,0.43164685,0.00029714414],"about_ca_topic_score_codex":0.014943715,"about_ca_topic_score_gemma":0.02208032,"teacher_disagreement_score":0.014943715,"about_ca_system_score_codex":0.0018397692,"about_ca_system_score_gemma":0.0025141684,"threshold_uncertainty_score":0.034251392},"labels":[],"label_agreement":null},{"id":"W6912291614","doi":"10.5281/zenodo.15485472","title":"Reproduction Package for CAV 2025 Article `Btor2-Select: Machine Learning Based Algorithm Selection for Hardware Model Checking'","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artifact (error); Selection (genetic algorithm); Login; Set (abstract data type); Selection algorithm; Test data","score_opus":0.02541640956097876,"score_gpt":0.2612174019710267,"score_spread":0.23580099241004795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912291614","genre_codex":"software","genre_gemma":"other","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010956062,0.00021707542,0.28498,0.0010049783,0.0016622653,0.0005536075,0.036228232,0.6046115,0.06964675],"genre_scores_gemma":[0.024620026,0.00038670588,0.15142718,0.0015708888,0.0009885499,0.0015062623,0.105634496,0.52144,0.19242595],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996267,0.00058213813,0.00034424313,0.00057127967,0.001986106,0.00024923767],"domain_scores_gemma":[0.9858807,0.0056755585,0.0005312989,0.004081156,0.003494425,0.00033686144],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031693275,0.0021003175,0.0012711593,0.0023314862,0.00057301717,0.0032972659,0.003698394,0.0020889433,0.6008426],"category_scores_gemma":[0.028850185,0.001264961,0.0018268584,0.001796887,0.00094480853,0.0029044764,0.002931447,0.002514649,0.34505743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033298988,0.00007658445,0.0002984307,0.0004804337,0.000034551857,0.0001400182,0.000060882965,0.002258028,0.003341002,0.009511549,0.87844896,0.10501654],"study_design_scores_gemma":[0.00038155838,0.000234904,0.0012044244,0.00035766818,0.000033594144,0.0004141949,0.00003666081,0.02498258,0.015516314,0.015519227,0.9411996,0.00011918499],"about_ca_topic_score_codex":0.0020125122,"about_ca_topic_score_gemma":0.001975225,"teacher_disagreement_score":0.9968307,"about_ca_system_score_codex":0.0011214921,"about_ca_system_score_gemma":0.0016253509,"threshold_uncertainty_score":0.5693496},"labels":[],"label_agreement":null},{"id":"W6912806176","doi":"10.5281/zenodo.6583266","title":"Learning to Generate Inversion-Resistant Model Explanations","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Inversion (geology); Rendering (computer graphics); Artificial neural network; Adversary; Deep neural networks; Threat model; Minimax; Adversarial system","score_opus":0.03594158688028365,"score_gpt":0.25028783498633206,"score_spread":0.2143462481060484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912806176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08829303,0.00026667817,0.9058511,0.0011360622,0.00006715554,0.000099254,0.00016945503,0.0013896836,0.0027275842],"genre_scores_gemma":[0.94306207,0.00014825088,0.054428417,0.00036121445,0.00003195451,0.00009040969,0.00020002411,0.00011037188,0.00156728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985952,0.0005507242,0.0000524313,0.00028640404,0.00035966275,0.00015566604],"domain_scores_gemma":[0.99460214,0.0035183,0.00055002596,0.0010202888,0.00020143004,0.00010781321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019928196,0.0010260103,0.00063062797,0.0005141069,0.00032259116,0.0009410846,0.0011912725,0.001609518,0.002134763],"category_scores_gemma":[0.01280816,0.00044081968,0.00094100035,0.00023424791,0.0018711941,0.0020329761,0.0027280813,0.00287804,0.00037499523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051989674,0.00012740155,0.0037814581,0.00019063143,0.00016973296,0.0005490361,0.00040970746,0.74490285,0.02383666,0.13845772,0.0033063907,0.0837485],"study_design_scores_gemma":[0.000020911519,0.000070269925,0.00016914173,0.00002009101,0.000016780603,0.00010026833,0.000022662554,0.94360715,0.006683733,0.048457507,0.0008183284,0.000013205242],"about_ca_topic_score_codex":0.0006337059,"about_ca_topic_score_gemma":0.00080369867,"teacher_disagreement_score":0.002134763,"about_ca_system_score_codex":0.00091704953,"about_ca_system_score_gemma":0.0009018132,"threshold_uncertainty_score":0.010539174},"labels":[],"label_agreement":null},{"id":"W6950499523","doi":"10.5281/zenodo.8206635","title":"Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vulnerability (computing); Deep learning; Scripting language; Field (mathematics); Pattern recognition (psychology)","score_opus":0.02343312981353438,"score_gpt":0.26780551819723336,"score_spread":0.24437238838369899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6950499523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6999618,0.004017802,0.087733805,0.0045188745,0.0014389611,0.0008551534,0.1443551,0.040931102,0.016187381],"genre_scores_gemma":[0.71137714,0.000731719,0.057364173,0.00083044637,0.00011215133,0.00033454623,0.22428465,0.001384177,0.0035810117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952651,0.0015091597,0.00035985396,0.0010928987,0.0013959616,0.00037696803],"domain_scores_gemma":[0.9895621,0.004702753,0.00067247415,0.0033075346,0.0014420557,0.0003130679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054331105,0.001394146,0.000748754,0.0017520773,0.000596528,0.0016020112,0.0020141331,0.0013701062,0.0027547954],"category_scores_gemma":[0.021722218,0.00045452235,0.0009766895,0.001394146,0.0011618459,0.0019543727,0.001753015,0.0020765213,0.0017723704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024624346,0.0013057292,0.0376191,0.0014714361,0.000870927,0.00067583215,0.0002587037,0.38308373,0.014354601,0.008740282,0.37237218,0.17678508],"study_design_scores_gemma":[0.00034259088,0.00064281444,0.022554982,0.00030127462,0.00012331366,0.0007806201,0.00023547586,0.87963235,0.036719985,0.014440254,0.044081848,0.00014453003],"about_ca_topic_score_codex":0.007397319,"about_ca_topic_score_gemma":0.007894871,"teacher_disagreement_score":0.007397319,"about_ca_system_score_codex":0.0017788403,"about_ca_system_score_gemma":0.0014168398,"threshold_uncertainty_score":0.028733373},"labels":[],"label_agreement":null},{"id":"W6958238404","doi":"10.60692/en3ds-q2b29","title":"Challenges of design, implementation, acceptability, and potential for, biomedical technologies in the Peruvian Amazon","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Medical Research Council","keywords":"Indigenous; Amazon rainforest; Psychological intervention; Biomedical technology; Inclusion (mineral); Emerging technologies; Digital health; Health technology; Enabling","score_opus":0.042797075548114426,"score_gpt":0.2734588667930057,"score_spread":0.23066179124489125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958238404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9190898,0.003958101,0.015976632,0.03767561,0.00010862674,0.00068656896,0.00010942673,0.00016286646,0.022232432],"genre_scores_gemma":[0.9889782,0.0015666517,0.0058839717,0.001593446,0.000026154625,0.00058217667,0.000033002438,0.00002163294,0.0013148205],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9726452,0.022894144,0.0007835431,0.0007271466,0.0017235006,0.0012264019],"domain_scores_gemma":[0.9648129,0.02637874,0.0020388837,0.0014536728,0.0038904687,0.0014254039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025009407,0.00030585882,0.00030735778,0.00064244115,0.0044428124,0.0050623706,0.0018706333,0.0017415208,0.0024725702],"category_scores_gemma":[0.04831735,0.00038321578,0.00033198306,0.0008205352,0.004729967,0.0033996613,0.0040792865,0.0012846726,0.0003019523],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014629899,0.00033756232,0.04642554,0.0027849702,0.00007327417,0.004776076,0.77575254,0.00067250384,0.010053195,0.016503472,0.004140081,0.13833447],"study_design_scores_gemma":[0.00005335673,0.00085114536,0.037126243,0.0022661574,0.000094754694,0.002371234,0.82794523,0.0014090127,0.0023241662,0.008430354,0.117040224,0.00008807229],"about_ca_topic_score_codex":0.010382948,"about_ca_topic_score_gemma":0.010465623,"teacher_disagreement_score":0.025009407,"about_ca_system_score_codex":0.00394001,"about_ca_system_score_gemma":0.0101505285,"threshold_uncertainty_score":0.13226396},"labels":[],"label_agreement":null},{"id":"W6958547972","doi":"10.6084/m9.figshare.28059540.v1","title":"Additional file 3 of Sex differences in mitochondrial gene expression during viral myocarditis","year":2024,"lang":"en","type":"dataset","venue":"Figshare","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre hospitalier universitaire de Québec","funders":"","keywords":"Gene expression; Expression (computer science); Mitochondrial DNA; Gene; Myocarditis; Viral Myocarditis; Differential (mechanical device); Transcriptome","score_opus":0.015468537812409103,"score_gpt":0.23754548932288888,"score_spread":0.2220769515104798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958547972","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012593699,0.000017244593,0.000084376756,0.000028930253,0.000014546267,0.00001217457,0.99934405,0.00017217404,0.0002006207],"genre_scores_gemma":[0.002123115,0.00004068261,0.0007201991,0.000133764,0.000023409677,0.00026580793,0.994669,0.00024009096,0.0017838142],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99929893,0.000091356014,0.000075327145,0.0002684785,0.00014309624,0.00012280604],"domain_scores_gemma":[0.995589,0.0027542831,0.00029820236,0.0005626341,0.0005788582,0.00021699358],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011229941,0.001732054,0.0015494019,0.0015224853,0.000843657,0.002062719,0.0022951826,0.0018910384,0.55062217],"category_scores_gemma":[0.009244343,0.0005322727,0.0014011232,0.002498657,0.0004231026,0.0011597024,0.0011910843,0.00132407,0.15553972],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018891583,0.00006076927,0.0027071682,0.0011749772,0.000060550195,0.00004363451,0.000028637614,0.00052296714,0.0002127405,0.0002906526,0.9909388,0.0037702287],"study_design_scores_gemma":[0.0029532332,0.00025211042,0.029684396,0.0014847425,0.0002560525,0.0005781893,0.000257361,0.0025481198,0.001790096,0.008460789,0.9515852,0.00014967548],"about_ca_topic_score_codex":0.007866518,"about_ca_topic_score_gemma":0.019340051,"teacher_disagreement_score":0.55062217,"about_ca_system_score_codex":0.001039838,"about_ca_system_score_gemma":0.001460808,"threshold_uncertainty_score":0.6409829},"labels":[],"label_agreement":null},{"id":"W6967972416","doi":"10.5281/zenodo.12411408","title":"ibid book pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Style (visual arts); Citation; Download; Biography; Economic Justice; Interpretation (philosophy)","score_opus":0.020349671605222903,"score_gpt":0.2502593524597456,"score_spread":0.2299096808545227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967972416","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014401984,0.00063834625,0.0005960136,0.00057378993,0.0016064231,0.00009454793,0.0023990627,0.0019345089,0.9920133],"genre_scores_gemma":[0.00045487087,0.000514224,0.0003648504,0.00038727996,0.0002721515,0.000026387426,0.0014802605,0.0008273736,0.9956727],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993051,0.000035873123,0.000028490213,0.00010584268,0.00045905728,0.00006552264],"domain_scores_gemma":[0.9984282,0.00016538588,0.000050457358,0.00016738968,0.00085388304,0.0003347051],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00041533203,0.0013300412,0.0011481122,0.002541359,0.0018733009,0.008584016,0.0018702522,0.002357743,0.9284003],"category_scores_gemma":[0.0027471373,0.0007944544,0.0010551764,0.002532695,0.0006208935,0.00572897,0.0025273955,0.0027777029,0.92419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016596245,0.000023647799,0.000028926235,0.00011624834,0.0000016541312,0.0000401791,0.000024129255,0.000032426695,0.00030288528,0.001623073,0.95820475,0.039585628],"study_design_scores_gemma":[0.000004330137,0.00000786519,0.000111448666,0.00004985771,0.0000013436699,0.000049268114,0.000030946554,0.000018519042,0.00011781781,0.0004124726,0.9991911,0.0000049758696],"about_ca_topic_score_codex":0.003276578,"about_ca_topic_score_gemma":0.006630608,"teacher_disagreement_score":0.07159972,"about_ca_system_score_codex":0.0014575592,"about_ca_system_score_gemma":0.0015108961,"threshold_uncertainty_score":0.10212821},"labels":[],"label_agreement":null},{"id":"W6977186968","doi":"10.6084/m9.figshare.25656300.v1","title":"Supplementary Material for: Longitudinal feasibility of the Montreal Cognitive Assessment (MoCA) in non-demented ALS patients","year":2024,"lang":"en","type":"dataset","venue":"Figshare","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Montreal Cognitive Assessment; Amyotrophic lateral sclerosis; Cohort; Cognition; Confidence interval; Cohort study; Confounding; Longitudinal study","score_opus":0.034524897066601355,"score_gpt":0.33776157437196186,"score_spread":0.3032366773053605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977186968","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034944206,0.0003501721,0.0017016523,0.0013116417,0.00063412206,0.00047400114,0.975473,0.0010087424,0.015552286],"genre_scores_gemma":[0.02894528,0.0010508659,0.011273639,0.0016911185,0.00079959,0.0028348723,0.89180696,0.0009670188,0.060630657],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99937755,0.00015371303,0.00012740122,0.0001407105,0.00013844842,0.00006224016],"domain_scores_gemma":[0.98950547,0.006235384,0.0006826434,0.00062242587,0.0023023246,0.0006517706],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016818118,0.00130184,0.001072271,0.0030764516,0.0009200931,0.0017779777,0.00179723,0.0013935978,0.7551874],"category_scores_gemma":[0.022054391,0.0004936429,0.00066978246,0.0033417125,0.00019137254,0.0012831893,0.0011233418,0.0007210813,0.24069324],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005484139,0.00016184668,0.004484473,0.0010992524,0.000043978944,0.00020145967,0.00006458127,0.0002111094,0.00016849658,0.0005796552,0.94981045,0.042626366],"study_design_scores_gemma":[0.0047531356,0.0013692778,0.14681065,0.005413902,0.0002829329,0.003516504,0.0006772711,0.0036724552,0.0020746207,0.017387876,0.8137356,0.00030590242],"about_ca_topic_score_codex":0.01022268,"about_ca_topic_score_gemma":0.0155384615,"teacher_disagreement_score":0.7551874,"about_ca_system_score_codex":0.0009667418,"about_ca_system_score_gemma":0.0015372788,"threshold_uncertainty_score":0.34919542},"labels":[],"label_agreement":null},{"id":"W6979234844","doi":"","title":"Stochastic Weight Sharing for Bayesian Neural Networks","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"H2020 Marie Skłodowska-Curie Actions; HORIZON EUROPE Framework Programme; European Commission","keywords":"Artificial neural network; Bayesian probability; Leverage (statistics); Inference; Gaussian process; Bayesian inference; Probabilistic logic; Bayesian network; Gaussian; Deep neural networks","score_opus":0.019261329382488,"score_gpt":0.2774973388431143,"score_spread":0.2582360094606263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979234844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055387663,0.00023258012,0.99277073,0.00021231602,0.000021886906,0.000019720555,0.00006545025,0.00016982107,0.0009686579],"genre_scores_gemma":[0.6963884,0.0010247511,0.29808113,0.00031330137,0.00017431546,0.00027890445,0.0004031503,0.00018978205,0.0031462514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983026,0.000682672,0.00009290509,0.00026044121,0.000559018,0.00010231798],"domain_scores_gemma":[0.9969657,0.0018112467,0.000287763,0.0005391868,0.00029101514,0.00010512535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036536527,0.00096606964,0.0009806873,0.0008828234,0.0005255087,0.0012696256,0.0018959055,0.0011040932,0.0023493716],"category_scores_gemma":[0.0136214,0.00061880157,0.0006686096,0.0008362485,0.001807819,0.003389951,0.0024029366,0.002452034,0.00045111365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006545527,0.00002704099,0.00036241947,0.00007309957,0.00003910802,0.00003278563,0.00005370557,0.78773564,0.0015075373,0.14964633,0.0011422773,0.059314582],"study_design_scores_gemma":[0.000005043553,0.000010776471,0.000051937983,0.000010099132,0.0000037443199,0.000010993441,0.000003591494,0.91416407,0.00043433424,0.08484626,0.00045333029,0.000005863017],"about_ca_topic_score_codex":0.0029142727,"about_ca_topic_score_gemma":0.0032380417,"teacher_disagreement_score":0.0036536527,"about_ca_system_score_codex":0.0016358607,"about_ca_system_score_gemma":0.0012505846,"threshold_uncertainty_score":0.019322574},"labels":[],"label_agreement":null},{"id":"W6979244033","doi":"","title":"Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Robustness (evolution); Software deployment; Adversarial system; Modular design; Threat model; Vulnerability (computing); Vulnerability assessment","score_opus":0.018275533358157795,"score_gpt":0.2967100467162475,"score_spread":0.2784345133580897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979244033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8478556,0.0016207382,0.12268393,0.0028211507,0.00036812903,0.0002124511,0.0017936259,0.008812902,0.013831398],"genre_scores_gemma":[0.98202467,0.0002548854,0.014378974,0.00026675384,0.00002310691,0.000070214526,0.0009631917,0.00036461538,0.0016537295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99842024,0.00062494754,0.00007931077,0.00022189143,0.0004249609,0.0002287626],"domain_scores_gemma":[0.9928188,0.0042817076,0.00056035677,0.0013842087,0.00062431645,0.00033062565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003446921,0.0009361619,0.00049396575,0.0005738047,0.00049087306,0.0010657152,0.0012192375,0.0012634642,0.0025444273],"category_scores_gemma":[0.013843453,0.00030193388,0.00073037983,0.0002624274,0.0014444635,0.002006667,0.0017639778,0.0022606289,0.0006580561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057562994,0.00014158254,0.005330151,0.00017124972,0.00009178136,0.000112053465,0.00011067314,0.95668817,0.0035420253,0.009993967,0.0049347836,0.018308003],"study_design_scores_gemma":[0.000026862686,0.00016799974,0.00039653326,0.00002355985,0.000012837934,0.000033028336,0.000032362837,0.99098223,0.003310172,0.0041251876,0.0008780016,0.000011345959],"about_ca_topic_score_codex":0.004562905,"about_ca_topic_score_gemma":0.0048105903,"teacher_disagreement_score":0.004562905,"about_ca_system_score_codex":0.0015479567,"about_ca_system_score_gemma":0.0016776107,"threshold_uncertainty_score":0.018229306},"labels":[],"label_agreement":null},{"id":"W6979343015","doi":"","title":"Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Software; Source lines of code; Robustness (evolution); Source code; Software deployment; Uncompressed video; Empirical research; Language model","score_opus":0.07452659093562705,"score_gpt":0.3712633562769646,"score_spread":0.2967367653413375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979343015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87553793,0.003269581,0.1097765,0.0022686701,0.00015971505,0.00033734538,0.0018533377,0.0018506936,0.004946204],"genre_scores_gemma":[0.98300135,0.0005013614,0.013614296,0.00020227779,0.000045074055,0.00010215046,0.0017856307,0.00018484468,0.0005629882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9946307,0.0025011485,0.0003088973,0.0007680838,0.001472179,0.00031899527],"domain_scores_gemma":[0.9003124,0.08289227,0.0045074597,0.0088927355,0.0026782572,0.00071675243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00946792,0.0011214071,0.00072352693,0.0013928289,0.00071277167,0.0012948748,0.0012850558,0.0012433485,0.0015038563],"category_scores_gemma":[0.08976205,0.00036813752,0.0007974142,0.0011335528,0.0025641616,0.0050657946,0.0018536008,0.0029727223,0.00048340188],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001281118,0.00054305216,0.038412377,0.0007613477,0.00032832808,0.00043296098,0.00061871606,0.82202363,0.004796533,0.017465783,0.009645292,0.10369088],"study_design_scores_gemma":[0.00004479599,0.0003601497,0.005122962,0.00008322583,0.00005819399,0.0003052426,0.00018109914,0.97455126,0.0037167866,0.013584839,0.0019533,0.000038156322],"about_ca_topic_score_codex":0.0042649223,"about_ca_topic_score_gemma":0.003490166,"teacher_disagreement_score":0.00946792,"about_ca_system_score_codex":0.0018141787,"about_ca_system_score_gemma":0.0013433901,"threshold_uncertainty_score":0.050071716},"labels":[],"label_agreement":null},{"id":"W6979923150","doi":"","title":"Analyzing and defending against adverserial samples in machine learning algorithms","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Adversarial system; Adversary; Adversarial machine learning; Embedding; Key (lock); Field (mathematics)","score_opus":0.019214650454490344,"score_gpt":0.25089414740123683,"score_spread":0.2316794969467465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979923150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08924362,0.0012392234,0.90446645,0.0016272112,0.00007997796,0.000102414415,0.00006806661,0.0004777614,0.0026953479],"genre_scores_gemma":[0.904326,0.0008535265,0.09165792,0.0003864522,0.00015555989,0.00014236814,0.00014086766,0.00016144599,0.0021758096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.988917,0.0053042686,0.00038041384,0.0013926219,0.0033349234,0.00067072833],"domain_scores_gemma":[0.91477126,0.0679731,0.0055916808,0.007888423,0.002800512,0.0009749605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011183867,0.0013935739,0.0016087306,0.0013913686,0.0009600752,0.0028725145,0.0019422089,0.0028073553,0.0013377799],"category_scores_gemma":[0.06886254,0.0010987122,0.001081636,0.0007802827,0.0063774483,0.0065277703,0.0050957724,0.005029955,0.00045165385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052524975,0.0001324648,0.00610209,0.0002225874,0.0001598913,0.00020843098,0.00034617572,0.80967027,0.005520802,0.12182135,0.0013362214,0.053954512],"study_design_scores_gemma":[0.000014568392,0.00012492329,0.00043140788,0.00003996495,0.0000151997265,0.00008322756,0.000044626788,0.937433,0.003416524,0.057744548,0.0006373072,0.000014649434],"about_ca_topic_score_codex":0.00088884146,"about_ca_topic_score_gemma":0.0007166779,"teacher_disagreement_score":0.011183867,"about_ca_system_score_codex":0.002272895,"about_ca_system_score_gemma":0.0015650062,"threshold_uncertainty_score":0.059146702},"labels":[],"label_agreement":null},{"id":"W6987775609","doi":"","title":"A unified Wasserstein distributional robustness framework for adversarial training","year":2022,"lang":"en","type":"article","venue":"Monash University Research Portal (Monash University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Adversarial system; Robustness (evolution); Adversary; Pointwise; Deep neural networks; Artificial neural network","score_opus":0.0714417221960287,"score_gpt":0.30382403129862084,"score_spread":0.23238230910259214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987775609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016506052,0.00021865267,0.9966983,0.00016424533,0.000028144923,0.000020368981,0.000027283782,0.00015715654,0.0010351061],"genre_scores_gemma":[0.46005,0.0019728942,0.5229463,0.00078446086,0.0006247948,0.00046878002,0.00048669692,0.000779538,0.011886542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963922,0.0013255411,0.00018265017,0.0007271544,0.001123336,0.000249129],"domain_scores_gemma":[0.9939926,0.0036132478,0.00054356776,0.00089332886,0.00067496113,0.00028231004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005162081,0.0020086018,0.0017054204,0.0015394115,0.0007333174,0.002200623,0.0031860266,0.0023827949,0.0031259744],"category_scores_gemma":[0.016554086,0.0007105251,0.0016155035,0.0010280013,0.003187731,0.004515416,0.004809878,0.0059873527,0.00088981754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079183475,0.000046452184,0.00056798285,0.00013683518,0.00007947717,0.00012177815,0.00010176497,0.6549047,0.0038167876,0.2869502,0.002331893,0.050862867],"study_design_scores_gemma":[0.0000069422767,0.00005338606,0.000097637225,0.00002632792,0.000012605498,0.00006616926,0.000008614479,0.91209155,0.0011947748,0.084674686,0.0017490531,0.00001826902],"about_ca_topic_score_codex":0.001540042,"about_ca_topic_score_gemma":0.0011074708,"teacher_disagreement_score":0.005162081,"about_ca_system_score_codex":0.0019967621,"about_ca_system_score_gemma":0.0014637427,"threshold_uncertainty_score":0.0273},"labels":[],"label_agreement":null},{"id":"W6996356034","doi":"","title":"The shifting landscape of data : learning to tame distributional shifts","year":2024,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Science; Samsung; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Université de Montréal; Canadian Institute for Advanced Research; Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Fonds de recherche du Québec – Nature et technologies; Deutscher Akademischer Austauschdienst; Institute for Catastrophic Loss Reduction; Microsoft Research; Canada Excellence Research Chairs, Government of Canada; U.S. Department of Energy","keywords":"Distribution (mathematics); ESPACE; Context (archaeology); Convergence (economics)","score_opus":0.012524873365663894,"score_gpt":0.22235611018738483,"score_spread":0.20983123682172095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996356034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.186008,0.0021577543,0.80349684,0.0025322866,0.00016873387,0.0001297131,0.00042824354,0.0020700095,0.0030083852],"genre_scores_gemma":[0.83211136,0.0009955013,0.16136052,0.0009311306,0.00022324458,0.00018643499,0.0009255808,0.0003965138,0.0028696985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948756,0.0022914927,0.00022845795,0.001764258,0.00058436726,0.00025590666],"domain_scores_gemma":[0.971512,0.019277265,0.001699283,0.0052496972,0.0016043887,0.0006573189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008497842,0.0010863919,0.001560222,0.0016115721,0.0008488104,0.0027435708,0.0022978364,0.0020638972,0.0022599772],"category_scores_gemma":[0.045579262,0.0007620375,0.0012507341,0.0014929852,0.0022896533,0.008723107,0.0045500924,0.0039710645,0.0013598204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093167555,0.0003245129,0.032353498,0.00029498807,0.0004423528,0.0002912554,0.0017565652,0.2601997,0.008059931,0.024254087,0.0040563936,0.66703504],"study_design_scores_gemma":[0.00004082796,0.00031848028,0.005596437,0.000058206642,0.00006465452,0.00018038774,0.0003300445,0.9189701,0.0031841,0.06746047,0.003741059,0.000055317032],"about_ca_topic_score_codex":0.0025187249,"about_ca_topic_score_gemma":0.0026145892,"teacher_disagreement_score":0.008497842,"about_ca_system_score_codex":0.0014095274,"about_ca_system_score_gemma":0.0011850586,"threshold_uncertainty_score":0.044941485},"labels":[],"label_agreement":null},{"id":"W6997296388","doi":"","title":"Winnipeg Is In The Mud","year":2021,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bar (unit); Sign (mathematics); Code (set theory)","score_opus":0.00772462142676103,"score_gpt":0.20560861857697435,"score_spread":0.19788399715021332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997296388","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086643547,0.0007627814,0.0016023081,0.0039390903,0.0019035639,0.000065383545,0.003477379,0.0021523992,0.98523074],"genre_scores_gemma":[0.0035593822,0.0003851692,0.00071500894,0.00060890417,0.000076188306,0.000015959446,0.0011228257,0.0009445682,0.992572],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968886,0.00002623847,0.000006955718,0.00008136221,0.00011962025,0.00007702882],"domain_scores_gemma":[0.99939406,0.000037530634,0.000014503388,0.000055853005,0.00015706233,0.0003410251],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00038476166,0.000714431,0.00042714356,0.0005868795,0.002523171,0.0034474193,0.00070075155,0.0008332255,0.60867643],"category_scores_gemma":[0.001485728,0.00033284313,0.00021860194,0.0006983432,0.00051198114,0.001909238,0.0021669215,0.0010993106,0.38208038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063234,0.0000106165035,0.0001336118,0.000035159665,0.0000022202078,0.000075242126,0.00007822809,0.00007354556,0.00021138068,0.006396105,0.9551621,0.03775867],"study_design_scores_gemma":[0.0000016629278,0.0000015768867,0.00007304629,0.000009435784,4.644854e-7,0.000009417427,0.000029082368,0.000029626595,0.000037146645,0.00020386942,0.9996032,0.0000014882667],"about_ca_topic_score_codex":0.07420088,"about_ca_topic_score_gemma":0.24955222,"teacher_disagreement_score":0.39132357,"about_ca_system_score_codex":0.0015867559,"about_ca_system_score_gemma":0.0030130236,"threshold_uncertainty_score":0.55817556},"labels":[],"label_agreement":null},{"id":"W7008130878","doi":"","title":"Approaching Neural Network Uncertainty Realism: Paper presented at Machine Learning for Autonomous Driving Workshop at the 33rd Conference on Neural Information Processing Systems, NeurIPS 2019, December 14th, 2019, Vancouver, Canada","year":2019,"lang":"en","type":"article","venue":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mahalanobis distance; Artificial neural network; Automotive industry; Domain (mathematical analysis); Quality (philosophy); Ground truth","score_opus":0.013948680213434142,"score_gpt":0.23391005732691939,"score_spread":0.21996137711348523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008130878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04546236,0.0013829637,0.9403931,0.0026083728,0.00029417127,0.00005650874,0.0001275706,0.0003318157,0.009343209],"genre_scores_gemma":[0.8723307,0.0013993568,0.11361284,0.00045899025,0.00039093502,0.00008721634,0.00026011877,0.00029107457,0.011168845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999073,0.00036534216,0.000030426676,0.00019424225,0.00027165384,0.00006533155],"domain_scores_gemma":[0.9953607,0.0034508468,0.00021525698,0.00032870134,0.00050796,0.00013659881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031297912,0.0010363079,0.000752674,0.00062582694,0.00053319,0.0021231228,0.0008803273,0.001299693,0.004170993],"category_scores_gemma":[0.011248135,0.00048092988,0.00058433984,0.00030293412,0.0017260573,0.0024160186,0.0032867722,0.0029728666,0.0005371683],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024418093,0.000057080742,0.002614207,0.00014868827,0.000114226124,0.00018131801,0.00024281941,0.77546656,0.006628641,0.069838345,0.0065756175,0.13788827],"study_design_scores_gemma":[0.000003948953,0.000045642577,0.00034791688,0.000025638352,0.000009830372,0.00003930319,0.000022670301,0.97124803,0.002273002,0.024188604,0.0017824875,0.000012924541],"about_ca_topic_score_codex":0.0028130463,"about_ca_topic_score_gemma":0.0034761834,"teacher_disagreement_score":0.004170993,"about_ca_system_score_codex":0.0013899422,"about_ca_system_score_gemma":0.00068321417,"threshold_uncertainty_score":0.01655215},"labels":[],"label_agreement":null},{"id":"W7020734135","doi":"","title":"Nature's Past Episode 050: Canadian Energy History","year":2015,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Energy consumption; Per capita; Energy (signal processing); Historiography; Consumption (sociology); Unit (ring theory); Natural history; Politics; Political history","score_opus":0.007323512262133897,"score_gpt":0.15046998286307584,"score_spread":0.14314647060094193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7020734135","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055445876,0.025208838,0.00033027728,0.0369072,0.0015475644,0.00006166418,0.023034213,0.00011714735,0.85734725],"genre_scores_gemma":[0.5459119,0.041015513,0.0010731736,0.016013803,0.00036492874,0.00009264096,0.014109663,0.000302245,0.3811161],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856913,0.00005030572,0.000029799608,0.00013330547,0.00067383156,0.0005435609],"domain_scores_gemma":[0.999047,0.00006256742,0.00005655763,0.00003517921,0.0005647848,0.00023389404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059029786,0.00037006667,0.0003091113,0.0040089716,0.02005736,0.0071592457,0.00126549,0.0013946672,0.0311688],"category_scores_gemma":[0.0019088764,0.00035041219,0.00034238808,0.014933197,0.0035001275,0.0024766086,0.0023854054,0.00266079,0.0020374397],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009573014,0.000034442743,0.019046145,0.00073956925,0.000049337705,0.0011232139,0.04192683,0.00028713347,0.00038293595,0.1996663,0.642846,0.093802415],"study_design_scores_gemma":[0.000001730313,0.0000018785415,0.015589678,0.00011993682,0.0000068787895,0.0000809298,0.0064566997,0.000024845645,0.000043951408,0.0010046589,0.97664875,0.000020083024],"about_ca_topic_score_codex":0.99726176,"about_ca_topic_score_gemma":0.99901533,"teacher_disagreement_score":0.14624326,"about_ca_system_score_codex":0.14624326,"about_ca_system_score_gemma":0.103827834,"threshold_uncertainty_score":0.9902368},"labels":[],"label_agreement":null},{"id":"W7023554395","doi":"","title":"Observation of the decay B0→ρ+ρ- and measurement of the branching fraction and polarization","year":2004,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut National de Physique Nucléaire et de Physique des Particules; Institute of High Energy Physics; Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; Bundesministerium für Bildung und Forschung; SLAC National Accelerator Laboratory; Alexander von Humboldt-Stiftung; Alfred P. Sloan Foundation; Deutsche Forschungsgemeinschaft; U.S. Department of Energy; National Science Foundation","keywords":"Polarization (electrochemistry); Branching fraction; Branching (polymer chemistry); Fraction (chemistry); Systematic error","score_opus":0.016355653624922355,"score_gpt":0.2085103844979674,"score_spread":0.19215473087304505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023554395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98996794,0.00025417408,0.0075412607,0.000050547755,0.000008406872,0.0000078676585,0.00048850983,0.00008806122,0.0015932467],"genre_scores_gemma":[0.9961026,0.00010343404,0.002846538,0.000017937256,0.0000073072415,0.000005273493,0.0005663949,0.000011336786,0.00033914586],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994407,0.0001829527,0.000020605787,0.00012409494,0.00014053004,0.000090979964],"domain_scores_gemma":[0.9987832,0.00035926077,0.00028070156,0.00017230255,0.00022485416,0.00017957787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011657042,0.00032349076,0.00038757882,0.00072673056,0.0006594546,0.00047209847,0.000361833,0.00031442507,0.0010886198],"category_scores_gemma":[0.0015582955,0.00030293528,0.000161513,0.0009567049,0.0004577342,0.00030123317,0.00061344175,0.0003702046,0.00033671694],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024271684,0.00034691434,0.40793836,0.00028264252,0.0004531819,0.0015068912,0.0006030834,0.009854393,0.52423567,0.0071877358,0.0016043345,0.043559674],"study_design_scores_gemma":[0.00015577453,0.0011680288,0.47060418,0.00006498437,0.00030561333,0.006636101,0.00034193174,0.05940597,0.44410124,0.0046560424,0.012338789,0.00022135994],"about_ca_topic_score_codex":0.0013433339,"about_ca_topic_score_gemma":0.00171895,"teacher_disagreement_score":0.0013433339,"about_ca_system_score_codex":0.00017882785,"about_ca_system_score_gemma":0.00019691183,"threshold_uncertainty_score":0.0061649084},"labels":[],"label_agreement":null},{"id":"W7024045394","doi":"","title":"A Pseudo Calabresian Sunset Down Under: The Anachronism of Disqualifying Australian Members of Parliament for Holding a Foreign Citizenship","year":2019,"lang":"en","type":"other","venue":"Swinburne Research Bank (Swinburne University of Technology)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Parliament; Constitution; Citizenship; Repeal; Anachronism; Commonwealth; High Court","score_opus":0.045227444324249716,"score_gpt":0.3179876504419607,"score_spread":0.272760206117711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024045394","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085417345,0.0023071999,0.022962898,0.12699258,0.003494266,0.00016958067,0.00024912815,0.0003154728,0.75809145],"genre_scores_gemma":[0.7484157,0.00064840453,0.005395874,0.043208692,0.0006259795,0.00011127106,0.00008924797,0.00015646413,0.20134826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99293035,0.0024128987,0.00019527756,0.0013602404,0.0019801054,0.0011210934],"domain_scores_gemma":[0.9966838,0.0015658552,0.00024979748,0.0006356139,0.00055468903,0.00031030632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067948992,0.00033878302,0.0004247858,0.0006492077,0.008645469,0.0054839826,0.001382101,0.0058249473,0.010897259],"category_scores_gemma":[0.01978208,0.000459189,0.0006051908,0.0005596768,0.010282297,0.003934925,0.0041707754,0.012413153,0.0019184129],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070827715,0.000025024696,0.0013513523,0.00005321492,0.000018481518,0.00077563757,0.0041502775,0.0004712758,0.0008673575,0.91074795,0.05387788,0.027590858],"study_design_scores_gemma":[0.00006566218,0.00014986741,0.009407567,0.0006797475,0.00008610914,0.001248327,0.00428032,0.0064518885,0.0054008043,0.28944036,0.6826473,0.00014210556],"about_ca_topic_score_codex":0.03720818,"about_ca_topic_score_gemma":0.061242674,"teacher_disagreement_score":0.03720818,"about_ca_system_score_codex":0.00436966,"about_ca_system_score_gemma":0.004844023,"threshold_uncertainty_score":0.07398319},"labels":[],"label_agreement":null},{"id":"W7024438943","doi":"","title":"Royal Bank of Canada Raises Iberdrola (OTCMKTS:IBDRY) Price Target to €12.75","year":2022,"lang":"en","type":"other","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Payment; Work (physics); Context (archaeology)","score_opus":0.006071461308999796,"score_gpt":0.2140257806979307,"score_spread":0.2079543193889309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024438943","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010021483,0.0028915452,0.0021409215,0.030096002,0.004246271,0.00015545433,0.013956515,0.001950153,0.9435611],"genre_scores_gemma":[0.004531411,0.000628588,0.00038221115,0.0014019483,0.00022659183,0.000018907871,0.0021440256,0.00044804384,0.9902182],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99810565,0.000072647446,0.00003722825,0.00016635621,0.0012774142,0.0003406262],"domain_scores_gemma":[0.9948737,0.00042513013,0.00012577004,0.00025379797,0.0028158796,0.0015058266],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015270176,0.0010526112,0.001000177,0.0015222112,0.002312975,0.005212992,0.001476629,0.004226449,0.5398189],"category_scores_gemma":[0.0068994327,0.00045868813,0.000680653,0.0010052985,0.0015104768,0.0012895967,0.0019873562,0.0033661663,0.27736494],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006821357,0.000018440396,0.00009179839,0.00003755267,0.0000037000113,0.000032836935,0.000010544876,0.0001683071,0.00012204555,0.0063973563,0.9641001,0.028949015],"study_design_scores_gemma":[0.000029004988,0.000021630849,0.0005616419,0.000059608436,0.000003310495,0.00003197625,0.00003450951,0.00056037167,0.00016511949,0.002190157,0.99632865,0.000014000745],"about_ca_topic_score_codex":0.4136734,"about_ca_topic_score_gemma":0.57839245,"teacher_disagreement_score":0.46018112,"about_ca_system_score_codex":0.007828261,"about_ca_system_score_gemma":0.01790633,"threshold_uncertainty_score":0.8225311},"labels":[],"label_agreement":null},{"id":"W7024823896","doi":"","title":"Stability testing and quantitation of certified reference materials","year":2008,"lang":"en","type":"article","venue":"NPARC","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Certified reference materials; Calibration; Certification; Quality assurance; Matrix (chemical analysis); Reference data; Quality (philosophy); Stability (learning theory)","score_opus":0.11644279792159017,"score_gpt":0.28531285112129345,"score_spread":0.16887005319970327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024823896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06725136,0.004782355,0.899761,0.0008848981,0.0007046961,0.0016893322,0.001361342,0.0017686129,0.021796301],"genre_scores_gemma":[0.42906472,0.006981766,0.5360616,0.001284009,0.0003822497,0.0033935395,0.0040676496,0.00052404526,0.018240426],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9835585,0.003826309,0.0008693056,0.0021064125,0.009237495,0.0004020431],"domain_scores_gemma":[0.99219006,0.001776602,0.0010610058,0.0012849328,0.0035857782,0.00010169266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009939201,0.0010963462,0.00086773623,0.0028393473,0.00078396045,0.0013501137,0.0022842535,0.0018081951,0.002668185],"category_scores_gemma":[0.022293257,0.00044711467,0.00077665295,0.0019929886,0.0016390919,0.0012396106,0.001532037,0.0016270711,0.0019284217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045655304,0.00057133014,0.004027807,0.0013911162,0.00018377231,0.00045552998,0.0005810378,0.029805597,0.73698074,0.027698431,0.008214794,0.18963319],"study_design_scores_gemma":[0.00004697673,0.00096414133,0.0028318802,0.00021623356,0.00007148206,0.00052200427,0.0001508454,0.049325984,0.89320815,0.009023382,0.04352227,0.000116704665],"about_ca_topic_score_codex":0.0012724301,"about_ca_topic_score_gemma":0.0012987267,"teacher_disagreement_score":0.009939201,"about_ca_system_score_codex":0.0016426921,"about_ca_system_score_gemma":0.0010737067,"threshold_uncertainty_score":0.052564204},"labels":[],"label_agreement":null},{"id":"W7026592638","doi":"","title":"Analysis of injury and fall hospitalization and associated risk factors among older adults in Saskatchewan, Canada, 1995/96 – 2004/05.","year":2010,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Health, Saskatchewan; University of Regina","keywords":"Poison control; Risk factor; MEDLINE; Occupational safety and health; Injury prevention; Incidence (geometry); Human factors and ergonomics; Population","score_opus":0.0023979527347440023,"score_gpt":0.18800123010925698,"score_spread":0.185603277374513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026592638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8244343,0.0031264846,0.0009962937,0.0016440391,0.00012987969,0.0001740213,0.16441691,0.00007537734,0.005002702],"genre_scores_gemma":[0.93498105,0.0025988268,0.00081425323,0.0003629654,0.000032548982,0.00014023068,0.052028906,0.000034196182,0.009007093],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991854,0.000060558406,0.000115962124,0.00013544952,0.00023658032,0.0002659736],"domain_scores_gemma":[0.99715984,0.000118770426,0.00038072414,0.00012902236,0.0017366767,0.00047497728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089621195,0.0007098192,0.0006670954,0.0027795844,0.0015542745,0.001045987,0.0026944946,0.00077036617,0.0028448517],"category_scores_gemma":[0.0022902219,0.0006421647,0.001160607,0.009561431,0.00063582236,0.00049894187,0.0015504047,0.0013862376,0.0007674617],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023037712,0.00006541298,0.9769875,0.00010121944,0.00039856194,0.0001448869,0.0003311777,0.0018863595,0.00022199417,0.00027077808,0.01161245,0.0077492455],"study_design_scores_gemma":[0.000013980226,0.0000133905505,0.99616915,0.00006461716,0.00007045414,0.000040605642,0.0010307508,0.0008603699,0.00008294887,0.000054399403,0.0015827764,0.000016511674],"about_ca_topic_score_codex":0.9974903,"about_ca_topic_score_gemma":0.99834037,"teacher_disagreement_score":0.029405242,"about_ca_system_score_codex":0.029405242,"about_ca_system_score_gemma":0.052254777,"threshold_uncertainty_score":0.21335095},"labels":[],"label_agreement":null},{"id":"W7028700700","doi":"","title":"Estimación de la incertidumbre en redes neuronales profundas","year":2020,"lang":"es","type":"article","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Context (archaeology); Maximum likelihood; Markov chain","score_opus":0.022633953985052593,"score_gpt":0.2710871621516835,"score_spread":0.2484532081666309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028700700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09913864,0.0018011273,0.8809931,0.0020005885,0.00016218827,0.000057195095,0.0005866303,0.0009023923,0.014358087],"genre_scores_gemma":[0.90396225,0.0024473767,0.07979637,0.00032753358,0.00011558224,0.00016143445,0.00063688256,0.00021174007,0.012340771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988663,0.00029814662,0.000058449783,0.0003902221,0.00025328438,0.0001335601],"domain_scores_gemma":[0.9932863,0.0041842205,0.00062248163,0.0008106528,0.00091701053,0.00017938433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025043825,0.0011518559,0.00094629196,0.0011000156,0.0005147208,0.0035543041,0.0016578672,0.0013736507,0.0065775746],"category_scores_gemma":[0.017684303,0.0008882284,0.0010961033,0.00091120874,0.0017293246,0.0050968486,0.0016852635,0.0030519424,0.0012399872],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002423249,0.000093118215,0.015275976,0.00038043663,0.00029525225,0.00032126473,0.0008714491,0.6334435,0.006871148,0.20645991,0.0028396896,0.13290597],"study_design_scores_gemma":[0.000020956357,0.000104784194,0.005040327,0.00016515961,0.000097469725,0.00021164262,0.0002352595,0.82873267,0.0033551424,0.15651473,0.0054492536,0.00007263611],"about_ca_topic_score_codex":0.009182772,"about_ca_topic_score_gemma":0.0069559286,"teacher_disagreement_score":0.009182772,"about_ca_system_score_codex":0.0020422477,"about_ca_system_score_gemma":0.0015341925,"threshold_uncertainty_score":0.022004187},"labels":[],"label_agreement":null},{"id":"W7034246359","doi":"","title":"The Tu/Ti Interrogative Morpheme in Québec French","year":2015,"lang":"en","type":"other","venue":"Institutional Research Information System (University of Udine)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; Dysgeusia; TSG101; Liquation; Emperipolesis; Diafiltration; Triacetin; Durvalumab","score_opus":0.05037792895340663,"score_gpt":0.29262346588113824,"score_spread":0.24224553692773163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034246359","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31704813,0.003925146,0.014783314,0.009448035,0.0012309956,0.00017515416,0.012401767,0.0012467089,0.63974077],"genre_scores_gemma":[0.8646284,0.00067542575,0.0023987659,0.00049799355,0.00012063575,0.000044055094,0.0020685478,0.00049090304,0.12907524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9992248,0.00023671378,0.00003151176,0.00016404255,0.00021884055,0.00012405938],"domain_scores_gemma":[0.9988709,0.0004405724,0.000055695295,0.00008281822,0.0005056262,0.000044314642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006798079,0.0009232471,0.00040042857,0.0014835733,0.0045944024,0.0038342022,0.0009018505,0.0015095113,0.03941416],"category_scores_gemma":[0.0034076273,0.00028988105,0.0002473128,0.0018340495,0.001887945,0.0015797459,0.0009026952,0.0017212321,0.0028959478],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010962638,0.0001267456,0.011943928,0.0007758635,0.00013154818,0.004849357,0.05555953,0.0035708905,0.017102148,0.45772246,0.29232326,0.15479799],"study_design_scores_gemma":[0.00005733257,0.000061004834,0.026534287,0.00038327096,0.000052339437,0.0016386403,0.01784848,0.0069020162,0.009227634,0.0062059145,0.9309246,0.0001644348],"about_ca_topic_score_codex":0.88997895,"about_ca_topic_score_gemma":0.8942432,"teacher_disagreement_score":0.88997895,"about_ca_system_score_codex":0.021980878,"about_ca_system_score_gemma":0.00644532,"threshold_uncertainty_score":0.22133791},"labels":[],"label_agreement":null},{"id":"W7034353057","doi":"","title":"Thermosensitive chitosan-based hydrogels for extrusion-based bioprinting and injectable scaffold for articular tissue engineering","year":2022,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Self-healing hydrogels; Microsphere; Tissue engineering; Biocompatible material; Scaffold","score_opus":0.006522330521912679,"score_gpt":0.19482522324492443,"score_spread":0.18830289272301176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034353057","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9329212,0.010116202,0.051387418,0.00018986194,0.00015887237,0.0002272987,0.0003628328,0.00033274537,0.0043035964],"genre_scores_gemma":[0.95324606,0.0044342084,0.03430033,0.00011080425,0.000031304608,0.00013169604,0.0002760029,0.00006777767,0.007401918],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998642,0.000011565902,0.000012369246,0.00002576945,0.000064591666,0.000021391372],"domain_scores_gemma":[0.9998957,0.00002308243,0.000040188082,0.000007868664,0.000019446758,0.000013799221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002226663,0.00034803193,0.0001636091,0.00028167613,0.000119241464,0.00021472758,0.00018297185,0.00033819553,0.0010729848],"category_scores_gemma":[0.00015781126,0.00015973485,0.00023216759,0.00022366234,0.0001856232,0.00028194455,0.00016747875,0.0003273945,0.00022602496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002880864,0.000009381131,0.00003314444,0.00005522612,0.000002061917,0.000025895662,0.000008068975,0.00010489494,0.9977526,0.00005316517,0.000023411834,0.0019034477],"study_design_scores_gemma":[0.000008268732,0.00011098229,0.0007897393,0.0000057825,0.000010707394,0.00009518092,0.0000062325307,0.0007175491,0.9967878,0.000015482921,0.0014441252,0.000008140778],"about_ca_topic_score_codex":0.00071285386,"about_ca_topic_score_gemma":0.0017036031,"teacher_disagreement_score":0.0010729848,"about_ca_system_score_codex":0.0003026847,"about_ca_system_score_gemma":0.00030820374,"threshold_uncertainty_score":0.0035894513},"labels":[],"label_agreement":null},{"id":"W7034421795","doi":"","title":"[Turtle Creek Chorale: Bert Martin and Rusty Allen at GALA Festival]","year":2004,"lang":"en","type":"other","venue":"The Portal to Texas History (University of North Texas)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Front (military); White (mutation); Myotis lucifugus; Turtle (robot)","score_opus":0.01373473398696258,"score_gpt":0.2033553603898918,"score_spread":0.1896206264029292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034421795","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031441005,0.0055835717,0.0022261264,0.026239516,0.014035477,0.00019819288,0.0039972756,0.0018977055,0.94267815],"genre_scores_gemma":[0.014624899,0.0014589368,0.0009079819,0.0027777345,0.0013138243,0.000058637754,0.0014297978,0.0005471139,0.97688097],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998448,0.00002008293,0.0000030771926,0.00003087303,0.00004300118,0.000058125468],"domain_scores_gemma":[0.99973685,0.000019163921,0.0000094631,0.000018514338,0.0000708597,0.00014508372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045065678,0.0011497505,0.00039699808,0.00074076466,0.004644129,0.0023535397,0.00094317703,0.0014707785,0.25765136],"category_scores_gemma":[0.000611342,0.0004298915,0.00037233782,0.00069199776,0.00071022625,0.0015569822,0.0020675214,0.0024137592,0.083965264],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013011642,0.0000040501154,0.00003631721,0.000008196234,7.638007e-7,0.00004199809,0.000066714754,0.000021011354,0.00004683827,0.00077638624,0.995621,0.0033635981],"study_design_scores_gemma":[0.000003439101,0.0000037615412,0.00035958577,0.00001682048,5.177195e-7,0.000037232658,0.0001573798,0.000022721284,0.000026154234,0.00018133319,0.9991868,0.000004185216],"about_ca_topic_score_codex":0.04641471,"about_ca_topic_score_gemma":0.21884532,"teacher_disagreement_score":0.25765136,"about_ca_system_score_codex":0.0016658526,"about_ca_system_score_gemma":0.0008990677,"threshold_uncertainty_score":0.86192966},"labels":[],"label_agreement":null},{"id":"W7037231901","doi":"","title":"Des moyens d'expression de l'intensité dans le langage des jeunes Québécois","year":2001,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sociolinguistics; Humanity; Semantic analysis (machine learning)","score_opus":0.006057530024147826,"score_gpt":0.16292338763811812,"score_spread":0.1568658576139703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037231901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77725226,0.005103655,0.034339514,0.003011662,0.00045514962,0.00008882179,0.003927051,0.0008042626,0.1750177],"genre_scores_gemma":[0.9264434,0.001907421,0.01589952,0.00036299712,0.00018817502,0.0001790044,0.0028902497,0.00072933256,0.051399883],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987514,0.00043141545,0.000082531806,0.00034048257,0.0002879163,0.000106253065],"domain_scores_gemma":[0.99583757,0.0023620594,0.0002936502,0.00049452466,0.000892602,0.00011952259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001355578,0.000547829,0.0003379369,0.0014791869,0.0018479485,0.0038180673,0.00033856602,0.0006172533,0.011094649],"category_scores_gemma":[0.0044282265,0.0005363687,0.0003844597,0.0011794509,0.0022075116,0.0027254869,0.0015719659,0.0016794262,0.0021397592],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010768279,0.00011284694,0.054935977,0.0020826992,0.00018181473,0.001445859,0.29431108,0.0009850638,0.099401556,0.21098518,0.025798038,0.308683],"study_design_scores_gemma":[0.00006664216,0.00021979408,0.27580276,0.0011886172,0.0001769536,0.0024313435,0.08292466,0.0036796965,0.035168078,0.024431216,0.5736216,0.0002887059],"about_ca_topic_score_codex":0.012978753,"about_ca_topic_score_gemma":0.023499778,"teacher_disagreement_score":0.98702127,"about_ca_system_score_codex":0.0015898569,"about_ca_system_score_gemma":0.0009218604,"threshold_uncertainty_score":0.037115335},"labels":[],"label_agreement":null},{"id":"W7038461005","doi":"","title":"Impacts of Changes in Cell Surface Components and Intraspecific Competition on the Interactions of Pseudomonas aeruginosa With Cystic Fibrosis Bronchial Epithelial Cells","year":2023,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Pseudomonas aeruginosa; Intraspecific competition; Cystic fibrosis; Competition (biology); Cell; Pseudomonadaceae","score_opus":0.013569505924821243,"score_gpt":0.22117892383462553,"score_spread":0.2076094179098043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038461005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9231041,0.0042527644,0.0053227074,0.003284073,0.0004313546,0.00011489945,0.021451596,0.00028035286,0.041758057],"genre_scores_gemma":[0.9720078,0.0017548968,0.0029105495,0.0007433416,0.00003404192,0.000151401,0.0056899292,0.00008254893,0.016625507],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996344,0.000055511984,0.000013785252,0.000047862264,0.00015492918,0.000093529234],"domain_scores_gemma":[0.9996332,0.00013767125,0.000046005815,0.000023590921,0.00006386909,0.000095685486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039517,0.00020076627,0.0002632855,0.00021046444,0.00043265667,0.00048213822,0.0002113549,0.00046392265,0.019897185],"category_scores_gemma":[0.0008253665,0.00012945036,0.00036421712,0.00022491634,0.0001639189,0.00022729982,0.00055748597,0.00084801007,0.0019172117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018895135,0.0005219523,0.020275358,0.0008706014,0.00020329155,0.00046178282,0.00033078477,0.009063565,0.8458951,0.005280213,0.059691552,0.055516243],"study_design_scores_gemma":[0.00022706004,0.002701751,0.39750832,0.0003439242,0.00034886782,0.0010873616,0.0021669765,0.03455269,0.43407393,0.0077710054,0.119049616,0.00016851717],"about_ca_topic_score_codex":0.003615936,"about_ca_topic_score_gemma":0.004361144,"teacher_disagreement_score":0.019897185,"about_ca_system_score_codex":0.0005356524,"about_ca_system_score_gemma":0.00057250756,"threshold_uncertainty_score":0.06656271},"labels":[],"label_agreement":null},{"id":"W7040500087","doi":"","title":"Eco – Hotel Sonsón","year":2021,"lang":"en","type":"dissertation","venue":"Digital Library (University of San Buenaventura Colombia)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Moorland; Exhibition; Natural (archaeology); Vegetation (pathology)","score_opus":0.006036745845847801,"score_gpt":0.185154049126394,"score_spread":0.1791173032805462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7040500087","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004668428,0.0012975704,0.0012313605,0.00088943314,0.0010805684,0.000035811587,0.0015801794,0.0006643883,0.98855233],"genre_scores_gemma":[0.012606547,0.0010738819,0.0009098788,0.0001258993,0.00010734943,0.000027762642,0.00093504676,0.00045287656,0.9837608],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997141,0.000027338374,0.00000850059,0.000101362064,0.00009907069,0.00004969601],"domain_scores_gemma":[0.99940133,0.000051053277,0.000029435767,0.00006979433,0.00012867466,0.00031973454],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025346817,0.0008305482,0.00035155087,0.0006847467,0.0010405901,0.0021108384,0.00042116336,0.00057315617,0.53129536],"category_scores_gemma":[0.00057474565,0.00018176301,0.00021999785,0.0007047569,0.00035560335,0.0013049379,0.001808264,0.0008729526,0.27819148],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043013744,0.00016454807,0.0025315299,0.0005983805,0.000018847048,0.00071937806,0.00041837362,0.00043216825,0.005764445,0.029538328,0.6069479,0.352436],"study_design_scores_gemma":[0.000004584047,0.000021911226,0.0016888337,0.00005795168,0.0000014023218,0.00012806054,0.00017553313,0.00013533022,0.0003960702,0.000593667,0.9967908,0.000005737359],"about_ca_topic_score_codex":0.0027290792,"about_ca_topic_score_gemma":0.009687012,"teacher_disagreement_score":0.46870464,"about_ca_system_score_codex":0.00068169093,"about_ca_system_score_gemma":0.0012533076,"threshold_uncertainty_score":0.66855025},"labels":[],"label_agreement":null},{"id":"W7082282613","doi":"10.48448/zr7a-0a35","title":"SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models","year":2025,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Guard (computer science); Router; Benchmark (surveying); Block (permutation group theory); Heuristics; Model selection; Selection (genetic algorithm)","score_opus":0.01736622173029912,"score_gpt":0.2995262720487557,"score_spread":0.2821600503184566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7082282613","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021947993,0.001209415,0.94943625,0.0012094335,0.0001730372,0.0002262783,0.0006538638,0.023479491,0.0016642786],"genre_scores_gemma":[0.62296695,0.0007277485,0.3580786,0.002358849,0.00032072217,0.00068688527,0.0045504025,0.0025109095,0.0077988915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973816,0.0011772155,0.00013926599,0.0006325143,0.00045106927,0.00021825363],"domain_scores_gemma":[0.99430335,0.003941778,0.0002909821,0.0008628582,0.00040031227,0.00020079328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034756518,0.0029552586,0.0018613144,0.0012891392,0.0007720778,0.0018674353,0.0036134801,0.002339658,0.005416293],"category_scores_gemma":[0.013532779,0.0010254966,0.0019157323,0.00064337865,0.0013233935,0.0037164981,0.0030201627,0.004840781,0.003209975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068825536,0.00043365496,0.0039330586,0.00043389093,0.0003222189,0.0005137786,0.0002565425,0.62196934,0.0079894615,0.013299889,0.031221362,0.31893855],"study_design_scores_gemma":[0.000027181428,0.000045593533,0.000067459514,0.000012855255,0.000014500702,0.00004019599,0.000017931556,0.98901635,0.0013782125,0.008602642,0.00076556986,0.000011519861],"about_ca_topic_score_codex":0.0040903436,"about_ca_topic_score_gemma":0.008836929,"teacher_disagreement_score":0.005416293,"about_ca_system_score_codex":0.0012329133,"about_ca_system_score_gemma":0.0024250457,"threshold_uncertainty_score":0.018381238},"labels":[],"label_agreement":null},{"id":"W7084056929","doi":"10.1109/yac66630.2025.11149732","title":"Uncertainty Propagation in AI-driven Autonomous Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Propagation of uncertainty; Modular design; Domain (mathematical analysis); Uncertainty analysis; Series (stratigraphy); Uncertainty quantification; Stability (learning theory)","score_opus":0.008201894414738653,"score_gpt":0.26883263568960797,"score_spread":0.2606307412748693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084056929","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013127934,0.00013639516,0.9847083,0.00015647002,0.000015676149,0.000023099543,0.000041275638,0.0001736015,0.0016170865],"genre_scores_gemma":[0.93485475,0.0002630021,0.06289082,0.00011304839,0.0000471655,0.00013805217,0.000088137014,0.000095280935,0.0015097561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972307,0.0008904806,0.00014121666,0.00053127407,0.00097489875,0.00023132235],"domain_scores_gemma":[0.9908489,0.0062730955,0.0011058371,0.0007294576,0.00083949405,0.00020321416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034628606,0.00092960196,0.0006550989,0.00090931717,0.00046177223,0.001595505,0.0013074571,0.0011836636,0.0015195322],"category_scores_gemma":[0.014028232,0.00060818024,0.0007710261,0.0004844753,0.0028261126,0.0021707583,0.0022380531,0.0018915613,0.00021286786],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032178545,0.000011877573,0.00041972398,0.000059972273,0.000022567314,0.00006613013,0.000089571215,0.9264025,0.0019846752,0.061775237,0.0001674544,0.008968178],"study_design_scores_gemma":[0.0000056780277,0.00002725144,0.0001439668,0.000012493336,0.0000067249653,0.000024219402,0.00001301378,0.9409988,0.0010926935,0.05714809,0.00051741005,0.000009769615],"about_ca_topic_score_codex":0.0024635333,"about_ca_topic_score_gemma":0.0011308431,"teacher_disagreement_score":0.0034628606,"about_ca_system_score_codex":0.0016671141,"about_ca_system_score_gemma":0.0011980308,"threshold_uncertainty_score":0.018313587},"labels":[],"label_agreement":null},{"id":"W7092191408","doi":"10.1145/3768725.3768734","title":"WeightSentry: Real-Time Bit-Flip Protection for Deep Neural Networks on GPUs","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Deep learning; Deep neural networks; Feature (linguistics); Noise (video)","score_opus":0.012408425133378491,"score_gpt":0.263320654613147,"score_spread":0.25091222947976854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7092191408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08278119,0.00088439113,0.8663016,0.0008200855,0.00075415056,0.0002127427,0.0006313654,0.029339418,0.018275024],"genre_scores_gemma":[0.80140847,0.00021016199,0.17832766,0.00048640583,0.00008306113,0.00016691606,0.0007152005,0.0022141535,0.016387917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929965,0.000113443646,0.00004519134,0.000113022055,0.0002898797,0.00013889228],"domain_scores_gemma":[0.998743,0.00029678567,0.00009806197,0.0005822616,0.00022171097,0.000058186743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076304836,0.0009754002,0.0005549002,0.00054366316,0.00042657123,0.0012534406,0.0018485062,0.0008986014,0.022290213],"category_scores_gemma":[0.003869459,0.000370471,0.00049013714,0.00039670753,0.00076190894,0.0017771197,0.002280298,0.0015042,0.0029531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027723452,0.00025114292,0.0022299942,0.0004935721,0.00022445282,0.00057020015,0.0003575886,0.13450232,0.08648904,0.051171433,0.074992195,0.64594567],"study_design_scores_gemma":[0.0002318606,0.00036591996,0.00059403933,0.00009897266,0.000057516143,0.00019302563,0.00009533306,0.79661065,0.12859188,0.045952685,0.027139753,0.00006830778],"about_ca_topic_score_codex":0.0015114032,"about_ca_topic_score_gemma":0.0030758902,"teacher_disagreement_score":0.022290213,"about_ca_system_score_codex":0.0007508511,"about_ca_system_score_gemma":0.0011900046,"threshold_uncertainty_score":0.07456815},"labels":[],"label_agreement":null},{"id":"W7104817253","doi":"","title":"A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher?","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Set (abstract data type); Perspective (graphical); Fidelity; Inference; Language model; Code (set theory); Software deployment","score_opus":0.05337935037745778,"score_gpt":0.35414837231113794,"score_spread":0.30076902193368016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104817253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3630555,0.0006815216,0.62273973,0.0021663227,0.00010042215,0.0002003499,0.00057992514,0.00633409,0.0041421237],"genre_scores_gemma":[0.9213823,0.00014871935,0.076421775,0.00037129363,0.000026186413,0.00009827574,0.00045988557,0.00025755013,0.0008340589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963587,0.0016729699,0.00016820792,0.00064224517,0.0008446064,0.00031332337],"domain_scores_gemma":[0.9827084,0.0111299,0.0011647912,0.0037475196,0.00082459464,0.00042484727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046452265,0.0010729643,0.00072697405,0.00067645765,0.00049585017,0.001329244,0.0023372676,0.0016499206,0.001602317],"category_scores_gemma":[0.034427393,0.0004963211,0.0007183525,0.00038143495,0.0031684842,0.005015707,0.0033433978,0.003373299,0.00043980038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013752638,0.0005089643,0.02645871,0.0006008615,0.00019854071,0.0005390363,0.00093946594,0.7415629,0.033278346,0.057022374,0.0032608462,0.13425462],"study_design_scores_gemma":[0.00003181723,0.00033227814,0.0007325032,0.00003844455,0.00001680662,0.0001226539,0.00008788127,0.96468383,0.013101992,0.02001027,0.00081836083,0.000023185063],"about_ca_topic_score_codex":0.0027703503,"about_ca_topic_score_gemma":0.0034446027,"teacher_disagreement_score":0.0046452265,"about_ca_system_score_codex":0.0010336639,"about_ca_system_score_gemma":0.0021002765,"threshold_uncertainty_score":0.02456659},"labels":[],"label_agreement":null},{"id":"W7105933270","doi":"10.23952/jano.7.2025.3.02","title":"Towards robust adversarial examples for deep neural networks","year":2025,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Artificial neural network; Deep learning; Deep neural networks; Key (lock); Feature (linguistics)","score_opus":0.010413139310401167,"score_gpt":0.24417596679944667,"score_spread":0.2337628274890455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105933270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00544674,0.000109799126,0.99308664,0.00009694176,0.00002470173,0.000025018853,0.000018197452,0.0002766625,0.000915231],"genre_scores_gemma":[0.522984,0.00031683923,0.4711274,0.0003080645,0.000115641196,0.00023541202,0.00020971091,0.00037067133,0.0043322546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983935,0.000651254,0.00006910223,0.00019584724,0.00057436415,0.00011591122],"domain_scores_gemma":[0.9963541,0.0023176188,0.00033617805,0.00050074863,0.000367415,0.0001239909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003010972,0.0015401352,0.001115393,0.0009399734,0.00038209895,0.0010067131,0.0015973503,0.0015766511,0.0022413468],"category_scores_gemma":[0.01049256,0.0006362497,0.0010545363,0.00040906316,0.002149838,0.0018267897,0.0035984053,0.0030735633,0.0005792751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008198295,0.000017965916,0.00020129673,0.00004625451,0.0000335176,0.0000497985,0.000031634358,0.93125516,0.0019378678,0.044240825,0.00060232,0.021501362],"study_design_scores_gemma":[0.000002983523,0.00001528812,0.000017615104,0.0000052728674,0.0000018849855,0.000010313774,0.0000021286396,0.98718226,0.00077047,0.01171245,0.00027604398,0.0000033193041],"about_ca_topic_score_codex":0.00087731786,"about_ca_topic_score_gemma":0.00074175856,"teacher_disagreement_score":0.003010972,"about_ca_system_score_codex":0.0009886675,"about_ca_system_score_gemma":0.00054703385,"threshold_uncertainty_score":0.015923738},"labels":[],"label_agreement":null},{"id":"W7105990745","doi":"10.2139/ssrn.5768480","title":"A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher?","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Pipeline (software); Perspective (graphical); Set (abstract data type); Fidelity; Inference; Language model; Code (set theory); Behavioral modeling","score_opus":0.026833304661548832,"score_gpt":0.3398161319375807,"score_spread":0.3129828272760319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105990745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04731305,0.00029326248,0.92521304,0.009459404,0.00010874124,0.000054880547,0.00015464683,0.00047775748,0.016925145],"genre_scores_gemma":[0.91808784,0.00017890814,0.074241966,0.0008503236,0.00016485612,0.000086198655,0.00013980594,0.00018727718,0.0060627805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951847,0.0026687272,0.00015952066,0.00070336985,0.0009255166,0.00035813474],"domain_scores_gemma":[0.96360373,0.028472695,0.001268552,0.0043355096,0.0015531986,0.0007664314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063300645,0.00080268114,0.0010950607,0.0010146309,0.0010142293,0.0027791313,0.0027580396,0.003992663,0.0066951844],"category_scores_gemma":[0.042245477,0.0006211826,0.0010425871,0.0005922122,0.008445974,0.0093762465,0.005648244,0.006862111,0.0006828285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103087565,0.000046153982,0.000797164,0.000069519636,0.000030718216,0.00015466659,0.0003309197,0.04767435,0.001102135,0.9305863,0.0014170507,0.017688],"study_design_scores_gemma":[0.000012392182,0.000034439934,0.0000987317,0.000023518114,0.000005848747,0.000042415973,0.000041708186,0.20500635,0.0012169371,0.7928204,0.0006844707,0.000012811868],"about_ca_topic_score_codex":0.001288998,"about_ca_topic_score_gemma":0.00088341907,"teacher_disagreement_score":0.0066951844,"about_ca_system_score_codex":0.0016073908,"about_ca_system_score_gemma":0.0013552984,"threshold_uncertainty_score":0.03347695},"labels":[],"label_agreement":null},{"id":"W7106679660","doi":"10.5281/zenodo.17713568","title":"EXPLOITING AND SECURING MACHINE LEARNING: A CYBERSECURITY PERSPECTIVE ON ADVERSARIAL VULNERABILITIES AND COUNTERMEASURES","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dawson College","funders":"","keywords":"Adversarial system; Perspective (graphical); Retraining; Resilience (materials science); Malware; Noise (video); Deep learning; Adversarial machine learning","score_opus":0.018410903611345942,"score_gpt":0.2567457876759767,"score_spread":0.23833488406463077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106679660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44241947,0.0016177737,0.53678536,0.004202956,0.00028531338,0.00016383764,0.00033369544,0.005300698,0.00889094],"genre_scores_gemma":[0.9253706,0.00046532942,0.07176267,0.00035397577,0.00004005056,0.00006213691,0.0002833365,0.00017373628,0.0014882201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99813986,0.0007085796,0.000091104666,0.0002800094,0.00057643815,0.00020386452],"domain_scores_gemma":[0.99516183,0.0018882023,0.00044430958,0.0019414494,0.00043388826,0.00013043631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030813534,0.0008094496,0.00047909873,0.00060236815,0.00042075306,0.0011370248,0.0010801726,0.00093703193,0.0009668422],"category_scores_gemma":[0.009850317,0.0002503958,0.00044146413,0.00041357684,0.00232089,0.0029390766,0.0019498188,0.002437688,0.00043164243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040434863,0.0004186683,0.015396241,0.00040388614,0.00020069377,0.000482192,0.0005226339,0.678353,0.049794998,0.039717194,0.005668702,0.20863743],"study_design_scores_gemma":[0.000014187096,0.0004368236,0.0032720915,0.00008856102,0.000027175643,0.00032643368,0.00011418852,0.9138177,0.051712595,0.024677876,0.005465487,0.000046894707],"about_ca_topic_score_codex":0.0012614178,"about_ca_topic_score_gemma":0.0012925798,"teacher_disagreement_score":0.0030813534,"about_ca_system_score_codex":0.00067037035,"about_ca_system_score_gemma":0.0007834545,"threshold_uncertainty_score":0.01629591},"labels":[],"label_agreement":null},{"id":"W7106688784","doi":"10.5281/zenodo.17713569","title":"EXPLOITING AND SECURING MACHINE LEARNING: A CYBERSECURITY PERSPECTIVE ON ADVERSARIAL VULNERABILITIES AND COUNTERMEASURES","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dawson College","funders":"","keywords":"Adversarial system; Perspective (graphical); Retraining; Resilience (materials science); Malware; Noise (video); Deep learning; Adversarial machine learning","score_opus":0.018410903611345942,"score_gpt":0.2567457876759767,"score_spread":0.23833488406463077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106688784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44241947,0.0016177737,0.53678536,0.004202956,0.00028531338,0.00016383764,0.00033369544,0.005300698,0.00889094],"genre_scores_gemma":[0.9253706,0.00046532942,0.07176267,0.00035397577,0.00004005056,0.00006213691,0.0002833365,0.00017373628,0.0014882201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813986,0.0007085796,0.000091104666,0.0002800094,0.00057643815,0.00020386452],"domain_scores_gemma":[0.99516183,0.0018882023,0.00044430958,0.0019414494,0.00043388826,0.00013043631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030813534,0.0008094496,0.00047909873,0.00060236815,0.00042075306,0.0011370248,0.0010801726,0.00093703193,0.0009668422],"category_scores_gemma":[0.009850317,0.0002503958,0.00044146413,0.00041357684,0.00232089,0.0029390766,0.0019498188,0.002437688,0.00043164243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040434863,0.0004186683,0.015396241,0.00040388614,0.00020069377,0.000482192,0.0005226339,0.678353,0.049794998,0.039717194,0.005668702,0.20863743],"study_design_scores_gemma":[0.000014187096,0.0004368236,0.0032720915,0.00008856102,0.000027175643,0.00032643368,0.00011418852,0.9138177,0.051712595,0.024677876,0.005465487,0.000046894707],"about_ca_topic_score_codex":0.0012614178,"about_ca_topic_score_gemma":0.0012925798,"teacher_disagreement_score":0.0030813534,"about_ca_system_score_codex":0.00067037035,"about_ca_system_score_gemma":0.0007834545,"threshold_uncertainty_score":0.01629591},"labels":[],"label_agreement":null},{"id":"W7108328114","doi":"10.1145/3767695.3769517","title":"Adversarial Attacks against Neural Ranking Models via In-Context Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Adversarial system; Ranking (information retrieval); Generalization; Set (abstract data type); Rank (graph theory); Scalability; Artificial neural network","score_opus":0.014655611215970992,"score_gpt":0.2690068626495586,"score_spread":0.2543512514335876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108328114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053459767,0.00064614986,0.9377335,0.0007332448,0.0001242361,0.00011452154,0.00016187567,0.0026956347,0.004331158],"genre_scores_gemma":[0.9118101,0.00027232367,0.08191174,0.0005803341,0.00010397626,0.00014257822,0.00023130904,0.00022690803,0.004720626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826366,0.00076937524,0.00007110955,0.00029962,0.00042320785,0.00017303012],"domain_scores_gemma":[0.9951133,0.0032412678,0.0003929924,0.0008518382,0.0002788275,0.00012180828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002138716,0.0012035003,0.00086975686,0.000439181,0.0005183865,0.00090312876,0.0013621885,0.0011592872,0.002065712],"category_scores_gemma":[0.010671182,0.00036321417,0.00060490303,0.00030972663,0.0015429858,0.0019423807,0.0022633367,0.0025884581,0.00082719274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002274129,0.00010149798,0.0013281706,0.00014741176,0.00008838538,0.000204978,0.00013150634,0.86541075,0.011796351,0.037261955,0.004630791,0.07867075],"study_design_scores_gemma":[0.000009325326,0.00006283927,0.00009380462,0.000007655594,0.000008389996,0.000053921274,0.000010052146,0.9826171,0.0034323123,0.0129368305,0.0007574018,0.000010367892],"about_ca_topic_score_codex":0.0014543509,"about_ca_topic_score_gemma":0.0020964334,"teacher_disagreement_score":0.002138716,"about_ca_system_score_codex":0.0008748569,"about_ca_system_score_gemma":0.0007826225,"threshold_uncertainty_score":0.011310756},"labels":[],"label_agreement":null},{"id":"W7108704788","doi":"10.5281/zenodo.17818478","title":"The Adversarial Conditioning Paradox: Why Attacked Inputs Are More Stable, Not Less","year":2025,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Exploit; Embedding; Conditioning; Jacobian matrix and determinant; Property (philosophy); Trigonometric functions","score_opus":0.043106748754775014,"score_gpt":0.2834536411488886,"score_spread":0.24034689239411358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108704788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47261,0.0007836372,0.5136032,0.0027531586,0.00020395975,0.000090863505,0.00039188605,0.0022775086,0.007285807],"genre_scores_gemma":[0.9775367,0.00010691712,0.020706743,0.00031991632,0.00003417335,0.000023033292,0.00019521413,0.00019550328,0.00088185054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970487,0.0010241782,0.00013487981,0.00081885,0.00075765996,0.0002157549],"domain_scores_gemma":[0.9818205,0.01098989,0.0021826313,0.003285263,0.0010850584,0.0006366016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00496375,0.0010546698,0.0007696402,0.0009335971,0.00058905187,0.002038922,0.00075726665,0.0015238205,0.0027137126],"category_scores_gemma":[0.02869787,0.0004298124,0.000589381,0.0004177956,0.0036073509,0.004158052,0.002206974,0.0032117225,0.0007924096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002430645,0.00032638432,0.055690344,0.0005613447,0.00057542196,0.00081719045,0.0007375772,0.4652279,0.13901559,0.079534225,0.009688264,0.2453951],"study_design_scores_gemma":[0.000054403445,0.0004858948,0.014309546,0.00007500533,0.00008093537,0.0006307466,0.0001587772,0.77836734,0.08893884,0.11452943,0.0022886312,0.00008037303],"about_ca_topic_score_codex":0.00077016104,"about_ca_topic_score_gemma":0.0007040838,"teacher_disagreement_score":0.00496375,"about_ca_system_score_codex":0.00084700953,"about_ca_system_score_gemma":0.00053378823,"threshold_uncertainty_score":0.026251137},"labels":[],"label_agreement":null},{"id":"W7108749442","doi":"10.5281/zenodo.17818479","title":"The Adversarial Conditioning Paradox: Why Attacked Inputs Are More Stable, Not Less","year":2025,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adversarial system; Exploit; Embedding; Conditioning; Jacobian matrix and determinant; Property (philosophy); Trigonometric functions","score_opus":0.043106748754775014,"score_gpt":0.2834536411488886,"score_spread":0.24034689239411358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108749442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47261,0.0007836372,0.5136032,0.0027531586,0.00020395975,0.000090863505,0.00039188605,0.0022775086,0.007285807],"genre_scores_gemma":[0.9775367,0.00010691712,0.020706743,0.00031991632,0.00003417335,0.000023033292,0.00019521413,0.00019550328,0.00088185054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970487,0.0010241782,0.00013487981,0.00081885,0.00075765996,0.0002157549],"domain_scores_gemma":[0.9818205,0.01098989,0.0021826313,0.003285263,0.0010850584,0.0006366016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00496375,0.0010546698,0.0007696402,0.0009335971,0.00058905187,0.002038922,0.00075726665,0.0015238205,0.0027137126],"category_scores_gemma":[0.02869787,0.0004298124,0.000589381,0.0004177956,0.0036073509,0.004158052,0.002206974,0.0032117225,0.0007924096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002430645,0.00032638432,0.055690344,0.0005613447,0.00057542196,0.00081719045,0.0007375772,0.4652279,0.13901559,0.079534225,0.009688264,0.2453951],"study_design_scores_gemma":[0.000054403445,0.0004858948,0.014309546,0.00007500533,0.00008093537,0.0006307466,0.0001587772,0.77836734,0.08893884,0.11452943,0.0022886312,0.00008037303],"about_ca_topic_score_codex":0.00077016104,"about_ca_topic_score_gemma":0.0007040838,"teacher_disagreement_score":0.00496375,"about_ca_system_score_codex":0.00084700953,"about_ca_system_score_gemma":0.00053378823,"threshold_uncertainty_score":0.026251137},"labels":[],"label_agreement":null},{"id":"W7109952297","doi":"10.4230/lipics.sat.2025.32","title":"Efficient Certified Reasoning for Binarized Neural Networks","year":2025,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Vetenskapsrådet; National Supercomputing Centre Singapore; University of Toronto; Innovation, Science and Economic Development Canada","keywords":"Speedup; Soundness; Scalability; Robustness (evolution); Benchmark (surveying); Artificial neural network; Formal verification; Pipeline (software)","score_opus":0.011078407872012327,"score_gpt":0.2723201104293548,"score_spread":0.26124170255734247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7109952297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021190953,0.00031296938,0.96059287,0.0009870584,0.00014558842,0.0002516159,0.00073472585,0.011273879,0.004510306],"genre_scores_gemma":[0.3833543,0.00043763543,0.60497606,0.0011233862,0.00009313283,0.00039802075,0.002180848,0.0024111327,0.005025515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99120504,0.002275681,0.0006002523,0.0014994513,0.003558357,0.00086109614],"domain_scores_gemma":[0.98203903,0.0110009005,0.0010123713,0.0029834525,0.0026830428,0.0002811444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043672477,0.0015854585,0.0010257458,0.0016242376,0.0013046787,0.003892875,0.0044051665,0.0020893228,0.010787371],"category_scores_gemma":[0.032730278,0.001292304,0.003540136,0.0008942168,0.0034035475,0.0067429584,0.0046992865,0.003822408,0.0016595972],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007807456,0.00029746396,0.0046751215,0.0016803436,0.00033728287,0.0010545973,0.00061206776,0.4827873,0.028829135,0.27244037,0.013755263,0.1927504],"study_design_scores_gemma":[0.000097294695,0.00005144624,0.0001749435,0.00012453359,0.000090518675,0.0001465138,0.00008767842,0.8291045,0.02252753,0.14068875,0.0068634977,0.00004280087],"about_ca_topic_score_codex":0.011627227,"about_ca_topic_score_gemma":0.024454648,"teacher_disagreement_score":0.011627227,"about_ca_system_score_codex":0.0040937113,"about_ca_system_score_gemma":0.0075761112,"threshold_uncertainty_score":0.036087334},"labels":[],"label_agreement":null},{"id":"W7109957739","doi":"10.4230/oasics.saia.2024.12","title":"A View on Vulnerabilites: The Security Challenges of XAI (Academic Track)","year":2025,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adversarial system; Transparency (behavior); Perspective (graphical); Evasion (ethics); Government (linguistics)","score_opus":0.017866769969852286,"score_gpt":0.2996563220959894,"score_spread":0.2817895521261371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7109957739","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013152435,0.09530274,0.37625998,0.46769258,0.005094734,0.0000790693,0.0005360027,0.0011524222,0.040730074],"genre_scores_gemma":[0.55048543,0.1521976,0.17716579,0.055388972,0.019154623,0.0004666429,0.00097062613,0.0009544609,0.0432159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944112,0.001926143,0.00027897058,0.0009753879,0.0020193383,0.00038893163],"domain_scores_gemma":[0.9733619,0.019135326,0.0012259316,0.0031621156,0.0024218736,0.00069290935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012871651,0.0009524989,0.00092581083,0.0029286444,0.0023459096,0.008704293,0.0020656087,0.006337663,0.006868122],"category_scores_gemma":[0.031152982,0.0007590475,0.0011403185,0.0021354205,0.012001181,0.020182014,0.005712834,0.01643721,0.0021672177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000594245,0.00003303616,0.0013375003,0.00028929184,0.000052819487,0.00017365284,0.0007738105,0.0057096076,0.00059661095,0.84021986,0.039777476,0.110976964],"study_design_scores_gemma":[0.0000072537487,0.000057413818,0.0009085234,0.00071983814,0.000024841782,0.00045176654,0.00040887826,0.019123906,0.00143751,0.83274543,0.14404434,0.00007029016],"about_ca_topic_score_codex":0.001750355,"about_ca_topic_score_gemma":0.0010089653,"teacher_disagreement_score":0.012871651,"about_ca_system_score_codex":0.0039256327,"about_ca_system_score_gemma":0.0016190136,"threshold_uncertainty_score":0.06807262},"labels":[],"label_agreement":null},{"id":"W7110910564","doi":"10.5555/3666122.3667167","title":"Optimal Transport Model Distributional Robustness","year":2023,"lang":"en","type":"article","venue":"Monash University Research Portal (Monash University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Robustness (evolution); Prior probability; Probabilistic logic; Bayesian probability; Minification; Statistical model","score_opus":0.047573814235277345,"score_gpt":0.28602365059338514,"score_spread":0.2384498363581078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110910564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011075468,0.00016471116,0.98668367,0.0002897605,0.000020962514,0.000027753133,0.0000847112,0.00049923564,0.0011537398],"genre_scores_gemma":[0.83153003,0.00052174076,0.16007593,0.0007004582,0.00012573245,0.00022218349,0.0008226488,0.0007175455,0.0052836756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978593,0.0007628466,0.00012226397,0.00052598375,0.00052921224,0.00020030297],"domain_scores_gemma":[0.99627703,0.0018445076,0.00052760117,0.00076364435,0.0004021242,0.00018513252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045859683,0.0016663643,0.0016890857,0.0013890114,0.0006257536,0.0016205073,0.0027673827,0.0022285266,0.0033944903],"category_scores_gemma":[0.01266257,0.00076666276,0.0019136328,0.0009107712,0.0025012386,0.004588124,0.004537107,0.0038431848,0.0006860734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010656694,0.000052100684,0.0007760178,0.00008351104,0.0000805495,0.000085226246,0.00005772622,0.9118077,0.0033339323,0.05765305,0.0012667697,0.024696888],"study_design_scores_gemma":[0.000005276906,0.000048373462,0.000094627125,0.000011830349,0.000008350405,0.00002813248,0.000008247124,0.9649304,0.0015117998,0.032926712,0.0004170294,0.000009213256],"about_ca_topic_score_codex":0.0021937839,"about_ca_topic_score_gemma":0.0016130036,"teacher_disagreement_score":0.0045859683,"about_ca_system_score_codex":0.002342401,"about_ca_system_score_gemma":0.0015976339,"threshold_uncertainty_score":0.02425319},"labels":[],"label_agreement":null},{"id":"W7111273077","doi":"10.18280/ijsse.150812","title":"A Review on Adversarial Attacks and Defenses on Image Classification Models","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Contextual image classification; Image (mathematics); Pattern recognition (psychology); Statistical classification","score_opus":0.01392410132267722,"score_gpt":0.2848217649671562,"score_spread":0.270897663644479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111273077","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001668122,0.84512264,0.1394024,0.0030396106,0.0016970304,0.000038769427,0.00009818431,0.00016267727,0.008770464],"genre_scores_gemma":[0.05172403,0.88385487,0.047038004,0.0024475646,0.0066434667,0.00010724711,0.0003307349,0.0001087532,0.007745275],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983876,0.0003772376,0.00017601596,0.00031332966,0.0006644807,0.00008134128],"domain_scores_gemma":[0.9960829,0.002922492,0.00021334764,0.00028726095,0.000445439,0.000048481346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023010985,0.0013170325,0.0017311646,0.0020967817,0.0004150993,0.0023321884,0.0016806329,0.0024735453,0.0027531742],"category_scores_gemma":[0.006766088,0.00063812925,0.0011412905,0.0030049572,0.0014902118,0.0039516594,0.0012308664,0.0032297247,0.0015667703],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009105101,0.000101868965,0.00066055777,0.0041108695,0.000201467,0.00012920401,0.00008936805,0.026246909,0.001957155,0.10681507,0.033258263,0.82633823],"study_design_scores_gemma":[0.000037850124,0.0004921627,0.0022460818,0.0039075054,0.0003861983,0.0023098108,0.0001421327,0.12006167,0.005845547,0.22105512,0.6433467,0.00016926906],"about_ca_topic_score_codex":0.00076147856,"about_ca_topic_score_gemma":0.00049253035,"teacher_disagreement_score":0.0027531742,"about_ca_system_score_codex":0.0009475613,"about_ca_system_score_gemma":0.0008007113,"threshold_uncertainty_score":0.01216948},"labels":[],"label_agreement":null},{"id":"W7112209861","doi":"","title":"ROBUST MACHINE LEARNING USING SUPERQUANTILES","year":2021,"lang":"","type":"dissertation","venue":"Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Adversarial system; Artificial neural network; Computation; Support vector machine; Online machine learning; Deep learning; Robotics","score_opus":0.054399841031698835,"score_gpt":0.2823500705063668,"score_spread":0.22795022947466795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7112209861","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02149065,0.0004668045,0.973807,0.00058385404,0.00004780378,0.000040389026,0.00005240024,0.00048592137,0.003025248],"genre_scores_gemma":[0.72628087,0.0011460928,0.26381356,0.00040579832,0.00018649845,0.00022300833,0.00046603917,0.0004458487,0.007032235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998629,0.0005887403,0.00005866013,0.00022102302,0.0003932038,0.00010943093],"domain_scores_gemma":[0.9963152,0.00212197,0.00037432287,0.00069341034,0.00040665083,0.00008846607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028639378,0.000873413,0.0008901822,0.0007569877,0.00044746962,0.0013205387,0.0012110976,0.0010345638,0.00249702],"category_scores_gemma":[0.009822405,0.00053957425,0.0008448859,0.00061773084,0.0019165328,0.0024181274,0.002945986,0.0032351667,0.0007618415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009719525,0.00004566702,0.00055394205,0.00009537788,0.000060304512,0.000060513685,0.000090672256,0.86613953,0.0045306995,0.064769246,0.00163358,0.06192332],"study_design_scores_gemma":[0.0000042598417,0.00004590991,0.00008725948,0.000013033064,0.0000034040954,0.000016853754,0.000007904033,0.9745062,0.0013637136,0.023323422,0.000621988,0.0000060636257],"about_ca_topic_score_codex":0.0014453462,"about_ca_topic_score_gemma":0.001278439,"teacher_disagreement_score":0.0028639378,"about_ca_system_score_codex":0.001158404,"about_ca_system_score_gemma":0.0009294509,"threshold_uncertainty_score":0.015146136},"labels":[],"label_agreement":null},{"id":"W7114916806","doi":"10.1162/tacl.a.62","title":"Towards More Realistic Extraction Attacks: An Adversarial Perspective","year":2025,"lang":"en","type":"article","venue":"Transactions of the Association for Computational Linguistics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Adversarial system; Adversary; Perspective (graphical); Training set; Memorization; Attack model","score_opus":0.014302450615151271,"score_gpt":0.33941185395921875,"score_spread":0.32510940334406746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114916806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100952886,0.00048858824,0.8818971,0.0041555213,0.0001829155,0.00024205248,0.00043843462,0.0013411449,0.010301379],"genre_scores_gemma":[0.9277842,0.00024725436,0.06723172,0.00082428014,0.00009896348,0.00012819657,0.0002241364,0.00018779647,0.003273391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99408674,0.0032100054,0.00023830436,0.000770865,0.0012329065,0.00046115275],"domain_scores_gemma":[0.95825267,0.031059982,0.0017231996,0.0075443345,0.0010532683,0.00036664083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006008617,0.00097610755,0.00083225744,0.0008660495,0.0009449301,0.0022548072,0.0014049147,0.0022856363,0.0032433681],"category_scores_gemma":[0.037091862,0.0005844443,0.0009360754,0.0005581822,0.0031941193,0.0051622307,0.004250068,0.004898091,0.0008256504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036714057,0.00020424133,0.0040273773,0.00017843627,0.00013131894,0.0006959915,0.00045326105,0.7845139,0.0124506485,0.15593332,0.0059489254,0.0350955],"study_design_scores_gemma":[0.000019364732,0.00006508721,0.00028849146,0.000039360028,0.000014907372,0.00023906337,0.00006456278,0.9081494,0.005793771,0.08284262,0.002458101,0.000025142965],"about_ca_topic_score_codex":0.0007787325,"about_ca_topic_score_gemma":0.0006660346,"teacher_disagreement_score":0.006008617,"about_ca_system_score_codex":0.0011507954,"about_ca_system_score_gemma":0.00088481914,"threshold_uncertainty_score":0.031776965},"labels":[],"label_agreement":null},{"id":"W7115209203","doi":"","title":"DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models","year":2024,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Army Research Office","keywords":"Orthonormal basis; Preprocessor; Dimensionality reduction; Deep learning; Pattern recognition (psychology); Classifier (UML); Artificial neural network; Curse of dimensionality; Projection (relational algebra)","score_opus":0.1046396780305977,"score_gpt":0.3136496063700711,"score_spread":0.2090099283394734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115209203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008984707,0.0002902483,0.9864097,0.00032836103,0.000055446762,0.000059193913,0.00011380952,0.002185667,0.0015728115],"genre_scores_gemma":[0.7548704,0.00037870574,0.23670602,0.0005915653,0.00009468204,0.00046933783,0.0006153168,0.0007117747,0.005562175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914694,0.00017643768,0.000041829204,0.00022276136,0.00031432728,0.00009766328],"domain_scores_gemma":[0.9983974,0.0007696001,0.00018692225,0.00023736696,0.00031789328,0.00009068897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021479004,0.001238831,0.0010165859,0.0006600705,0.00051386683,0.0013604797,0.0024054435,0.0013390726,0.003196753],"category_scores_gemma":[0.0063693468,0.0006047494,0.0007316069,0.0004830592,0.0016093724,0.0017065054,0.0029749859,0.0037829517,0.0006771261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008057465,0.00007928295,0.000534722,0.000085746164,0.000033292563,0.00005620368,0.000050694267,0.8970411,0.0023234321,0.019263124,0.0029487251,0.0775031],"study_design_scores_gemma":[0.000003864333,0.000007723828,0.000013689905,0.0000032243092,0.0000011136444,0.00000224719,0.0000015942974,0.9961934,0.00038417918,0.003136958,0.0002498842,0.0000021330943],"about_ca_topic_score_codex":0.012433501,"about_ca_topic_score_gemma":0.012960213,"teacher_disagreement_score":0.012433501,"about_ca_system_score_codex":0.0023234056,"about_ca_system_score_gemma":0.002618672,"threshold_uncertainty_score":0.024722278},"labels":[],"label_agreement":null},{"id":"W7116655798","doi":"10.1016/b978-0-443-14109-6.00011-0","title":"Am (A)I hallucinating? Nonrobustness, hallucinations, and unpredictable performance of AI for MR image reconstruction","year":2025,"lang":"en","type":"book-chapter","venue":"Advances in magnetic resonance technology and applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Trustworthiness; Image (mathematics); Key (lock); Iterative reconstruction; Range (aeronautics); Medical imaging","score_opus":0.003720495113728217,"score_gpt":0.2427824457376022,"score_spread":0.23906195062387398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116655798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19191806,0.028369784,0.3770276,0.2110575,0.007988517,0.00008897239,0.00036331575,0.0014385547,0.1817477],"genre_scores_gemma":[0.9076872,0.009922024,0.028970545,0.014246575,0.0024467884,0.000041811476,0.00008211099,0.00021583309,0.036387138],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963284,0.00013448436,0.00001310209,0.000059624734,0.00011179969,0.000048159298],"domain_scores_gemma":[0.9979486,0.001084797,0.0002469515,0.00027438204,0.0002643252,0.00018095343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012575387,0.00028123122,0.00022666418,0.00018577086,0.00048611683,0.002014249,0.00046618565,0.0017066629,0.0033272475],"category_scores_gemma":[0.0096059125,0.00019713727,0.00020691253,0.00017488051,0.0033724543,0.0035089767,0.00069794495,0.0031422938,0.001365351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072515645,0.00012224453,0.009400591,0.00055949186,0.00022806636,0.0023651267,0.0070607346,0.011348819,0.03996487,0.40220168,0.11348067,0.41254252],"study_design_scores_gemma":[0.000054953525,0.00051127863,0.007431136,0.00048582072,0.00011034842,0.013644386,0.0059923064,0.05693663,0.022044424,0.7329001,0.15966445,0.00022415897],"about_ca_topic_score_codex":0.00047150056,"about_ca_topic_score_gemma":0.00033671453,"teacher_disagreement_score":0.0033272475,"about_ca_system_score_codex":0.0002171188,"about_ca_system_score_gemma":0.00020233788,"threshold_uncertainty_score":0.01113075},"labels":[],"label_agreement":null},{"id":"W7116857466","doi":"10.64898/2025.12.19.25342673","title":"The Forgotten Shield: Safety Grafting in Parameter-Space for Medical MLLMs","year":2025,"lang":"","type":"article","venue":"medRxiv","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Process (computing); Forgetting; Benchmark (surveying); Patient safety; System safety; Medical device; Work (physics)","score_opus":0.012434679877591639,"score_gpt":0.2984077255061033,"score_spread":0.28597304562851167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116857466","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18829565,0.0011650284,0.79018813,0.001650109,0.00024590644,0.000207026,0.00044298018,0.011542441,0.006262749],"genre_scores_gemma":[0.91469806,0.00017237099,0.082191184,0.0004918449,0.00004875863,0.000096844546,0.0003833251,0.00057969353,0.0013379705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964185,0.0018716791,0.0001588168,0.00050413137,0.00080085726,0.00024610254],"domain_scores_gemma":[0.990568,0.005585756,0.0007104523,0.0022708524,0.0005965275,0.00026848167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055407532,0.0011109298,0.0006558492,0.00071558624,0.0005466278,0.0014625479,0.0014799506,0.0018294373,0.0030585553],"category_scores_gemma":[0.022011356,0.0004157321,0.00085018366,0.000282386,0.0022930794,0.0025240616,0.003734446,0.003059688,0.00093967747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065689295,0.00018740402,0.0033159554,0.00020249798,0.00011432945,0.0002264736,0.00025105354,0.8671192,0.012079059,0.022121243,0.0045874207,0.08913856],"study_design_scores_gemma":[0.000021800473,0.00016815563,0.000220127,0.000029175539,0.000013121858,0.000067421744,0.000040431274,0.9751418,0.00935949,0.013454292,0.0014646464,0.00001954083],"about_ca_topic_score_codex":0.0013711132,"about_ca_topic_score_gemma":0.0012922948,"teacher_disagreement_score":0.0055407532,"about_ca_system_score_codex":0.0009255012,"about_ca_system_score_gemma":0.0012774987,"threshold_uncertainty_score":0.029302657},"labels":[],"label_agreement":null},{"id":"W7116896472","doi":"10.26599/tst.2024.9010243","title":"From Model Parameters to Data Quality: Implicit Factor Evaluation of Model Extraction Attacks","year":2025,"lang":"en","type":"article","venue":"Tsinghua Science & Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Quality (philosophy); Range (aeronautics); Key (lock); Annotation; Focus (optics); Deep learning; Data quality","score_opus":0.15482746084436322,"score_gpt":0.4612913278443817,"score_spread":0.3064638670000185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116896472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5816902,0.0018694787,0.4090821,0.0016815307,0.00017269177,0.00024793923,0.0003651864,0.0013874514,0.0035034649],"genre_scores_gemma":[0.97586006,0.00011449919,0.023424847,0.00010341832,0.000022890275,0.000034278582,0.00014233465,0.000072477844,0.00022518841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9907879,0.0051513696,0.00055673614,0.0008958908,0.002067315,0.0005407341],"domain_scores_gemma":[0.8932444,0.0822528,0.0058454517,0.013162932,0.004364915,0.0011294717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016741555,0.0015081752,0.001075493,0.0011008201,0.00054031564,0.0016164551,0.0009961388,0.001470784,0.001098953],"category_scores_gemma":[0.106338166,0.00039629874,0.00071428734,0.0006312949,0.0022659139,0.0036391858,0.0023014182,0.0027243984,0.00022133828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015661643,0.00022244266,0.014211575,0.00024711055,0.00026839,0.00015147167,0.00013161493,0.907728,0.006628472,0.011963534,0.0015144721,0.055366796],"study_design_scores_gemma":[0.000024751063,0.00024875003,0.0011210139,0.000033077307,0.000026043419,0.000062685234,0.000034069864,0.9873518,0.005234836,0.005605428,0.0002377164,0.000019858455],"about_ca_topic_score_codex":0.0016129944,"about_ca_topic_score_gemma":0.0012725779,"teacher_disagreement_score":0.016741555,"about_ca_system_score_codex":0.0016068699,"about_ca_system_score_gemma":0.0011568519,"threshold_uncertainty_score":0.088538885},"labels":[],"label_agreement":null},{"id":"W7116959228","doi":"10.5281/zenodo.18016506","title":"Structural Retention Index (SRI): A Collapse Index Extension for Orthogonal Stability Assessment","year":2025,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glycemic Index Laboratories","funders":"","keywords":"Discriminative model; Extension (predicate logic); Stability (learning theory); Metric (unit); Index (typography); Computation; Key (lock); Base (topology); Fragility","score_opus":0.05215174655622574,"score_gpt":0.3044627794009185,"score_spread":0.25231103284469275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116959228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066205405,0.0017666369,0.8721784,0.0009859928,0.00040549593,0.00064283435,0.016874794,0.02068395,0.020256462],"genre_scores_gemma":[0.54040515,0.0007838593,0.4014448,0.000756137,0.00032344682,0.0013405245,0.04133144,0.0050765052,0.008538068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647397,0.00082101446,0.0002889958,0.0007705721,0.0014062767,0.00023918803],"domain_scores_gemma":[0.9858725,0.0064396937,0.0015747758,0.0031094165,0.0026172218,0.00038644133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005443458,0.0020992954,0.000993714,0.004817764,0.0010035106,0.0030103663,0.001895193,0.0015082706,0.0105143525],"category_scores_gemma":[0.031623308,0.000516387,0.0014133777,0.0024751988,0.0014067481,0.0035063382,0.0034899123,0.0030366136,0.0045616427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009089603,0.00042845338,0.05449313,0.001339956,0.00063826516,0.00042371303,0.00080111343,0.19096671,0.018102221,0.05185173,0.11139013,0.56865567],"study_design_scores_gemma":[0.000086932145,0.0005608029,0.013567653,0.0002792626,0.00012505963,0.0004625327,0.00023403954,0.8168235,0.021298034,0.11291472,0.03348583,0.00016168616],"about_ca_topic_score_codex":0.0029097106,"about_ca_topic_score_gemma":0.0041750157,"teacher_disagreement_score":0.0105143525,"about_ca_system_score_codex":0.0012137752,"about_ca_system_score_gemma":0.0019085177,"threshold_uncertainty_score":0.035174012},"labels":[],"label_agreement":null},{"id":"W7116969159","doi":"10.5281/zenodo.18016507","title":"Structural Retention Index (SRI): A Collapse Index Extension for Orthogonal Stability Assessment","year":2025,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glycemic Index Laboratories","funders":"","keywords":"Discriminative model; Extension (predicate logic); Stability (learning theory); Metric (unit); Index (typography); Computation; Key (lock); Base (topology); Fragility","score_opus":0.05215174655622574,"score_gpt":0.3044627794009185,"score_spread":0.25231103284469275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116969159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066205405,0.0017666369,0.8721784,0.0009859928,0.00040549593,0.00064283435,0.016874794,0.02068395,0.020256462],"genre_scores_gemma":[0.54040515,0.0007838593,0.4014448,0.000756137,0.00032344682,0.0013405245,0.04133144,0.0050765052,0.008538068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647397,0.00082101446,0.0002889958,0.0007705721,0.0014062767,0.00023918803],"domain_scores_gemma":[0.9858725,0.0064396937,0.0015747758,0.0031094165,0.0026172218,0.00038644133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005443458,0.0020992954,0.000993714,0.004817764,0.0010035106,0.0030103663,0.001895193,0.0015082706,0.0105143525],"category_scores_gemma":[0.031623308,0.000516387,0.0014133777,0.0024751988,0.0014067481,0.0035063382,0.0034899123,0.0030366136,0.0045616427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009089603,0.00042845338,0.05449313,0.001339956,0.00063826516,0.00042371303,0.00080111343,0.19096671,0.018102221,0.05185173,0.11139013,0.56865567],"study_design_scores_gemma":[0.000086932145,0.0005608029,0.013567653,0.0002792626,0.00012505963,0.0004625327,0.00023403954,0.8168235,0.021298034,0.11291472,0.03348583,0.00016168616],"about_ca_topic_score_codex":0.0029097106,"about_ca_topic_score_gemma":0.0041750157,"teacher_disagreement_score":0.0105143525,"about_ca_system_score_codex":0.0012137752,"about_ca_system_score_gemma":0.0019085177,"threshold_uncertainty_score":0.035174012},"labels":[],"label_agreement":null},{"id":"W7117079245","doi":"","title":"SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples","year":2025,"lang":"","type":"article","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Benchmark (surveying); Deconvolution; Sample (material); Process (computing); Lossy compression; Distribution (mathematics); Gaussian; Bridge (graph theory)","score_opus":0.1562647322113571,"score_gpt":0.2190490939221826,"score_spread":0.06278436171082552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117079245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033484288,0.0001040424,0.9951344,0.00017688103,0.000019917685,0.000025292964,0.00007001904,0.00046437394,0.0006566473],"genre_scores_gemma":[0.45318487,0.0005476923,0.53604287,0.00072111975,0.00014181,0.0004142,0.0011480955,0.00079642906,0.007002877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986236,0.0005618894,0.00005061786,0.0003121512,0.0003421716,0.0001095757],"domain_scores_gemma":[0.9963831,0.0020892902,0.0003486425,0.00071494374,0.00030408154,0.00016000254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052936757,0.0016670115,0.0016400862,0.00097601366,0.00059724797,0.0014661534,0.0035415813,0.0027029365,0.0029919967],"category_scores_gemma":[0.01362099,0.00097051734,0.001417408,0.0007881284,0.0026809464,0.003156043,0.0047405562,0.0042674094,0.0011637954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023766827,0.00009820932,0.0012464202,0.00017229436,0.000089306406,0.00014271992,0.00015303906,0.83212733,0.004103719,0.089421466,0.0035610434,0.06864682],"study_design_scores_gemma":[0.000007061625,0.00002378494,0.000072434865,0.000011575549,0.000004720761,0.00003203466,0.000007301136,0.97230023,0.00094845524,0.025973564,0.0006094496,0.0000092768805],"about_ca_topic_score_codex":0.00231493,"about_ca_topic_score_gemma":0.0023981335,"teacher_disagreement_score":0.0052936757,"about_ca_system_score_codex":0.0014393274,"about_ca_system_score_gemma":0.0018508619,"threshold_uncertainty_score":0.027996004},"labels":[],"label_agreement":null},{"id":"W7117165756","doi":"10.1145/3756681.3756985","title":"Large Language Models as Robust Data Generators in Software Analytics: Are We There Yet?","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Adversarial system; Robustness (evolution); Software quality; Software; Data quality; Data modeling; Source code; Software metric; Context (archaeology)","score_opus":0.04894563841330968,"score_gpt":0.31692934843922793,"score_spread":0.26798371002591825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117165756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12434042,0.0063501517,0.8336653,0.011967674,0.0010631429,0.0007168527,0.0037052387,0.013095423,0.0050957915],"genre_scores_gemma":[0.6829662,0.0020312704,0.30020913,0.0032814106,0.00035658779,0.00081592007,0.00643512,0.0012565472,0.0026478262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9857431,0.00953697,0.0007107591,0.0016291841,0.0020030146,0.00037687627],"domain_scores_gemma":[0.9402594,0.04111735,0.002526663,0.011652176,0.0036240094,0.0008203657],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.018497052,0.002022671,0.0013018945,0.0012705807,0.00074627926,0.0037350333,0.0031270315,0.0024487518,0.0024865237],"category_scores_gemma":[0.10055139,0.00086607755,0.0016700045,0.0011042615,0.0027133669,0.0074817073,0.0040024426,0.006179212,0.002686807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012660658,0.0006958626,0.02551604,0.0013755184,0.0007137245,0.00051918026,0.0016153296,0.55291057,0.012338227,0.02603595,0.025147064,0.35186642],"study_design_scores_gemma":[0.00008538026,0.0003411493,0.0015648071,0.00024474188,0.000061976585,0.00018627736,0.00028520598,0.94902223,0.008136431,0.031819668,0.00816645,0.00008564645],"about_ca_topic_score_codex":0.0033027425,"about_ca_topic_score_gemma":0.0055196895,"teacher_disagreement_score":0.98150295,"about_ca_system_score_codex":0.001431237,"about_ca_system_score_gemma":0.002183934,"threshold_uncertainty_score":0.097822905},"labels":[],"label_agreement":null},{"id":"W7117166472","doi":"10.1109/imeta66706.2025.11306865","title":"Securing the Metaverse and Medical MeTAI: Threat Taxonomy, Adversary Models, Risk Quantification, and a Data-Driven Defense Architecture","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Adversary; Architecture; Scheme (mathematics); Taxonomy (biology); Threat model; Systems architecture; Adversarial system; Black box","score_opus":0.04371025167840364,"score_gpt":0.2903100882237529,"score_spread":0.24659983654534928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117166472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019594215,0.00045940685,0.9618477,0.0042629577,0.000088614695,0.0001557549,0.00013542277,0.00075114676,0.012704764],"genre_scores_gemma":[0.78156114,0.0005173906,0.21218301,0.0007269428,0.00015645126,0.00019345887,0.00022776055,0.00016161303,0.004272264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9905121,0.0032806285,0.0006071557,0.0012349742,0.0035637927,0.00080129993],"domain_scores_gemma":[0.9817758,0.006140181,0.0023035705,0.0070531117,0.0018542988,0.00087303447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011045705,0.00091353286,0.0007777102,0.0018923501,0.0016009558,0.00914249,0.0025341325,0.003014081,0.0026129645],"category_scores_gemma":[0.019481458,0.0007614572,0.0010732291,0.0010232033,0.008214804,0.0125533575,0.009495965,0.005724338,0.0007973611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015216235,0.00007824419,0.00385882,0.00010998605,0.000049779403,0.00023385439,0.0008938044,0.05380236,0.0042694304,0.88864964,0.0019708078,0.04593107],"study_design_scores_gemma":[0.000019633217,0.00021006365,0.0010971985,0.00024557323,0.00004406131,0.0005282536,0.0005583335,0.41109896,0.012940984,0.5517727,0.021397322,0.00008682108],"about_ca_topic_score_codex":0.0012677937,"about_ca_topic_score_gemma":0.0008849444,"teacher_disagreement_score":0.011045705,"about_ca_system_score_codex":0.0029809363,"about_ca_system_score_gemma":0.0024939636,"threshold_uncertainty_score":0.05841601},"labels":[],"label_agreement":null},{"id":"W7117255613","doi":"10.20944/preprints202512.2112.v1","title":"Calibrated Trust in AI for Security Operations: A Conceptual Framework for Analyst–AI Collaboration","year":2025,"lang":"","type":"preprint","venue":"Preprints.org","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tetra Tech (Canada); Ontario Tech University","funders":"","keywords":"Trustworthiness; Conceptual framework; Key (lock); Foundation (evidence); Automation; Empirical research; Conceptual model; Computational trust","score_opus":0.07830353351326334,"score_gpt":0.40239399267078335,"score_spread":0.32409045915752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117255613","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029795785,0.00037117407,0.94381833,0.006381356,0.000070748734,0.00020049633,0.000090144225,0.00018939647,0.019082658],"genre_scores_gemma":[0.90496147,0.00023668744,0.093158595,0.00033523238,0.00007405715,0.00025238414,0.00004595814,0.00006117764,0.000874443],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9756422,0.015938293,0.0009708041,0.002641296,0.0037271963,0.0010801608],"domain_scores_gemma":[0.91148174,0.05296289,0.014842637,0.012258164,0.005760046,0.002694558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030695645,0.0011756965,0.0008425355,0.003044859,0.0024916916,0.00882716,0.0031159755,0.0042106844,0.0038711098],"category_scores_gemma":[0.09786185,0.0008343804,0.0010610315,0.0015109587,0.02038462,0.011320434,0.007636798,0.004689811,0.0005056655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039697665,0.000040123494,0.0017922182,0.000080949874,0.00005577772,0.00014548261,0.0021198457,0.054646082,0.0006534707,0.9307364,0.00054600614,0.009143982],"study_design_scores_gemma":[0.000024928508,0.000066048524,0.0007102619,0.00009821109,0.000026371066,0.00009724053,0.0004827435,0.10547245,0.0005390055,0.88909984,0.0033395225,0.000043377644],"about_ca_topic_score_codex":0.0025988556,"about_ca_topic_score_gemma":0.0010559512,"teacher_disagreement_score":0.030695645,"about_ca_system_score_codex":0.0054514618,"about_ca_system_score_gemma":0.004755024,"threshold_uncertainty_score":0.16233605},"labels":[],"label_agreement":null},{"id":"W7117482120","doi":"10.1109/dicta68720.2025.11302451","title":"Robosurg: Resilience of Vision-Language Models Against Adversarial Attacks in Robotic Surgery","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Adversarial system; Robustness (evolution); Resilience (materials science); Context (archaeology); Compromise; Adversarial machine learning; Robot","score_opus":0.014439942651241905,"score_gpt":0.29538154054945664,"score_spread":0.2809415978982147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117482120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20181417,0.0015478155,0.77844167,0.0027028974,0.00028414663,0.00016067228,0.00042820815,0.007085699,0.007534837],"genre_scores_gemma":[0.9528825,0.00032238054,0.04338336,0.00046001235,0.00005088727,0.0000756639,0.0003799232,0.00030951746,0.0021356156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986216,0.0005055261,0.0000521032,0.00026426447,0.00035812927,0.0001984447],"domain_scores_gemma":[0.99652255,0.00210455,0.00032386996,0.0006872156,0.00023101199,0.00013083857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001981866,0.0010706771,0.0006506559,0.00041420854,0.00059126667,0.001053358,0.0013144591,0.0012328998,0.0020375834],"category_scores_gemma":[0.009519491,0.0004310127,0.0006769142,0.00020212693,0.0017604866,0.0019210394,0.0031420353,0.0021896616,0.00047516078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021347667,0.000057351244,0.0011953032,0.00007076715,0.000055943903,0.00013135583,0.00012792053,0.9552196,0.005716938,0.010111543,0.0026310706,0.02446884],"study_design_scores_gemma":[0.0000070996653,0.00006290234,0.00017585715,0.000010574043,0.000006716036,0.000039226983,0.000022821621,0.9876074,0.0025154338,0.008770982,0.00077082904,0.0000101437845],"about_ca_topic_score_codex":0.0057755406,"about_ca_topic_score_gemma":0.0044165496,"teacher_disagreement_score":0.0057755406,"about_ca_system_score_codex":0.0011009146,"about_ca_system_score_gemma":0.0012991106,"threshold_uncertainty_score":0.011483848},"labels":[],"label_agreement":null},{"id":"W7117557791","doi":"10.5281/zenodo.18080355","title":"The Instruction Stack Audit Framework (ISAF): A Technical Methodology for Tracing AI Accountability Across Nine Abstraction Layers","year":2025,"lang":"","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Accountability; Audit; Corporate governance; Documentation; Transparency (behavior); Abstraction; Traceability; Protocol stack","score_opus":0.08141551385463358,"score_gpt":0.3767917135260739,"score_spread":0.2953761996714403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117557791","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040732087,0.00010982728,0.982872,0.0009011893,0.00007319396,0.0005836909,0.0003752403,0.0041273404,0.006884354],"genre_scores_gemma":[0.06802933,0.00019916202,0.9267852,0.00021597196,0.000055606804,0.00080672017,0.00061233825,0.0006320061,0.0026636533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9743825,0.009054654,0.0037501536,0.0026616636,0.008429472,0.0017215199],"domain_scores_gemma":[0.95004773,0.013070196,0.0063625644,0.0169728,0.012364291,0.0011824125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032025542,0.0018944542,0.0009806126,0.009115864,0.0039916052,0.010365667,0.0045136088,0.0036329892,0.0057758386],"category_scores_gemma":[0.07062179,0.0018968344,0.0026940384,0.0031071824,0.008058134,0.014838381,0.0077600917,0.007364034,0.0021407227],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008746699,0.00014612442,0.005443161,0.0004917802,0.000078001285,0.00046606266,0.0036943506,0.029245505,0.0027730507,0.7566651,0.0086478945,0.1922615],"study_design_scores_gemma":[0.000066692555,0.0004137863,0.0029889888,0.0018569942,0.00017490731,0.0011705182,0.002468597,0.19813935,0.021108637,0.5492687,0.22187684,0.00046590398],"about_ca_topic_score_codex":0.018174676,"about_ca_topic_score_gemma":0.012822282,"teacher_disagreement_score":0.032025542,"about_ca_system_score_codex":0.0055953455,"about_ca_system_score_gemma":0.023176912,"threshold_uncertainty_score":0.16936928},"labels":[],"label_agreement":null},{"id":"W7117581194","doi":"10.5281/zenodo.18080354","title":"The Instruction Stack Audit Framework (ISAF): A Technical Methodology for Tracing AI Accountability Across Nine Abstraction Layers","year":2025,"lang":"","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Accountability; Audit; Corporate governance; Documentation; Transparency (behavior); Abstraction; Traceability; Protocol stack","score_opus":0.08141551385463358,"score_gpt":0.3767917135260739,"score_spread":0.2953761996714403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117581194","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040732087,0.00010982728,0.982872,0.0009011893,0.00007319396,0.0005836909,0.0003752403,0.0041273404,0.006884354],"genre_scores_gemma":[0.06802933,0.00019916202,0.9267852,0.00021597196,0.000055606804,0.00080672017,0.00061233825,0.0006320061,0.0026636533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9743825,0.009054654,0.0037501536,0.0026616636,0.008429472,0.0017215199],"domain_scores_gemma":[0.95004773,0.013070196,0.0063625644,0.0169728,0.012364291,0.0011824125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032025542,0.0018944542,0.0009806126,0.009115864,0.0039916052,0.010365667,0.0045136088,0.0036329892,0.0057758386],"category_scores_gemma":[0.07062179,0.0018968344,0.0026940384,0.0031071824,0.008058134,0.014838381,0.0077600917,0.007364034,0.0021407227],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008746699,0.00014612442,0.005443161,0.0004917802,0.000078001285,0.00046606266,0.0036943506,0.029245505,0.0027730507,0.7566651,0.0086478945,0.1922615],"study_design_scores_gemma":[0.000066692555,0.0004137863,0.0029889888,0.0018569942,0.00017490731,0.0011705182,0.002468597,0.19813935,0.021108637,0.5492687,0.22187684,0.00046590398],"about_ca_topic_score_codex":0.018174676,"about_ca_topic_score_gemma":0.012822282,"teacher_disagreement_score":0.032025542,"about_ca_system_score_codex":0.0055953455,"about_ca_system_score_gemma":0.023176912,"threshold_uncertainty_score":0.16936928},"labels":[],"label_agreement":null},{"id":"W7117690214","doi":"10.1109/wincom65874.2025.11313441","title":"Combating Neural Network Adversaries in Autonomous Vehicles: A 6G-Ready Defense Framework","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Robustness (evolution); Artificial neural network; Convolutional neural network; Resilience (materials science); Convolution (computer science); Deep learning","score_opus":0.015277332647441265,"score_gpt":0.2833489313422372,"score_spread":0.26807159869479597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117690214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042165015,0.0008191578,0.93549836,0.0022083074,0.00026552624,0.00006437137,0.00015240096,0.00095825386,0.017868588],"genre_scores_gemma":[0.93828976,0.00080096204,0.052599143,0.00048067115,0.000119847115,0.00007076208,0.00012636402,0.00011738355,0.007395191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967813,0.00007700206,0.000008531276,0.00005239709,0.000116240255,0.000067676556],"domain_scores_gemma":[0.9995813,0.00013149122,0.000055638244,0.00009415725,0.00009167389,0.000045837012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007157469,0.00074770366,0.00042131013,0.00033444085,0.00034401487,0.0008609516,0.0010127723,0.0012785516,0.0020925943],"category_scores_gemma":[0.0013139979,0.0002311335,0.0004190277,0.00015096087,0.0012832797,0.0019268419,0.0017313419,0.0018657998,0.000541369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007703545,0.000035405246,0.0006200983,0.000050582505,0.00003450289,0.00013389991,0.000067149755,0.84619015,0.007742574,0.11707819,0.0028700852,0.025100281],"study_design_scores_gemma":[0.0000040001473,0.000044848366,0.000096607226,0.000012453265,0.000006323255,0.000037135702,0.000014858637,0.96504116,0.0017288697,0.029317617,0.0036879566,0.000008108093],"about_ca_topic_score_codex":0.0022032862,"about_ca_topic_score_gemma":0.0021330644,"teacher_disagreement_score":0.0022032862,"about_ca_system_score_codex":0.00078550423,"about_ca_system_score_gemma":0.0007193033,"threshold_uncertainty_score":0.0070004463},"labels":[],"label_agreement":null},{"id":"W7118507482","doi":"10.1109/vtc2025-fall65116.2025.11310106","title":"ScoreCAM and Segmentation-Based Adversarial Attacks in Autonomous Vehicles","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Adversarial system; Convolutional neural network; Gradient descent; Pipeline (software); Traffic sign recognition; Artificial neural network; Deep learning","score_opus":0.01065650281643442,"score_gpt":0.28266526283281734,"score_spread":0.2720087600163829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118507482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093717955,0.0006404032,0.89590335,0.0006952101,0.00016809034,0.00010497319,0.00014935786,0.004311515,0.0043091886],"genre_scores_gemma":[0.920902,0.00025983367,0.074518986,0.00029757054,0.000052608993,0.000048939426,0.0002675581,0.00019491477,0.0034575656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989114,0.00035160247,0.00004136084,0.000215606,0.00035164255,0.0001284575],"domain_scores_gemma":[0.99884737,0.00047142216,0.000112020476,0.0003677349,0.00014519066,0.000056280827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010281061,0.000896824,0.00080322096,0.0005470669,0.0003377472,0.0009162265,0.0009970037,0.001120793,0.0014815946],"category_scores_gemma":[0.0033574607,0.00033290818,0.0007602952,0.00036845787,0.001237704,0.0014981733,0.0017755935,0.0013765797,0.0005663543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027316378,0.00008533333,0.0016588848,0.000060184186,0.00009030005,0.00028072423,0.000111997404,0.816788,0.01841611,0.020543603,0.0042364113,0.13745534],"study_design_scores_gemma":[0.0000046761043,0.00003163318,0.0001630289,0.0000042310035,0.0000046855903,0.000045021814,0.000007607327,0.99041545,0.0046565263,0.0038685922,0.00079184875,0.000006796422],"about_ca_topic_score_codex":0.0037548821,"about_ca_topic_score_gemma":0.0021573305,"teacher_disagreement_score":0.0037548821,"about_ca_system_score_codex":0.0008819199,"about_ca_system_score_gemma":0.00074212835,"threshold_uncertainty_score":0.007466078},"labels":[],"label_agreement":null},{"id":"W7118955112","doi":"10.1109/vtc2025-fall65116.2025.11310677","title":"Dual-VAE with Truncated Gaussian: An Unsupervised Defense Against Model Poisoning in Federated Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Autoencoder; MNIST database; Robustness (evolution); Federated learning; Anomaly detection; Gaussian; Noise (video); Gaussian network model; Unsupervised learning","score_opus":0.014358358084121703,"score_gpt":0.2637639597112908,"score_spread":0.2494056016271691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118955112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023746999,0.00015429649,0.97414804,0.0002047202,0.00002494671,0.000023847442,0.0000403977,0.0011393271,0.0005173737],"genre_scores_gemma":[0.84158814,0.000103990846,0.15562062,0.0003459293,0.00003400725,0.00006813191,0.0001691964,0.00016742147,0.0019025902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982597,0.00068707165,0.0000777561,0.00040125774,0.00038428002,0.00018997007],"domain_scores_gemma":[0.9961904,0.0014137272,0.00035135992,0.0014030586,0.0004869003,0.00015451324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002725423,0.0008648711,0.0012560843,0.0005740966,0.00060084666,0.0010699502,0.0020541716,0.001658639,0.0006827332],"category_scores_gemma":[0.0095779,0.00050908397,0.0009842573,0.00051851425,0.0018693152,0.0025560984,0.0030639614,0.002526245,0.00031809142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003226203,0.00019068083,0.004595908,0.00007330594,0.00022443406,0.00019984684,0.0002967784,0.7415719,0.0134902485,0.039756548,0.0032652707,0.19601242],"study_design_scores_gemma":[0.0000073964693,0.000035628516,0.0001393857,0.0000049631317,0.0000058171713,0.000059710026,0.000011865807,0.98496485,0.0029011436,0.01149652,0.0003643101,0.000008387167],"about_ca_topic_score_codex":0.0025823507,"about_ca_topic_score_gemma":0.0033603755,"teacher_disagreement_score":0.002725423,"about_ca_system_score_codex":0.00081056455,"about_ca_system_score_gemma":0.0013560266,"threshold_uncertainty_score":0.014413595},"labels":[],"label_agreement":null},{"id":"W7123349202","doi":"10.1109/esem64174.2025.00015","title":"Toward Real-Time Intrusion Detection for Autonomous Vehicles: A Vision for Deep Learning-Based Security Frameworks","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quanser (Canada)","funders":"","keywords":"Anomaly detection; Intrusion detection system; Software; Set (abstract data type); Overfitting; Software system; Software security assurance","score_opus":0.008648166611859515,"score_gpt":0.2826300980068625,"score_spread":0.273981931395003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7123349202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011091467,0.0016671881,0.98234653,0.001884555,0.00006679371,0.000046097928,0.00008228753,0.0011990338,0.0016160583],"genre_scores_gemma":[0.49605227,0.0035154088,0.49537858,0.001004792,0.00016541965,0.000234452,0.00047575432,0.00022670036,0.0029466795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933404,0.00017230026,0.000041112984,0.00016640933,0.00021212446,0.0000739899],"domain_scores_gemma":[0.99816614,0.0007473176,0.00015866474,0.0002958317,0.0004974797,0.00013461105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023258452,0.0010703589,0.0006181876,0.0009516848,0.00028090295,0.0015417638,0.0022680752,0.0013963191,0.0010925708],"category_scores_gemma":[0.0042866687,0.0005058202,0.0007100895,0.000734875,0.0015360219,0.0027415925,0.001537846,0.0038370083,0.0004232789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006807891,0.00022057083,0.0022304542,0.00022064679,0.0001096285,0.000056774858,0.00013459534,0.72555107,0.005362502,0.054548316,0.0039340863,0.20756334],"study_design_scores_gemma":[0.00000345653,0.00003211743,0.00015617038,0.000023503128,0.000006425581,0.000009058723,0.000012087394,0.97570443,0.0011434307,0.021283317,0.0016192618,0.000006795886],"about_ca_topic_score_codex":0.0056489455,"about_ca_topic_score_gemma":0.0035727085,"teacher_disagreement_score":0.0056489455,"about_ca_system_score_codex":0.0018256927,"about_ca_system_score_gemma":0.001655226,"threshold_uncertainty_score":0.013246357},"labels":[],"label_agreement":null},{"id":"W7125594525","doi":"10.1109/ai-si66213.2025.11341460","title":"A Two-Phase Evolutionary Framework to Boost Adversarial Robustness in Neural Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adversarial system; Robustness (evolution); MNIST database; Artificial neural network; Gradient descent; Deep neural networks; Scalability","score_opus":0.012423496661929828,"score_gpt":0.3174910083424262,"score_spread":0.3050675116804964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125594525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04189939,0.00037666922,0.9535761,0.0003611589,0.00006737225,0.00010992246,0.00004005314,0.0008422966,0.0027271002],"genre_scores_gemma":[0.7632896,0.00021985512,0.23154438,0.00037691218,0.000074599404,0.0002565453,0.00017001438,0.0001649515,0.0039031585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991309,0.00032044892,0.000038919752,0.00016043433,0.0002463974,0.00010290627],"domain_scores_gemma":[0.9985753,0.00065817963,0.00011911996,0.00023657437,0.0003290206,0.00008178365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025877836,0.0011918298,0.0008965783,0.0006948933,0.0005050371,0.0006261996,0.001953669,0.0012561914,0.0012559323],"category_scores_gemma":[0.004640868,0.00057455583,0.00062590477,0.00038580678,0.0011806221,0.0013794426,0.002071832,0.0016347271,0.00029745212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078438396,0.00007605686,0.00096900755,0.000031920503,0.00004336996,0.00007098358,0.00005886379,0.9096551,0.005079804,0.010348676,0.0011178548,0.07246999],"study_design_scores_gemma":[0.0000074538984,0.000038976115,0.00006163352,0.0000029404546,0.000005453076,0.000015338723,0.0000029264975,0.9968244,0.0008407762,0.001921166,0.00027581106,0.0000032921575],"about_ca_topic_score_codex":0.00216254,"about_ca_topic_score_gemma":0.0027608292,"teacher_disagreement_score":0.0025877836,"about_ca_system_score_codex":0.0007559803,"about_ca_system_score_gemma":0.0010228032,"threshold_uncertainty_score":0.013685644},"labels":[],"label_agreement":null},{"id":"W7125601193","doi":"10.1109/metroxraine66377.2025.11340047","title":"Adversarial Machine Learning in Cyber Social Security","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian University Press","keywords":"Adversarial system; Adversarial machine learning; Evasion (ethics); Convolutional neural network; Deep learning; Traffic sign recognition; Artificial neural network","score_opus":0.009839734133684094,"score_gpt":0.27485248470224766,"score_spread":0.26501275056856355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125601193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12233688,0.0040166453,0.8476428,0.0053798524,0.0002901608,0.000111117675,0.000188698,0.00034031135,0.01969353],"genre_scores_gemma":[0.9753675,0.0010083697,0.020937245,0.0002142568,0.000116927826,0.00006630603,0.000052024054,0.000024498093,0.0022129049],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979382,0.0011927187,0.00006106535,0.00023458558,0.00038832,0.00018516851],"domain_scores_gemma":[0.98874235,0.009040663,0.00087224116,0.0007811892,0.0003813819,0.00018213903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031664455,0.0007147851,0.0007639017,0.0007473144,0.0006086369,0.0013563398,0.0006806066,0.0015049789,0.0016632773],"category_scores_gemma":[0.009159222,0.00034266812,0.00047697418,0.00045610953,0.003674755,0.002168677,0.0016919087,0.002416975,0.00021321622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009486665,0.00005844776,0.001961733,0.0000987305,0.00006935934,0.00017988727,0.00013071786,0.7441527,0.00183846,0.22910176,0.0014113665,0.020901911],"study_design_scores_gemma":[0.000008187107,0.000036760575,0.00048147692,0.0000242335,0.000006566872,0.000038065067,0.000034537017,0.8856652,0.00058629,0.112079,0.0010252179,0.000014452459],"about_ca_topic_score_codex":0.0018137298,"about_ca_topic_score_gemma":0.0010245182,"teacher_disagreement_score":0.0031664455,"about_ca_system_score_codex":0.0013595879,"about_ca_system_score_gemma":0.00084067386,"threshold_uncertainty_score":0.016745985},"labels":[],"label_agreement":null},{"id":"W7125633080","doi":"10.1109/cascon66301.2025.00098","title":"A Preliminary Systematic Review on LLM-Based System Assurance: Is Generative AI All You Need?","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Feature (linguistics); Generative grammar; Matching (statistics); Set (abstract data type); Generative model","score_opus":0.015898158044467182,"score_gpt":0.29609748766002714,"score_spread":0.28019932961555993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125633080","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014244103,0.98374015,0.00632197,0.0052225487,0.00044547877,0.0006050294,0.00035119423,0.00005283629,0.0018364227],"genre_scores_gemma":[0.07867164,0.886026,0.01935372,0.011604753,0.0006170813,0.0019858666,0.00076866616,0.00007667783,0.0008955092],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9743965,0.013629395,0.0062036067,0.0014443673,0.003951229,0.00037490757],"domain_scores_gemma":[0.7927256,0.1788183,0.012732347,0.0052443496,0.009546059,0.00093338965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03534668,0.0011676685,0.004138142,0.0053565316,0.00081693806,0.004308629,0.0031997734,0.0031811588,0.008988623],"category_scores_gemma":[0.22359495,0.0010015821,0.00567891,0.0030157636,0.002498228,0.0053953733,0.0036002828,0.0033190032,0.0011912328],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005804624,0.00007400923,0.0015917916,0.5784132,0.006288913,0.00011860289,0.0010210668,0.0009049491,0.00046565552,0.0070800353,0.008894068,0.39456728],"study_design_scores_gemma":[0.00046924612,0.0007624331,0.0034325446,0.8359536,0.024303425,0.00048049953,0.00084159075,0.0008524781,0.0007974788,0.012199458,0.11978704,0.0001203175],"about_ca_topic_score_codex":0.0047431802,"about_ca_topic_score_gemma":0.014783219,"teacher_disagreement_score":0.03534668,"about_ca_system_score_codex":0.004420911,"about_ca_system_score_gemma":0.015789088,"threshold_uncertainty_score":0.18693328},"labels":[],"label_agreement":null},{"id":"W7125643653","doi":"10.1109/bigdatase66491.2025.00018","title":"Woodpecker: A Locally Deployed Large Language Model for Protecting Sensitive Information via RAG and Semantic Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"123 Certification (Canada)","funders":"","keywords":"Language model; Semantics (computer science); Information model; Natural language; Feature (linguistics); Key (lock)","score_opus":0.01084179017092142,"score_gpt":0.26379328822362597,"score_spread":0.25295149805270456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125643653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012505256,0.00028532377,0.9722998,0.00029375328,0.00014836802,0.000074519725,0.0001930658,0.012814188,0.0013856002],"genre_scores_gemma":[0.66145545,0.00030169435,0.31963852,0.0012999855,0.00017707609,0.00025142852,0.0010857087,0.0018702579,0.013919823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988796,0.00035168265,0.000041829895,0.0002698876,0.00031142862,0.00014566355],"domain_scores_gemma":[0.9979791,0.00085722853,0.000108323795,0.0007916829,0.00017491571,0.00008875429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019097868,0.0011366047,0.0014783787,0.0005327711,0.00057476974,0.0012300238,0.0026887436,0.0019735103,0.0037336408],"category_scores_gemma":[0.0051879752,0.00047896244,0.00094377703,0.00036324761,0.0016825966,0.0031819376,0.0038848382,0.003261175,0.0020857558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011515236,0.00039660992,0.0007186245,0.0001733678,0.00025959153,0.00036836474,0.00016181613,0.6034727,0.032176413,0.04962847,0.02383604,0.2876565],"study_design_scores_gemma":[0.000017488252,0.000049135964,0.00003753313,0.0000048801335,0.000011156035,0.000033677996,0.000008327585,0.97738224,0.0049719247,0.01640024,0.0010680011,0.000015291946],"about_ca_topic_score_codex":0.0023510351,"about_ca_topic_score_gemma":0.0032774531,"teacher_disagreement_score":0.0037336408,"about_ca_system_score_codex":0.00066972536,"about_ca_system_score_gemma":0.0014173499,"threshold_uncertainty_score":0.0124902725},"labels":[],"label_agreement":null},{"id":"W7125907148","doi":"10.1109/ase63991.2025.00380","title":"TrustVis: A Multi-Dimensional Trustworthiness Evaluation Framework for Large Language Models","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Alberta","funders":"","keywords":"Trustworthiness; Robustness (evolution); Voting; Natural language; Key (lock); User interface; Personalization; Feature (linguistics)","score_opus":0.03575871934778612,"score_gpt":0.3657391003013337,"score_spread":0.3299803809535476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125907148","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005475028,0.0002897295,0.98577785,0.0003913261,0.000046623718,0.00021215914,0.0004656433,0.005618279,0.0017234177],"genre_scores_gemma":[0.34911418,0.00037323084,0.6441572,0.00029691728,0.00010668084,0.00081487064,0.001787999,0.0016769199,0.0016720819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9862113,0.007568224,0.0010384908,0.001026122,0.0038149313,0.00034099206],"domain_scores_gemma":[0.9763109,0.014008219,0.002562126,0.002866863,0.0035725434,0.0006792731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015854383,0.001791159,0.001134819,0.0033504043,0.0012190279,0.0043452694,0.0019697563,0.0015133425,0.0039778035],"category_scores_gemma":[0.06772964,0.0008600377,0.001947669,0.0012043873,0.002146412,0.005811939,0.0051580104,0.0029886016,0.001025312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007075457,0.00024016002,0.00988797,0.0009379892,0.00072417554,0.00039444826,0.0013388536,0.50248003,0.007871266,0.20722613,0.026813727,0.24137774],"study_design_scores_gemma":[0.000028478476,0.00008896923,0.00039319848,0.000080378544,0.000037022768,0.00007884193,0.00008128756,0.8956646,0.0025387472,0.096230686,0.0047325753,0.000045252273],"about_ca_topic_score_codex":0.00607796,"about_ca_topic_score_gemma":0.0076945582,"teacher_disagreement_score":0.015854383,"about_ca_system_score_codex":0.0028954493,"about_ca_system_score_gemma":0.0033705987,"threshold_uncertainty_score":0.083847046},"labels":[],"label_agreement":null},{"id":"W7125923544","doi":"10.1109/smc58881.2025.11343149","title":"Vision-Based Covert Attack and Hybrid Adversary Detection for Autonomous Vehicles Using Generative Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covert; Discriminative model; Adversary; Drone; Generative model; Generative grammar; Artificial neural network; SIGNAL (programming language)","score_opus":0.018362235807249962,"score_gpt":0.30421267467072505,"score_spread":0.2858504388634751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125923544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09782875,0.0003449108,0.8973711,0.00029326617,0.00005006486,0.00004590238,0.00004642535,0.00083851145,0.003181065],"genre_scores_gemma":[0.9615799,0.00012455758,0.036323402,0.00011177212,0.000024619527,0.000023978975,0.000063007355,0.000053249325,0.0016954351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992872,0.00015232338,0.00001640574,0.00017955716,0.00024510367,0.00011941003],"domain_scores_gemma":[0.9988226,0.00050161936,0.00020404153,0.00024773888,0.00014417856,0.00007966573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006467187,0.0008138034,0.00060643686,0.00063801615,0.00034203476,0.0006948506,0.0010517567,0.0008966355,0.00093131466],"category_scores_gemma":[0.0021398875,0.00041423162,0.0006833291,0.00025560596,0.0014766579,0.0013666794,0.0018294305,0.0011297701,0.00032054388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002607864,0.00010275487,0.003249099,0.00007889888,0.000115787174,0.00040668485,0.0001982122,0.8064093,0.038919684,0.027595675,0.0011136988,0.1215494],"study_design_scores_gemma":[0.0000021234366,0.00003230734,0.00023232662,0.0000030554954,0.0000054626366,0.000087506356,0.0000069038197,0.99159133,0.004420978,0.0033725193,0.00023952757,0.00000595741],"about_ca_topic_score_codex":0.0015572201,"about_ca_topic_score_gemma":0.0016680356,"teacher_disagreement_score":0.0015572201,"about_ca_system_score_codex":0.0009633252,"about_ca_system_score_gemma":0.00046174298,"threshold_uncertainty_score":0.006989479},"labels":[],"label_agreement":null},{"id":"W7125931344","doi":"10.1109/smc58881.2025.11342479","title":"Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de l'Outaouais; Concordia University; Institut National de la Recherche Scientifique","funders":"","keywords":"Adversarial system; Robustness (evolution); Intrusion detection system; Classifier (UML); Intrusion; Adversarial machine learning; Vulnerability (computing); Stacking","score_opus":0.02108942544885226,"score_gpt":0.2808944724446748,"score_spread":0.2598050469958225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125931344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038289648,0.00043651936,0.9574741,0.00028514536,0.00008396692,0.000059743215,0.000045686167,0.0019176946,0.001407509],"genre_scores_gemma":[0.7903863,0.00034986666,0.20588288,0.000332758,0.0001046782,0.00008276464,0.00018756183,0.00011564014,0.0025575114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899596,0.0002428054,0.00005398818,0.00021670925,0.00033739622,0.00015318292],"domain_scores_gemma":[0.9983494,0.00045752287,0.00019910748,0.00037692543,0.0005158103,0.00010119514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023582692,0.0012977244,0.001155406,0.00089296733,0.000448409,0.0009789556,0.001494964,0.0011984651,0.00094417855],"category_scores_gemma":[0.0034468116,0.00044197802,0.0008867977,0.00041876017,0.00058042735,0.002234001,0.0021608851,0.0019630557,0.00041584368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019893066,0.00023603487,0.0038798975,0.0000832732,0.00029091045,0.00013752676,0.00012468915,0.70728886,0.029264914,0.004972638,0.002696003,0.25082627],"study_design_scores_gemma":[0.000002732017,0.00005296171,0.0002657568,0.0000038243256,0.000021999287,0.00002722177,0.000006657394,0.9939779,0.0037221203,0.0014303393,0.00048144162,0.000007130186],"about_ca_topic_score_codex":0.0016462331,"about_ca_topic_score_gemma":0.0019609735,"teacher_disagreement_score":0.0023582692,"about_ca_system_score_codex":0.00054708985,"about_ca_system_score_gemma":0.0006393623,"threshold_uncertainty_score":0.012471914},"labels":[],"label_agreement":null},{"id":"W7125954427","doi":"10.1109/smc58881.2025.11342778","title":"An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Science Foundation","keywords":"Trojan; Vulnerability (computing); Resilience (materials science); Covert; Work (physics); Interdiction; Vulnerability assessment; Deep learning","score_opus":0.019202064687958258,"score_gpt":0.3311105165461078,"score_spread":0.3119084518581495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125954427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895564,0.00031596265,0.0067291423,0.00016743675,0.00009279668,0.000072149895,0.00073124055,0.000667244,0.0016675066],"genre_scores_gemma":[0.99431396,0.00010466571,0.0040545086,0.000055998305,0.000012740354,0.000030567036,0.00094285334,0.00003333915,0.00045138187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990589,0.00022963311,0.00007040406,0.00022034415,0.00028863104,0.00013222218],"domain_scores_gemma":[0.99625033,0.0018500744,0.0004583008,0.0008149886,0.00050906994,0.00011730906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009106324,0.00072935555,0.00036679563,0.0007156429,0.0003920012,0.00040081757,0.0006815874,0.0007531985,0.0008636967],"category_scores_gemma":[0.0051274737,0.00019862026,0.00033387027,0.00041670655,0.000888407,0.0011075379,0.0007292555,0.0008277407,0.0002761258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015866122,0.0015191785,0.055788726,0.000784474,0.00038066052,0.0015532437,0.00061341794,0.7598408,0.077151686,0.004284188,0.011470068,0.085026935],"study_design_scores_gemma":[0.00009231221,0.0019634913,0.022429796,0.00008274015,0.00006726195,0.0007801024,0.00035361922,0.8751231,0.092353456,0.002457166,0.004236983,0.00005997757],"about_ca_topic_score_codex":0.003075718,"about_ca_topic_score_gemma":0.0048385146,"teacher_disagreement_score":0.003075718,"about_ca_system_score_codex":0.0005991583,"about_ca_system_score_gemma":0.00037013897,"threshold_uncertainty_score":0.0061156154},"labels":[],"label_agreement":null},{"id":"W7125955600","doi":"10.1109/ase63991.2025.00102","title":"Backdoors in Code Summarizers: How Bad Is It?","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Education","keywords":"Backdoor; Code (set theory); Task (project management); Inference; Automatic summarization; Software","score_opus":0.02351066552657653,"score_gpt":0.30651631207980184,"score_spread":0.2830056465532253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125955600","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7174645,0.008984788,0.22020106,0.0075154137,0.0009374645,0.0007600932,0.0022703968,0.034985352,0.006880918],"genre_scores_gemma":[0.9217334,0.0011676188,0.06961759,0.0014542661,0.00023075691,0.00014400654,0.001831826,0.0014838756,0.0023367223],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.97803473,0.009025987,0.0017115357,0.0040488276,0.006064548,0.0011142753],"domain_scores_gemma":[0.8760553,0.0696778,0.014487543,0.031620488,0.006652433,0.0015064401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014335872,0.0015991057,0.0014432493,0.002224244,0.0014695382,0.004061852,0.0020908716,0.0025833787,0.001753504],"category_scores_gemma":[0.15269044,0.0011793835,0.0011772998,0.0016142258,0.002842361,0.011803377,0.0033790555,0.003576229,0.0021364875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039258306,0.0008355575,0.09754688,0.0020678423,0.0008254088,0.0013085564,0.008908678,0.04912115,0.032117322,0.011542383,0.028712029,0.7630884],"study_design_scores_gemma":[0.000549265,0.007404062,0.06190565,0.0021142517,0.0017496899,0.0059807813,0.009370315,0.5908066,0.16571791,0.07270569,0.0808685,0.0008272591],"about_ca_topic_score_codex":0.002745005,"about_ca_topic_score_gemma":0.0028719667,"teacher_disagreement_score":0.014335872,"about_ca_system_score_codex":0.0013650566,"about_ca_system_score_gemma":0.0019611993,"threshold_uncertainty_score":0.075816214},"labels":[],"label_agreement":null},{"id":"W7126269717","doi":"10.21428/594757db.25036134","title":"Generating Malicious Demonstration Policies to Exploit Vulnerabilities in Inverse Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Exploit; Adversarial system; Reinforcement learning; Task (project management); Function (biology); Imitation","score_opus":0.0159369100862271,"score_gpt":0.28140667507142925,"score_spread":0.26546976498520214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126269717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20458062,0.00037515763,0.7880805,0.0006114578,0.00010547773,0.00016405656,0.000108009954,0.0015367693,0.0044378615],"genre_scores_gemma":[0.94348997,0.00009217973,0.05475263,0.00015531249,0.000012433718,0.00011386629,0.00008020561,0.000115450704,0.0011879769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986873,0.00052092597,0.0000642559,0.00025890485,0.00029970004,0.00016888701],"domain_scores_gemma":[0.9905551,0.006262578,0.0010003096,0.0014090223,0.00044082516,0.0003322565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022513426,0.001019972,0.0006208837,0.00034236556,0.00035080628,0.00067929435,0.0012866105,0.0009786717,0.0021108526],"category_scores_gemma":[0.017974203,0.0003870986,0.00042598933,0.0001646404,0.0016918604,0.0012995475,0.0019969179,0.0021578448,0.00043576947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034718867,0.00020456998,0.0033193412,0.00018217262,0.00007684232,0.00032616727,0.0002032046,0.9191124,0.017552484,0.020859778,0.0012538704,0.03656205],"study_design_scores_gemma":[0.000019798548,0.00011519644,0.00029059316,0.000019256287,0.000009415281,0.00006624316,0.00002078304,0.98438084,0.0059730196,0.008440374,0.0006529788,0.000011515288],"about_ca_topic_score_codex":0.0011965516,"about_ca_topic_score_gemma":0.0012557828,"teacher_disagreement_score":0.0022513426,"about_ca_system_score_codex":0.00066394854,"about_ca_system_score_gemma":0.0010886203,"threshold_uncertainty_score":0.011906385},"labels":[],"label_agreement":null},{"id":"W7126383326","doi":"10.21428/594757db.1a0d2332","title":"Humans Don’t Get Fooled:Does Predictive Coding Defend Against Adversarial Attack?","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Robustness (evolution); Artificial neural network; Predictive coding; Backpropagation; Coding (social sciences); Perspective (graphical); Fragility","score_opus":0.015573063848640795,"score_gpt":0.27624527978344765,"score_spread":0.26067221593480683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126383326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3367178,0.0026616843,0.5493784,0.027340762,0.0011972269,0.00012506361,0.00030526795,0.0018873394,0.08038638],"genre_scores_gemma":[0.9816201,0.00032914296,0.013167721,0.0013216228,0.000070307186,0.000026854017,0.000052094772,0.00012404661,0.0032881731],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987924,0.00046295868,0.000030825388,0.00022419053,0.00030361107,0.00018592643],"domain_scores_gemma":[0.9914625,0.005023979,0.00078008836,0.0018484575,0.00048706002,0.0003979242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002440823,0.00054996856,0.00050335744,0.00032786987,0.00082692713,0.0018968506,0.0009676196,0.002360716,0.0056175054],"category_scores_gemma":[0.023368414,0.000303838,0.00032802712,0.0001787559,0.0038846522,0.0046218624,0.002345143,0.0025467821,0.0012845485],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016572549,0.00022074785,0.010379316,0.0003287841,0.00029310034,0.0009045841,0.0020400144,0.24395317,0.051300414,0.4091744,0.029236607,0.25051165],"study_design_scores_gemma":[0.00005752553,0.0002841485,0.0020585258,0.00012649976,0.000046708017,0.00061282434,0.00050848204,0.45340154,0.015599786,0.51183015,0.0153998835,0.00007394593],"about_ca_topic_score_codex":0.00124238,"about_ca_topic_score_gemma":0.0009361191,"teacher_disagreement_score":0.0056175054,"about_ca_system_score_codex":0.00058785494,"about_ca_system_score_gemma":0.0006306935,"threshold_uncertainty_score":0.01879239},"labels":[],"label_agreement":null},{"id":"W7126405082","doi":"10.21428/594757db.b3a27711","title":"On Neural Process Pooling Operator Choice and PosteriorCalibration","year":2024,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pooling; Inference; Conditioning; Artificial neural network; Process (computing); Predictive inference; Term (time)","score_opus":0.00951831460353636,"score_gpt":0.2883884421740188,"score_spread":0.27887012757048246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126405082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023947265,0.00042636154,0.970625,0.0012358812,0.0000388496,0.000039814306,0.00006783799,0.00022592294,0.0033931113],"genre_scores_gemma":[0.7768525,0.0010443587,0.21622974,0.0010006042,0.00021598647,0.00021255904,0.00022573471,0.00040277152,0.0038158272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966564,0.0016671944,0.00015379295,0.0006963042,0.0005814911,0.00024487515],"domain_scores_gemma":[0.9740623,0.020451313,0.0015698042,0.0026290638,0.00078503153,0.0005024312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014298329,0.0014417521,0.0013692615,0.0008650186,0.0009965963,0.0028087464,0.002454913,0.0027393547,0.003878562],"category_scores_gemma":[0.05950194,0.0008177594,0.0011907915,0.0009463292,0.004910636,0.0063301874,0.0066037057,0.0047186892,0.00047738716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037514634,0.00008465235,0.0018325339,0.00020563783,0.00013982912,0.00023928343,0.00041132845,0.52110153,0.0064687445,0.41283214,0.0015816356,0.05472749],"study_design_scores_gemma":[0.000027410926,0.00009439095,0.00038545774,0.00007057592,0.000026937925,0.00010548304,0.000042119704,0.76035315,0.004126068,0.23372217,0.0010061755,0.000040069543],"about_ca_topic_score_codex":0.0024127306,"about_ca_topic_score_gemma":0.0018146253,"teacher_disagreement_score":0.014298329,"about_ca_system_score_codex":0.001608451,"about_ca_system_score_gemma":0.0016456618,"threshold_uncertainty_score":0.07561773},"labels":[],"label_agreement":null},{"id":"W7127370637","doi":"10.1109/icrcicn68210.2025.11364988","title":"Adversial Prompt Injection in Large Language Models: Taxonomy, Exploits, and Mitigation Frameworks","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adversarial system; Exploit; Covert; Context (archaeology); Confidentiality; Isolation (microbiology); Key (lock); Unintended consequences","score_opus":0.01256992635687382,"score_gpt":0.2610636639407804,"score_spread":0.24849373758390658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127370637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045044884,0.0019350138,0.93800575,0.0024645287,0.00009464693,0.00027822444,0.00013165287,0.0061200946,0.005925218],"genre_scores_gemma":[0.7690974,0.0012862703,0.22532986,0.00091183814,0.00012298534,0.0003406618,0.00021767337,0.0008478215,0.0018456125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98899764,0.004745472,0.00067381765,0.0012823654,0.0034709761,0.00082971016],"domain_scores_gemma":[0.9654157,0.019200152,0.0030894491,0.010453206,0.00142971,0.0004117633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008803441,0.0019380104,0.0008078618,0.0016619524,0.0010084406,0.0032677383,0.003078579,0.0025595473,0.0020218003],"category_scores_gemma":[0.036615226,0.0010474365,0.0016505679,0.00080056523,0.006352608,0.006019386,0.008081829,0.005560673,0.0006776562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045239192,0.00038991222,0.019640123,0.000871691,0.00035885812,0.0012461018,0.0029692817,0.37195206,0.024155727,0.3566789,0.008600278,0.2126847],"study_design_scores_gemma":[0.000035660258,0.0002057762,0.0008363259,0.00027550658,0.00010719192,0.0011355396,0.00033305984,0.8195774,0.023074586,0.14241147,0.011902748,0.00010475868],"about_ca_topic_score_codex":0.0010969981,"about_ca_topic_score_gemma":0.0010615723,"teacher_disagreement_score":0.008803441,"about_ca_system_score_codex":0.0017137023,"about_ca_system_score_gemma":0.0019885076,"threshold_uncertainty_score":0.046557605},"labels":[],"label_agreement":null},{"id":"W7127395196","doi":"10.1109/ccece64018.2025.11364449","title":"MetJam: Metamorphic Testing for Data Synthesize and Quality Assurance of an ML-based Jamming (DOS) Detector in 5G","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada); University of Ottawa","funders":"","keywords":"Correctness; Process (computing); Domain (mathematical analysis); Detector; sort; Subject-matter expert; Quality assurance; Denial-of-service attack; Quality (philosophy)","score_opus":0.1179929271528875,"score_gpt":0.3738083530836773,"score_spread":0.2558154259307898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127395196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1369895,0.00035401326,0.8120078,0.0010883603,0.00018349606,0.00039591908,0.0007454653,0.04032269,0.007912845],"genre_scores_gemma":[0.85905546,0.00006341547,0.13680513,0.00041770356,0.000044823373,0.0001753735,0.0005248498,0.00084963144,0.0020636013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934685,0.0023725156,0.00045174794,0.0010338316,0.0022494167,0.00042398795],"domain_scores_gemma":[0.9858067,0.006522663,0.0016765576,0.0041212505,0.0016070405,0.000265768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004667377,0.0009516458,0.00066315674,0.0019593989,0.0005970528,0.002065602,0.0016929109,0.001397561,0.003676304],"category_scores_gemma":[0.022435693,0.00045880838,0.00059882685,0.00049271045,0.001789155,0.0031478836,0.0019056585,0.0017602276,0.0010626455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003191262,0.00080039975,0.03712254,0.00064243336,0.00029337849,0.0024468966,0.0012616528,0.21519528,0.1540172,0.08125928,0.025438532,0.47833115],"study_design_scores_gemma":[0.000086027525,0.0007101422,0.0017642902,0.00009339518,0.000035003224,0.0007214897,0.000092267925,0.82629377,0.1406074,0.021816233,0.0077192765,0.00006080181],"about_ca_topic_score_codex":0.00089841645,"about_ca_topic_score_gemma":0.0010719784,"teacher_disagreement_score":0.004667377,"about_ca_system_score_codex":0.0010752794,"about_ca_system_score_gemma":0.0010594977,"threshold_uncertainty_score":0.024683714},"labels":[],"label_agreement":null},{"id":"W7127450737","doi":"10.1109/ccece64018.2025.11364511","title":"Double Deep Q-Learning for Autonomous Cyber Defense Agent Training","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Training (meteorology); Multi-agent system; Intelligent agent; Autonomous agent; Training set","score_opus":0.03222072242703986,"score_gpt":0.2988361695255839,"score_spread":0.266615447098544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127450737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03846824,0.0006158999,0.9526964,0.0005904482,0.00015866302,0.000099753765,0.00012662334,0.0021181053,0.005125852],"genre_scores_gemma":[0.82179815,0.00018006434,0.16978775,0.00067537883,0.00007385514,0.00018784718,0.0003980584,0.00017597168,0.006722879],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995503,0.0001473408,0.000018938692,0.00010078067,0.000086224965,0.00009637602],"domain_scores_gemma":[0.99874544,0.0007017939,0.0000739129,0.00013535231,0.00023974011,0.00010372272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015149652,0.000819004,0.0007281049,0.00035784836,0.00035560082,0.00053678296,0.0015154241,0.0012187063,0.004777024],"category_scores_gemma":[0.0034902226,0.00049249985,0.00033900273,0.0002981497,0.00078511244,0.00092421076,0.001461801,0.002013128,0.0009032683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017986313,0.00014240792,0.001372156,0.00008572713,0.000045017747,0.000052446037,0.000048185513,0.85659313,0.0019931176,0.007892675,0.0043936516,0.12720162],"study_design_scores_gemma":[0.000008866332,0.00002681768,0.00004761412,0.0000033307588,0.00000198301,0.0000044168496,0.0000029928083,0.9971679,0.00031692037,0.0020731639,0.0003443657,0.0000017073711],"about_ca_topic_score_codex":0.0041484768,"about_ca_topic_score_gemma":0.005405413,"teacher_disagreement_score":0.004777024,"about_ca_system_score_codex":0.0008212912,"about_ca_system_score_gemma":0.0015504357,"threshold_uncertainty_score":0.01598072},"labels":[],"label_agreement":null},{"id":"W7128225249","doi":"10.1162/tacl.a.27","title":"Investigating Adversarial Trigger Transfer in Large Language Models","year":2025,"lang":"en","type":"article","venue":"Transactions of the Association for Computational Linguistics","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Adversarial system; Security token; Offensive; Robustness (evolution); Language model; Threat model; Language understanding","score_opus":0.012176640850124919,"score_gpt":0.28045051901156787,"score_spread":0.26827387816144294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128225249","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52153987,0.00038276665,0.46985766,0.0012337972,0.00008825446,0.00017300705,0.00032516473,0.0021830373,0.0042164745],"genre_scores_gemma":[0.97947866,0.00005598449,0.019152492,0.00014631981,0.00001855554,0.00008373985,0.00018380203,0.00014864243,0.00073170196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99552226,0.0028290402,0.00013800144,0.0005897134,0.00055975374,0.00036115455],"domain_scores_gemma":[0.96929574,0.025448058,0.0015361719,0.0025690463,0.0006957865,0.0004551501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068124244,0.0009049687,0.0008230606,0.000564665,0.00060630945,0.0011329133,0.0014539334,0.0013897966,0.0029727123],"category_scores_gemma":[0.03289236,0.0004792129,0.000870406,0.00034329575,0.0020583812,0.0025988359,0.0022712348,0.0028829256,0.000415513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017051138,0.00008950493,0.0019414898,0.00006111289,0.00005557678,0.00011659157,0.00016378007,0.9713169,0.0020780643,0.012993211,0.0006445194,0.010368811],"study_design_scores_gemma":[0.000010252478,0.00005567039,0.00015957677,0.000005990378,0.000006206842,0.000011932813,0.000021842834,0.9839468,0.0009916908,0.014630656,0.00015164926,0.000007609715],"about_ca_topic_score_codex":0.0022953595,"about_ca_topic_score_gemma":0.0020846555,"teacher_disagreement_score":0.0068124244,"about_ca_system_score_codex":0.0015041413,"about_ca_system_score_gemma":0.0011411286,"threshold_uncertainty_score":0.036027968},"labels":[],"label_agreement":null},{"id":"W7128336567","doi":"","title":"An Object-Level Entropy-Based Adversarial Attack for Image Privacy","year":2025,"lang":"en","type":"article","venue":"Explore Bristol Research","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Infection and Immunity","funders":"","keywords":"Interpretability; Adversarial system; Contextual image classification; USable; Image (mathematics); Rendering (computer graphics); Usability; Deep learning; Human visual system model; Seam carving","score_opus":0.16807654020733656,"score_gpt":0.4535561052356736,"score_spread":0.28547956502833705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128336567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0144594535,0.00019891611,0.9812734,0.000563844,0.000059263486,0.000026390117,0.00005265768,0.00019821261,0.0031677913],"genre_scores_gemma":[0.859293,0.0005080769,0.13121922,0.0005501407,0.00020958913,0.000066799046,0.00016860034,0.00014825643,0.007836294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983777,0.00048266895,0.000053202177,0.0002550049,0.0006450846,0.0001863426],"domain_scores_gemma":[0.9970366,0.0017100322,0.00019402389,0.00073001976,0.00021134916,0.00011806475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018962403,0.00073577056,0.00096790266,0.0006137055,0.0004431251,0.0012757016,0.001219622,0.0014901473,0.002332953],"category_scores_gemma":[0.0067155296,0.00041364462,0.0009543639,0.00061093405,0.0020699343,0.0028844995,0.0041585756,0.0028288148,0.0005358497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046614418,0.00012327355,0.0010725468,0.0001330077,0.0001710451,0.00026446872,0.00013490477,0.5145786,0.034830812,0.3375428,0.0038877297,0.10679459],"study_design_scores_gemma":[0.0000083673,0.000049273553,0.00019855746,0.000011384706,0.00001452063,0.000113968694,0.000007981501,0.94375044,0.0051608603,0.049782313,0.00088754,0.000014850864],"about_ca_topic_score_codex":0.00036753804,"about_ca_topic_score_gemma":0.00036495953,"teacher_disagreement_score":0.002332953,"about_ca_system_score_codex":0.0007436412,"about_ca_system_score_gemma":0.00059781806,"threshold_uncertainty_score":0.010028422},"labels":[],"label_agreement":null},{"id":"W7129668034","doi":"10.1109/iceconf65644.2025.11379512","title":"A Secured Artificial Intelligence (AI) Assisted Personal Data Prediction and Leakage Prevention System Using Deep Learning Logic","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Homomorphic encryption; Adversarial system; Leakage (economics); Data pre-processing; Encryption; Cryptography; Information privacy; Preprocessor","score_opus":0.0816347288720864,"score_gpt":0.3464994231527416,"score_spread":0.26486469428065523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129668034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11150985,0.00023964555,0.8701517,0.0005020993,0.00007952478,0.00017179205,0.0004833032,0.013509554,0.0033526],"genre_scores_gemma":[0.7435364,0.00020211103,0.24770214,0.0004984474,0.00003176917,0.00011454011,0.0012986815,0.00009514474,0.0065208874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995678,0.000059682356,0.000040382867,0.00011510681,0.00015206219,0.00006501364],"domain_scores_gemma":[0.9994881,0.000104208295,0.000067920104,0.00016488579,0.00013883677,0.00003609302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071551156,0.00045465317,0.0004977821,0.0005785909,0.0003473218,0.0007016457,0.0011582208,0.00053195114,0.0018452221],"category_scores_gemma":[0.0010966815,0.00021996762,0.0004890535,0.0005727952,0.00031874684,0.0015684068,0.0012144946,0.00076585985,0.00066200463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010008008,0.0007379154,0.009010326,0.0001871011,0.00019790899,0.00057823636,0.00018518657,0.10895419,0.05977645,0.009756222,0.012115663,0.79749995],"study_design_scores_gemma":[0.000020773987,0.00010269213,0.00082923414,0.000012199289,0.00003083971,0.00010838157,0.000019326606,0.9655484,0.027663236,0.0030920417,0.0025551713,0.000017766188],"about_ca_topic_score_codex":0.0033199703,"about_ca_topic_score_gemma":0.0035247917,"teacher_disagreement_score":0.0033199703,"about_ca_system_score_codex":0.00078906515,"about_ca_system_score_gemma":0.0010983857,"threshold_uncertainty_score":0.0066013336},"labels":[],"label_agreement":null},{"id":"W7131128688","doi":"10.1109/iccvw69036.2025.00656","title":"AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Adversarial system; Domain adaptation; Classifier (UML); Robustness (evolution); Training set; Artificial neural network; Deep neural networks; Domain (mathematical analysis)","score_opus":0.01148330666009143,"score_gpt":0.255925985013127,"score_spread":0.2444426783530356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131128688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013840646,0.00041197846,0.9776487,0.00018866191,0.00015806632,0.00011564633,0.00009418358,0.005474207,0.0020678756],"genre_scores_gemma":[0.50119734,0.0004298317,0.48724288,0.0007873705,0.00016210553,0.0002762111,0.00093635794,0.0007842044,0.008183585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988937,0.00025220326,0.000063127045,0.0003015439,0.00037057124,0.00011893825],"domain_scores_gemma":[0.99750996,0.00066189043,0.00016907153,0.0010967588,0.00047252877,0.000089765475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018413776,0.0016899913,0.0011796576,0.0010007665,0.00051775,0.00087525835,0.0028958363,0.0011289374,0.002493087],"category_scores_gemma":[0.006736638,0.00052799727,0.0009885767,0.0005894431,0.00084631203,0.0018440323,0.0024546774,0.0031920355,0.0019660448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029174186,0.00024846918,0.003368023,0.00014215244,0.00021414796,0.00023895696,0.00014769468,0.36486748,0.04464005,0.010129816,0.017597482,0.55811393],"study_design_scores_gemma":[0.000019118706,0.000070594026,0.0003529979,0.000014376706,0.000021417858,0.00015768467,0.000017597496,0.9744402,0.013817383,0.0071335086,0.0039311396,0.000023938006],"about_ca_topic_score_codex":0.0034639535,"about_ca_topic_score_gemma":0.005196692,"teacher_disagreement_score":0.0034639535,"about_ca_system_score_codex":0.00063717115,"about_ca_system_score_gemma":0.0010709689,"threshold_uncertainty_score":0.0097382665},"labels":[],"label_agreement":null},{"id":"W7131142533","doi":"10.1109/iccvw69036.2025.00311","title":"Watch, Listen, Understand, Mislead: Tri-Modal Adversarial Attacks on Short Videos for Content Appropriateness Evaluation","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Adversarial system; Offensive; Robustness (evolution); Key (lock); Semantics (computer science)","score_opus":0.14491009052087453,"score_gpt":0.371232071841216,"score_spread":0.22632198132034145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131142533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55078125,0.004490051,0.4034071,0.004323065,0.0012237941,0.000966058,0.007021003,0.0111447815,0.016642854],"genre_scores_gemma":[0.9328438,0.0004806062,0.05631229,0.000835968,0.00015021536,0.00023021919,0.00529786,0.00030744952,0.0035416496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973545,0.001275472,0.000120380326,0.00036201306,0.0006740079,0.0002136206],"domain_scores_gemma":[0.9932662,0.004418955,0.0004578825,0.0010254087,0.00051737,0.00031416665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037043109,0.0013910943,0.00063163193,0.00075340056,0.0005074273,0.0010971783,0.0010524377,0.0014918474,0.0027256473],"category_scores_gemma":[0.015285441,0.00021044718,0.00067028246,0.00029742345,0.0012536609,0.0020614187,0.0019146841,0.0022411933,0.0010637018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002739607,0.0008264423,0.015213748,0.00097358227,0.0005019548,0.00069340947,0.0004533007,0.6703061,0.028521862,0.017123386,0.044651188,0.21799538],"study_design_scores_gemma":[0.00005645165,0.00044911308,0.0019343487,0.00006925735,0.000029024253,0.00021446552,0.00011030393,0.97306144,0.012378097,0.008190622,0.0034677857,0.000039038412],"about_ca_topic_score_codex":0.003175972,"about_ca_topic_score_gemma":0.0041759615,"teacher_disagreement_score":0.0037043109,"about_ca_system_score_codex":0.0012228412,"about_ca_system_score_gemma":0.00078512856,"threshold_uncertainty_score":0.019590497},"labels":[],"label_agreement":null},{"id":"W7131300647","doi":"10.1109/icoiics67115.2025.11390487","title":"Adversarial Vulnerabilities in Machine Learning: Differential Analysis of Gradient and XGBoost-Based Attack Mechanisms","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic","funders":"","keywords":"Adversarial system; Interpretability; Exploit; Adversarial machine learning; Robustness (evolution); Key (lock); Compromise; Threat model","score_opus":0.013935880116683666,"score_gpt":0.2717333226875287,"score_spread":0.257797442570845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131300647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033669725,0.006341448,0.9500415,0.0018967558,0.00016455146,0.00008214069,0.00006182249,0.0005172137,0.0072248247],"genre_scores_gemma":[0.9191745,0.005294603,0.07139677,0.00068744924,0.00019714975,0.00016305025,0.000081447535,0.00016845045,0.0028366346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997045,0.0013042685,0.0001302324,0.00036102493,0.0009123889,0.00024706224],"domain_scores_gemma":[0.99160016,0.0059139924,0.000845759,0.00088284555,0.0006162268,0.0001409771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005714747,0.0011136656,0.0011005544,0.0012170167,0.0005033152,0.0022305737,0.0015112032,0.0019032729,0.0012967212],"category_scores_gemma":[0.015109121,0.0004957053,0.0010388008,0.00072520785,0.0032400216,0.0030347807,0.0022647197,0.003238533,0.00029370308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013793337,0.000062060666,0.0033417176,0.00037355942,0.00024232418,0.00019280994,0.00018453546,0.6158606,0.0037552365,0.28177512,0.002531475,0.0915427],"study_design_scores_gemma":[0.000012633982,0.000121408826,0.0009612385,0.00011928674,0.000044848544,0.00022317111,0.000038637685,0.86189204,0.0032501614,0.13006511,0.0032368852,0.000034583863],"about_ca_topic_score_codex":0.000874706,"about_ca_topic_score_gemma":0.0005219185,"teacher_disagreement_score":0.005714747,"about_ca_system_score_codex":0.0014748065,"about_ca_system_score_gemma":0.0008085219,"threshold_uncertainty_score":0.030222833},"labels":[],"label_agreement":null},{"id":"W7132995918","doi":"","title":"Towards Trustworthy Machine Learning in High-Stakes Decision-Making Systems","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trustworthiness; Task (project management); Heuristic; Strengths and weaknesses; Adversarial system; Focus (optics); Artificial neural network; Function (biology); Cognitive reframing","score_opus":0.021686160515245984,"score_gpt":0.3387278517736889,"score_spread":0.31704169125844295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132995918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016866261,0.0006937069,0.9711869,0.0059628035,0.00010228294,0.000095627205,0.00006222178,0.00017172382,0.0048585073],"genre_scores_gemma":[0.76494306,0.0010849674,0.228669,0.0013743344,0.0004973031,0.0003434624,0.000121045225,0.00018045286,0.0027863325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97412777,0.017055256,0.0010427239,0.0028272187,0.0038615998,0.0010854702],"domain_scores_gemma":[0.87174034,0.10349535,0.007788631,0.009648567,0.005056666,0.0022705228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038095698,0.0017817104,0.0026426418,0.0016607557,0.0022999954,0.009122986,0.0036822855,0.0063045616,0.0034359433],"category_scores_gemma":[0.13395332,0.0016362524,0.0015715184,0.001259954,0.011524406,0.011650206,0.009857598,0.011235028,0.0008661288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016375068,0.00008523482,0.002098885,0.00020295817,0.00010460213,0.00021584067,0.0006977267,0.27047312,0.0007710888,0.70644504,0.0012450054,0.017496714],"study_design_scores_gemma":[0.000023209617,0.000034586716,0.00014735264,0.00005377744,0.00001024132,0.000032320124,0.000051154526,0.36614564,0.00044327736,0.6320514,0.0009850819,0.00002189886],"about_ca_topic_score_codex":0.0024412677,"about_ca_topic_score_gemma":0.0014921458,"teacher_disagreement_score":0.038095698,"about_ca_system_score_codex":0.004522911,"about_ca_system_score_gemma":0.004542227,"threshold_uncertainty_score":0.20147169},"labels":[],"label_agreement":null},{"id":"W7133000605","doi":"","title":"Recovering Utility in LDP Schemes by Training with Noise^2","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Noise (video); Key (lock); Scheme (mathematics); Training set; Set (abstract data type); Training (meteorology); Robustness (evolution)","score_opus":0.02678681143849532,"score_gpt":0.3332081642835441,"score_spread":0.3064213528450488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133000605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03549715,0.00054386223,0.9577064,0.0012906638,0.000079780766,0.00021032887,0.0005010817,0.0014643602,0.0027063873],"genre_scores_gemma":[0.82381386,0.00040880174,0.16958012,0.00090958265,0.00017157475,0.00039895633,0.0006906511,0.0002269695,0.0037995758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923057,0.0031144011,0.00040500774,0.0016042652,0.0017321281,0.0008384392],"domain_scores_gemma":[0.9768171,0.013766462,0.0010362567,0.006916626,0.0009982364,0.00046528032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010396588,0.001597501,0.0024749672,0.0011313014,0.001727554,0.0025803237,0.0045927474,0.00303641,0.0031954148],"category_scores_gemma":[0.037671767,0.00096308655,0.0012557654,0.0017856176,0.0038314406,0.007836817,0.00733021,0.0054230997,0.001100265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013729963,0.00029950155,0.004218136,0.00036868665,0.00019451216,0.00043195314,0.00060653745,0.6381761,0.0059165987,0.16238907,0.010115111,0.17591083],"study_design_scores_gemma":[0.00005603039,0.000076119766,0.00024406248,0.0000265835,0.000018769908,0.0001205037,0.000034942754,0.8848831,0.0021712377,0.110978074,0.0013669308,0.000023532144],"about_ca_topic_score_codex":0.002487503,"about_ca_topic_score_gemma":0.0022520155,"teacher_disagreement_score":0.010396588,"about_ca_system_score_codex":0.00393865,"about_ca_system_score_gemma":0.0031655682,"threshold_uncertainty_score":0.05498308},"labels":[],"label_agreement":null},{"id":"W7133010653","doi":"","title":"Secure and private machine learning in hardware","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Noise (video); Edge device; Popularity; Hardware acceleration; Power (physics); Latency (audio); Key (lock); Safeguard; Order (exchange)","score_opus":0.013803681738989892,"score_gpt":0.3156183685128924,"score_spread":0.3018146867739025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133010653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10054167,0.0017991933,0.8581713,0.0030060126,0.0007003671,0.00024121268,0.0005854761,0.010078647,0.024876146],"genre_scores_gemma":[0.8836947,0.00069058524,0.10267606,0.00070877525,0.000215847,0.00031527117,0.000449826,0.00032000247,0.010928991],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99519074,0.0009890872,0.00033077202,0.00075807277,0.0017999731,0.0009313999],"domain_scores_gemma":[0.993353,0.00166036,0.00044098074,0.0038336774,0.0005782374,0.00013378271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002070073,0.0009871451,0.0008632512,0.0006950556,0.0011187968,0.0033432627,0.0019475631,0.0013257337,0.0076363976],"category_scores_gemma":[0.007997605,0.0007478665,0.0008904923,0.0007318426,0.002238902,0.0055628805,0.0042575495,0.0027908843,0.002659496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016127329,0.00032729877,0.0033245618,0.00058059546,0.00017976592,0.0005108302,0.00046957488,0.23605473,0.034587592,0.5014647,0.021496369,0.19939123],"study_design_scores_gemma":[0.00013765173,0.0003353377,0.0005268742,0.00014750054,0.00006493618,0.0002484683,0.00009342235,0.75991553,0.04032713,0.17015119,0.027982648,0.000069335605],"about_ca_topic_score_codex":0.00096702564,"about_ca_topic_score_gemma":0.0013143796,"teacher_disagreement_score":0.0076363976,"about_ca_system_score_codex":0.0018732882,"about_ca_system_score_gemma":0.0024344248,"threshold_uncertainty_score":0.025546312},"labels":[],"label_agreement":null},{"id":"W7133310216","doi":"10.1109/icinvents64613.2025.11401412","title":"JEE 360 - Empowering JEE Aspirants Through AI-Driven Personalized Learning and Instant Doubt Resolution","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Variety (cybernetics); Adaptability; Quality (philosophy); Key (lock); Architecture; Session (web analytics); Personalized learning","score_opus":0.01770820267148726,"score_gpt":0.3239603213248075,"score_spread":0.30625211865332025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133310216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18441594,0.00048717973,0.65880764,0.001329565,0.00028271272,0.0006451055,0.0011739754,0.09917737,0.053680524],"genre_scores_gemma":[0.6058751,0.00025251435,0.33372572,0.0006604583,0.000059024867,0.00028103575,0.0027693554,0.0021202073,0.054256525],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942195,0.000162385,0.000030450075,0.00012845453,0.00018305048,0.00007377543],"domain_scores_gemma":[0.99896395,0.00032292766,0.000046505596,0.000304916,0.00018319149,0.00017842691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015170482,0.00057439104,0.00033459457,0.00046754992,0.00034274292,0.0012959588,0.0012811938,0.0007386161,0.011022908],"category_scores_gemma":[0.0044948645,0.0003013952,0.00039504966,0.00020958713,0.0006845718,0.0022616617,0.004522484,0.0011033219,0.008051203],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00170955,0.0011958999,0.0092279175,0.00062491634,0.00008442028,0.0012441784,0.0038178063,0.031615205,0.08964315,0.037970584,0.07087365,0.7519927],"study_design_scores_gemma":[0.00031239592,0.0011582264,0.0056422204,0.00014862989,0.00006780324,0.001338621,0.0016560452,0.49105954,0.1078063,0.07226555,0.31829494,0.0002497652],"about_ca_topic_score_codex":0.00058532174,"about_ca_topic_score_gemma":0.0012322189,"teacher_disagreement_score":0.011022908,"about_ca_system_score_codex":0.00026764837,"about_ca_system_score_gemma":0.0005887704,"threshold_uncertainty_score":0.036875248},"labels":[],"label_agreement":null},{"id":"W7134187329","doi":"10.1109/icngn67480.2025.11413763","title":"Perception’s Blind Spot: A Survey of Localized Vulnerabilities in Semantic Segmentation for Autonomous Vehicles","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Fredericton; University of New Brunswick","funders":"","keywords":"Segmentation; Field (mathematics); Feature (linguistics); Key (lock); Image segmentation","score_opus":0.031884570587542904,"score_gpt":0.32565995722502256,"score_spread":0.29377538663747965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134187329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017653957,0.017255541,0.9538294,0.0021062437,0.00022882308,0.00008305399,0.000143653,0.0011199791,0.007579329],"genre_scores_gemma":[0.8176994,0.017898716,0.15598986,0.0013583412,0.0006738142,0.00011729902,0.00046503937,0.00069566036,0.0051018284],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973308,0.00067926507,0.00013084532,0.0005649907,0.0010436217,0.0002504151],"domain_scores_gemma":[0.99339116,0.0040453966,0.00049371173,0.0012062871,0.000738216,0.00012530148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032698696,0.0011438864,0.0017403846,0.0015861583,0.00074808183,0.002575052,0.002379528,0.0032504543,0.001991861],"category_scores_gemma":[0.0118457675,0.0008584326,0.0012881487,0.0015872183,0.0035124165,0.0058110217,0.0041531385,0.0029660875,0.0005475344],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002698759,0.00010928363,0.0031274462,0.001021954,0.0003479888,0.00043824568,0.00045402808,0.3732433,0.010512158,0.17197125,0.012684202,0.42582035],"study_design_scores_gemma":[0.00001670072,0.0001873869,0.0012963942,0.0002608051,0.00009986556,0.0008187621,0.0002262185,0.7402662,0.012022542,0.22701511,0.017719837,0.00007020177],"about_ca_topic_score_codex":0.0024199523,"about_ca_topic_score_gemma":0.0014792123,"teacher_disagreement_score":0.0032698696,"about_ca_system_score_codex":0.0013955242,"about_ca_system_score_gemma":0.0013990112,"threshold_uncertainty_score":0.017292917},"labels":[],"label_agreement":null},{"id":"W7134190636","doi":"10.1109/bigdata66926.2025.11400845","title":"Federated Neural Architecture Search with Model-Agnostic Meta Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Meta learning (computer science); Artificial neural network; Feature (linguistics); Architecture; Key (lock)","score_opus":0.02470278793916895,"score_gpt":0.2837344653759475,"score_spread":0.2590316774367785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134190636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06503275,0.0005813228,0.92375153,0.00096957944,0.00016167009,0.000094052455,0.00016466533,0.0026327013,0.0066117966],"genre_scores_gemma":[0.8944021,0.00008640398,0.10061298,0.00040652964,0.000083386745,0.00012269385,0.00021993316,0.00024492864,0.0038210857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989938,0.0003677513,0.00005382715,0.00021841304,0.00020276353,0.00016335075],"domain_scores_gemma":[0.99728394,0.0014919343,0.00021222817,0.000626453,0.00028135354,0.00010405397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021110584,0.001596523,0.0017582723,0.0010676373,0.0006037174,0.0015062634,0.0025710466,0.0032011229,0.0039644465],"category_scores_gemma":[0.0078088213,0.0008557608,0.0012667539,0.0006603776,0.0013926538,0.0024829002,0.0034564638,0.002687161,0.00083678734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022683863,0.0001109121,0.00067728513,0.0000562295,0.000121008976,0.00012853029,0.00003686731,0.93430513,0.0020619037,0.014152107,0.0025326344,0.04559054],"study_design_scores_gemma":[0.000010667722,0.000022337197,0.00002956899,0.0000050184817,0.000009470865,0.000014777694,0.000003912899,0.99232775,0.00038272105,0.007094477,0.00009588533,0.0000034181617],"about_ca_topic_score_codex":0.0016234663,"about_ca_topic_score_gemma":0.0024400104,"teacher_disagreement_score":0.0039644465,"about_ca_system_score_codex":0.0010345235,"about_ca_system_score_gemma":0.0013738779,"threshold_uncertainty_score":0.013262391},"labels":[],"label_agreement":null},{"id":"W7134195250","doi":"10.1109/bigdata66926.2025.11401939","title":"Exposing Privacy Vulnerabilities in Federated Learning: A GAN-Based Model Inversion Attack","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Information privacy; Inversion (geology); Confidentiality; Privacy protection; Threat model","score_opus":0.031582521886884846,"score_gpt":0.30445082103506405,"score_spread":0.2728682991481792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134195250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065769814,0.00037444182,0.9226048,0.0022270638,0.00015078705,0.000117344855,0.00023831018,0.0022639045,0.0062535238],"genre_scores_gemma":[0.954363,0.00011495225,0.043189306,0.00053767173,0.00005238621,0.000058070586,0.00013404284,0.000100973986,0.0014497704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953928,0.002029385,0.00014908494,0.00065637456,0.0012515944,0.0005208242],"domain_scores_gemma":[0.9917985,0.004144778,0.00036643405,0.0030948392,0.00045993834,0.00013550924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004495295,0.0008408517,0.0010840307,0.00056659937,0.0005992268,0.0017175628,0.0015886613,0.0023822736,0.0019431795],"category_scores_gemma":[0.015727747,0.0004875617,0.0012215414,0.00052075845,0.001954929,0.0040148743,0.0046594758,0.0044112564,0.0004507573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092882104,0.00037011155,0.0039691213,0.00018405894,0.00037370477,0.000838456,0.00034591884,0.59689194,0.018020904,0.2375989,0.010664475,0.1298136],"study_design_scores_gemma":[0.00001760368,0.0000441599,0.00017422959,0.000018387775,0.0000187298,0.00016694884,0.00001919519,0.9149797,0.004046,0.07981551,0.0006854645,0.000014118902],"about_ca_topic_score_codex":0.00072364794,"about_ca_topic_score_gemma":0.0007462082,"teacher_disagreement_score":0.004495295,"about_ca_system_score_codex":0.00094398606,"about_ca_system_score_gemma":0.001256986,"threshold_uncertainty_score":0.02377367},"labels":[],"label_agreement":null},{"id":"W7135164249","doi":"","title":"Adaptive Gradient Clipping for Robust Federated Learning","year":2025,"lang":"en","type":"article","venue":"Research at the University of Copenhagen (University of Copenhagen)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Institute for Catastrophic Loss Reduction","keywords":"Robustness (evolution); Clipping (morphology); Gradient descent; Adversarial system; Convergence (economics); Benchmark (surveying)","score_opus":0.05110527818891028,"score_gpt":0.2919151308250512,"score_spread":0.24080985263614094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135164249","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010118216,0.00016792356,0.9879358,0.00016742929,0.00003701181,0.000028726774,0.000029772886,0.00062074006,0.0008944512],"genre_scores_gemma":[0.80942315,0.0002681984,0.18620442,0.0003700615,0.000104214836,0.00017871709,0.00016745583,0.00023185283,0.0030519308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881005,0.00037447872,0.000056920024,0.00031559975,0.00031437865,0.00012859915],"domain_scores_gemma":[0.9968823,0.0016588421,0.0002798522,0.0005866298,0.00041347404,0.00017897341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002459599,0.0013484553,0.0012030957,0.00055903196,0.0005671032,0.00093344634,0.0017876822,0.001419178,0.0015935032],"category_scores_gemma":[0.010755945,0.00047387465,0.000532731,0.0004934782,0.0017504492,0.0015480431,0.0025313748,0.00236667,0.00047808143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014641504,0.00006490173,0.00059188483,0.00008513161,0.00004847291,0.00011187021,0.00007807684,0.91370106,0.004835527,0.019616297,0.002291789,0.058428638],"study_design_scores_gemma":[0.0000072992993,0.000026585874,0.000047880774,0.000004981988,0.0000033069507,0.000017035773,0.0000049832465,0.9913033,0.0013225798,0.006967057,0.00029003364,0.000005002221],"about_ca_topic_score_codex":0.0016086854,"about_ca_topic_score_gemma":0.0013459001,"teacher_disagreement_score":0.002459599,"about_ca_system_score_codex":0.00094956445,"about_ca_system_score_gemma":0.0012311912,"threshold_uncertainty_score":0.01300776},"labels":[],"label_agreement":null},{"id":"W7151381060","doi":"10.1109/icosec67334.2025.11459748","title":"Adaptive Adversarial Prompt Detection in Large Language Models via Quantum-Resistant Cryptography and Spectral–Spatial Wave Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adversarial system; Cryptography; Key (lock); Scheme (mathematics); Identification (biology); Natural language","score_opus":0.00926456394725601,"score_gpt":0.2385108515904655,"score_spread":0.2292462876432095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7151381060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02503898,0.000090922746,0.9725246,0.00034660622,0.000022901146,0.000048256803,0.00005284942,0.0007319042,0.0011430416],"genre_scores_gemma":[0.85632,0.00018160407,0.13707083,0.00036954813,0.00006194555,0.00018331961,0.00018559642,0.00019799793,0.0054289424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986773,0.00044253966,0.00005500265,0.0002831789,0.00036579036,0.00017609526],"domain_scores_gemma":[0.99678254,0.0019760332,0.00045179104,0.0003482807,0.00032064045,0.00012073669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016860008,0.00093780464,0.0008029038,0.0006895202,0.00051171717,0.0012764345,0.0016642379,0.0009354308,0.0018658317],"category_scores_gemma":[0.0078105726,0.0005988591,0.00088959886,0.0003867836,0.0019019056,0.0028738312,0.0024591333,0.0022176248,0.00055091374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019958809,0.00007768014,0.00086333835,0.00006323843,0.000045348126,0.0001471238,0.00012452118,0.8708764,0.007824767,0.06400949,0.0013552265,0.05441321],"study_design_scores_gemma":[0.0000028341558,0.000008174369,0.000026173853,0.0000016788488,0.0000022406937,0.0000065001427,0.0000033646488,0.98924667,0.0006548249,0.009958412,0.00008567994,0.0000033835788],"about_ca_topic_score_codex":0.0038590373,"about_ca_topic_score_gemma":0.0052341474,"teacher_disagreement_score":0.0038590373,"about_ca_system_score_codex":0.0016739483,"about_ca_system_score_gemma":0.0017093394,"threshold_uncertainty_score":0.0121454},"labels":[],"label_agreement":null},{"id":"W7162310612","doi":"10.3997/2214-4609.202510306","title":"Enhancing Uncertainty Quantification Performance via Deep Learning-Assisted Markov Chain Monte Carlo","year":2025,"lang":"","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Monte Carlo method; Markov chain Monte Carlo; Uncertainty quantification; Markov chain; Uncertainty analysis; Measurement uncertainty","score_opus":0.01120442573902244,"score_gpt":0.2595734411563984,"score_spread":0.24836901541737597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162310612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055832874,0.0001780503,0.9381501,0.0004946332,0.000047251346,0.000036752146,0.00009081473,0.0013682995,0.0038012415],"genre_scores_gemma":[0.8480822,0.00012076365,0.14985868,0.00017559434,0.000029125085,0.000052731324,0.00016445533,0.0001807648,0.0013357092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993807,0.00019364907,0.000024844398,0.000081754275,0.00024050882,0.000078472585],"domain_scores_gemma":[0.9953625,0.003205347,0.0002855425,0.0004200475,0.00057305425,0.0001534463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025039942,0.0007067726,0.0006797564,0.0005379123,0.00041515505,0.00095055246,0.0010813628,0.0008196122,0.0024153271],"category_scores_gemma":[0.008775433,0.00040794123,0.0004898779,0.00034711193,0.0009853852,0.0011818885,0.001633242,0.0018065425,0.0004262745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074036165,0.000022686145,0.0007789113,0.000027230355,0.000015556177,0.000031091906,0.000021934631,0.9713467,0.0017859503,0.007303983,0.00046849495,0.0181235],"study_design_scores_gemma":[0.0000013735316,0.00000306551,0.000021416485,0.000001888543,7.647859e-7,0.0000023685698,9.1927336e-7,0.99824095,0.0004194778,0.00125165,0.000054663615,0.0000014618505],"about_ca_topic_score_codex":0.009380034,"about_ca_topic_score_gemma":0.00896227,"teacher_disagreement_score":0.009380034,"about_ca_system_score_codex":0.0011258175,"about_ca_system_score_gemma":0.0018906982,"threshold_uncertainty_score":0.01865089},"labels":[],"label_agreement":null}]}