{"meta":{"query_hash":"7a3d6d71d764","filters":{"venue":"2007 IEEE/SP 14th Workshop on Statistical Signal Processing"},"cohort_total":4,"direct_labels_cover":0,"predictions_cover":4,"exported":4,"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/7a3d6d71d764","api":"https://metacan.xera.ac/api/v1/cohort?venue=2007+IEEE%2FSP+14th+Workshop+on+Statistical+Signal+Processing"},"results":[{"id":"W1977335817","doi":"10.1109/ssp.2007.4301337","title":"Distributed Average Consensus using Probabilistic Quantization","year":2007,"lang":"en","type":"article","venue":"2007 IEEE/SP 14th Workshop on Statistical Signal Processing","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":143,"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":"Quantization (signal processing); Probabilistic logic; Computer science; Computation; Distributed algorithm; Wireless sensor network; Consensus; Algorithm; Consensus algorithm; Theoretical computer science; Distributed computing; Artificial intelligence; Multi-agent system","score_opus":0.040121011471013546,"score_gpt":0.30982728312438823,"score_spread":0.2697062716533747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977335817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031671952,0.00010162583,0.9956495,0.00008058348,0.000025192787,0.000018490784,0.000011554202,0.00014956348,0.0007963273],"genre_scores_gemma":[0.64726865,0.00043905844,0.3492176,0.00018120128,0.00013993258,0.00025922293,0.00016742403,0.000116791234,0.0022100522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773335,0.0006045653,0.000136436,0.00048274884,0.00091667444,0.0001261799],"domain_scores_gemma":[0.9959681,0.0023106947,0.00036232593,0.0005261936,0.00073356554,0.000099090495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025022144,0.00064618356,0.0012494089,0.0008506504,0.000654935,0.0011940422,0.0018101163,0.0008588401,0.0012956484],"category_scores_gemma":[0.009657795,0.00040827013,0.0006797305,0.0010930133,0.001514808,0.0026587988,0.0017622019,0.0013941056,0.00032277574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049018996,0.000022750688,0.00028490063,0.000058841477,0.000036770893,0.00003871093,0.00010189939,0.87844133,0.002021628,0.07770679,0.00082430785,0.040413022],"study_design_scores_gemma":[0.0000097827215,0.000020207848,0.000033191554,0.0000034084844,0.0000042010965,0.000011777492,0.000005400162,0.97615916,0.000539687,0.02273504,0.00047116578,0.000006935519],"about_ca_topic_score_codex":0.0021864052,"about_ca_topic_score_gemma":0.0012587785,"teacher_disagreement_score":0.0025022144,"about_ca_system_score_codex":0.0011184601,"about_ca_system_score_gemma":0.0012889397,"threshold_uncertainty_score":0.013233125},"labels":[],"label_agreement":null},{"id":"W2018580924","doi":"10.1109/ssp.2007.4301330","title":"Cooperative Swarms for Clustering Phoneme Data","year":2007,"lang":"en","type":"article","venue":"2007 IEEE/SP 14th Workshop on Statistical Signal Processing","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","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":"Cluster analysis; Particle swarm optimization; Computer science; TIMIT; Swarm behaviour; Fuzzy clustering; Data mining; Cluster (spacecraft); Artificial intelligence; Machine learning; Hidden Markov model","score_opus":0.048086470628656675,"score_gpt":0.32522379493236486,"score_spread":0.2771373243037082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018580924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017949719,0.00029045728,0.98028743,0.00009068285,0.00007385461,0.00004854708,0.00006201526,0.00037538385,0.00082183984],"genre_scores_gemma":[0.47987494,0.0004020369,0.5152747,0.00011179643,0.0001405295,0.0002825771,0.00054256123,0.000104734165,0.003266063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993487,0.00015587742,0.000048521324,0.00018114511,0.00020679156,0.000058939804],"domain_scores_gemma":[0.9990896,0.00030622497,0.00011587363,0.00017688074,0.00026599478,0.000045470075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008054997,0.0008067179,0.0010893798,0.0012115487,0.00062713993,0.0008458267,0.0012461666,0.0008671292,0.0007590688],"category_scores_gemma":[0.0026097256,0.00048023244,0.00085356284,0.0013767049,0.00050404546,0.0010936346,0.00094694574,0.00071255123,0.00042195583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031908083,0.00016269211,0.0044648633,0.00025607063,0.000305151,0.00025156757,0.000589354,0.61488765,0.030588688,0.017907735,0.0056155073,0.32465163],"study_design_scores_gemma":[0.000013290311,0.000045239332,0.0005443612,0.0000048061656,0.000019705409,0.00004862576,0.000042051033,0.99188143,0.0026089847,0.0029372906,0.0018405056,0.0000137142815],"about_ca_topic_score_codex":0.004469555,"about_ca_topic_score_gemma":0.003579617,"teacher_disagreement_score":0.004469555,"about_ca_system_score_codex":0.0005697783,"about_ca_system_score_gemma":0.00052243617,"threshold_uncertainty_score":0.008887053},"labels":[],"label_agreement":null},{"id":"W2126833874","doi":"10.1109/ssp.2007.4301292","title":"Compressed Network Monitoring","year":2007,"lang":"en","type":"article","venue":"2007 IEEE/SP 14th Workshop on Statistical Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","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":"McGill University","funders":"","keywords":"Computer science; Metric (unit); Exploit; Path (computing); Wavelet; Compressed sensing; Algorithm; Routing (electronic design automation); Set (abstract data type); Basis (linear algebra); Network monitoring; Real-time computing; Data mining; Mathematics; Artificial intelligence; Computer network","score_opus":0.02825340884641831,"score_gpt":0.29022574400761975,"score_spread":0.26197233516120144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126833874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016082885,0.00038900893,0.9785418,0.00035505468,0.00010164264,0.00008718182,0.00035734355,0.00063720386,0.0034480128],"genre_scores_gemma":[0.41290915,0.0009642341,0.57995087,0.00030008017,0.00028584054,0.0002428639,0.0013753622,0.00013904388,0.0038325351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991775,0.00016299529,0.00003270022,0.00014364845,0.00043181732,0.000051327497],"domain_scores_gemma":[0.9988475,0.00045861563,0.0001698591,0.00029275418,0.00019898446,0.00003218406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055486214,0.0006282081,0.00057684176,0.00087578857,0.0003925283,0.00073610997,0.0008952116,0.00064802513,0.001928374],"category_scores_gemma":[0.0036404617,0.00024621194,0.00030133198,0.00092137,0.00048024088,0.001637171,0.0011842597,0.0010285813,0.0004158871],"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":[0.0006159681,0.00013311008,0.0029214597,0.00035479438,0.000082399194,0.0003582099,0.0003323127,0.21907397,0.079444885,0.050991654,0.009183532,0.6365077],"study_design_scores_gemma":[0.000029640858,0.000107261054,0.001536128,0.000040248335,0.00002187755,0.00048815456,0.000065786335,0.9387061,0.029342633,0.019298062,0.0103340205,0.000030139407],"about_ca_topic_score_codex":0.0012299072,"about_ca_topic_score_gemma":0.00131665,"teacher_disagreement_score":0.001928374,"about_ca_system_score_codex":0.00047125792,"about_ca_system_score_gemma":0.0006286747,"threshold_uncertainty_score":0.0064510107},"labels":[],"label_agreement":null},{"id":"W2138556100","doi":"10.1109/ssp.2007.4301362","title":"On Nonparametric Identification of Multi-Channel Hammerstein Systems","year":2007,"lang":"en","type":"article","venue":"2007 IEEE/SP 14th Workshop on Statistical Signal Processing","topic":"Control Systems and Identification","field":"Engineering","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 Manitoba","funders":"","keywords":"Pointwise; Nonparametric statistics; A priori and a posteriori; Kernel (algebra); Generalization; Identification (biology); Mathematics; Channel (broadcasting); System identification; Kernel density estimation; Mathematical optimization; Computer science; Pointwise convergence; Parametric statistics; Nonlinear system; Dynamical systems theory; Applied mathematics; Data modeling; Statistics","score_opus":0.023217300327855336,"score_gpt":0.27682802985251975,"score_spread":0.2536107295246644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138556100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009382817,0.00051334564,0.9888399,0.00013474446,0.00003433332,0.000016684045,0.000017817058,0.00004786297,0.0010124013],"genre_scores_gemma":[0.8042044,0.0029689714,0.1854063,0.00017281831,0.00033011826,0.00022979054,0.0002235437,0.00009625578,0.00636778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986389,0.00070657354,0.00005383477,0.00022002563,0.00029427296,0.00008634104],"domain_scores_gemma":[0.9936447,0.005104985,0.00044201183,0.00034660308,0.0003964546,0.000065211796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028699206,0.0007820119,0.001170903,0.0008108477,0.00041572753,0.0010295052,0.0007470107,0.0013048317,0.0009001447],"category_scores_gemma":[0.011358935,0.00041870365,0.00064749824,0.00089561095,0.001863894,0.0016737483,0.0014896408,0.0013934339,0.00018953336],"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":[0.000074849886,0.00003535055,0.000620697,0.00017159113,0.00008568497,0.00015576479,0.00013051451,0.85130477,0.002284523,0.09773652,0.0006005546,0.046799168],"study_design_scores_gemma":[0.0000035176952,0.000018378694,0.0002173322,0.000009411308,0.0000061413557,0.000035455774,0.000010553665,0.96420914,0.00040457066,0.03467408,0.0004009121,0.000010540448],"about_ca_topic_score_codex":0.0021514988,"about_ca_topic_score_gemma":0.0011100295,"teacher_disagreement_score":0.0028699206,"about_ca_system_score_codex":0.00063060847,"about_ca_system_score_gemma":0.00076062314,"threshold_uncertainty_score":0.015177786},"labels":[],"label_agreement":null}]}