{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":4,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":4,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"b7378f147e44","filters":{"venue":"Information Processing in Sensor Networks"}},"results":[{"id":"W2162814504","doi":"","title":"Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements","year":2011,"lang":"en","type":"article","venue":"Information Processing in Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"RSS; Wireless sensor network; Testbed; Computer science; Particle filter; Monte Carlo method; Real-time computing; Tracking (education); Tracking system; Calibration; SIGNAL (programming language); Artificial intelligence; Kalman filter; Computer network","authors":[{"name":"Xi Chen","is_ca":true},{"name":"Andrea Edelstein","is_ca":true},{"name":"Yunpeng Li","is_ca":false},{"name":"Mark Coates","is_ca":true},{"name":"Michael Rabbat","is_ca":true},{"name":"Aidong Men","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04320824435457003,"gpt":0.2417035649233902,"spread":0.1984953205688202,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002590673,0.0008190516,0.0009847329,0.0009063684,0.0005435225,0.0009571412,0.00176648,0.001034005,0.001504767],"category_scores_gemma":[0.00834376,0.0008131263,0.0008361035,0.0008639145,0.0009938007,0.001457534,0.00118959,0.0009184185,0.0004377459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001286056,"about_ca_system_score_gemma":0.001455048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008045721,"about_ca_topic_score_gemma":0.00776126,"domain_scores_codex":[0.9985479,0.0005078191,0.00006031206,0.0002235447,0.0005706614,0.00008971844],"domain_scores_gemma":[0.9953566,0.003233717,0.0003605612,0.0004234043,0.0005080885,0.0001175355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001201368,0.0000439935,0.0009983707,0.00004799163,0.00004484297,0.00005420637,0.00005427564,0.9535325,0.001687595,0.01119849,0.0003294809,0.03188804],"study_design_scores_gemma":[0.0000097632,0.00001582846,0.00009032391,0.000003248501,0.00000544173,0.00001708781,0.000002270858,0.9970658,0.000494607,0.002013956,0.0002761045,0.00000555819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006629046,0.00007367064,0.9923156,0.00003955501,0.00001392851,0.00003051355,0.0000158285,0.0002810792,0.0006007478],"genre_scores_gemma":[0.5127707,0.0002257187,0.4837417,0.00009522323,0.00006043695,0.0003128891,0.0001875458,0.0001580659,0.002447708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008045721,"threshold_uncertainty_score":0.01599783,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2117656114","doi":"10.5555/1602165.1602222","title":"Demo abstract: Application of WINTeR industrial testbed to the analysis of closed-loop control systems in wireless sensor networks","year":2009,"lang":"en","type":"article","venue":"Information Processing in Sensor Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Cape Breton University","funders":"","keywords":"Testbed; Computer science; Wireless sensor network; Network topology; Context (archaeology); Wireless; Wireless network; Dynamical systems theory; Topology control; Interference (communication); Distributed computing; Topology (electrical circuits); Key distribution in wireless sensor networks; Computer network; Telecommunications; Engineering; Electrical engineering","authors":[{"name":"Martin J. Murillo","is_ca":true},{"name":"Jeff Slipp","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01121400653931337,"gpt":0.2343345776357725,"spread":0.2231205710964591,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005271067,0.0003678528,0.0002077842,0.0003126051,0.0002805018,0.000271412,0.0005678667,0.0002716059,0.005744821],"category_scores_gemma":[0.0008895315,0.0001049157,0.0002533399,0.000266091,0.0002454542,0.0004811319,0.0003909716,0.0003415126,0.000271646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004418373,"about_ca_system_score_gemma":0.0002340171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003412151,"about_ca_topic_score_gemma":0.00386584,"domain_scores_codex":[0.9998344,0.00006836012,0.000006988583,0.00001660039,0.00005110342,0.00002239902],"domain_scores_gemma":[0.9995678,0.0002060743,0.00002080188,0.00009248387,0.0000719678,0.00004078578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007830645,0.0003197451,0.005218865,0.0002410715,0.00006567677,0.0009455268,0.0002075752,0.8807764,0.02902585,0.02209732,0.01695319,0.04336561],"study_design_scores_gemma":[0.00005406655,0.0001732854,0.001426831,0.00001127407,0.000007432942,0.00008013121,0.00004724505,0.9761577,0.01160626,0.003620002,0.006805167,0.00001067551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5041201,0.0003663103,0.4306671,0.0009919831,0.0004133797,0.0003921691,0.004458697,0.008271025,0.05031924],"genre_scores_gemma":[0.9533414,0.0001520259,0.04186563,0.00004417663,0.00002569595,0.0001393481,0.001347329,0.000124397,0.00296001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005744821,"threshold_uncertainty_score":0.01921833,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2473751517","doi":"10.5555/2959355.2959416","title":"Accelerating embedded deep learning using DeepX: demonstration abstract","year":2016,"lang":"en","type":"article","venue":"Information Processing in Sensor Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Bell (Canada)","funders":"","keywords":"Deep learning; Computer science; Convolutional neural network; Artificial intelligence; Inference; Artificial neural network; Deep neural networks; Software; Mobile device; Computer architecture; Resource (disambiguation); Embedded system; Machine learning; Computer engineering; Operating system; Computer network","authors":[{"name":"Nicholas D. Lane","is_ca":true},{"name":"Sourav Bhattacharya","is_ca":true},{"name":"Petko Georgiev","is_ca":false},{"name":"Claudio Forlivesi","is_ca":true},{"name":"Fahim Kawsar","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02504612971637403,"gpt":0.2743456450988635,"spread":0.2492995153824895,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005855101,0.0005865512,0.0002770287,0.0001744299,0.0001829037,0.0004123271,0.001202202,0.0005232912,0.008699649],"category_scores_gemma":[0.0009761945,0.000204669,0.0002021092,0.0002639868,0.0003520147,0.0007969881,0.0007318928,0.001242581,0.001332421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003942812,"about_ca_system_score_gemma":0.0006307356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004418351,"about_ca_topic_score_gemma":0.003700818,"domain_scores_codex":[0.9998015,0.00002767909,0.000008692469,0.00002673626,0.00009457455,0.00004079453],"domain_scores_gemma":[0.9995891,0.0001220126,0.0000220404,0.00006026358,0.0001260924,0.0000805151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003356841,0.001410613,0.007251534,0.0008189894,0.0002513121,0.001507901,0.0004120294,0.2864585,0.1753359,0.03652461,0.1880258,0.298646],"study_design_scores_gemma":[0.0003301914,0.0005210376,0.001215923,0.00003417514,0.00001622608,0.0001058968,0.00004141459,0.9082207,0.0623903,0.005372868,0.02172784,0.00002338869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4990113,0.001668706,0.4016564,0.004674688,0.001629176,0.0003692685,0.002748214,0.0467345,0.04150761],"genre_scores_gemma":[0.8274736,0.0004410799,0.1554967,0.0004668246,0.00006001842,0.0001910471,0.001304003,0.0007018592,0.01386493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008699649,"threshold_uncertainty_score":0.02910322,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2082758143","doi":"10.5555/2602339.2602384","title":"Poster abstract: precision improvement of aircrafts attitude estimation through gyro sensors data fusion in a redundant inertial measurement unit","year":2014,"lang":"en","type":"article","venue":"Information Processing in Sensor Networks","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Inertial measurement unit; Sensor fusion; Computer science; Gyroscope; Inertial frame of reference; Variance (accounting); Inertial navigation system; Units of measurement; Angular velocity; Real-time computing; Computer vision; Engineering; Aerospace engineering; Physics","authors":[{"name":"Teodor Lucian Grigorie","is_ca":false},{"name":"Ruxandra Mihaela Botez","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02649086215780831,"gpt":0.2574214201559367,"spread":0.2309305579981284,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003560265,0.0004008051,0.0002925243,0.0003604411,0.0001796968,0.0004874739,0.0003468169,0.0002934277,0.001731999],"category_scores_gemma":[0.0006646091,0.0001216685,0.0002851705,0.0003664741,0.0001651544,0.0003711577,0.0003391896,0.0003226355,0.0007858999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001407624,"about_ca_system_score_gemma":0.0001929731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004703742,"about_ca_topic_score_gemma":0.0005109634,"domain_scores_codex":[0.9997095,0.00004967863,0.00001531347,0.00007117683,0.0001348006,0.0000195607],"domain_scores_gemma":[0.9997026,0.00003785966,0.00002801563,0.00006543117,0.0001503463,0.00001565739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000931375,0.0001128221,0.003594589,0.0002999746,0.0001429885,0.0002299434,0.0001847651,0.04116574,0.426736,0.003986258,0.01062432,0.5119911],"study_design_scores_gemma":[0.00008699283,0.001153406,0.01327942,0.00004344955,0.0001882176,0.0004737299,0.00005384576,0.559034,0.3994288,0.00235785,0.02382751,0.00007281392],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1962019,0.00150589,0.7907407,0.0005547123,0.0007745752,0.00007489994,0.0003096276,0.002026208,0.007811415],"genre_scores_gemma":[0.8156525,0.0005686539,0.1744475,0.0001078324,0.0003917488,0.00003607464,0.0004060706,0.0001177738,0.008271856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001731999,"threshold_uncertainty_score":0.005794108,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}