{"meta":{"query_hash":"54aa244634c5","filters":{"venue":"ISRN Signal Processing"},"cohort_total":8,"direct_labels_cover":0,"predictions_cover":8,"exported":8,"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/54aa244634c5","api":"https://metacan.xera.ac/api/v1/cohort?venue=ISRN+Signal+Processing"},"results":[{"id":"W1965030114","doi":"10.5402/2011/725108","title":"Iterative Smooth Variable Structure Filter for Parameter Estimation","year":2011,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","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":"McMaster University","funders":"","keywords":"Control theory (sociology); Hyperplane; Robustness (evolution); Actuator; Computer science; Filter (signal processing); Stability (learning theory); Convergence (economics); Mathematics; Control (management)","score_opus":0.026181641126502696,"score_gpt":0.23262873545055118,"score_spread":0.20644709432404848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965030114","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":[0.0020054472,0.00014631738,0.9968755,0.00003241622,0.000025612131,0.000012933878,0.000018460854,0.0002772367,0.00060599006],"genre_scores_gemma":[0.24737455,0.0007669075,0.7420138,0.00012521785,0.000106743624,0.0002048955,0.0003647081,0.00010521327,0.008938009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958485,0.000088548586,0.00002020377,0.00007056201,0.0002048491,0.000030893214],"domain_scores_gemma":[0.9994518,0.00026138264,0.000044116696,0.00007107933,0.00015900326,0.0000125572715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000639273,0.0004818993,0.0005981381,0.00052942015,0.00026632927,0.00054338365,0.0005454167,0.0007655108,0.0030844852],"category_scores_gemma":[0.002254004,0.00022547395,0.0004334672,0.0005935749,0.0002907249,0.00061811844,0.00039428103,0.00086250546,0.0009226121],"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":[0.00029273794,0.00005633243,0.0007157454,0.00021358035,0.00008340827,0.000089470865,0.00017259963,0.24643399,0.041347694,0.030687967,0.00478749,0.6751189],"study_design_scores_gemma":[0.00001749056,0.00006074913,0.00029862387,0.000013358475,0.000010032763,0.000040791692,0.000006998843,0.983872,0.006702187,0.0033286477,0.0056350436,0.000014056741],"about_ca_topic_score_codex":0.0034789862,"about_ca_topic_score_gemma":0.003743535,"teacher_disagreement_score":0.0034789862,"about_ca_system_score_codex":0.00041904507,"about_ca_system_score_gemma":0.0009934665,"threshold_uncertainty_score":0.010318637},"labels":[],"label_agreement":null},{"id":"W1977989201","doi":"10.5402/2012/503707","title":"A Hybrid RSS/TOA Method for 3D Positioning in an Indoor Environment","year":2012,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":24,"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":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Non-line-of-sight propagation; RSS; Multipath propagation; Computer science; Path loss; Nakagami distribution; Time of arrival; Real-time computing; Wireless; Fading; Telecommunications; Decoding methods","score_opus":0.01513369963839353,"score_gpt":0.2630037525629649,"score_spread":0.24787005292457134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977989201","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":[0.00517272,0.00008000465,0.99265915,0.000027425122,0.000051671734,0.000022691125,0.000042127012,0.0011458143,0.0007984721],"genre_scores_gemma":[0.1287786,0.00016175381,0.8670867,0.00006503977,0.000070255046,0.00012057952,0.0002049713,0.00016694318,0.003345141],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929297,0.0001434995,0.000030831547,0.00013648179,0.00035616156,0.000040022765],"domain_scores_gemma":[0.9994947,0.000110974,0.00005662114,0.00010576163,0.00020846994,0.000023525065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037830326,0.00087507407,0.0007423862,0.0015395802,0.00040626992,0.00071618636,0.0011914304,0.000698454,0.0018995609],"category_scores_gemma":[0.0012885757,0.0004850395,0.0008750299,0.0013921559,0.00030187835,0.0007782751,0.00073093444,0.00052521226,0.002146179],"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":[0.0002112435,0.000112818016,0.0016699068,0.00021227385,0.00017877945,0.00017824778,0.00021044242,0.06037498,0.12503783,0.004386543,0.0028824345,0.8045445],"study_design_scores_gemma":[0.00005338646,0.0003115343,0.0026544977,0.000027434515,0.000112234186,0.0011227868,0.000105551364,0.9159521,0.060599733,0.0020553558,0.01686177,0.0001435734],"about_ca_topic_score_codex":0.0014930789,"about_ca_topic_score_gemma":0.0024341051,"teacher_disagreement_score":0.0018995609,"about_ca_system_score_codex":0.00024352755,"about_ca_system_score_gemma":0.00049041474,"threshold_uncertainty_score":0.0063546896},"labels":[],"label_agreement":null},{"id":"W1987360397","doi":"10.5402/2011/683972","title":"Decoding of Turbo Codes in Symmetric Alpha-Stable Noise","year":2011,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Power Line Communications and Noise","field":"Engineering","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 Manitoba","funders":"Telekom Malaysia Berhad; Ministério da Ciência, Tecnologia e Inovação","keywords":"Turbo equalizer; Turbo code; Soft-decision decoder; Turbo; Computer science; Serial concatenated convolutional codes; Algorithm; Noise (video); Decoding methods; Probability density function; Mathematics; Low-density parity-check code; Concatenated error correction code; Statistics; Engineering; Artificial intelligence; Block code; Error floor","score_opus":0.03706631559175234,"score_gpt":0.24428517739566996,"score_spread":0.20721886180391763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987360397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1726204,0.00027437683,0.8235939,0.00016562993,0.00002791494,0.000016702013,0.000035385725,0.00010939539,0.003156268],"genre_scores_gemma":[0.9640992,0.00030571173,0.033753682,0.000049667433,0.000023588553,0.000016508708,0.000041258332,0.000022319375,0.0016881275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918836,0.0003027499,0.000043576598,0.00008258603,0.00029368835,0.00008898387],"domain_scores_gemma":[0.99682117,0.0021601324,0.00029313203,0.0001471375,0.00051544746,0.00006287296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011491221,0.0004920226,0.00058016414,0.0004137028,0.00036619275,0.0007203309,0.00034830262,0.0006996922,0.0003374736],"category_scores_gemma":[0.0076625887,0.00024115345,0.0002989803,0.00059864856,0.0010391001,0.0010320575,0.0005611607,0.00046010566,0.00020267598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023852295,0.000020841624,0.002160732,0.00008366581,0.00003584488,0.00036894763,0.00027465422,0.88125926,0.014675743,0.06817709,0.0003256934,0.032379013],"study_design_scores_gemma":[0.0000036371628,0.000029880208,0.00015454789,0.0000056976214,0.000003842507,0.00006226893,0.000015622438,0.98836356,0.0051950067,0.006012089,0.00014779078,0.0000060560415],"about_ca_topic_score_codex":0.0022918961,"about_ca_topic_score_gemma":0.0013467285,"teacher_disagreement_score":0.0022918961,"about_ca_system_score_codex":0.00062035787,"about_ca_system_score_gemma":0.0012103077,"threshold_uncertainty_score":0.0060771704},"labels":[],"label_agreement":null},{"id":"W2026615873","doi":"10.5402/2011/651790","title":"A Novel Method of Small Target Detection in Sea Clutter","year":2011,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Radar Systems and Signal Processing","field":"Engineering","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":"National University of Defense Technology; McMaster University","keywords":"Clutter; Scattering; Stationary target indication; Remote sensing; Radar; Radar horizon; Polarimetry; Constant false alarm rate; Computer science; Entropy (arrow of time); Moving target indication; Artificial intelligence; Continuous-wave radar; Radar imaging; Geology; Physics; Optics; Telecommunications","score_opus":0.032098173613445885,"score_gpt":0.23725173091027058,"score_spread":0.2051535572968247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026615873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027748062,0.00018969268,0.99621385,0.000042173815,0.00006819038,0.000016530787,0.000024928224,0.00020323572,0.0004665474],"genre_scores_gemma":[0.063413665,0.00038858506,0.9321273,0.0001230666,0.00012397765,0.00007352239,0.00012611534,0.00008470327,0.0035390032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995065,0.000078393736,0.000020592071,0.00013999967,0.00021767193,0.000036775884],"domain_scores_gemma":[0.99958795,0.00013188431,0.000036286525,0.000058410067,0.00015276382,0.000032681004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043865733,0.0007040043,0.00089401455,0.0011275508,0.00041390065,0.00075656944,0.00077719527,0.00080685207,0.001437923],"category_scores_gemma":[0.0010200017,0.00028652992,0.0006035345,0.0009016456,0.000615217,0.0009956501,0.0009772838,0.0009604659,0.0011438776],"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":[0.00028898066,0.00008226842,0.0007388643,0.0002629187,0.00008607115,0.00021151069,0.00013850695,0.014028635,0.26891923,0.017946126,0.003430881,0.69386595],"study_design_scores_gemma":[0.000064318185,0.0002991731,0.0017206161,0.00002953927,0.00007225428,0.002039729,0.000061300714,0.82259285,0.1373038,0.013768848,0.021944657,0.0001029219],"about_ca_topic_score_codex":0.00044601533,"about_ca_topic_score_gemma":0.0006245706,"teacher_disagreement_score":0.001437923,"about_ca_system_score_codex":0.0002488175,"about_ca_system_score_gemma":0.00046181623,"threshold_uncertainty_score":0.0048103333},"labels":[],"label_agreement":null},{"id":"W2085121419","doi":"10.5402/2011/672353","title":"Edge-Detection in Noisy Images Using Independent Component Analysis","year":2011,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Blind Source Separation Techniques","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","funders":"","keywords":"Artificial intelligence; Independent component analysis; Edge detection; Computer science; Computer vision; Pattern recognition (psychology); Phase congruency; Enhanced Data Rates for GSM Evolution; Noise (video); Image (mathematics); Digital image; Gaussian; Image gradient; Image processing","score_opus":0.042546742337180844,"score_gpt":0.2830846228225765,"score_spread":0.24053788048539568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085121419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014420857,0.00049752975,0.9838702,0.00005180202,0.000028832002,0.00002190954,0.000024100242,0.00040716643,0.00067766197],"genre_scores_gemma":[0.14258327,0.00087888644,0.85520095,0.000045408848,0.00007177487,0.00005264806,0.00013403737,0.00008729934,0.0009456632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995433,0.00011418886,0.000026554706,0.0000816806,0.0002125265,0.000021657946],"domain_scores_gemma":[0.99933076,0.0003214534,0.00007826948,0.000072560084,0.00018111733,0.00001588748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005257387,0.0006620737,0.00073658366,0.0015691313,0.00024348183,0.00064593944,0.00047809738,0.0007455311,0.00075301476],"category_scores_gemma":[0.0020174829,0.00024172089,0.00045543053,0.0011681764,0.00050960225,0.0011175707,0.00048776806,0.00058903615,0.00054091745],"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":[0.00033267608,0.00009323883,0.0012483869,0.00032750843,0.00014658053,0.00027221238,0.00015048221,0.0434264,0.18317926,0.0072618285,0.0015987117,0.7619628],"study_design_scores_gemma":[0.00003483685,0.00016223699,0.0035388723,0.00005237446,0.00009578384,0.0005036287,0.00004786309,0.8607055,0.12059518,0.008336906,0.0058585005,0.00006824185],"about_ca_topic_score_codex":0.00059597765,"about_ca_topic_score_gemma":0.0007834224,"teacher_disagreement_score":0.0015691313,"about_ca_system_score_codex":0.0002098819,"about_ca_system_score_gemma":0.00024504965,"threshold_uncertainty_score":0.0027804375},"labels":[],"label_agreement":null},{"id":"W2092200183","doi":"10.5402/2011/120351","title":"Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle","year":2011,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":49,"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":"","keywords":"Kalman filter; Robustness (evolution); Extended Kalman filter; Battery (electricity); Electric vehicle; Automotive engineering; Control theory (sociology); Condition monitoring; Computer science; Engineering; Particle filter; Power (physics); Electrical engineering","score_opus":0.03357398038346488,"score_gpt":0.28208493725165534,"score_spread":0.24851095686819047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092200183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0152454255,0.00028007364,0.983622,0.000059497517,0.000014231495,0.000025051719,0.0000105825675,0.00014940665,0.000593673],"genre_scores_gemma":[0.86511123,0.000629941,0.13222057,0.00004976754,0.000040074705,0.00010365316,0.00005528954,0.00002766744,0.0017618649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998493,0.000037436457,0.000012904707,0.000038112696,0.000048000013,0.000014236134],"domain_scores_gemma":[0.99956983,0.0002316784,0.00008069033,0.000019100968,0.00009070234,0.000008024934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046454393,0.0005318872,0.00042647365,0.00038300522,0.00021287578,0.0004880725,0.00044594132,0.00038644506,0.000688609],"category_scores_gemma":[0.0020822573,0.00020806177,0.00023815379,0.0002327332,0.00027526863,0.0006009469,0.0003672843,0.00041536507,0.00014927695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016536406,0.00006420486,0.001759339,0.00018222046,0.000067744935,0.00010819634,0.0002696119,0.6040187,0.020729633,0.008650901,0.0007711018,0.36321297],"study_design_scores_gemma":[0.000018812403,0.00010213208,0.0009455922,0.00001756332,0.000020476346,0.000039118848,0.000036890546,0.9911849,0.0040950407,0.0023727678,0.0011524494,0.000014217704],"about_ca_topic_score_codex":0.004956212,"about_ca_topic_score_gemma":0.0033372894,"teacher_disagreement_score":0.004956212,"about_ca_system_score_codex":0.0002769547,"about_ca_system_score_gemma":0.0003535319,"threshold_uncertainty_score":0.009854734},"labels":[],"label_agreement":null},{"id":"W2092706628","doi":"10.5402/2011/563678","title":"Application of Hilbert-Huang Transform in Generating Spectrum-Compatible Earthquake Time Histories","year":2011,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":28,"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; University Network of Excellence in Nuclear Engineering","keywords":"Hilbert–Huang transform; Earthquake simulation; Seismology; Response spectrum; Time–frequency analysis; Hilbert spectral analysis; Earthquake prediction; Spectrum (functional analysis); Seismic microzonation; Geology; Computer science; Algorithm; Mathematics; Physics; Statistics; Telecommunications; Energy (signal processing)","score_opus":0.019350800216741088,"score_gpt":0.24614645132533092,"score_spread":0.22679565110858982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092706628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014611619,0.000063837026,0.98472244,0.000025864489,0.000006467954,0.000016272179,0.00002042252,0.000098912235,0.0004342054],"genre_scores_gemma":[0.30385348,0.0002609108,0.69480115,0.000026663862,0.000027565362,0.000085263266,0.0001309412,0.000076871904,0.00073711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997379,0.00009097551,0.00001745431,0.000039765906,0.000103235056,0.000010722485],"domain_scores_gemma":[0.9994715,0.000363275,0.00003943487,0.00004673647,0.000065731525,0.000013291341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092478417,0.0004429855,0.00022413582,0.00052172504,0.00015198461,0.00027257996,0.00026297016,0.00030729978,0.0010295371],"category_scores_gemma":[0.0022911942,0.00016142307,0.0003130226,0.00052840763,0.00029632414,0.0005719003,0.00031003152,0.00029466284,0.00020210439],"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":[0.00022093057,0.00012106019,0.0016553928,0.00019427841,0.00007141612,0.00023523881,0.00022845356,0.23808615,0.108404815,0.028783143,0.0006042133,0.62139493],"study_design_scores_gemma":[0.00002241002,0.00013119676,0.0014772974,0.000009896808,0.000027354801,0.00013314011,0.000027736862,0.9517819,0.036402423,0.0072465977,0.0027164922,0.000023570476],"about_ca_topic_score_codex":0.00044520656,"about_ca_topic_score_gemma":0.0006527425,"teacher_disagreement_score":0.0010295371,"about_ca_system_score_codex":0.00015583755,"about_ca_system_score_gemma":0.0003816707,"threshold_uncertainty_score":0.00489074},"labels":[],"label_agreement":null},{"id":"W2141403181","doi":"10.5402/2012/628706","title":"Cochlear Implant Speech Processing Using Wavelet Transform","year":2012,"lang":"en","type":"article","venue":"ISRN Signal Processing","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","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":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Cochlear implant; Wavelet packet decomposition; Wavelet; Speech recognition; Computer science; Wavelet transform; Network packet; Algorithm; Artificial intelligence; Audiology; Medicine","score_opus":0.05903101370192804,"score_gpt":0.31925882734081196,"score_spread":0.2602278136388839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141403181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017548941,0.0000817152,0.9813161,0.00003139137,0.000028345843,0.00001993261,0.000027013559,0.00017619958,0.0007704155],"genre_scores_gemma":[0.27927107,0.00039433292,0.71716195,0.000030545285,0.00003339358,0.00006717992,0.0001324815,0.00007627126,0.0028327643],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999052,0.000015785226,0.000005615539,0.000012111659,0.00005324965,0.000007986369],"domain_scores_gemma":[0.99991286,0.0000339827,0.0000064651167,0.000012356054,0.00002886267,0.0000054555926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023612949,0.00034538243,0.00019311169,0.00032400095,0.00015262215,0.00032791708,0.00026804244,0.00029202027,0.0013456137],"category_scores_gemma":[0.0004425822,0.00013327124,0.00034835824,0.00027367548,0.00019772723,0.0003248252,0.00027817654,0.0003388166,0.00041328205],"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":[0.00029878222,0.00008797511,0.0007563246,0.00018502901,0.00007001096,0.00025332085,0.00012154652,0.119071975,0.3929323,0.012965126,0.0011222325,0.47213534],"study_design_scores_gemma":[0.00002004759,0.0001548618,0.00079263304,0.000015418016,0.000029383638,0.00031104867,0.000025096368,0.8927866,0.09771498,0.0033164304,0.0048108604,0.000022562761],"about_ca_topic_score_codex":0.00053660054,"about_ca_topic_score_gemma":0.0004917802,"teacher_disagreement_score":0.0013456137,"about_ca_system_score_codex":0.00013002538,"about_ca_system_score_gemma":0.00025732938,"threshold_uncertainty_score":0.0045015216},"labels":[],"label_agreement":null}]}