{"meta":{"query_hash":"e7cc34a8ed74","filters":{"venue":"Journal of Computational Neuroscience"},"cohort_total":72,"direct_labels_cover":0,"predictions_cover":72,"exported":72,"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/e7cc34a8ed74","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Computational+Neuroscience"},"results":[{"id":"W1480943299","doi":"10.1023/a:1011264913465","title":"Self-Organizing Task Modules and Explicit Coordinate Systems in a Neural Network Model for 3-D Saccades","year":2001,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":23,"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":"Saccade; Computer science; Artificial neural network; Artificial intelligence; Orientation (vector space); Coordinate system; Computer vision; Eye movement; Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.04133644084327417,"score_gpt":0.28380142650669965,"score_spread":0.24246498566342548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480943299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42201313,0.0003022513,0.57169044,0.00094032154,0.00006155621,0.000051515246,0.00023247504,0.00027691934,0.004431333],"genre_scores_gemma":[0.9818649,0.00011748603,0.015580417,0.000025684065,0.00001612743,0.00005756868,0.000048909278,0.000035558747,0.0022533021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998777,0.000040847477,0.0000068885297,0.000034561566,0.000016595206,0.000023359087],"domain_scores_gemma":[0.99960905,0.0001870549,0.0000633495,0.000038600072,0.00006406495,0.000037833273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046143212,0.00042050806,0.00062016305,0.00029587813,0.0004831596,0.00091652764,0.0010831445,0.0011054982,0.001602671],"category_scores_gemma":[0.0018858712,0.0005712245,0.0006651411,0.00051565457,0.00075436215,0.0015908078,0.00058150716,0.0008896137,0.00025912217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007887996,0.000039064653,0.0008229595,0.000021375636,0.00004208641,0.000066808265,0.0001493616,0.96406066,0.0026783547,0.024982598,0.00032406606,0.006733771],"study_design_scores_gemma":[0.000006591114,0.00000739917,0.0002370467,0.0000011641067,0.0000047665826,0.000006680062,0.000004999329,0.99484116,0.000091020935,0.0047615194,0.00003422474,0.0000034496506],"about_ca_topic_score_codex":0.010966723,"about_ca_topic_score_gemma":0.0106876,"teacher_disagreement_score":0.010966723,"about_ca_system_score_codex":0.000857289,"about_ca_system_score_gemma":0.000808196,"threshold_uncertainty_score":0.021805763},"labels":[],"label_agreement":null},{"id":"W153317153","doi":"10.1023/a:1025881007453","title":"The Influences of Ih on Temporal Summation in Hippocampal CA1 Pyramidal Neurons: A Modeling Study","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of British Columbia","funders":"","keywords":"Neuroscience; Hippocampal formation; Summation; Hyperpolarization (physics); Normalization (sociology); Physics; Chemistry; Biology; Nuclear magnetic resonance","score_opus":0.05220251626336465,"score_gpt":0.304852205805885,"score_spread":0.2526496895425204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W153317153","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":[0.8779948,0.0006657602,0.09149872,0.0010715603,0.000060366077,0.000050228315,0.0002348036,0.0001967593,0.0282269],"genre_scores_gemma":[0.99715805,0.00021759688,0.0009419838,0.00003394257,0.00002089437,0.000011630633,0.000017655753,0.0000263208,0.0015719836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990976,0.000020243593,0.0000032171786,0.00001464562,0.00001667115,0.000035433764],"domain_scores_gemma":[0.99943584,0.00033008328,0.00006333562,0.000037366346,0.00006799686,0.000065244116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002813114,0.0004026679,0.00052805996,0.0002673296,0.00045024027,0.0005561331,0.0008898862,0.00091242767,0.001965071],"category_scores_gemma":[0.0018704339,0.00038579592,0.0008410965,0.00025530928,0.0006115731,0.0012498343,0.0004813261,0.0006168534,0.00018584935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025149595,0.00016453728,0.0019532384,0.00010214482,0.00006593783,0.00055767904,0.00023913154,0.9185381,0.040578157,0.03058089,0.0007625924,0.006206044],"study_design_scores_gemma":[0.000013773836,0.000018285798,0.0007818293,0.0000025121462,0.000016691316,0.000029547364,0.000016370414,0.99560934,0.0006919458,0.0027026592,0.00010997626,0.0000070159726],"about_ca_topic_score_codex":0.015118418,"about_ca_topic_score_gemma":0.008919988,"teacher_disagreement_score":0.015118418,"about_ca_system_score_codex":0.000723538,"about_ca_system_score_gemma":0.0006725996,"threshold_uncertainty_score":0.030060828},"labels":[],"label_agreement":null},{"id":"W1557539572","doi":"10.1023/a:1021902717424","title":"Clustering in Small Networks of Excitatory Neurons with Heterogeneous Coupling Strengths","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":24,"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; Pacific Institute for the Mathematical Sciences","keywords":"Excitatory postsynaptic potential; Coupling (piping); Neuroscience; Cluster analysis; Cluster (spacecraft); Stability (learning theory); Computer science; Physics; Biological system; Statistical physics; Biology; Inhibitory postsynaptic potential; Artificial intelligence; Computer network; Materials science; Machine learning","score_opus":0.025589834310315036,"score_gpt":0.25082859534898494,"score_spread":0.2252387610386699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557539572","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":[0.82877517,0.0003271014,0.16705047,0.0003571884,0.000041427666,0.0000346796,0.00006784253,0.00020099466,0.0031451248],"genre_scores_gemma":[0.9927772,0.00007620805,0.0057686837,0.000033707547,0.000025596219,0.000019427762,0.000044444387,0.000033013337,0.0012216957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976784,0.000070129434,0.000013358895,0.000060024027,0.00004175481,0.000046933208],"domain_scores_gemma":[0.997968,0.0010342243,0.00028033392,0.00017809612,0.0002592801,0.00028008458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072809996,0.00032160577,0.0007723354,0.0009247331,0.0006558657,0.0009026969,0.0012541835,0.0010475607,0.0011776155],"category_scores_gemma":[0.004509797,0.0007402506,0.00059604895,0.000416125,0.0012148578,0.0012480929,0.00093820586,0.00042704016,0.0002133651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034238337,0.0001006935,0.0066021774,0.00014462024,0.00023695866,0.00056959776,0.00037369883,0.90041167,0.030518407,0.048051406,0.0014106252,0.011237711],"study_design_scores_gemma":[0.000028245771,0.00003392002,0.0034733238,0.0000072035405,0.000028972767,0.00009251695,0.00007043885,0.96702176,0.001621794,0.02741304,0.00018986777,0.00001901946],"about_ca_topic_score_codex":0.0024929056,"about_ca_topic_score_gemma":0.003248825,"teacher_disagreement_score":0.0024929056,"about_ca_system_score_codex":0.00071163516,"about_ca_system_score_gemma":0.00031056904,"threshold_uncertainty_score":0.005163312},"labels":[],"label_agreement":null},{"id":"W1574056899","doi":"10.1023/a:1011200713411","title":"Do Neocortical Pyramidal Neurons Display Stochastic Resonance?","year":2001,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":62,"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 Laval","funders":"National Institute of Neurological Disorders and Stroke; Medical Research Council; Centre National de la Recherche Scientifique","keywords":"Neuroscience; Pyramidal cell; Neocortex; Stochastic resonance; Cerebral cortex; Subthreshold conduction; Functional magnetic resonance imaging; Neuron; Noise (video); Biology; Physics; Computer science; Artificial intelligence; Hippocampus","score_opus":0.01399359609118782,"score_gpt":0.27656881516089804,"score_spread":0.2625752190697102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574056899","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":[0.9860535,0.00006270678,0.011726078,0.0005857372,0.000014216803,0.000002910375,0.000035067264,0.000058409023,0.0014613834],"genre_scores_gemma":[0.9993961,0.00002509874,0.000431476,0.000025563471,0.000004175017,8.809104e-7,0.000007931392,0.0000048508145,0.000103992104],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992335,0.0000120807435,0.0000041046605,0.000015337067,0.000019468653,0.000025737447],"domain_scores_gemma":[0.9993782,0.0003058251,0.000111186375,0.00006823412,0.0000677862,0.00006878608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037087259,0.00011386021,0.00024419554,0.00018863317,0.00012892079,0.0005369953,0.00032724373,0.0007919272,0.0007738158],"category_scores_gemma":[0.0032644486,0.00022804439,0.00015153068,0.0001489336,0.00041690285,0.0012583662,0.0002633465,0.00023209478,0.00019848868],"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":[0.0013847305,0.00022034999,0.23567213,0.00024778658,0.00040000738,0.0022889525,0.0020396162,0.06520761,0.44423276,0.14428043,0.0038657899,0.10015995],"study_design_scores_gemma":[0.00014177753,0.00014811497,0.19065775,0.000027154672,0.00010874068,0.001757084,0.0012839664,0.5103301,0.03524702,0.25874895,0.0014725816,0.000076787415],"about_ca_topic_score_codex":0.0003507884,"about_ca_topic_score_gemma":0.00054281164,"teacher_disagreement_score":0.0007919272,"about_ca_system_score_codex":0.00018921358,"about_ca_system_score_gemma":0.00010201423,"threshold_uncertainty_score":0.0025886893},"labels":[],"label_agreement":null},{"id":"W185261604","doi":"10.1023/a:1025860724292","title":"Noise-Stabilized Long-Distance Synchronization in Populations of Model Neurons","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":15,"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 Toronto","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada","keywords":"Synchronization (alternating current); Stability (learning theory); Noise (video); Theory of computation; Computer science; Excitatory postsynaptic potential; Coupling (piping); Neuroscience; Population; Biological system; Hippocampal formation; Inhibitory postsynaptic potential; Rhythm; Communication noise; Control theory (sociology); Physics; Artificial intelligence; Algorithm; Biology; Telecommunications; Machine learning","score_opus":0.0244600532575549,"score_gpt":0.2869515739693038,"score_spread":0.2624915207117489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W185261604","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":[0.94934684,0.0001107583,0.047493454,0.0003041962,0.000044854998,0.000014868659,0.000042125153,0.00009952702,0.0025435349],"genre_scores_gemma":[0.99864894,0.000024640152,0.0008088881,0.000014327134,0.0000053826247,0.0000068305612,0.0000141444,0.000008542184,0.0004684015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988115,0.000038483828,0.000008867848,0.000027382657,0.000020541234,0.000023609244],"domain_scores_gemma":[0.999087,0.0004308034,0.00015343651,0.000063083404,0.0001224492,0.00014330678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006494156,0.00022942838,0.00050304225,0.0003276152,0.0004283289,0.0007238838,0.0005921275,0.00077647244,0.0011379784],"category_scores_gemma":[0.0037868891,0.00026270453,0.0003831566,0.000183464,0.0007596934,0.0008239932,0.0006497211,0.00050613325,0.00012783693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040551423,0.00011268856,0.006996162,0.0000930144,0.0001405317,0.00053380604,0.00040787627,0.85607713,0.04975224,0.077006236,0.0009015261,0.00757324],"study_design_scores_gemma":[0.000025028265,0.000033782148,0.0011309253,0.00000449254,0.000017145074,0.00004221528,0.000035913818,0.98633933,0.001243853,0.011035314,0.00008218999,0.000009874078],"about_ca_topic_score_codex":0.0018995039,"about_ca_topic_score_gemma":0.0020961342,"teacher_disagreement_score":0.0018995039,"about_ca_system_score_codex":0.00073298835,"about_ca_system_score_gemma":0.0005487671,"threshold_uncertainty_score":0.0053182244},"labels":[],"label_agreement":null},{"id":"W1965202386","doi":"10.1007/s10827-014-0539-z","title":"Spike detection methods for polytrodes and high density microelectrode arrays","year":2014,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":30,"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; Canadian Institutes of Health Research","keywords":"Spike (software development); Computer science; Pattern recognition (psychology); Cluster analysis; Multielectrode array; Microelectrode; Waveform; Artificial intelligence; Temporal resolution; Noise (video); Spike sorting; Spike train; Physics; Electrode","score_opus":0.022314704195287874,"score_gpt":0.30708537870658176,"score_spread":0.2847706745112939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965202386","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":[0.011303158,0.0001929958,0.98625004,0.00011341971,0.000049039383,0.000026826045,0.000104326224,0.000470781,0.0014894336],"genre_scores_gemma":[0.29758933,0.00048610283,0.6931423,0.00022081267,0.00008859388,0.00018321924,0.00027930565,0.00016140124,0.00784892],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957794,0.00006915795,0.000022924409,0.000085364474,0.00020752798,0.00003708557],"domain_scores_gemma":[0.999244,0.0003525756,0.0000755108,0.00012414697,0.00017382951,0.000029999477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032476886,0.00039324575,0.00038501556,0.000559847,0.0002819214,0.0006514442,0.0011835796,0.00088345865,0.0029841694],"category_scores_gemma":[0.0018222982,0.00037232062,0.00037826717,0.00060848054,0.00040825887,0.0008540603,0.00077103934,0.0007965364,0.00087031187],"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":[0.00040151487,0.00013402317,0.0023040827,0.0003448596,0.00012490428,0.00026986064,0.00015118463,0.06564514,0.40483814,0.06330609,0.0046223514,0.45785776],"study_design_scores_gemma":[0.000020126043,0.00006324566,0.0018486126,0.000013991623,0.000013692582,0.00027405412,0.000029481103,0.9058022,0.06955958,0.01722133,0.0051156445,0.000038062564],"about_ca_topic_score_codex":0.0010617842,"about_ca_topic_score_gemma":0.0029640645,"teacher_disagreement_score":0.0029841694,"about_ca_system_score_codex":0.00047634964,"about_ca_system_score_gemma":0.00039357576,"threshold_uncertainty_score":0.009983003},"labels":[],"label_agreement":null},{"id":"W1967029134","doi":"10.1007/s10827-009-0149-3","title":"Maximum decoding abilities of temporal patterns and synchronized firings: application to auditory neurons responding to click trains and amplitude modulated white noise","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"Auditory cortex; Stimulus (psychology); Computer science; Decoding methods; White noise; Noise (video); Neural decoding; Speech recognition; Frequency modulation; Amplitude; Amplitude modulation; Modulation (music); Neuroscience; Artificial intelligence; Algorithm; Acoustics; Physics; Psychology; Telecommunications","score_opus":0.018547993313099864,"score_gpt":0.2775302885080052,"score_spread":0.25898229519490534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967029134","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":[0.6291651,0.0003195918,0.3670879,0.00020093074,0.000021669106,0.000037986025,0.00011827507,0.00040695464,0.0026415691],"genre_scores_gemma":[0.9462037,0.00018039392,0.05283054,0.00001672056,0.000018429579,0.000026828995,0.00005593264,0.000050080638,0.0006174987],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987984,0.000034007906,0.000010435402,0.000025526915,0.000031955966,0.000018188126],"domain_scores_gemma":[0.99845314,0.0012591728,0.000067025285,0.000059200396,0.000096482356,0.000064943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000644221,0.0004422168,0.00034907385,0.00045333238,0.00022937612,0.00045666422,0.00048369507,0.0007214614,0.0010282428],"category_scores_gemma":[0.0046973135,0.0002727851,0.00047553144,0.0005809474,0.0004106706,0.00071383617,0.000751576,0.00034543907,0.00009519779],"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":[0.0011771182,0.00023364871,0.005798932,0.00057479384,0.00012511825,0.00077880744,0.0005054261,0.32031497,0.35935074,0.024864903,0.00048819583,0.2857874],"study_design_scores_gemma":[0.000021529246,0.000100679696,0.0020155055,0.000008967144,0.000032029995,0.00017301948,0.000040210187,0.962703,0.02873799,0.0059296065,0.00021623104,0.000021179463],"about_ca_topic_score_codex":0.001110632,"about_ca_topic_score_gemma":0.0009641171,"teacher_disagreement_score":0.001110632,"about_ca_system_score_codex":0.00030255053,"about_ca_system_score_gemma":0.0002967581,"threshold_uncertainty_score":0.003439784},"labels":[],"label_agreement":null},{"id":"W1972440292","doi":"10.1007/s10827-009-0177-z","title":"Canonical bicoherence analysis of dynamic EEG data","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":15,"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":"University of California, San Diego; Centre National de la Recherche Scientifique; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Bicoherence; Electroencephalography; Computer science; Pattern recognition (psychology); Artificial intelligence; Quadratic equation; Speech recognition; Mathematics; Bispectrum; Spectral density; Neuroscience; Psychology","score_opus":0.04167537222689209,"score_gpt":0.3541892814301985,"score_spread":0.3125139092033064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972440292","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":[0.08789934,0.00084326224,0.9038951,0.0003845143,0.00022422962,0.00006127612,0.0007079741,0.0008272561,0.005157178],"genre_scores_gemma":[0.66595405,0.0018699248,0.32418522,0.00010676567,0.00018459948,0.00010385187,0.0016651835,0.00042783434,0.005502649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996947,0.00007764748,0.000020154699,0.000060859198,0.0001010844,0.000045672507],"domain_scores_gemma":[0.9994567,0.0001796892,0.00003052004,0.00011091424,0.00019965992,0.000022466087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004519657,0.0005164839,0.0003143061,0.0011786838,0.00030148315,0.0007444889,0.00032709527,0.00026785312,0.0036250423],"category_scores_gemma":[0.0035472938,0.00015894888,0.0003699954,0.0017299891,0.00041391348,0.0008524225,0.00050839974,0.00055760733,0.00088217587],"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":[0.00078267587,0.00011781891,0.0019374721,0.000464886,0.00014801652,0.00040592396,0.00036849553,0.047601115,0.1499515,0.04745161,0.008137118,0.74263334],"study_design_scores_gemma":[0.000044360055,0.00021651459,0.013203314,0.000079796635,0.00010759552,0.0012431307,0.00034016202,0.8298639,0.096489646,0.04213952,0.0161652,0.000106854364],"about_ca_topic_score_codex":0.0018754748,"about_ca_topic_score_gemma":0.0025818446,"teacher_disagreement_score":0.0036250423,"about_ca_system_score_codex":0.00018178138,"about_ca_system_score_gemma":0.00066109264,"threshold_uncertainty_score":0.012126923},"labels":[],"label_agreement":null},{"id":"W1981912231","doi":"10.1007/s10827-007-0046-6","title":"Saccade-related remapping of target representations between topographic maps: a neural network study","year":2007,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":24,"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":"Saccade; Computer science; Artificial intelligence; Artificial neural network; Computer vision; Feedforward neural network; Task (project management); Gaze; Pattern recognition (psychology); Eye movement","score_opus":0.0765333720448252,"score_gpt":0.3701281115412113,"score_spread":0.2935947394963861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981912231","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":[0.9487883,0.00026933535,0.04899556,0.00017950982,0.000020264366,0.000033236483,0.000099123485,0.00009384751,0.0015209693],"genre_scores_gemma":[0.99335456,0.00009191794,0.0058657536,0.0000116209285,0.000007206892,0.000010440842,0.000047206628,0.000017273767,0.0005941464],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999318,0.000018179082,0.000003487635,0.000019481819,0.000014682232,0.000012355228],"domain_scores_gemma":[0.9992176,0.00048473358,0.00008473166,0.000088406545,0.00008452417,0.000039998686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003567655,0.00016704256,0.00022673488,0.00033740798,0.0002436381,0.00044291327,0.0004966541,0.00032214614,0.0009754958],"category_scores_gemma":[0.0032309608,0.00024416833,0.00030871105,0.000462119,0.0003338321,0.0008786815,0.00032053777,0.0004868161,0.00010403213],"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":[0.0016970874,0.00055847137,0.031378202,0.00028437257,0.0003645511,0.0015154065,0.0013223172,0.17490008,0.5983139,0.030583348,0.0021308924,0.15695132],"study_design_scores_gemma":[0.00005389419,0.00014570304,0.038971763,0.000009373796,0.0000666276,0.0005543076,0.00015707327,0.9196303,0.028045852,0.01189341,0.000449304,0.000022397704],"about_ca_topic_score_codex":0.003726905,"about_ca_topic_score_gemma":0.003067586,"teacher_disagreement_score":0.003726905,"about_ca_system_score_codex":0.00035031533,"about_ca_system_score_gemma":0.00025397495,"threshold_uncertainty_score":0.007410407},"labels":[],"label_agreement":null},{"id":"W1993152810","doi":"10.1007/s10827-009-0209-8","title":"Nonlinear cross-frequency interactions in primary auditory cortex spectrotemporal receptive fields: a Wiener–Volterra analysis","year":2010,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":24,"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":"Auditory cortex; Receptive field; Nonlinear system; Theory of computation; Neuroscience; Computer science; Psychology; Physics; Algorithm","score_opus":0.022722916537659617,"score_gpt":0.31018029835801364,"score_spread":0.287457381820354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993152810","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":[0.62348527,0.0006120463,0.3725916,0.00023971358,0.000020422916,0.000013565042,0.00008302119,0.00012705372,0.0028271626],"genre_scores_gemma":[0.979321,0.00024767706,0.017640667,0.000019684914,0.00002145742,0.000009212892,0.0000644851,0.00005384567,0.0026219797],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999552,0.000010350018,0.000002607975,0.000012203282,0.000011299479,0.000008350121],"domain_scores_gemma":[0.9998078,0.00011639145,0.000016949936,0.0000175178,0.000027767359,0.000013618697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030643816,0.00020434064,0.0001937031,0.0003329361,0.00017941144,0.00039158834,0.00020343879,0.00029783775,0.0009937244],"category_scores_gemma":[0.00082961447,0.0002551015,0.000517319,0.00022875781,0.00030780467,0.00061653985,0.00029083414,0.00036749634,0.00021594901],"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":[0.00037199273,0.00012668951,0.0076655014,0.00019655786,0.00029380078,0.0007039498,0.00047324275,0.295184,0.5242011,0.0830655,0.0013090061,0.08640858],"study_design_scores_gemma":[0.000004126669,0.000014250857,0.009275961,0.0000035166433,0.000031854397,0.00012196677,0.000020700001,0.976035,0.006201287,0.008024709,0.00025238094,0.000014175453],"about_ca_topic_score_codex":0.0015355275,"about_ca_topic_score_gemma":0.0019458659,"teacher_disagreement_score":0.0015355275,"about_ca_system_score_codex":0.00022090039,"about_ca_system_score_gemma":0.00018509616,"threshold_uncertainty_score":0.0033243299},"labels":[],"label_agreement":null},{"id":"W1997722418","doi":"10.1007/s10827-011-0313-4","title":"In vivo conditions influence the coding of stimulus features by bursts of action potentials","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"William Osler Health System; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Stimulus (psychology); Bursting; In vivo; Neuroscience; Electric fish; Stimulation; Neural coding; Sensory system; Electrophysiology; Physics; Biology; Psychology; Cognitive psychology","score_opus":0.04104396509895564,"score_gpt":0.3027882839221808,"score_spread":0.2617443188232252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997722418","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":[0.9928954,0.00032965178,0.0049853614,0.0001422254,0.00008344646,0.000024392257,0.00035228496,0.00007266061,0.001114556],"genre_scores_gemma":[0.9973947,0.0003229306,0.0012130397,0.00008668026,0.000035806934,0.00004068042,0.00033479417,0.00016916101,0.00040224372],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996736,0.000078085446,0.000037315327,0.00006781904,0.000054475222,0.000088799236],"domain_scores_gemma":[0.99802667,0.0011849129,0.00025392385,0.00015643825,0.000090403715,0.00028762507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004460117,0.0005480499,0.0005237018,0.00019838064,0.00032351268,0.0011191464,0.0004464807,0.00050647225,0.0030459303],"category_scores_gemma":[0.0037794886,0.0003961779,0.00020683167,0.00013272959,0.0008287849,0.00083779136,0.00060301065,0.00089930627,0.00037291113],"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":[0.0010834947,0.00005985478,0.0008432019,0.000047487043,0.000013763094,0.000046158068,0.00007601446,0.00030230594,0.9958181,0.0002678732,0.00010432002,0.001337496],"study_design_scores_gemma":[0.00032021324,0.0017011459,0.08294208,0.00004875877,0.00022222254,0.00054674223,0.00046656,0.013051861,0.89616835,0.0025741393,0.0018821054,0.00007596416],"about_ca_topic_score_codex":0.0006333378,"about_ca_topic_score_gemma":0.0008782704,"teacher_disagreement_score":0.0030459303,"about_ca_system_score_codex":0.00041881626,"about_ca_system_score_gemma":0.00036133247,"threshold_uncertainty_score":0.010189593},"labels":[],"label_agreement":null},{"id":"W2004433795","doi":"10.1007/s10827-010-0256-1","title":"Can homeostatic plasticity in deafferented primary auditory cortex lead to travelling waves of excitation?","year":2010,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Hearing, Cochlea, Tinnitus, Genetics","field":"Neuroscience","cited_by":41,"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; McMaster University","funders":"","keywords":"Auditory cortex; Homeostatic plasticity; Neuroscience; Excitatory postsynaptic potential; Neuroplasticity; Tinnitus; Inhibitory postsynaptic potential; Stimulus (psychology); Auditory system; Hearing loss; Physics; Psychology; Biology; Synaptic plasticity; Audiology; Metaplasticity; Medicine; Cognitive psychology","score_opus":0.02901421840892079,"score_gpt":0.286030537217559,"score_spread":0.25701631880863823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004433795","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":[0.89752644,0.00035796638,0.09709408,0.00071562664,0.00016213908,0.000018107296,0.00012042584,0.00072377635,0.0032815374],"genre_scores_gemma":[0.99570256,0.00015538603,0.003498102,0.000040165378,0.0000091365155,0.00000844677,0.00003199101,0.00004810945,0.00050607114],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999285,0.000009373318,0.000005151278,0.000013152505,0.000017271304,0.000026586582],"domain_scores_gemma":[0.9998228,0.00006279846,0.000028336464,0.000038690745,0.000019883988,0.00002750598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020389905,0.00015848003,0.00024351524,0.00010985855,0.0001415511,0.00047666556,0.0006612628,0.00038561007,0.00166037],"category_scores_gemma":[0.0015198453,0.00014939059,0.0003582682,0.000119110555,0.00038887284,0.0010453808,0.0003486925,0.00041472152,0.00020439073],"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":[0.00049327424,0.00009371942,0.009174642,0.00022626037,0.0001465334,0.0011095316,0.00023340859,0.038616925,0.83032006,0.065019384,0.0014065107,0.05315992],"study_design_scores_gemma":[0.000119967524,0.00031369194,0.05766306,0.000036523674,0.00019961124,0.0014729727,0.0005244041,0.58082217,0.21543838,0.14056924,0.0027594112,0.00008054117],"about_ca_topic_score_codex":0.0005528611,"about_ca_topic_score_gemma":0.0007844091,"teacher_disagreement_score":0.00166037,"about_ca_system_score_codex":0.00017324291,"about_ca_system_score_gemma":0.0002264333,"threshold_uncertainty_score":0.0055544972},"labels":[],"label_agreement":null},{"id":"W2009340072","doi":"10.1007/s10827-012-0399-3","title":"Quantitative prediction of vasopressin secretion using a computational population model of rat magnocellular neurons","year":2012,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neuroendocrine regulation and behavior","field":"Psychology","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":"Université Laval; Centre hospitalier de l'Université Laval","funders":"Canadian Institutes of Health Research","keywords":"Vasopressin; Neuroscience; Population; Secretion; Computational model; Computer science; Biology; Artificial intelligence; Endocrinology; Medicine","score_opus":0.12949717819104384,"score_gpt":0.37744039625975345,"score_spread":0.24794321806870961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009340072","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":[0.80974287,0.00022533882,0.18389384,0.0011176352,0.00007744612,0.000026450525,0.0001903078,0.0002809125,0.004445215],"genre_scores_gemma":[0.99375385,0.00003481547,0.005582742,0.000037389433,0.000013225973,0.00002158746,0.000047834812,0.000018288083,0.00049020373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999263,0.00002201575,0.0000034945379,0.000017948763,0.0000124524195,0.000017868015],"domain_scores_gemma":[0.9986669,0.0010190423,0.00008672372,0.00004054094,0.00011632384,0.00007057639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004441445,0.00036288903,0.0006986501,0.00028298135,0.00041606135,0.0007601739,0.00089003844,0.0010241338,0.0014831567],"category_scores_gemma":[0.0024506522,0.0004692625,0.00054317166,0.0002977554,0.00063318515,0.0007636495,0.00043687545,0.00090366544,0.00010895716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017050683,0.000009093541,0.00036671266,0.000005772345,0.000007937898,0.000014966999,0.0000070667884,0.99713,0.00029099814,0.0013519797,0.00006326475,0.0007352402],"study_design_scores_gemma":[0.0000016740155,0.0000015880761,0.000042397936,3.0673928e-7,7.0995793e-7,0.0000011453154,8.068727e-7,0.9996044,0.000026101636,0.00031589266,0.000004308095,6.946126e-7],"about_ca_topic_score_codex":0.013497781,"about_ca_topic_score_gemma":0.009305726,"teacher_disagreement_score":0.013497781,"about_ca_system_score_codex":0.0011481909,"about_ca_system_score_gemma":0.0011534749,"threshold_uncertainty_score":0.026838422},"labels":[],"label_agreement":null},{"id":"W2014805078","doi":"10.1007/s10827-009-0207-x","title":"Spatial coherence and stationarity of local field potentials in an isolated whole hippocampal preparation in vitro","year":2010,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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 Toronto; University of British Columbia; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Local field potential; Hippocampal formation; Neuroscience; Coherence (philosophical gambling strategy); Hippocampus; Biological system; Physics; Computer science; Pattern recognition (psychology); Artificial intelligence; Psychology; Biology","score_opus":0.017317075922419854,"score_gpt":0.2885876617015396,"score_spread":0.2712705857791197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014805078","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":[0.98950106,0.0004202979,0.008567876,0.000071109695,0.000020654883,0.000011994921,0.00015859176,0.000053356052,0.0011950694],"genre_scores_gemma":[0.9970305,0.0002726583,0.0022340396,0.000016963573,0.000013233817,0.000008139926,0.00012352184,0.000022066364,0.0002787917],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999143,0.000015542182,0.0000069316566,0.000022096723,0.000017474396,0.00002365677],"domain_scores_gemma":[0.9995333,0.00030207966,0.00002804772,0.00004845047,0.00003530276,0.00005287764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019973026,0.00017182759,0.00026377395,0.0001767642,0.0002817892,0.0004135737,0.0004772034,0.00032769144,0.00066819874],"category_scores_gemma":[0.0010438608,0.00026862085,0.00024514968,0.00021971109,0.0006711179,0.00055529596,0.00037552288,0.0006228268,0.00014983669],"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":[0.00019462923,0.000022170883,0.0005264979,0.000054162578,0.000025434732,0.0001481903,0.0001902146,0.0016819229,0.99429095,0.0007328375,0.000071323266,0.0020617405],"study_design_scores_gemma":[0.00018474451,0.0006755273,0.08342706,0.00004342995,0.0002403764,0.001106148,0.00074938394,0.102825664,0.80544597,0.003871163,0.0013713323,0.000059285612],"about_ca_topic_score_codex":0.0034790474,"about_ca_topic_score_gemma":0.0050049457,"teacher_disagreement_score":0.0034790474,"about_ca_system_score_codex":0.00025803325,"about_ca_system_score_gemma":0.0003243375,"threshold_uncertainty_score":0.006917596},"labels":[],"label_agreement":null},{"id":"W2028170053","doi":"10.1007/s10827-011-0321-4","title":"New determinants of firing rates and patterns of vasopressinergic magnocellular neurons: predictions using a mathematical model of osmodetection","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neuroscience of respiration and sleep","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre hospitalier de l'Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Ottawa","keywords":"Neuroscience; Theory of computation; Computer science; Psychology; Algorithm","score_opus":0.12012604962425898,"score_gpt":0.3122296023598891,"score_spread":0.19210355273563012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028170053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39044863,0.000350494,0.59727985,0.0027804207,0.00012644244,0.000038863876,0.00022839272,0.00022481152,0.008522038],"genre_scores_gemma":[0.98384374,0.0001948588,0.013412844,0.000114975475,0.000051398,0.000040471696,0.000059425296,0.00008770955,0.002194562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998572,0.000035047007,0.000008305577,0.000032438227,0.000031848824,0.00003508436],"domain_scores_gemma":[0.9982565,0.0010023903,0.00027290563,0.00009512193,0.00021500897,0.00015804524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008847146,0.00039141293,0.0006464666,0.0005017682,0.00041162694,0.0012720635,0.0015120786,0.0011294866,0.001841431],"category_scores_gemma":[0.0062717064,0.0007319444,0.00081060355,0.00037203304,0.0011673749,0.0025661795,0.0006416674,0.0012659084,0.00022035935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004369317,0.000056402703,0.003789533,0.000051951687,0.00003302156,0.00022506477,0.0001142358,0.7962862,0.006331598,0.18786645,0.0009866293,0.0042152423],"study_design_scores_gemma":[0.000005057551,0.0000031319812,0.00039536454,0.0000017728903,0.0000024618487,0.000021441614,0.000007903976,0.9800259,0.00014905933,0.019336557,0.000045033205,0.0000062971776],"about_ca_topic_score_codex":0.003122602,"about_ca_topic_score_gemma":0.0032850043,"teacher_disagreement_score":0.003122602,"about_ca_system_score_codex":0.0011627243,"about_ca_system_score_gemma":0.0007743159,"threshold_uncertainty_score":0.008436203},"labels":[],"label_agreement":null},{"id":"W2035808699","doi":"10.1023/b:jcns.0000025690.02886.93","title":"Comparison of Coding Capabilities of Type I and Type II Neurons","year":2004,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":48,"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":"Amplitude; Noise (video); Type (biology); ENCODE; Computer science; Neural coding; Coding (social sciences); Range (aeronautics); Subthreshold conduction; Statistical physics; Mathematics; Algorithm; Biological system; Artificial intelligence; Physics; Statistics","score_opus":0.026032529123687344,"score_gpt":0.3121023383102013,"score_spread":0.2860698091865139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035808699","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":[0.9923913,0.00008863777,0.00438799,0.00005453008,0.000009582889,0.0000055591618,0.00019875579,0.00004001043,0.0028237274],"genre_scores_gemma":[0.9975834,0.00007133594,0.0013101853,0.000019258401,0.000005007362,0.0000056090325,0.00023621903,0.000020961432,0.0007479816],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998902,0.000017233395,0.0000091014235,0.000017807914,0.000024835716,0.00004083046],"domain_scores_gemma":[0.99722207,0.0015900523,0.00019373305,0.00023008673,0.00042990985,0.00033411814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003211823,0.0002332726,0.00023039598,0.00050148036,0.00017357856,0.0010035632,0.00032908304,0.00040076842,0.0021979022],"category_scores_gemma":[0.0037286917,0.0001425502,0.00029755902,0.00028282302,0.00023619167,0.0005716697,0.00038295868,0.00030298292,0.00032375703],"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":[0.002110984,0.00018208564,0.116210766,0.0002937196,0.0002475223,0.0003995779,0.0011721516,0.02355108,0.72279495,0.022049975,0.0015481169,0.109438956],"study_design_scores_gemma":[0.00012584557,0.0007103264,0.48215815,0.00012707304,0.00037706664,0.0016459019,0.0017467005,0.31857237,0.16646805,0.025083207,0.0028550322,0.00013024254],"about_ca_topic_score_codex":0.0012336933,"about_ca_topic_score_gemma":0.0009242354,"teacher_disagreement_score":0.0021979022,"about_ca_system_score_codex":0.0002466765,"about_ca_system_score_gemma":0.00029075012,"threshold_uncertainty_score":0.00735265},"labels":[],"label_agreement":null},{"id":"W2038197036","doi":"10.1007/s10827-014-0521-9","title":"Stimulation-induced ectopicity and propagation windows in model damaged axons","year":2014,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Ion channel regulation and function","field":"Biochemistry, Genetics and Molecular Biology","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":"Ottawa Hospital; Carleton University; University of Ottawa; Cégep de l'Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Ottawa Hospital Research Institute","keywords":"Neuroscience; Node of Ranvier; Sodium channel; Biophysics; Axon; Action potential; Chemistry; Stimulation; Excitatory postsynaptic potential; Electrophysiology; Myelin; Inhibitory postsynaptic potential; Biology; Central nervous system; Sodium","score_opus":0.022853067757200984,"score_gpt":0.26932251398553825,"score_spread":0.24646944622833727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038197036","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":[0.99238974,0.00009184005,0.005143235,0.000056553927,0.000008556903,0.00001065645,0.0001687202,0.000051235274,0.0020794198],"genre_scores_gemma":[0.99740463,0.000106105836,0.0015241605,0.000009739191,0.0000014750003,0.00001746806,0.000071837545,0.0000105954405,0.00085404963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999678,0.000005446359,0.0000017850234,0.0000053332087,0.000008882465,0.000010631588],"domain_scores_gemma":[0.99988735,0.00003677099,0.000027778204,0.000009832195,0.000015612253,0.000022616843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007266,0.00024712895,0.00022015019,0.00018241825,0.00022778078,0.00030832045,0.00046673525,0.00065478927,0.0014561857],"category_scores_gemma":[0.00023540658,0.000117324525,0.0003272481,0.00016978836,0.00028109344,0.00022397946,0.00027649038,0.00032297615,0.00011628269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008741762,0.000046622285,0.0023808067,0.000066898465,0.00002590574,0.00043118835,0.00010410228,0.9694188,0.024154713,0.0019698916,0.00013584243,0.0011777746],"study_design_scores_gemma":[0.00001734795,0.00018538871,0.0015872061,0.000013842722,0.000021100626,0.00009943348,0.00010436034,0.98928225,0.0071873413,0.0011146009,0.0003762923,0.000010913838],"about_ca_topic_score_codex":0.0055134506,"about_ca_topic_score_gemma":0.0036401593,"teacher_disagreement_score":0.0055134506,"about_ca_system_score_codex":0.00050125766,"about_ca_system_score_gemma":0.0004246137,"threshold_uncertainty_score":0.010962725},"labels":[],"label_agreement":null},{"id":"W2038605385","doi":"10.1007/s10827-009-0191-1","title":"Alternative time representation in dopamine models","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":23,"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 du Québec à Montréal; Quebec Network for Research on Aging; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Representation (politics); Computer science; Task (project management); TRACE (psycholinguistics); Artificial intelligence; Interval (graph theory); Constant (computer programming); Machine learning; Mathematics","score_opus":0.04523521444104836,"score_gpt":0.3047986170484456,"score_spread":0.2595634026073972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038605385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13718753,0.0007281271,0.8440529,0.0025455854,0.00021129582,0.00001743272,0.00029372188,0.00016751322,0.014795933],"genre_scores_gemma":[0.95176345,0.00042693203,0.03939944,0.00015577204,0.00010766331,0.000049515373,0.00018339962,0.00007295265,0.007840914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970156,0.00014274847,0.000014770761,0.00004777418,0.000053606334,0.000039481096],"domain_scores_gemma":[0.9988085,0.000728223,0.000113915834,0.00012499781,0.00010142664,0.00012291565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097536953,0.00039366752,0.00077172334,0.00053717755,0.00039264507,0.0017222195,0.0013020416,0.0015304222,0.0042684088],"category_scores_gemma":[0.00417802,0.00037917757,0.00066306035,0.0006800059,0.00087657117,0.0031890085,0.00094443455,0.0013681067,0.0003393469],"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":[0.000069392016,0.000020941658,0.00026652683,0.000030349865,0.000025661973,0.0000580612,0.000075328346,0.21590054,0.0005287115,0.7762058,0.0007445216,0.006074242],"study_design_scores_gemma":[0.000007674813,0.0000061954397,0.000046001212,0.0000036483932,0.000003881584,0.000012225814,0.000009157507,0.78385305,0.000054923465,0.21573253,0.00026419412,0.0000065033687],"about_ca_topic_score_codex":0.0025517426,"about_ca_topic_score_gemma":0.0028897198,"teacher_disagreement_score":0.0042684088,"about_ca_system_score_codex":0.0009895288,"about_ca_system_score_gemma":0.00062262267,"threshold_uncertainty_score":0.014279246},"labels":[],"label_agreement":null},{"id":"W2047330754","doi":"10.1007/s10827-011-0369-1","title":"Redundant information encoding in primary motor cortex during natural and prosthetic motor control","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":25,"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":"American Stroke Association; Stroke Association; Heart and Stroke Foundation of Canada; American Heart Association; National Science Foundation","keywords":"Primary motor cortex; Motor control; Motor cortex; Neuroscience; Control (management); Computer science; Theory of computation; Encoding (memory); Natural (archaeology); Cognitive science; Motor coordination; Psychology; Artificial intelligence; Biology; Programming language","score_opus":0.016565795634969828,"score_gpt":0.2346782506339795,"score_spread":0.2181124549990097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047330754","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":[0.9866903,0.00060099,0.009242408,0.00012261815,0.000061971194,0.000046572124,0.00037992673,0.00007911532,0.0027760176],"genre_scores_gemma":[0.9978326,0.00008295026,0.0013249468,0.00002755357,0.000021694947,0.000024127103,0.00013920305,0.000034776407,0.00051216927],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997495,0.00005785128,0.000020947331,0.000047885584,0.00006160017,0.0000622245],"domain_scores_gemma":[0.9987544,0.00090391457,0.000114727,0.00007280109,0.00008753186,0.00006660755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004557625,0.00039053313,0.00039497172,0.0003509863,0.00020330578,0.0005990292,0.00024182418,0.0004118004,0.0027210836],"category_scores_gemma":[0.0071143317,0.000263784,0.00024968252,0.00034206492,0.0005094077,0.000639853,0.00039051526,0.0004511762,0.00020276701],"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":[0.0092261685,0.00024979142,0.009554938,0.0005615486,0.00014162592,0.00046437708,0.001085543,0.004316812,0.9040587,0.0051408275,0.0013237945,0.063875906],"study_design_scores_gemma":[0.00043603088,0.0011797076,0.7953279,0.0001304333,0.00021516407,0.0021177977,0.00051253766,0.0649082,0.11859108,0.014717147,0.0017814038,0.000082597275],"about_ca_topic_score_codex":0.00086644443,"about_ca_topic_score_gemma":0.0012133094,"teacher_disagreement_score":0.0027210836,"about_ca_system_score_codex":0.00020220828,"about_ca_system_score_gemma":0.00028589246,"threshold_uncertainty_score":0.009102881},"labels":[],"label_agreement":null},{"id":"W2047533506","doi":"10.1007/s10827-009-0156-4","title":"Implications of gain modulation in brainstem circuits: VOR control system","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Vestibular and auditory disorders","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Stimulus (psychology); Neuroscience; Brainstem; Automatic gain control; Psychology; Reflex; Vestibulo–ocular reflex; Computer science; Cognitive psychology","score_opus":0.023512879225880877,"score_gpt":0.2760405743145461,"score_spread":0.25252769508866524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047533506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47030553,0.0010113106,0.48609722,0.003970066,0.0005646825,0.000079004574,0.00032867037,0.0008152633,0.036828246],"genre_scores_gemma":[0.9886113,0.000109624074,0.009534107,0.00008307269,0.000051059727,0.000018280702,0.000025460142,0.00004072401,0.0015264516],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999863,0.000027471773,0.0000073086776,0.000037450543,0.000037020513,0.000027684757],"domain_scores_gemma":[0.99976104,0.000111878566,0.000020329066,0.000026546613,0.000047782993,0.000032335443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019900811,0.0002497487,0.00026305916,0.00014258908,0.0003293225,0.0010377996,0.0004383635,0.0005326317,0.0051281354],"category_scores_gemma":[0.0018987302,0.00013456147,0.00020235447,0.00012153945,0.0005214874,0.0013022735,0.00047803335,0.0005130779,0.00038115765],"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":[0.0005704345,0.00017596534,0.0064730234,0.00018152599,0.00009109629,0.0008428051,0.00041111547,0.049726315,0.54416376,0.30613247,0.0026180844,0.08861339],"study_design_scores_gemma":[0.000118640855,0.0003534946,0.024784438,0.00004504014,0.00008544255,0.001057749,0.0003304974,0.5759102,0.05967528,0.33359355,0.0039738803,0.00007181603],"about_ca_topic_score_codex":0.00064006133,"about_ca_topic_score_gemma":0.00049104646,"teacher_disagreement_score":0.0051281354,"about_ca_system_score_codex":0.00035478457,"about_ca_system_score_gemma":0.00035829862,"threshold_uncertainty_score":0.01715535},"labels":[],"label_agreement":null},{"id":"W2048239311","doi":"10.1023/b:jcns.0000037677.58916.6b","title":"To Burst or Not to Burst?","year":2004,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":46,"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; Canadian Institutes of Health Research","keywords":"Bursting; Neuroscience; Stimulus (psychology); Neuron; Electric fish; Sensory system; Biological neuron model; Tonic (physiology); Computer science; Biology; Psychology","score_opus":0.03255442705621986,"score_gpt":0.30223966101221933,"score_spread":0.26968523395599947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048239311","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":[0.7973819,0.0027830442,0.10350647,0.06562766,0.0025515303,0.00011003667,0.0017969181,0.0012653844,0.024977056],"genre_scores_gemma":[0.986844,0.00031947557,0.008674906,0.001269912,0.00019912334,0.000022819579,0.00016536393,0.00012873915,0.0023756935],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994636,0.00018079486,0.000039894345,0.0001499248,0.00006837049,0.00009740091],"domain_scores_gemma":[0.9954579,0.0024603077,0.00044729238,0.00024980307,0.0005085598,0.00087609893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019078783,0.0002839448,0.0005038768,0.00044536954,0.0004913386,0.0017847074,0.00065404177,0.0018407012,0.008402526],"category_scores_gemma":[0.01708129,0.00025303982,0.00031340544,0.00024017823,0.001067225,0.0028153225,0.00059626176,0.0014428295,0.0018457542],"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":[0.005977952,0.0010480587,0.2591978,0.0005320388,0.0005354732,0.000919463,0.0013165136,0.015813885,0.039467033,0.059627317,0.07075723,0.54480726],"study_design_scores_gemma":[0.00076498947,0.00091394543,0.10739468,0.0003928376,0.0006003624,0.0037860714,0.005624577,0.44545022,0.016550122,0.39090273,0.027318342,0.00030120768],"about_ca_topic_score_codex":0.001481345,"about_ca_topic_score_gemma":0.0027383051,"teacher_disagreement_score":0.008402526,"about_ca_system_score_codex":0.00042848245,"about_ca_system_score_gemma":0.0007038158,"threshold_uncertainty_score":0.028109252},"labels":[],"label_agreement":null},{"id":"W2049372373","doi":"10.1023/b:jcns.0000004838.67584.77","title":"Membrane Resonance and Stochastic Resonance Modulate Firing Patterns of Thalamocortical Neurons","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","cited_by":23,"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; University of British Columbia","keywords":"Stochastic resonance; Subthreshold conduction; Noise (video); Resonance (particle physics); Physics; Neuroscience; Sine wave; Bistability; Biological system; Nuclear magnetic resonance; Computer science; Biology; Artificial intelligence; Quantum mechanics","score_opus":0.012429460312207525,"score_gpt":0.24967198284522943,"score_spread":0.23724252253302192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049372373","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":[0.99304914,0.000054220287,0.005659523,0.00011523547,0.000022165463,0.0000066366383,0.000031770036,0.000030763837,0.001030663],"genre_scores_gemma":[0.9992786,0.000020814747,0.0004964145,0.000015334137,0.000007661713,0.0000026035225,0.000015643556,0.000013043162,0.00014981345],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998996,0.000023282782,0.000006777214,0.000015647009,0.00002505861,0.000029576877],"domain_scores_gemma":[0.99944097,0.0002955329,0.00006608156,0.00003133061,0.00006790472,0.00009813781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022118806,0.00017252302,0.00021486908,0.00023845144,0.00019288441,0.0005526195,0.00024128065,0.00040197506,0.0008109319],"category_scores_gemma":[0.0031590883,0.00021146334,0.00025728813,0.00016685933,0.00026162647,0.00037330794,0.0004537604,0.000385127,0.00015145296],"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":[0.0008981968,0.00012236877,0.020027855,0.00008315099,0.00013987007,0.00034761752,0.0005719829,0.031335384,0.91259086,0.0062028533,0.0005588142,0.027121076],"study_design_scores_gemma":[0.00017232649,0.00027411745,0.22432236,0.00003413915,0.00016377105,0.0006079253,0.00041942307,0.679025,0.07245533,0.021613257,0.0008268576,0.0000855288],"about_ca_topic_score_codex":0.0003942842,"about_ca_topic_score_gemma":0.00061761757,"teacher_disagreement_score":0.0008109319,"about_ca_system_score_codex":0.00029447637,"about_ca_system_score_gemma":0.00015291233,"threshold_uncertainty_score":0.0027127862},"labels":[],"label_agreement":null},{"id":"W2054154492","doi":"10.1007/s10827-011-0370-8","title":"Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Neurophysiology; Artificial neural network; Stochastic modelling; Theory of computation; Neural coding; Stochastic process; Control theory (sociology); Mathematics; Neuroscience; Artificial intelligence; Control (management); Algorithm; Biology; Statistics","score_opus":0.032179845385054785,"score_gpt":0.25275555816201817,"score_spread":0.2205757127769634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054154492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30154756,0.00056084077,0.6829372,0.0023160013,0.00017915994,0.00006109059,0.00019805209,0.00031826377,0.01188175],"genre_scores_gemma":[0.9878753,0.00017089341,0.008730539,0.00012148927,0.00006988068,0.000043823857,0.00007263164,0.00009781069,0.0028177632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970406,0.00011074898,0.000012839726,0.00007107808,0.000045595774,0.000055556426],"domain_scores_gemma":[0.9979215,0.0013059356,0.00023523111,0.00012544463,0.00021786464,0.00019409019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012391065,0.00081504043,0.0012577286,0.00084507343,0.00073003466,0.001629865,0.0017052841,0.0016956788,0.0025024822],"category_scores_gemma":[0.0061674602,0.00063213956,0.00083648,0.000359344,0.0023492542,0.0024949838,0.0012292437,0.0013145168,0.00018302491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098332624,0.00007461151,0.00081910257,0.00006113792,0.00007494204,0.0001906944,0.000110917295,0.6885995,0.0041882037,0.30067927,0.0011591974,0.0039441343],"study_design_scores_gemma":[0.0000054408865,0.000007476591,0.00018164964,0.000002318228,0.000004069983,0.000011217475,0.000005339759,0.9738458,0.00009512203,0.025779435,0.000055133056,0.000007038627],"about_ca_topic_score_codex":0.0055948202,"about_ca_topic_score_gemma":0.005443074,"teacher_disagreement_score":0.0055948202,"about_ca_system_score_codex":0.0011459406,"about_ca_system_score_gemma":0.00086955924,"threshold_uncertainty_score":0.011124492},"labels":[],"label_agreement":null},{"id":"W2056158129","doi":"10.1007/s10827-006-0007-5","title":"Functional organization within a neural network trained to update target representations across 3-D saccades","year":2006,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Canadian Institutes of Health Research","funders":"Canada Research Chairs","keywords":"Artificial neural network; Computer science; Theory of computation; Functional connectivity; Artificial intelligence; Neuroscience; Cognitive science; Machine learning; Psychology; Algorithm","score_opus":0.016873428491170187,"score_gpt":0.272620901615778,"score_spread":0.2557474731246078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056158129","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":[0.9372114,0.00019937535,0.058042873,0.00052052323,0.00011813522,0.0000354262,0.0002117843,0.00041627084,0.0032443346],"genre_scores_gemma":[0.99395245,0.000040811716,0.0047125774,0.000024211478,0.00000711141,0.00000905473,0.00005656104,0.000036274123,0.0011609076],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991965,0.000011208049,0.000004580356,0.000029017341,0.000013574356,0.000022035687],"domain_scores_gemma":[0.99952817,0.000171037,0.000047605183,0.00004641309,0.00013104297,0.00007564357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025433916,0.00024779147,0.0002757007,0.00025385924,0.00036465784,0.0006355882,0.00053278555,0.00095495273,0.001569126],"category_scores_gemma":[0.0017771239,0.00031833746,0.00035983662,0.00026421796,0.0003002692,0.0005290625,0.00032516386,0.00067060866,0.00018867059],"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":[0.0009086167,0.00035259954,0.020446835,0.00014286717,0.00027254366,0.00071493565,0.0004539067,0.33266023,0.54774195,0.0067859055,0.002500168,0.08701955],"study_design_scores_gemma":[0.000017648661,0.00009409177,0.015888538,0.0000065008694,0.000030293659,0.000100634315,0.000041825027,0.9631632,0.018888095,0.0015347842,0.00022090587,0.000013508849],"about_ca_topic_score_codex":0.0073677115,"about_ca_topic_score_gemma":0.0052789045,"teacher_disagreement_score":0.0073677115,"about_ca_system_score_codex":0.000670825,"about_ca_system_score_gemma":0.000616566,"threshold_uncertainty_score":0.01464963},"labels":[],"label_agreement":null},{"id":"W2060140164","doi":"10.1007/s10827-005-0331-1","title":"Two-Cell to N-Cell Heterogeneous, Inhibitory Networks: Precise Linking of Multistable and Coherent Properties","year":2005,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Photoreceptor and optogenetics research","field":"Neuroscience","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 Waterloo; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inhibitory postsynaptic potential; Population; Network dynamics; Computer science; Neuroscience; Cell type; Network model; Theory of computation; Biological system; Cell; Biology; Artificial intelligence; Mathematics; Algorithm; Genetics","score_opus":0.040535210322981935,"score_gpt":0.29407701451030893,"score_spread":0.25354180418732697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060140164","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":[0.76431704,0.00025795362,0.22261263,0.00029420428,0.000103519815,0.00004765825,0.0000763801,0.00050691585,0.011783642],"genre_scores_gemma":[0.9930542,0.00006225668,0.005757351,0.000042314976,0.000015147081,0.000011371243,0.000026423153,0.000036715155,0.0009941276],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998598,0.000023784967,0.000006778547,0.00003641393,0.000032945278,0.000040282142],"domain_scores_gemma":[0.999569,0.00012871686,0.00003814717,0.00006356567,0.00007653573,0.0001241065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039301655,0.00032951345,0.00033563064,0.00020974268,0.00039132635,0.00097446615,0.0008967172,0.0003660296,0.0016588428],"category_scores_gemma":[0.0018845631,0.00025813485,0.00026806464,0.0002341193,0.000458877,0.001135814,0.0007967656,0.00050914555,0.00027785156],"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":[0.00051792996,0.00025014664,0.005692502,0.00011356121,0.000090548136,0.0005950114,0.00032102407,0.0537391,0.8412268,0.041217692,0.0014249671,0.05481063],"study_design_scores_gemma":[0.000094381685,0.00018292459,0.01452922,0.000014662234,0.00008412609,0.00036828616,0.00016340229,0.81593984,0.1413787,0.02529779,0.0018999439,0.000046806123],"about_ca_topic_score_codex":0.00089485216,"about_ca_topic_score_gemma":0.0018312093,"teacher_disagreement_score":0.0016588428,"about_ca_system_score_codex":0.0004688071,"about_ca_system_score_gemma":0.00037174445,"threshold_uncertainty_score":0.005549431},"labels":[],"label_agreement":null},{"id":"W2061658222","doi":"10.1007/s10827-006-0017-3","title":"A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms","year":2007,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Neil Squire Society; University of British Columbia","funders":"Western Canada Research Grid","keywords":"Rhythm; Electroencephalography; Computer science; Artificial intelligence; Brain–computer interface; Pattern recognition (psychology); Movement (music); Beta Rhythm; Feature vector; Channel (broadcasting); Feature (linguistics); Neuroscience; Psychology; Physics","score_opus":0.026199537660031892,"score_gpt":0.2906722932587094,"score_spread":0.2644727555986775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061658222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21140671,0.00056957395,0.77118474,0.00032023218,0.0006730097,0.00052060495,0.0006443541,0.007961038,0.0067198295],"genre_scores_gemma":[0.7209424,0.00035893553,0.2656261,0.0007828916,0.0002843713,0.00043357804,0.00051571865,0.0004554106,0.010600591],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998318,0.000029890605,0.000011747769,0.000047659625,0.00006809279,0.000010758729],"domain_scores_gemma":[0.9996214,0.00016227867,0.000028992008,0.000042194028,0.000086882064,0.00005833642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036165366,0.00031791982,0.00031440557,0.0003707713,0.0001490845,0.00042469674,0.00060155836,0.0005290207,0.004058281],"category_scores_gemma":[0.0010493806,0.00014090055,0.00020756421,0.00025110872,0.00016463852,0.00047220875,0.00036132146,0.00028701822,0.0010404756],"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":[0.00077450875,0.00030523003,0.0030318878,0.0002696428,0.000108587774,0.00036081055,0.00018470135,0.0015322784,0.7140376,0.001982495,0.0056012697,0.27181095],"study_design_scores_gemma":[0.0010551014,0.00438258,0.054317664,0.00008260395,0.0005881743,0.008264832,0.0001123405,0.2993054,0.5723578,0.0069248877,0.05237254,0.00023616386],"about_ca_topic_score_codex":0.00018003881,"about_ca_topic_score_gemma":0.00028235614,"teacher_disagreement_score":0.004058281,"about_ca_system_score_codex":0.00008693643,"about_ca_system_score_gemma":0.00016460022,"threshold_uncertainty_score":0.013576269},"labels":[],"label_agreement":null},{"id":"W2062658342","doi":"10.1007/s10827-010-0284-x","title":"Asymmetric electrotonic coupling between the soma and dendrites alters the bistable firing behaviour of reduced models","year":2010,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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 Alberta","funders":"","keywords":"Bistability; Soma; Asymmetry; Coupling (piping); Dendrite (mathematics); Nonlinear system; Depolarization; Physics; Neuroscience; Biophysics; Materials science; Mathematics; Biology; Geometry","score_opus":0.03264997979988747,"score_gpt":0.27557713713627646,"score_spread":0.24292715733638898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062658342","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":[0.9417691,0.00008987888,0.046467163,0.000556416,0.00006554798,0.00001152525,0.00013914499,0.00032278404,0.010578353],"genre_scores_gemma":[0.9964431,0.00005099851,0.0019612126,0.00004116698,0.000010808515,0.000010223044,0.000065973625,0.00008436193,0.0013322225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986374,0.000043952627,0.0000066915873,0.00002152571,0.000032368327,0.0000316199],"domain_scores_gemma":[0.99949396,0.00018270631,0.0000677459,0.00010160229,0.000047115227,0.00010687616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002575245,0.00037772485,0.00059270713,0.0004874413,0.00028637398,0.000988046,0.0008491008,0.0008834461,0.003270347],"category_scores_gemma":[0.0018388228,0.00029729304,0.0006109772,0.00016024636,0.00062413974,0.0010090705,0.00053578516,0.0007919816,0.0003007766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036309162,0.00018666875,0.002312541,0.00013762596,0.00014207675,0.0004669406,0.00016070796,0.6662117,0.15973066,0.16006625,0.0016456485,0.008576075],"study_design_scores_gemma":[0.000018135312,0.0000202896,0.00081993587,0.0000034374314,0.000018863066,0.000051586485,0.000019940893,0.97764844,0.0020790542,0.01913552,0.00017194577,0.0000128614065],"about_ca_topic_score_codex":0.0021235289,"about_ca_topic_score_gemma":0.0023402593,"teacher_disagreement_score":0.003270347,"about_ca_system_score_codex":0.00063212606,"about_ca_system_score_gemma":0.0004313619,"threshold_uncertainty_score":0.010940433},"labels":[],"label_agreement":null},{"id":"W2066303770","doi":"10.1023/b:jcns.0000023870.23322.0a","title":"Novel Bursting Patterns Emerging from Model Inhibitory Networks with Synaptic Depression","year":2004,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"Toronto Rehabilitation Institute; University Health Network","funders":"","keywords":"Bursting; Neuroscience; Hippocampus; Inhibitory postsynaptic potential; Synaptic plasticity; Metaplasticity; Hippocampal formation; Neural coding; Computer science; Biology; Psychology","score_opus":0.026148727706014084,"score_gpt":0.25065189461834575,"score_spread":0.22450316691233166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066303770","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":[0.8690537,0.0003070274,0.121567756,0.00053479703,0.00011630496,0.000037706483,0.00022401249,0.00032459098,0.007834035],"genre_scores_gemma":[0.996262,0.00005926911,0.002749554,0.000020762285,0.000014544869,0.000009914333,0.000051207655,0.000024839486,0.0008078226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992645,0.000016585305,0.0000054461225,0.000013548801,0.000017491366,0.000020402726],"domain_scores_gemma":[0.99944156,0.00022822047,0.00009301259,0.00005103761,0.00008985254,0.00009629828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003085654,0.0004040695,0.00043611857,0.0003640677,0.00030170023,0.0006896254,0.0007293164,0.000993743,0.0019480037],"category_scores_gemma":[0.0017960542,0.00026559443,0.00043128477,0.00023392118,0.00050793594,0.0006763219,0.00033909938,0.00069979485,0.00015828144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007219058,0.000198872,0.012814355,0.00033235687,0.00028471943,0.003384902,0.00054357,0.6242444,0.11239165,0.21540126,0.0051834695,0.02449852],"study_design_scores_gemma":[0.000020480016,0.000026226186,0.0013956194,0.0000063486136,0.000014461861,0.00022989057,0.000030798466,0.98123986,0.001498523,0.015348798,0.00017957178,0.000009426878],"about_ca_topic_score_codex":0.000999027,"about_ca_topic_score_gemma":0.0012296074,"teacher_disagreement_score":0.0019480037,"about_ca_system_score_codex":0.00031552382,"about_ca_system_score_gemma":0.00020018574,"threshold_uncertainty_score":0.006516695},"labels":[],"label_agreement":null},{"id":"W2067449620","doi":"10.1007/s10827-011-0328-x","title":"Stochastic amplification of calcium-activated potassium currents in Ca2+ microdomains","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Ion channel regulation and function","field":"Biochemistry, Genetics and Molecular Biology","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 Toronto","funders":"","keywords":"Gating; Calcium; Biophysics; Calcium in biology; Conductance; Subthreshold conduction; Chemistry; Calcium-activated potassium channel; Calcium signaling; Voltage-dependent calcium channel; Potassium channel; Physics; Biology; Condensed matter physics","score_opus":0.05274722280212289,"score_gpt":0.2912032985411387,"score_spread":0.23845607573901578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067449620","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":[0.8572727,0.00014763518,0.13653918,0.00043104353,0.00005544905,0.000022384158,0.000056190423,0.0003761463,0.0050991797],"genre_scores_gemma":[0.996759,0.00004226826,0.0025131926,0.000021443853,0.0000127361545,0.0000064727265,0.000015789858,0.000023530856,0.00060555123],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984515,0.00002399749,0.0000059776758,0.000029011306,0.000056107936,0.000039735674],"domain_scores_gemma":[0.999435,0.0003389493,0.000057395777,0.00004505888,0.000057788042,0.00006569366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002968849,0.00016848608,0.00028640428,0.00021666384,0.00029587952,0.0005186647,0.0004049958,0.00029545388,0.0013897046],"category_scores_gemma":[0.0017449603,0.00020605887,0.00024298554,0.00015016722,0.00042345867,0.0006273127,0.0007984218,0.0003878172,0.00015232454],"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":[0.0008060395,0.00013375914,0.0052811755,0.00014233521,0.00006350593,0.00081848213,0.00032370532,0.29419523,0.56747705,0.10109029,0.0012278819,0.028440505],"study_design_scores_gemma":[0.000026809796,0.000032893815,0.0024832895,0.0000030973797,0.000010979469,0.00012503036,0.000028379563,0.9595906,0.02202914,0.015306714,0.00034418993,0.000018708366],"about_ca_topic_score_codex":0.0006141839,"about_ca_topic_score_gemma":0.0010014146,"teacher_disagreement_score":0.0013897046,"about_ca_system_score_codex":0.00045130952,"about_ca_system_score_gemma":0.00029165714,"threshold_uncertainty_score":0.004649043},"labels":[],"label_agreement":null},{"id":"W2076228010","doi":"10.1007/s10827-007-0070-6","title":"Mechanism of gain modulation at single neuron and network levels","year":2008,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Columbia College","funders":"James G. Boswell Foundation; National Institutes of Health; National Science Foundation","keywords":"Sigmoid function; Modulation (music); Constraint (computer-aided design); Artificial neural network; Computer science; Nonlinear system; Multiplicative function; Transformation (genetics); Neuron; Transfer function; Control theory (sociology); Biological neuron model; Mathematics; Artificial intelligence; Neuroscience; Physics; Mathematical analysis","score_opus":0.060475400109319746,"score_gpt":0.2592585087980384,"score_spread":0.19878310868871868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076228010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36881742,0.0010987901,0.5624661,0.0029884481,0.0007463949,0.00011697079,0.00025181114,0.0018458404,0.06166825],"genre_scores_gemma":[0.98110366,0.00022402493,0.014028572,0.00011256004,0.00008118297,0.000043099546,0.00002067584,0.00007960827,0.004306569],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998524,0.000014337247,0.000005593191,0.000038530023,0.000056509863,0.00003256662],"domain_scores_gemma":[0.9997248,0.00008544055,0.00002399213,0.000055399225,0.00005736404,0.000053104595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000266343,0.00028148232,0.0004610478,0.0004249954,0.00035637867,0.0008732291,0.0010099952,0.0007481324,0.0063639204],"category_scores_gemma":[0.0011726343,0.00024720177,0.00029654065,0.00016133813,0.00057635264,0.0015629802,0.0006539542,0.00067014166,0.0009062868],"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":[0.00019780855,0.00012912639,0.00082570966,0.00011236984,0.00005182589,0.00042346926,0.00020206615,0.026573418,0.5426094,0.38784516,0.0023691917,0.038660463],"study_design_scores_gemma":[0.000075354365,0.00013762845,0.004085806,0.000023117938,0.000037879818,0.0008603625,0.00009003834,0.6022161,0.049170703,0.3400381,0.0031939484,0.000070911774],"about_ca_topic_score_codex":0.00031676833,"about_ca_topic_score_gemma":0.00021806068,"teacher_disagreement_score":0.0063639204,"about_ca_system_score_codex":0.00041430249,"about_ca_system_score_gemma":0.00029476822,"threshold_uncertainty_score":0.021289468},"labels":[],"label_agreement":null},{"id":"W2079025781","doi":"10.1007/s10827-013-0442-z","title":"Bifurcations of large networks of two-dimensional integrate and fire neurons","year":2013,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":37,"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","keywords":"Theory of computation; Cognitive science; Computer science; Neuroscience; Statistical physics; Biology; Psychology; Physics; Algorithm","score_opus":0.019418366431519833,"score_gpt":0.2713257036505362,"score_spread":0.25190733721901637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079025781","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":[0.94261986,0.00045729036,0.046384152,0.0008473793,0.000078193334,0.000034158635,0.0000998722,0.00018857438,0.009290435],"genre_scores_gemma":[0.9970764,0.00007977568,0.0017661273,0.000029214825,0.000014537433,0.000019561043,0.000031586947,0.000017759918,0.00096499175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984074,0.00005537464,0.000009474885,0.000027305876,0.000034699027,0.00003240529],"domain_scores_gemma":[0.9987948,0.000702384,0.00014824119,0.00006891778,0.00009347207,0.00019219579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007140345,0.00030464146,0.000662712,0.0010548568,0.0007450095,0.0011322831,0.0006397689,0.0012751654,0.0025305313],"category_scores_gemma":[0.004415164,0.0004901154,0.00066103815,0.00029940705,0.001268031,0.001385854,0.0010281849,0.00080196245,0.00022144594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003870868,0.00013351958,0.0066024824,0.0001242502,0.00016049143,0.0005835999,0.00055239926,0.78508,0.018807197,0.17544428,0.00212387,0.01000089],"study_design_scores_gemma":[0.000023002674,0.000016693439,0.0013088792,0.000010054961,0.000011158938,0.000050504852,0.000050369596,0.96689487,0.00050320406,0.030927565,0.0001906506,0.000013018952],"about_ca_topic_score_codex":0.0018946443,"about_ca_topic_score_gemma":0.0016064284,"teacher_disagreement_score":0.0025305313,"about_ca_system_score_codex":0.0011793914,"about_ca_system_score_gemma":0.0003513028,"threshold_uncertainty_score":0.008557141},"labels":[],"label_agreement":null},{"id":"W2079901150","doi":"10.1007/s10827-013-0477-1","title":"Hierarchical control of two-dimensional gaze saccades","year":2013,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":40,"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 Institutes of Health","keywords":"Gaze; Computer science; Head (geology); Eye movement; Artificial intelligence; Motor control; Control (management); Process (computing); Computer vision; Psychology; Neuroscience","score_opus":0.025576072184995437,"score_gpt":0.27296731126191254,"score_spread":0.2473912390769171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079901150","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":[0.7997885,0.00076793483,0.18420784,0.00047960877,0.00012301999,0.000045826822,0.0002774118,0.0009524701,0.013357351],"genre_scores_gemma":[0.9930918,0.000067128494,0.0056289546,0.000014756063,0.000008173075,0.000010459963,0.00004593005,0.000043536325,0.0010891983],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998369,0.000022568818,0.000009998648,0.0000482813,0.000037949376,0.000044345277],"domain_scores_gemma":[0.99943227,0.00019790472,0.000074762254,0.00008268265,0.00011694845,0.00009530919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002691541,0.0002629085,0.00031431418,0.00034096203,0.0004041681,0.0009974611,0.0004378763,0.00041086815,0.0021025944],"category_scores_gemma":[0.0017093273,0.00032230958,0.00033496734,0.00024277293,0.00031744948,0.00066535344,0.0008846603,0.00044985436,0.00034035326],"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":[0.00055960804,0.00016485051,0.00947131,0.00015063657,0.00013434255,0.00025419533,0.00064697186,0.1320342,0.7182465,0.052070558,0.0021875093,0.084079295],"study_design_scores_gemma":[0.00006430216,0.00010785431,0.053872734,0.000018252038,0.000028745777,0.00010280269,0.00012748511,0.8930019,0.023095913,0.028328434,0.0012056956,0.00004598252],"about_ca_topic_score_codex":0.005135838,"about_ca_topic_score_gemma":0.0067146043,"teacher_disagreement_score":0.005135838,"about_ca_system_score_codex":0.0007256825,"about_ca_system_score_gemma":0.00057762605,"threshold_uncertainty_score":0.010211885},"labels":[],"label_agreement":null},{"id":"W2083168684","doi":"10.1007/s10827-010-0280-1","title":"Spiking neurons that keep the rhythm","year":2010,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":8,"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":"Rhythm; Computer science; Neuroscience; Stimulus (psychology); Perception; Biological neural network; Cognitive psychology; Psychology; Physics","score_opus":0.03663516832875864,"score_gpt":0.2759021981167879,"score_spread":0.23926702978802927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083168684","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":[0.6821753,0.0005761417,0.2683621,0.0017249059,0.0011145973,0.00006562141,0.00036526637,0.0012465273,0.04436948],"genre_scores_gemma":[0.9810853,0.00018154041,0.01264489,0.00032351504,0.000097927346,0.000024501762,0.0001632416,0.00024541715,0.0052337362],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999889,0.000011083556,0.0000074560367,0.000038020982,0.000026993923,0.000027398964],"domain_scores_gemma":[0.9997223,0.00003589022,0.000030478523,0.00007998997,0.000060173516,0.00007111138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019977808,0.00026323958,0.00034386554,0.00019923801,0.00041526216,0.0013202821,0.00066906377,0.0007432271,0.0024088463],"category_scores_gemma":[0.0013676052,0.0002474175,0.0004931239,0.00023168666,0.0005070282,0.0012375048,0.00079336995,0.0008619562,0.0010645381],"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":[0.000252792,0.000105857536,0.0077856923,0.0001405338,0.00031050536,0.00046644258,0.00044992176,0.024352742,0.77768403,0.08533318,0.0040724436,0.099045865],"study_design_scores_gemma":[0.0002615245,0.0005163268,0.030171447,0.00008933856,0.00048842805,0.0025121942,0.00078643986,0.51435,0.22085252,0.19892363,0.030900236,0.00014803893],"about_ca_topic_score_codex":0.00032515856,"about_ca_topic_score_gemma":0.0004532585,"teacher_disagreement_score":0.0024088463,"about_ca_system_score_codex":0.00019398144,"about_ca_system_score_gemma":0.0003271982,"threshold_uncertainty_score":0.008058369},"labels":[],"label_agreement":null},{"id":"W2085283475","doi":"10.1007/s10827-010-0301-0","title":"The Wagon Wheel Illusions and models of orientation selection","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","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":"McGill University","funders":"","keywords":"Illusion; Theory of computation; Orientation (vector space); Selection (genetic algorithm); Computer science; Cognitive psychology; Cognitive science; Artificial intelligence; Psychology; Computer vision; Mathematics; Algorithm; Geometry","score_opus":0.11404043865246143,"score_gpt":0.3257467666592032,"score_spread":0.21170632800674177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085283475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41755116,0.001859445,0.5475663,0.0077169887,0.00032912206,0.0000335475,0.00029573607,0.00044796726,0.02419972],"genre_scores_gemma":[0.985044,0.00043076448,0.012502765,0.00016654516,0.00006762099,0.000015863105,0.000059581485,0.000047798883,0.0016650397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999793,0.00007784924,0.00000952931,0.000032836244,0.000046300964,0.00004050097],"domain_scores_gemma":[0.99869823,0.00067815353,0.00016660841,0.00021819437,0.000120380064,0.00011842473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007556623,0.00029038446,0.00045207812,0.00065144297,0.00032630135,0.001996533,0.0011620412,0.0012592941,0.0029505123],"category_scores_gemma":[0.004956167,0.0003483996,0.00062584964,0.00056704594,0.0014367043,0.0037407174,0.000992027,0.0013001504,0.0002836461],"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":[0.00019617764,0.000037447015,0.001953939,0.000050722952,0.000043359705,0.00017869368,0.00031276728,0.052248675,0.007444789,0.9174482,0.0020854832,0.017999776],"study_design_scores_gemma":[0.000025866448,0.000013243531,0.00086136226,0.000008028503,0.000009121907,0.00009778919,0.00005411976,0.30264243,0.00061896426,0.6950667,0.00058506173,0.000017321749],"about_ca_topic_score_codex":0.0016823626,"about_ca_topic_score_gemma":0.0011292305,"teacher_disagreement_score":0.0029505123,"about_ca_system_score_codex":0.00042218217,"about_ca_system_score_gemma":0.00036861582,"threshold_uncertainty_score":0.00987047},"labels":[],"label_agreement":null},{"id":"W2092494689","doi":"10.1007/s10827-009-0145-7","title":"Derivation of cable parameters for a reduced model that retains asymmetric voltage attenuation of reconstructed spinal motor neuron dendrites","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neurobiology and Insect Physiology Research","field":"Neuroscience","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 Alberta","funders":"","keywords":"Soma; Attenuation; Coupling (piping); Plateau (mathematics); Bistability; Nonlinear system; Physics; Biological neuron model; Neuroscience; Voltage; Biological system; Computer science; Control theory (sociology); Neuron; Mechanics; Materials science; Mathematics; Mathematical analysis; Optoelectronics; Optics; Biology","score_opus":0.08508613363963757,"score_gpt":0.3321099760303575,"score_spread":0.24702384239071995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092494689","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":[0.024144612,0.00016583582,0.96731937,0.00021627624,0.0000491777,0.000053010783,0.00037447206,0.00038275405,0.0072944597],"genre_scores_gemma":[0.6794276,0.00069889054,0.2984998,0.00026327814,0.000074062904,0.00036891794,0.00092669216,0.0013341935,0.018406644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993455,0.000009824402,0.0000044825356,0.000011505744,0.00002922122,0.000010436292],"domain_scores_gemma":[0.9997141,0.00008526429,0.000031181175,0.00004270847,0.00010113766,0.000025560781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030864394,0.0006399241,0.00069026416,0.000503368,0.00038810362,0.0007579435,0.0013327694,0.0018869756,0.004243886],"category_scores_gemma":[0.0011018667,0.000642574,0.000716919,0.0004173092,0.00043339326,0.00080904673,0.00051768386,0.001281654,0.0015754123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015900514,0.000023061011,0.00032364912,0.00007725984,0.000018568127,0.00020798997,0.000052520438,0.9612349,0.0100048445,0.020101583,0.0007939665,0.007145675],"study_design_scores_gemma":[0.000004056174,0.000004358825,0.000089364694,0.000007579001,0.000004926062,0.000035316538,0.00000992124,0.99571157,0.000479165,0.0029540104,0.0006933247,0.000006386621],"about_ca_topic_score_codex":0.011539735,"about_ca_topic_score_gemma":0.011976847,"teacher_disagreement_score":0.011539735,"about_ca_system_score_codex":0.001082375,"about_ca_system_score_gemma":0.0014262192,"threshold_uncertainty_score":0.022945106},"labels":[],"label_agreement":null},{"id":"W2094205817","doi":"10.1007/s10827-011-0374-4","title":"Improved measures of phase-coupling between spikes and the Local Field Potential","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":172,"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":"Spike (software development); Local field potential; Spike train; Computer science; Coherence (philosophical gambling strategy); Point process; Phase synchronization; Bursting; Pairwise comparison; Synchronization (alternating current); Field (mathematics); Phase (matter); Artificial intelligence; Mathematics; Physics; Neuroscience; Statistics","score_opus":0.05894597146057224,"score_gpt":0.27996350252508295,"score_spread":0.22101753106451072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094205817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060576495,0.00057592604,0.9354237,0.0001620162,0.00011411314,0.00007065918,0.0005088796,0.0004270431,0.002141268],"genre_scores_gemma":[0.5785933,0.00062692625,0.41601107,0.00017636396,0.00026089812,0.00026772713,0.0010622252,0.00043881944,0.0025626824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890184,0.00027851036,0.0001246439,0.0002771392,0.00036214982,0.000055671742],"domain_scores_gemma":[0.99508756,0.0020714132,0.0009491772,0.00095800345,0.00076817395,0.00016560109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018545329,0.0008205238,0.0005860829,0.0015283099,0.00025230963,0.00090519467,0.0014737422,0.00079763884,0.0020735462],"category_scores_gemma":[0.013922181,0.00028003473,0.0006141877,0.0017771171,0.0007467111,0.0027235497,0.0013266735,0.0013928039,0.0004724192],"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":[0.0005811195,0.00027567253,0.012709164,0.0008612857,0.00054473436,0.0006039472,0.00044517202,0.22933482,0.24600685,0.18819383,0.003975172,0.3164681],"study_design_scores_gemma":[0.00004942788,0.0004899174,0.022767724,0.000054617576,0.00014958347,0.000810836,0.00007577789,0.8341661,0.041461606,0.092736684,0.0070553427,0.00018239713],"about_ca_topic_score_codex":0.0005964843,"about_ca_topic_score_gemma":0.00061922753,"teacher_disagreement_score":0.0020735462,"about_ca_system_score_codex":0.00041301973,"about_ca_system_score_gemma":0.00050753856,"threshold_uncertainty_score":0.009807885},"labels":[],"label_agreement":null},{"id":"W2094637627","doi":"10.1007/s10827-005-6555-2","title":"Velocity-Based Planning of Rapid Elbow Movements Expands the Control Scheme of the Equilibrium Point Hypothesis","year":2005,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":9,"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":"Kinjo Gakuin University; McGill University","keywords":"Kinematics; Control theory (sociology); Coactivation; Motor control; Computer science; Trajectory; Equilibrium point; Mathematics; Physics; Control (management); Physical medicine and rehabilitation; Classical mechanics; Mathematical analysis; Psychology; Electromyography; Artificial intelligence; Differential equation","score_opus":0.019223693467603747,"score_gpt":0.23331182769775757,"score_spread":0.21408813423015383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094637627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03819373,0.00008994013,0.95589525,0.00024495032,0.000058908627,0.000030552066,0.000033406406,0.0003014044,0.005151813],"genre_scores_gemma":[0.9187746,0.00011743775,0.07911291,0.00004497646,0.00003659522,0.00006417138,0.000040131392,0.00007966571,0.0017294949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997358,0.00007119262,0.000016743657,0.00007841636,0.000067399895,0.000030435885],"domain_scores_gemma":[0.9987614,0.00076399074,0.00011389515,0.00017830943,0.00012615077,0.000056197467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078981405,0.00045146164,0.00047401176,0.00039180732,0.0003953332,0.000982292,0.0009311896,0.0006098473,0.003699106],"category_scores_gemma":[0.004086057,0.00041156216,0.0003278714,0.00027793978,0.0008473029,0.0013968518,0.00087771326,0.0006925661,0.00037499308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029717054,0.000087736196,0.0015824462,0.0001264667,0.00006824025,0.00018769265,0.0002838657,0.608109,0.02806628,0.22063686,0.0009944094,0.1395598],"study_design_scores_gemma":[0.00004416979,0.00011065265,0.0007775715,0.000013232437,0.000012404966,0.00004291527,0.000018281096,0.9009693,0.0032747004,0.09393104,0.0007830925,0.000022588354],"about_ca_topic_score_codex":0.0014960335,"about_ca_topic_score_gemma":0.0011761105,"teacher_disagreement_score":0.003699106,"about_ca_system_score_codex":0.00028143293,"about_ca_system_score_gemma":0.0006740755,"threshold_uncertainty_score":0.012374699},"labels":[],"label_agreement":null},{"id":"W2095787854","doi":"10.1007/s10827-008-0126-2","title":"Analyzing multiple spike trains with nonparametric granger causality","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":92,"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":"National Institute of Mental Health; National Institutes of Health; Wellcome Trust; Research Nova Scotia; Jawaharlal Nehru Centre for Advanced Scientific Research; Defence Research and Development Organisation; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Spike (software development); Granger causality; Spike train; Computer science; Train; Autoregressive model; Nonparametric statistics; Artificial intelligence; Causality (physics); Neuroscience; Pattern recognition (psychology); Machine learning; Econometrics; Mathematics; Biology","score_opus":0.03514176299764425,"score_gpt":0.28116616366436187,"score_spread":0.2460244006667176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095787854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10140902,0.0002097404,0.89694315,0.00019063146,0.000063590705,0.000022964246,0.00009856799,0.0005281988,0.0005341575],"genre_scores_gemma":[0.82565516,0.00027580638,0.17157146,0.000065523374,0.00011957385,0.000055341003,0.00037811606,0.00019630158,0.0016825517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993425,0.00025357652,0.000043969238,0.00011080916,0.00018115315,0.00006796372],"domain_scores_gemma":[0.9929172,0.005232582,0.00059619406,0.000704956,0.0004004322,0.00014862709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024291084,0.0008249195,0.00081212784,0.0019453105,0.0005493124,0.0013811567,0.0010566708,0.0012309068,0.0017150621],"category_scores_gemma":[0.014519981,0.0007717498,0.0009146729,0.001873357,0.0008271844,0.0027235537,0.0017181482,0.0019835418,0.0002652968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010541568,0.00021757852,0.0094829565,0.00015309583,0.00038983667,0.00053645525,0.0001305737,0.72634065,0.013217449,0.06015205,0.0012821167,0.18704301],"study_design_scores_gemma":[0.000016427144,0.000021415924,0.00065658684,0.0000040103864,0.000009887142,0.000060141774,0.000009393303,0.97586244,0.00082985597,0.022344237,0.00017448465,0.000011200907],"about_ca_topic_score_codex":0.0015444027,"about_ca_topic_score_gemma":0.0024032805,"teacher_disagreement_score":0.0024291084,"about_ca_system_score_codex":0.00042036737,"about_ca_system_score_gemma":0.0011257152,"threshold_uncertainty_score":0.0128465295},"labels":[],"label_agreement":null},{"id":"W2101845075","doi":"10.1023/b:jcns.0000037683.55688.7e","title":"Oscillations in Large-Scale Cortical Networks: Map-Based Model","year":2004,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":192,"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 Laval","funders":"","keywords":"Bursting; Computer science; Theory of computation; Scale (ratio); Set (abstract data type); Neuroscience; Network model; Computational model; Biological neuron model; Replicate; Artificial intelligence; Physics; Artificial neural network; Algorithm; Biology; Mathematics","score_opus":0.026511538652979387,"score_gpt":0.2750349812715427,"score_spread":0.2485234426185633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101845075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1671316,0.0018623562,0.80349636,0.0040227966,0.0002465456,0.00007409442,0.000577373,0.00059676473,0.021992086],"genre_scores_gemma":[0.9767543,0.00086335844,0.014716731,0.00014757874,0.00013240057,0.000104126164,0.00013053996,0.000092583294,0.0070583844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999835,0.00006316081,0.0000069556677,0.000041761996,0.000030634368,0.0000224609],"domain_scores_gemma":[0.9991191,0.0005220411,0.00009250373,0.000057750385,0.000113627655,0.0000949066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006292819,0.0006210867,0.0011300999,0.00067346,0.00057251164,0.0013845171,0.0018042146,0.002581151,0.0022720678],"category_scores_gemma":[0.0034926194,0.000641686,0.0007966054,0.0009045135,0.0013797896,0.0027823246,0.0011264328,0.0012680884,0.0004127568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034362467,0.000019849496,0.00034521078,0.00003482141,0.000032008087,0.000072581104,0.000044960212,0.9649131,0.0006752203,0.030813444,0.00063786574,0.0023764342],"study_design_scores_gemma":[0.0000049843366,0.0000026108944,0.00005639067,0.0000014794568,0.0000034400337,0.0000076185584,0.0000032990608,0.99153143,0.000020423135,0.008299065,0.00006640188,0.0000027515123],"about_ca_topic_score_codex":0.008203592,"about_ca_topic_score_gemma":0.0046892525,"teacher_disagreement_score":0.008203592,"about_ca_system_score_codex":0.0008185613,"about_ca_system_score_gemma":0.00062299985,"threshold_uncertainty_score":0.016311705},"labels":[],"label_agreement":null},{"id":"W2101848767","doi":"10.1023/a:1014942129705","title":"A Spiking Neuron Model for Binocular Rivalry","year":2002,"lang":"en","type":"review","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":346,"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":"National Institutes of Health; Alfred P. Sloan Foundation","keywords":"Dominance (genetics); Binocular rivalry; Ocular dominance; Population; Stimulus (psychology); Statistics; Statistical physics; Psychology; Mathematics; Neuroscience; Cognitive psychology; Visual cortex; Visual perception; Physics; Biology; Medicine; Perception","score_opus":0.16201727588120474,"score_gpt":0.355482686739886,"score_spread":0.19346541085868124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101848767","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":[0.019851774,0.08185407,0.83519703,0.0051469393,0.0022558598,0.000058441583,0.00057873724,0.00063099636,0.05442616],"genre_scores_gemma":[0.4917379,0.22169109,0.21087329,0.0019598396,0.0017429056,0.0003277741,0.0009352793,0.00044251213,0.070289336],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999565,0.000008407761,0.000004286286,0.00000942133,0.000016808539,0.000004564242],"domain_scores_gemma":[0.9999355,0.000025868454,0.000004080107,0.000006509965,0.00002303207,0.0000050104018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021140529,0.00064318924,0.0009604563,0.00038233184,0.00025071617,0.00046827228,0.0014290975,0.0014703044,0.0018601392],"category_scores_gemma":[0.00034177367,0.00025601327,0.0006459,0.00068544626,0.0006154085,0.0009917137,0.00042409674,0.00065258355,0.00082875055],"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":[0.000046080713,0.00004044235,0.00044292765,0.0010877067,0.00010945834,0.0007136872,0.00009683397,0.32786417,0.01032521,0.4890638,0.01878587,0.15142381],"study_design_scores_gemma":[0.000025582549,0.00002598891,0.0003223175,0.0001482709,0.00006220999,0.00083515415,0.000032719483,0.62837034,0.0017978171,0.30574596,0.062592745,0.000040780295],"about_ca_topic_score_codex":0.0028197113,"about_ca_topic_score_gemma":0.0029900784,"teacher_disagreement_score":0.0028197113,"about_ca_system_score_codex":0.0006780008,"about_ca_system_score_gemma":0.000638094,"threshold_uncertainty_score":0.0062227845},"labels":[],"label_agreement":null},{"id":"W2117829171","doi":"10.1023/a:1014921628797","title":"Ghostbursting: A Novel Neuronal Burst Mechanism","year":2002,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":138,"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":"Mechanism (biology); Neuroscience; Theory of computation; Cognitive science; Computer science; Psychology; Epistemology; Philosophy; Algorithm","score_opus":0.06232537740556444,"score_gpt":0.26540134423372225,"score_spread":0.20307596682815782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117829171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40258214,0.00079433725,0.5884769,0.00054481084,0.00053571025,0.00008243267,0.000171696,0.0015267268,0.0052852817],"genre_scores_gemma":[0.9665454,0.00019108178,0.031526994,0.000082913815,0.00006352047,0.000019778456,0.00004628486,0.000110917514,0.001413237],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998814,0.00001929693,0.000009943387,0.000029357956,0.000037945345,0.00002219242],"domain_scores_gemma":[0.99919325,0.00023095326,0.00012324062,0.00018760187,0.00006703545,0.00019784382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036319462,0.00029369773,0.00036123637,0.00031321158,0.00034968735,0.0006381244,0.0009499324,0.0009106333,0.0017979686],"category_scores_gemma":[0.0022168506,0.00021041789,0.0002742237,0.00023498521,0.0004739056,0.0010066015,0.0009713147,0.000761123,0.00023309962],"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":[0.0014293661,0.00018372477,0.007967214,0.00032359484,0.00013992902,0.0034498388,0.00077869784,0.03184288,0.6798441,0.14747097,0.0044250167,0.12214471],"study_design_scores_gemma":[0.00032449927,0.00043717198,0.00572484,0.00004587882,0.00011716673,0.005684296,0.00012684098,0.7936493,0.07186979,0.11555964,0.0063599013,0.000100672834],"about_ca_topic_score_codex":0.00015000909,"about_ca_topic_score_gemma":0.00013533677,"teacher_disagreement_score":0.0017979686,"about_ca_system_score_codex":0.00020566082,"about_ca_system_score_gemma":0.00022935314,"threshold_uncertainty_score":0.006014824},"labels":[],"label_agreement":null},{"id":"W2120386405","doi":"10.1023/a:1023269128622","title":"Type I Burst Excitability","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":42,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric fish; Bursting; Neuroscience; Bifurcation; Physics; Depolarization; Tonic (physiology); Excitatory postsynaptic potential; Computer science; Biology; Nonlinear system; Fish <Actinopterygii>; Biophysics; Inhibitory postsynaptic potential","score_opus":0.04301014640233819,"score_gpt":0.2881509563589454,"score_spread":0.2451408099566072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120386405","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":[0.83259976,0.0007669942,0.12179896,0.0006076642,0.00036462044,0.00012623119,0.000810673,0.0011879884,0.041737113],"genre_scores_gemma":[0.99053544,0.00013683278,0.0032666319,0.00006735308,0.000041897845,0.00003553844,0.00018475938,0.000110969704,0.0056205182],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988425,0.0000124815815,0.000009650733,0.00003276501,0.000031526844,0.000029417515],"domain_scores_gemma":[0.9995503,0.000099936326,0.00006222503,0.00010320663,0.00008521099,0.00009918822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002210734,0.00031822216,0.0002989669,0.00026485708,0.00039423292,0.0008232465,0.00047195156,0.0005107117,0.007251417],"category_scores_gemma":[0.0013929544,0.00017046579,0.0003694232,0.00019711003,0.00020916709,0.00076983304,0.00055125466,0.0005711763,0.0012423972],"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":[0.0009620272,0.00011594577,0.011050737,0.00016095728,0.00009386602,0.00062447356,0.0002985293,0.0054684486,0.87295413,0.0458628,0.0035258827,0.058882155],"study_design_scores_gemma":[0.00021314782,0.0008675882,0.10582348,0.00009140886,0.0002972632,0.010381951,0.0007206098,0.3632909,0.38819477,0.11215165,0.017828578,0.00013859863],"about_ca_topic_score_codex":0.00033242485,"about_ca_topic_score_gemma":0.00033092953,"teacher_disagreement_score":0.007251417,"about_ca_system_score_codex":0.00037522463,"about_ca_system_score_gemma":0.00027897517,"threshold_uncertainty_score":0.024258375},"labels":[],"label_agreement":null},{"id":"W2127253481","doi":"10.1023/a:1024426903582","title":"Spike Generating Dynamics and the Conditions for Spike-Time Precision in Cortical Neurons","year":2003,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":65,"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":"National Science Foundation","keywords":"Aperiodic graph; Stimulus (psychology); Spike (software development); Spike train; Time constant; Neuron; Computer science; Neuroscience; Mathematics; Psychology","score_opus":0.023032629899550277,"score_gpt":0.2854782459386307,"score_spread":0.2624456160390804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127253481","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":[0.64498025,0.00040088827,0.33630073,0.0019144203,0.00013819925,0.00007517877,0.00053900183,0.00056505576,0.015086313],"genre_scores_gemma":[0.993057,0.000074855496,0.005992244,0.000033252833,0.000034572924,0.000037684025,0.00008351673,0.00007726724,0.00060960423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993243,0.00010771667,0.000065820146,0.00011832381,0.00020227603,0.00018152356],"domain_scores_gemma":[0.99232835,0.004492119,0.0009743146,0.00053541095,0.0008984632,0.00077134854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013224669,0.00050585903,0.00084866275,0.0008264769,0.0009322176,0.0021202455,0.0010244289,0.0017983374,0.004399411],"category_scores_gemma":[0.024966829,0.00063391187,0.0005541394,0.00046398566,0.0015118474,0.0036505575,0.0014575919,0.0016802057,0.00046121658],"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":[0.0005190968,0.000103393395,0.0033085733,0.00016085363,0.000042907246,0.0010086682,0.0007098468,0.06359744,0.07897295,0.832976,0.0013929593,0.017207345],"study_design_scores_gemma":[0.00014151569,0.00007664071,0.0048710173,0.000033087665,0.000018646795,0.00064639613,0.00019264365,0.49583113,0.015966697,0.4815307,0.0006149383,0.00007652786],"about_ca_topic_score_codex":0.0010104814,"about_ca_topic_score_gemma":0.00079891266,"teacher_disagreement_score":0.004399411,"about_ca_system_score_codex":0.00071656075,"about_ca_system_score_gemma":0.0010424729,"threshold_uncertainty_score":0.014717519},"labels":[],"label_agreement":null},{"id":"W2134928318","doi":"10.1023/a:1026535704537","title":"Nonlinear Thermodynamic Models of Voltage-Dependent Currents","year":2000,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":69,"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 Laval; Université du Québec","funders":"Medical Research Council; National Institutes of Health","keywords":"Nonlinear system; Statistical physics; Voltage; Formalism (music); Theory of computation; Physics; Computer science; Quantum mechanics; Algorithm","score_opus":0.03228280863277329,"score_gpt":0.2776547979777715,"score_spread":0.2453719893449982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134928318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2710777,0.0020031992,0.63504314,0.006013,0.0006440504,0.00012173672,0.0005790214,0.0004157315,0.08410238],"genre_scores_gemma":[0.95538306,0.0009783595,0.011974053,0.00033895532,0.00023590587,0.0001683286,0.0001848633,0.00019498251,0.03054151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996779,0.00009832626,0.00001624911,0.000040802017,0.00010257509,0.00006412934],"domain_scores_gemma":[0.9987024,0.0006897189,0.00010578862,0.0001266717,0.00022786277,0.00014749815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010551322,0.000520892,0.0011569788,0.0010519009,0.0010949194,0.00203814,0.002645368,0.0019314123,0.0067101945],"category_scores_gemma":[0.005262098,0.0006958048,0.00086588814,0.0007821922,0.0027752458,0.0038688031,0.0015532197,0.0013914409,0.00090093113],"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":[0.000025520603,0.000047544636,0.00022830022,0.00004499633,0.00001755068,0.00008123544,0.000095135845,0.32454985,0.0011186381,0.67099357,0.0010601345,0.0017374742],"study_design_scores_gemma":[0.000007962564,0.0000046389714,0.00008382202,0.0000041833023,0.0000033012484,0.000016687934,0.00001476654,0.8512321,0.000097744,0.14811252,0.00041161102,0.000010634561],"about_ca_topic_score_codex":0.0049147974,"about_ca_topic_score_gemma":0.0047206045,"teacher_disagreement_score":0.0067101945,"about_ca_system_score_codex":0.0019041618,"about_ca_system_score_gemma":0.0013878735,"threshold_uncertainty_score":0.022447884},"labels":[],"label_agreement":null},{"id":"W2142316811","doi":"10.1007/s10827-011-0372-6","title":"Network bursting using experimentally constrained single compartment CA3 hippocampal neuron models with adaptation","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":34,"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 Toronto; University of Waterloo; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bursting; Neuroscience; Hippocampal formation; Network model; Context (archaeology); Excitatory postsynaptic potential; Computer science; Glutamatergic; Population; Network dynamics; Biological system; Biology; Artificial intelligence; Glutamate receptor; Mathematics; Inhibitory postsynaptic potential","score_opus":0.1690258683120918,"score_gpt":0.27494383384103105,"score_spread":0.10591796552893926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142316811","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":[0.66207355,0.0004914267,0.32625085,0.0008640683,0.00008784483,0.0000897855,0.00042798376,0.00038366835,0.009330799],"genre_scores_gemma":[0.99229336,0.000089204965,0.006322486,0.00002461369,0.000010012148,0.000050669292,0.00007152463,0.000047642905,0.0010905741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998863,0.000039050417,0.000007658437,0.000030066378,0.000017521646,0.00001949327],"domain_scores_gemma":[0.99932134,0.00041419663,0.000085632135,0.000055643945,0.000074045995,0.00004906263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004396151,0.00058801693,0.0007130543,0.0002730099,0.00039074055,0.0005641489,0.001302333,0.0015371873,0.0015062031],"category_scores_gemma":[0.002467956,0.0005076189,0.0007421492,0.00031553526,0.00074121973,0.001048233,0.00065456657,0.0008665068,0.0001467676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024333975,0.000009491646,0.00029156596,0.000013200448,0.000010316652,0.000033661203,0.000020118474,0.9957094,0.0007477772,0.0024887023,0.00006451014,0.000586986],"study_design_scores_gemma":[0.0000029100036,0.000002117309,0.000055417924,9.27887e-7,0.0000016626208,0.0000027158642,0.0000018674942,0.9993882,0.00006717814,0.00045854895,0.000016473516,0.0000019643142],"about_ca_topic_score_codex":0.022005439,"about_ca_topic_score_gemma":0.016326461,"teacher_disagreement_score":0.022005439,"about_ca_system_score_codex":0.0009945333,"about_ca_system_score_gemma":0.00086289254,"threshold_uncertainty_score":0.043754697},"labels":[],"label_agreement":null},{"id":"W2142969506","doi":"10.1007/s10827-006-7199-6","title":"A model that integrates eye velocity commands to keep track of smooth eye displacements","year":2006,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":21,"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":"National Institutes of Health","keywords":"Theory of computation; Track (disk drive); Computer science; Eye movement; Computer vision; Artificial intelligence; Algorithm","score_opus":0.07279120838529415,"score_gpt":0.35121918423212595,"score_spread":0.27842797584683177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142969506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21577036,0.00048831385,0.74275404,0.004148028,0.0009751163,0.00012058547,0.0012059357,0.002622081,0.031915456],"genre_scores_gemma":[0.9506625,0.00028855432,0.033336412,0.00034181064,0.00008028928,0.0001152967,0.00034322945,0.00012108726,0.014710841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999244,0.00001125286,0.00000469916,0.000032012205,0.00001559371,0.000012058093],"domain_scores_gemma":[0.9998217,0.000060553935,0.000019151132,0.000028389179,0.000039620467,0.00003060856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021080044,0.0004309131,0.00050567294,0.0002681352,0.00041995768,0.0012522074,0.0013604176,0.0022150679,0.0050127334],"category_scores_gemma":[0.000982095,0.00035061053,0.00066457235,0.00030486318,0.0004684031,0.0014573823,0.00063144893,0.0007536602,0.0010524908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035275467,0.00011978152,0.0015732249,0.00012897386,0.00014095365,0.00049484434,0.00016700424,0.8966553,0.019124357,0.05475942,0.0042041778,0.022279171],"study_design_scores_gemma":[0.00004142499,0.000027607628,0.000115872215,0.0000028228399,0.000021224932,0.0000340754,0.0000075679613,0.9935143,0.0006132496,0.004927404,0.0006854365,0.000008866578],"about_ca_topic_score_codex":0.007281869,"about_ca_topic_score_gemma":0.003702865,"teacher_disagreement_score":0.007281869,"about_ca_system_score_codex":0.0005218705,"about_ca_system_score_gemma":0.0009273677,"threshold_uncertainty_score":0.01676923},"labels":[],"label_agreement":null},{"id":"W2146508384","doi":"10.1007/s10827-007-0033-y","title":"Threshold fatigue and information transfer","year":2007,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":63,"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; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Noise (video); Information transmission; Electric fish; Transmission (telecommunications); Robustness (evolution); SIGNAL (programming language); Auditory fatigue; Physics; Neuroscience; Computer science; Telecommunications; Chemistry; Artificial intelligence; Noise exposure; Psychology; Audiology; Biology; Fish <Actinopterygii>; Medicine","score_opus":0.03418796517847549,"score_gpt":0.27711641367955164,"score_spread":0.24292844850107614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146508384","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":[0.7700244,0.0016842561,0.20628753,0.002593416,0.00014339459,0.000044592663,0.00008393321,0.0002530687,0.018885452],"genre_scores_gemma":[0.9962464,0.00009449574,0.0020913028,0.000048865324,0.000025050247,0.000010308817,0.0000115239545,0.000017318107,0.0014547715],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999658,0.00009943247,0.000022303762,0.000079058176,0.00006916303,0.00007209431],"domain_scores_gemma":[0.99445456,0.0039158654,0.0003968568,0.00047001542,0.00043746314,0.00032518894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010999547,0.0002488448,0.0004463898,0.0004234956,0.0003858971,0.0010591435,0.00046815688,0.0012901655,0.005970499],"category_scores_gemma":[0.015848827,0.00024350456,0.00024696358,0.00023266266,0.0010603801,0.0028348363,0.0010411266,0.00085733074,0.00041496693],"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":[0.001875012,0.00070968916,0.015485474,0.00050141965,0.00021160879,0.0010062495,0.0021457016,0.20516388,0.10077979,0.5041065,0.0047340523,0.16328056],"study_design_scores_gemma":[0.00007716169,0.00040481766,0.018688576,0.000035048095,0.000044427907,0.00047069072,0.00029129666,0.48705626,0.008630415,0.48308808,0.0011597263,0.00005354563],"about_ca_topic_score_codex":0.0006402447,"about_ca_topic_score_gemma":0.0003657955,"teacher_disagreement_score":0.005970499,"about_ca_system_score_codex":0.0005025381,"about_ca_system_score_gemma":0.0004367622,"threshold_uncertainty_score":0.019973338},"labels":[],"label_agreement":null},{"id":"W2146600851","doi":"10.1007/s10827-005-6558-z","title":"A Controlled Attractor Network Model of Path Integration in the Rat","year":2005,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","cited_by":131,"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":"Path integration; Representation (politics); Attractor; Computer science; Theory of computation; Path (computing); Mechanism (biology); Multiplicative function; Network model; Population; Variety (cybernetics); Theoretical computer science; Artificial intelligence; Algorithm; Mathematics; Physics; Computer network","score_opus":0.08464200681426108,"score_gpt":0.36549197291531954,"score_spread":0.28084996610105845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146600851","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":[0.7031329,0.00051059976,0.27557135,0.0007919317,0.00008301933,0.00004892203,0.00042576186,0.00032268377,0.019112809],"genre_scores_gemma":[0.9849657,0.00018955945,0.010649488,0.000018024768,0.000009049949,0.00003085523,0.000069339054,0.000020138348,0.00404778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999306,0.000022785456,0.0000026042474,0.000019165185,0.000012467731,0.000012236836],"domain_scores_gemma":[0.9998461,0.00005891512,0.000030991156,0.000012312085,0.000031512143,0.000020177025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018175157,0.00036504184,0.00046090127,0.0003258304,0.00029902734,0.0006349915,0.0006446753,0.00062169466,0.0021365872],"category_scores_gemma":[0.00058830756,0.00020310273,0.00042058202,0.00031825795,0.0005738277,0.0007394972,0.00032834095,0.0004705119,0.00014643338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006855707,0.00002643499,0.00051149016,0.000026234924,0.00003889941,0.00008519284,0.00004477986,0.92859614,0.0037130963,0.06176723,0.00041739544,0.00470456],"study_design_scores_gemma":[0.000009334786,0.000015187152,0.00013926555,0.000001524481,0.000006601721,0.000008670503,0.0000049651862,0.99341726,0.00011989824,0.0061391,0.00013450472,0.0000036807735],"about_ca_topic_score_codex":0.01771238,"about_ca_topic_score_gemma":0.011354491,"teacher_disagreement_score":0.01771238,"about_ca_system_score_codex":0.0008504725,"about_ca_system_score_gemma":0.0009921348,"threshold_uncertainty_score":0.035218596},"labels":[],"label_agreement":null},{"id":"W2151507971","doi":"10.1007/s10827-015-0577-1","title":"Examining the limits of cellular adaptation bursting mechanisms in biologically-based excitatory networks of the hippocampus","year":2015,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":23,"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; University Health Network","funders":"","keywords":"Bursting; Neuroscience; Excitatory postsynaptic potential; Context (archaeology); Population; Computer science; Computational model; Hippocampus; Network model; Inhibitory postsynaptic potential; Pyramidal cell; Biological system; Artificial intelligence; Biology","score_opus":0.10899725911933682,"score_gpt":0.26924694892392176,"score_spread":0.16024968980458493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151507971","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":[0.98140484,0.0002971762,0.015938174,0.000274602,0.000011162864,0.0000051968013,0.000017881775,0.000025777053,0.0020253146],"genre_scores_gemma":[0.99913317,0.00006742913,0.0006856009,0.0000063218185,0.0000041353865,0.0000030093956,0.0000047697295,0.000003570363,0.00009209546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999124,0.0000314379,0.0000063815774,0.000016577638,0.000013889761,0.000019194998],"domain_scores_gemma":[0.99855036,0.0010645748,0.00010521957,0.00010286433,0.00008030312,0.000096599666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005293528,0.00017808052,0.00020960851,0.00020963069,0.00021784434,0.0007605655,0.0005552642,0.00046525343,0.00092301],"category_scores_gemma":[0.006209938,0.00020650653,0.00015525792,0.00010978252,0.0006620078,0.001731336,0.0007132989,0.0005853491,0.000065263994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010504451,0.00024206568,0.031080674,0.00037444595,0.00025631452,0.0006171342,0.001041409,0.44490212,0.30993032,0.15524073,0.0007193916,0.05454502],"study_design_scores_gemma":[0.00003789626,0.00014353973,0.02031914,0.00002412769,0.000048407506,0.0001632626,0.00034538042,0.8847674,0.014324389,0.07944112,0.00036373627,0.000021568929],"about_ca_topic_score_codex":0.0009915039,"about_ca_topic_score_gemma":0.00075076206,"teacher_disagreement_score":0.0009915039,"about_ca_system_score_codex":0.0003046063,"about_ca_system_score_gemma":0.00024293525,"threshold_uncertainty_score":0.003087759},"labels":[],"label_agreement":null},{"id":"W2155815687","doi":"10.1023/a:1012837415096","title":"Turning On and Off with Excitation: The Role of Spike-Timing Asynchrony and Synchrony in Sustained Neural Activity","year":2001,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":178,"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":"Neuroscience; Excitatory postsynaptic potential; Efferent; Stimulus (psychology); Working memory; Psychology; Computer science; Inhibitory postsynaptic potential; Cognition; Afferent; Cognitive psychology","score_opus":0.016918622930818472,"score_gpt":0.25495051554215187,"score_spread":0.2380318926113334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155815687","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":[0.8975555,0.0007771039,0.09479774,0.00046660795,0.00011346162,0.000015257005,0.000102305385,0.00016282257,0.00600925],"genre_scores_gemma":[0.9966324,0.00009185451,0.00294426,0.000013471946,0.000024218856,0.0000044553512,0.000013520197,0.000020828558,0.00025504173],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990416,0.000019298437,0.000008135564,0.000025313908,0.000023718836,0.00001936765],"domain_scores_gemma":[0.99886703,0.00066864473,0.00015673222,0.00009099939,0.000052920917,0.00016378658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042889517,0.00019058134,0.00030137796,0.00023976596,0.00028001601,0.0010979123,0.00045169712,0.0004502952,0.0014695576],"category_scores_gemma":[0.0043174503,0.00025537453,0.00020934027,0.00020544874,0.0005625123,0.0012405596,0.00064760126,0.0005295191,0.00013400217],"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":[0.0022644897,0.00030552232,0.034360483,0.00026551215,0.00020944203,0.0013881598,0.00094443816,0.13892362,0.4445757,0.17034262,0.0017168833,0.2047031],"study_design_scores_gemma":[0.00015102452,0.0002087086,0.028252307,0.00002912694,0.0000980872,0.000559161,0.00018193528,0.7977432,0.016588967,0.15512663,0.0010052245,0.000055602708],"about_ca_topic_score_codex":0.00039810492,"about_ca_topic_score_gemma":0.0005017017,"teacher_disagreement_score":0.0014695576,"about_ca_system_score_codex":0.00019808275,"about_ca_system_score_gemma":0.00029663846,"threshold_uncertainty_score":0.0049161315},"labels":[],"label_agreement":null},{"id":"W2159565530","doi":"10.1007/s10827-015-0562-8","title":"Cooperativity between remote sites of ectopic spiking allows afterdischarge to be initiated and maintained at different locations","year":2015,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Pain Mechanisms and Treatments","field":"Medicine","cited_by":6,"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 Toronto; Hospital for Sick Children","funders":"Canadian Institutes of Health Research; National Institutes of Health; National Institute of Neurological Disorders and Stroke; Howard Hughes Medical Institute","keywords":"Soma; Neuroscience; Axon; Ectopic expression; Antidromic; Stimulus (psychology); Biological neural network; Biology; Electrophysiology; Psychology","score_opus":0.10503280097322204,"score_gpt":0.3482196855936448,"score_spread":0.24318688462042276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159565530","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":[0.9270145,0.00016642443,0.06844408,0.00018859151,0.00013919293,0.00005223365,0.000094319446,0.00048538504,0.003415267],"genre_scores_gemma":[0.9967849,0.000027746735,0.002516095,0.000034415978,0.000010507637,0.000015335278,0.00003967099,0.00005158912,0.00051990413],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957246,0.00004656012,0.000045947207,0.00012047534,0.000112910464,0.00010152183],"domain_scores_gemma":[0.99871445,0.00031419902,0.00018989788,0.00041668254,0.00012058977,0.00024414904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077525247,0.0003630243,0.00084830547,0.00028814343,0.0005046454,0.0010981032,0.0016504499,0.00072389696,0.0020701864],"category_scores_gemma":[0.0021397287,0.0003410324,0.0007557563,0.00016718931,0.00070494425,0.00085889647,0.0016292738,0.0011116518,0.00047445972],"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":[0.0008388339,0.00042933793,0.0053491574,0.000105754756,0.00010780305,0.00039410216,0.00014497622,0.015222906,0.94816595,0.008370112,0.0003910878,0.020479945],"study_design_scores_gemma":[0.0004629768,0.0010376776,0.047050178,0.000044131022,0.00031168116,0.0015011339,0.00035411862,0.47674856,0.44278875,0.026640346,0.0029303355,0.0001300344],"about_ca_topic_score_codex":0.00043395406,"about_ca_topic_score_gemma":0.0010954395,"teacher_disagreement_score":0.0020701864,"about_ca_system_score_codex":0.00066084345,"about_ca_system_score_gemma":0.00053024257,"threshold_uncertainty_score":0.0069254637},"labels":[],"label_agreement":null},{"id":"W2280694712","doi":"10.1007/s10827-015-0583-3","title":"A stochastic model of input effectiveness during irregular gamma rhythms","year":2015,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"General Dynamics (Canada); Royal Ottawa Mental Health Centre; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Rhythm; Computer science; Neuroscience; Theory of computation; Cognitive science; Artificial intelligence; Psychology; Medicine; Algorithm; Internal medicine","score_opus":0.052300412986069265,"score_gpt":0.2795901379205156,"score_spread":0.22728972493444635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280694712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37496805,0.00044187403,0.61210054,0.0017026764,0.00013447479,0.000057360314,0.00050095655,0.00035561153,0.009738401],"genre_scores_gemma":[0.992071,0.0001384295,0.004471569,0.00006466347,0.00004067232,0.000028208971,0.00008449406,0.00006286278,0.0030381996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973375,0.000071014205,0.0000144329415,0.00006044153,0.000049177856,0.000071173126],"domain_scores_gemma":[0.9979755,0.0012642979,0.00024231762,0.00011784799,0.00021708083,0.0001828241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011357837,0.0005289947,0.00078106945,0.0004486698,0.00041757643,0.0012167558,0.001290777,0.0016131189,0.0026316],"category_scores_gemma":[0.0065360004,0.0006971323,0.0007399201,0.00042490082,0.000933453,0.001471514,0.0007883459,0.0015135349,0.00032069153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009001076,0.000033313005,0.00094515656,0.000039205846,0.000034489334,0.00015823857,0.00008355032,0.93568826,0.0052545094,0.05444289,0.0005239243,0.0027064888],"study_design_scores_gemma":[0.000005022798,0.000006669807,0.0002720007,0.0000026997664,0.000004677091,0.000017343942,0.000004175483,0.99516004,0.0001120177,0.004364248,0.000045261313,0.0000058929418],"about_ca_topic_score_codex":0.0056247283,"about_ca_topic_score_gemma":0.0038445957,"teacher_disagreement_score":0.0056247283,"about_ca_system_score_codex":0.0008783428,"about_ca_system_score_gemma":0.00066344027,"threshold_uncertainty_score":0.011183977},"labels":[],"label_agreement":null},{"id":"W2328001100","doi":"10.1007/s10827-016-0591-y","title":"Large extracellular space leads to neuronal susceptibility to ischemic injury in a Na+/K + pumps–dependent manner","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":20,"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":"","keywords":"Extracellular; Depolarization; Neuroscience; Ischemia; Electrophysiology; Biophysics; Homeostasis; Mechanism (biology); Chemistry; Biology; Cell biology; Internal medicine; Medicine; Physics","score_opus":0.021355025352184254,"score_gpt":0.2800888475498562,"score_spread":0.25873382219767194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328001100","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":[0.99503505,0.0003897618,0.0031028767,0.00020217914,0.000032150627,0.000009965044,0.00007342121,0.00007124212,0.001083373],"genre_scores_gemma":[0.9989894,0.00018197345,0.0003841152,0.000030499337,0.000007559079,0.000008331942,0.000044331653,0.000010881566,0.00034302665],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998838,0.000016236949,0.000011378757,0.000021062671,0.000030101017,0.000037411628],"domain_scores_gemma":[0.9997265,0.00004371848,0.00010257832,0.00003318946,0.000028800188,0.000065185086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001476956,0.00032109625,0.0003459822,0.00016888448,0.0002349467,0.0005483096,0.00022706132,0.0003677906,0.0019595854],"category_scores_gemma":[0.00041390123,0.00016567142,0.00036229103,0.00012321115,0.000433467,0.00048026835,0.00055831653,0.0006132533,0.00020225764],"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":[0.0005722001,0.00005389508,0.0007055436,0.000044064298,0.000027904458,0.00042893775,0.0000321544,0.00051930733,0.9951793,0.0006731539,0.00017355321,0.00159009],"study_design_scores_gemma":[0.000146484,0.0013769701,0.042200774,0.000023376386,0.0001827399,0.0031735378,0.00036090074,0.012791772,0.9323674,0.0052805706,0.0020424924,0.0000530706],"about_ca_topic_score_codex":0.0003311283,"about_ca_topic_score_gemma":0.00045676873,"teacher_disagreement_score":0.0019595854,"about_ca_system_score_codex":0.0002272673,"about_ca_system_score_gemma":0.00025828846,"threshold_uncertainty_score":0.0065554976},"labels":[],"label_agreement":null},{"id":"W2333322910","doi":"10.1007/s10827-016-0598-4","title":"A unified model for two modes of bursting in GnRH neurons","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","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":"McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","keywords":"Bursting; Neuroscience; Physics; Tetrodotoxin; Membrane potential; Conductance; Bifurcation; Chemistry; Biophysics; Biology; Nonlinear system","score_opus":0.03038917705222616,"score_gpt":0.30239863576209597,"score_spread":0.2720094587098698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333322910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3410262,0.00075216324,0.62060934,0.0032406566,0.00036469626,0.000089346926,0.00061538536,0.00040202247,0.032900106],"genre_scores_gemma":[0.9757983,0.0002539105,0.01635769,0.00016277026,0.00007528772,0.00010376604,0.00009904125,0.000055327164,0.0070939222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998454,0.000037039743,0.000011646962,0.000038806287,0.000029422066,0.000037571725],"domain_scores_gemma":[0.9997037,0.000105864194,0.000043145406,0.00002753816,0.0000523351,0.000067453795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040863908,0.00041257523,0.0011839309,0.0005165359,0.00076761784,0.0015913971,0.0019650373,0.002465458,0.0035397306],"category_scores_gemma":[0.0010313316,0.00046914507,0.0012590206,0.00049384974,0.0009483678,0.0016412555,0.0012111294,0.0010245587,0.0004391864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009921402,0.00006047297,0.0015669363,0.00009174747,0.00009537008,0.0005288869,0.00028132254,0.6085609,0.010666299,0.3713939,0.0014474659,0.0052075386],"study_design_scores_gemma":[0.000027501026,0.000017326593,0.00021621952,0.000005440731,0.00001722241,0.00005811527,0.000025163781,0.9660977,0.00013013437,0.033071022,0.0003169812,0.000017160077],"about_ca_topic_score_codex":0.0038176805,"about_ca_topic_score_gemma":0.0032410412,"teacher_disagreement_score":0.0038176805,"about_ca_system_score_codex":0.00094630086,"about_ca_system_score_gemma":0.0011706231,"threshold_uncertainty_score":0.011841595},"labels":[],"label_agreement":null},{"id":"W2512532751","doi":"10.1007/s10827-016-0619-3","title":"Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Dynamics (Canada); University of Ottawa","funders":"","keywords":"Computer science; Robustness (evolution); Biological neural network; Chaotic; Artificial neural network; Exploit; Coding (social sciences); Electronic circuit; Nerve net; Neuroscience; Artificial intelligence; Mathematics; Machine learning; Physics; Biology","score_opus":0.03753003493952895,"score_gpt":0.2528041465687272,"score_spread":0.21527411162919824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512532751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30382353,0.00072765537,0.6618303,0.0044337427,0.00023420344,0.00008624199,0.00022830472,0.0003836834,0.028252264],"genre_scores_gemma":[0.97860307,0.00019654103,0.015791079,0.0001932052,0.000069562695,0.000053423126,0.000046846886,0.000060339557,0.0049860314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998574,0.000053553013,0.0000070280307,0.000025628327,0.000028935952,0.000027424816],"domain_scores_gemma":[0.99929786,0.00033922438,0.00011140818,0.000049194732,0.00009455315,0.00010772779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004882849,0.0005122889,0.0007502135,0.0005023257,0.0005202038,0.0011816764,0.0012663914,0.0024494694,0.0020765115],"category_scores_gemma":[0.0038428192,0.0006318658,0.0006760574,0.00032303648,0.0013742945,0.0013005468,0.0021002237,0.0013348846,0.00016405713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003777542,0.00003177971,0.00052467163,0.00004609,0.000040811443,0.00018065871,0.00009333123,0.8613828,0.0019469252,0.13110699,0.0011776697,0.0034305025],"study_design_scores_gemma":[0.0000056451245,0.0000050593308,0.000040456252,0.0000023316964,0.0000030096421,0.000013828176,0.000008365501,0.9803726,0.000059119484,0.019348904,0.00013659887,0.0000041949047],"about_ca_topic_score_codex":0.0024551537,"about_ca_topic_score_gemma":0.001846394,"teacher_disagreement_score":0.0024551537,"about_ca_system_score_codex":0.0005630283,"about_ca_system_score_gemma":0.00077497994,"threshold_uncertainty_score":0.0069466233},"labels":[],"label_agreement":null},{"id":"W2771558206","doi":"10.1007/s10827-017-0663-7","title":"New class of reduced computationally efficient neuronal models for large-scale simulations of brain dynamics","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":39,"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; Institut Universitaire en Santé Mentale de Québec","funders":"Office of Naval Research; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Neuroscience; Oscillation (cell signaling); Network dynamics; Memory consolidation; Electroencephalography; Physics; Nerve net; Computer science; Slow-wave sleep; Psychology; Biology; Mathematics; Hippocampus","score_opus":0.04037556942916797,"score_gpt":0.31164658281787383,"score_spread":0.27127101338870585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771558206","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":[0.036909513,0.0007400885,0.94362116,0.00071972224,0.00045656017,0.00013024763,0.0005751066,0.0009412886,0.015906272],"genre_scores_gemma":[0.6334179,0.0013470111,0.33578026,0.0007780164,0.00063220866,0.0011647186,0.0020967366,0.0015617678,0.023221316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971765,0.00008120199,0.000015807,0.00003086975,0.00011672954,0.000037788843],"domain_scores_gemma":[0.9992861,0.00026570124,0.000060965584,0.00016497451,0.00014002046,0.000082224265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006219616,0.0010062011,0.0018572472,0.0008379854,0.00066335587,0.0011645594,0.0030076671,0.001660645,0.0045496756],"category_scores_gemma":[0.0029490034,0.0005614314,0.001549427,0.00057133747,0.0006370907,0.0014069685,0.0013570086,0.0023029384,0.0010610295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009396848,0.000163051,0.0005444221,0.00011558277,0.00012946731,0.00012071017,0.000053694934,0.8605011,0.0045713154,0.11003966,0.00568437,0.017982578],"study_design_scores_gemma":[0.000009727843,0.0000066607545,0.00003726475,0.0000020813766,0.0000054380657,0.000009023361,0.0000020091468,0.9900123,0.00012808738,0.009026525,0.000757017,0.000003768649],"about_ca_topic_score_codex":0.0036794387,"about_ca_topic_score_gemma":0.0057393904,"teacher_disagreement_score":0.0045496756,"about_ca_system_score_codex":0.0007315787,"about_ca_system_score_gemma":0.0009719687,"threshold_uncertainty_score":0.015220225},"labels":[],"label_agreement":null},{"id":"W2897659848","doi":"10.1007/s10827-018-0697-5","title":"A numerical simulation of neural fields on curved geometries","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"Trent University; Nottingham Trent University","keywords":"Geodesic; Computer science; Neuroimaging; Collocation (remote sensing); Artificial neural network; Partial differential equation; Algorithm; Mathematics; Mathematical analysis; Artificial intelligence; Neuroscience; Machine learning","score_opus":0.07208191479361976,"score_gpt":0.33350791787193346,"score_spread":0.2614260030783137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897659848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45269912,0.00032813847,0.5185221,0.0011241761,0.00015447423,0.00016556379,0.0007991065,0.00080341625,0.02540388],"genre_scores_gemma":[0.85804874,0.0002017808,0.13656282,0.00010303852,0.000020117639,0.00023559763,0.00034465577,0.00008302323,0.004400249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999874,0.000037570724,0.000007802952,0.000023076911,0.000038022477,0.00001959962],"domain_scores_gemma":[0.9994734,0.0003389924,0.00004353095,0.00005282125,0.0000617248,0.000029569796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029054424,0.00032033003,0.00029719275,0.0003472579,0.00036597587,0.00063929317,0.00052851945,0.001424477,0.002254199],"category_scores_gemma":[0.0022433354,0.0002508632,0.00043902942,0.00040842762,0.000789726,0.00045129212,0.0006497118,0.00051669247,0.0002476167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022054297,0.00001636782,0.0004890512,0.000025703679,0.0000073568917,0.00009114362,0.000082334416,0.98559266,0.0033364303,0.0074179033,0.000249507,0.0026693884],"study_design_scores_gemma":[0.0000055335818,0.000007410183,0.00011447443,0.0000032962269,9.261143e-7,0.000011747058,0.0000097993125,0.99776816,0.0003799072,0.001360169,0.00033508855,0.0000034759194],"about_ca_topic_score_codex":0.007331297,"about_ca_topic_score_gemma":0.003551333,"teacher_disagreement_score":0.007331297,"about_ca_system_score_codex":0.0004636525,"about_ca_system_score_gemma":0.0007744188,"threshold_uncertainty_score":0.01457727},"labels":[],"label_agreement":null},{"id":"W2913597122","doi":"10.1007/s10827-019-00711-x","title":"Outgrowing seizures in Childhood Absence Epilepsy: time delays and bistability","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","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 Waterloo; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GABAA receptor; Neuroscience; Epilepsy; Bistability; Reticular connective tissue; Thalamus; Neuron; Biology; Psychology; Physics; Medicine; Receptor; Internal medicine; Anatomy","score_opus":0.024160722678273204,"score_gpt":0.31393513725426325,"score_spread":0.28977441457599007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913597122","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":[0.9898352,0.00027529293,0.008015111,0.00012740704,0.000011590793,0.000004490188,0.0001025465,0.000049810682,0.0015785202],"genre_scores_gemma":[0.9994665,0.00003875656,0.0003425783,0.0000046106115,0.000002702927,0.0000011785071,0.000020881853,0.000005087955,0.00011757637],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999373,0.000008101085,0.0000051651855,0.000011995177,0.000014578519,0.000022918395],"domain_scores_gemma":[0.999315,0.00034724848,0.00017847208,0.000036964175,0.000031620362,0.000090767295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013925764,0.00020521754,0.00018941323,0.00036024285,0.00010350847,0.00042647144,0.0001474932,0.00021612884,0.0012016944],"category_scores_gemma":[0.0021526846,0.00010119939,0.00012124568,0.00018371064,0.00024444575,0.00045325654,0.00033797653,0.00028491236,0.000067769804],"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":[0.0024703618,0.00022344032,0.1533996,0.00023173638,0.00012675831,0.0037613867,0.00044414552,0.1525422,0.5211537,0.032882713,0.0008790862,0.13188474],"study_design_scores_gemma":[0.00014293363,0.00048911903,0.33317304,0.0000507509,0.000088392626,0.0062882514,0.00046767993,0.52209336,0.067466766,0.06797281,0.0016936621,0.000073248964],"about_ca_topic_score_codex":0.001359393,"about_ca_topic_score_gemma":0.0017224343,"teacher_disagreement_score":0.001359393,"about_ca_system_score_codex":0.0003031358,"about_ca_system_score_gemma":0.00022801395,"threshold_uncertainty_score":0.0040200353},"labels":[],"label_agreement":null},{"id":"W2961010205","doi":"10.1007/s10827-019-00721-9","title":"Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hôpital du Sacré-Cœur de Montréal; Concordia University; École de Technologie Supérieure; McGill University; Canadian Sleep & Circadian Network; Montreal Neurological Institute and Hospital","funders":"Agence Nationale de la Recherche","keywords":"Electroencephalography; Scaling; Coherence (philosophical gambling strategy); Magnetoencephalography; Amplitude; Neuroimaging; Local field potential; Logarithm","score_opus":0.03966869608664201,"score_gpt":0.2902784136519733,"score_spread":0.2506097175653313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961010205","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":[0.9921002,0.000053025855,0.005801581,0.0001870614,0.000007844411,0.000021817168,0.00014906612,0.000034787114,0.0016445855],"genre_scores_gemma":[0.9985061,0.00002471287,0.001082718,0.000019082316,0.00000401391,0.000014066123,0.00007080723,0.00001248064,0.0002658605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998797,0.000053810134,0.000006178686,0.000026410366,0.000013090886,0.000020768834],"domain_scores_gemma":[0.9954804,0.0038601444,0.00017887178,0.00020688256,0.00016813324,0.000105523584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074267347,0.00033349966,0.00038601796,0.0005374144,0.0003520528,0.00067361054,0.0006940693,0.0012338773,0.0021623096],"category_scores_gemma":[0.006677443,0.00033270384,0.0007439421,0.00042968066,0.0007520624,0.00096928875,0.00032004248,0.00069747784,0.00014807124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004965488,0.0004140231,0.013472272,0.00008084335,0.00014821073,0.00070812664,0.00026054177,0.96848965,0.0058675515,0.006674425,0.0009017269,0.0024861319],"study_design_scores_gemma":[0.00007266213,0.00006922329,0.0037281842,0.000004464772,0.000024406965,0.000056036497,0.00003340579,0.99387497,0.00028878218,0.0017606892,0.00007613031,0.0000110969995],"about_ca_topic_score_codex":0.009494538,"about_ca_topic_score_gemma":0.0050293184,"teacher_disagreement_score":0.009494538,"about_ca_system_score_codex":0.0004812729,"about_ca_system_score_gemma":0.00031095775,"threshold_uncertainty_score":0.01887858},"labels":[],"label_agreement":null},{"id":"W3022973983","doi":"10.1007/s10827-020-00744-7","title":"Ring models of binocular rivalry and fusion","year":2020,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","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":"McGill University","funders":"","keywords":"Binocular rivalry; Ocular dominance column; Visual cortex; Rivalry; Computer science; Monocular; Neuroscience; Binocular vision; Visual system; Visual perception; Artificial intelligence; Psychology; Perception; Ocular dominance","score_opus":0.1174292081212723,"score_gpt":0.32706761790969585,"score_spread":0.20963840978842355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022973983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23924968,0.0019307577,0.6733194,0.0029261564,0.00027340496,0.00008113435,0.0004896085,0.0008165509,0.08091335],"genre_scores_gemma":[0.9677759,0.0004598355,0.015988065,0.00013126661,0.00008154479,0.000055003537,0.000089396795,0.000144401,0.015274559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999539,0.0002048147,0.000020771819,0.00005419834,0.000076120275,0.00010515363],"domain_scores_gemma":[0.99794275,0.001137053,0.0001904991,0.00028080656,0.0002261014,0.00022283137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013329211,0.0005472401,0.001334061,0.0009894543,0.000767661,0.0018230001,0.0019501612,0.002183509,0.010085372],"category_scores_gemma":[0.0050889086,0.0005062565,0.0011307703,0.0007345277,0.0016437545,0.004586604,0.002063803,0.0012950709,0.0010809483],"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":[0.00010547897,0.000020613912,0.00019066797,0.000035269353,0.000020009893,0.000082260936,0.000099169905,0.12581067,0.0012208493,0.8679842,0.0013823137,0.003048512],"study_design_scores_gemma":[0.000020546326,0.000016930451,0.000118781245,0.0000052353894,0.0000074562186,0.000035371533,0.000028134376,0.80220807,0.00019961881,0.19684252,0.00050305895,0.000014232212],"about_ca_topic_score_codex":0.0038507388,"about_ca_topic_score_gemma":0.0019837788,"teacher_disagreement_score":0.010085372,"about_ca_system_score_codex":0.001131198,"about_ca_system_score_gemma":0.00068849,"threshold_uncertainty_score":0.03373891},"labels":[],"label_agreement":null},{"id":"W3081913253","doi":"10.1007/s10827-012-0387-7","title":"Coupled left-shift of Nav channels: modeling the Na+-loading and dysfunctional excitability of damaged axons","year":2012,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Ion channel regulation and function","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Axon; Biophysics; Node of Ranvier; Chemistry; Neuroscience; CLs upper limits; Sodium channel; Gating; Myelin; Medicine; Biology; Central nervous system; Sodium","score_opus":0.03141585383121796,"score_gpt":0.27209627396519165,"score_spread":0.24068042013397367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081913253","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":[0.89083517,0.00026761915,0.09617212,0.0006845969,0.00008190024,0.000038859176,0.00019789801,0.00027382243,0.0114480145],"genre_scores_gemma":[0.99506384,0.00007595087,0.0027547975,0.00006439839,0.000012239287,0.000020165398,0.000031853255,0.000045669392,0.0019311385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998956,0.000025048572,0.0000049064215,0.000021751746,0.000018971523,0.00003380949],"domain_scores_gemma":[0.99976534,0.00005459698,0.00003478232,0.00002982333,0.000044105625,0.00007133683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029992528,0.00046785196,0.0005830803,0.00031495938,0.00038542468,0.0005733988,0.0014775222,0.0020128253,0.0018144465],"category_scores_gemma":[0.0009559605,0.00034493918,0.0008570975,0.00022360306,0.0009377885,0.0008864305,0.00069759483,0.00063474796,0.00022156422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005701954,0.000041977146,0.00062220194,0.000019086281,0.000018127523,0.00017658198,0.000046307472,0.9836971,0.006700095,0.0071587902,0.00020576104,0.0012569916],"study_design_scores_gemma":[0.000004322494,0.0000047279254,0.00009286154,8.3304553e-7,0.0000026542514,0.000008835726,0.000004763554,0.99875534,0.000168358,0.00091771496,0.000037340084,0.000002214806],"about_ca_topic_score_codex":0.015415879,"about_ca_topic_score_gemma":0.011302963,"teacher_disagreement_score":0.015415879,"about_ca_system_score_codex":0.00092563353,"about_ca_system_score_gemma":0.0009590225,"threshold_uncertainty_score":0.030652285},"labels":[],"label_agreement":null},{"id":"W3101936733","doi":"10.1007/s10827-020-00760-7","title":"Frontal eye field inactivation alters the readout of superior colliculus activity for saccade generation in a task-dependent manner","year":2020,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Vestibular and auditory disorders","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Western University; Robarts Clinical Trials; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Superior colliculus; Saccade; Neuroscience; Brainstem; Saccadic masking; Eye movement; Inferior colliculus; Context (archaeology); Bursting; Computer science; Psychology; Biology; Nucleus","score_opus":0.04468657941661293,"score_gpt":0.29480141568490525,"score_spread":0.25011483626829234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101936733","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":[0.9885316,0.00020179948,0.008148722,0.00015520904,0.00010141727,0.000025429821,0.0010579518,0.0002741198,0.0015036426],"genre_scores_gemma":[0.9924016,0.00020830262,0.0026188272,0.00014525511,0.000016527723,0.000036941718,0.0006349476,0.00021641089,0.003721126],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998109,0.000013799736,0.000017201783,0.00006553992,0.000041970998,0.00005064552],"domain_scores_gemma":[0.9994802,0.00012560101,0.00017411535,0.000065144544,0.00005757139,0.000097481236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002468626,0.0006383802,0.00028026977,0.00042931954,0.00016537406,0.0006027535,0.00043620818,0.00047136215,0.0040650186],"category_scores_gemma":[0.00067418633,0.0002752311,0.0002979559,0.00013584191,0.00058504124,0.00039689176,0.00032112963,0.00082045497,0.00056086923],"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":[0.00011640951,0.000020462277,0.00019256503,0.0000072679654,0.000004480936,0.000019383626,0.0000070242622,0.000039609058,0.9988373,0.000057741145,0.000034845234,0.00066281384],"study_design_scores_gemma":[0.000046718855,0.0002487848,0.033000775,0.000014005422,0.000046356923,0.0001958574,0.000049604783,0.0031943296,0.9619194,0.00019701436,0.001070906,0.000016318409],"about_ca_topic_score_codex":0.0035486745,"about_ca_topic_score_gemma":0.0066966717,"teacher_disagreement_score":0.0040650186,"about_ca_system_score_codex":0.00041621603,"about_ca_system_score_gemma":0.00058364356,"threshold_uncertainty_score":0.0135988},"labels":[],"label_agreement":null},{"id":"W3133984305","doi":"10.1007/s10827-021-00784-7","title":"The unknown but knowable relationship between Presaccadic Accumulation of activity and Saccade initiation","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":13,"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; York University","funders":"National Eye Institute; National Institute of Mental Health; Canadian Institutes of Health Research","keywords":"Saccade; Neuroscience; Brainstem; Gaze; Psychology; Computer science; Eye movement; Artificial intelligence","score_opus":0.1431403461423382,"score_gpt":0.3530376391504902,"score_spread":0.209897293008152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133984305","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":[0.89803404,0.0018449556,0.09028628,0.0006442104,0.00015200545,0.0000371791,0.0011267059,0.00070862944,0.00716595],"genre_scores_gemma":[0.9949527,0.000224754,0.003628714,0.00003122098,0.000029817933,0.000010832347,0.00018084343,0.00006879279,0.00087242795],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979645,0.00003202518,0.000018606108,0.00008605977,0.000035564517,0.000031346244],"domain_scores_gemma":[0.99806815,0.0010761785,0.00036987534,0.00017781697,0.00018327752,0.00012474244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000320764,0.00023821443,0.0002796905,0.00032170847,0.00023774,0.0010875547,0.00035125285,0.00050535874,0.0034864927],"category_scores_gemma":[0.005953552,0.000304769,0.00023207598,0.0002920776,0.00027108146,0.0011136507,0.00041904647,0.0006437126,0.00050511316],"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":[0.0014013281,0.00034076866,0.22114272,0.0006597646,0.0006623889,0.0008132276,0.00051945954,0.021431724,0.59012926,0.01654718,0.0021103118,0.14424184],"study_design_scores_gemma":[0.000042956606,0.0002811244,0.8104534,0.00006562606,0.0001392689,0.0007944007,0.00018366199,0.119225934,0.051059768,0.01612183,0.0015593905,0.00007261642],"about_ca_topic_score_codex":0.0011667685,"about_ca_topic_score_gemma":0.001753101,"teacher_disagreement_score":0.0034864927,"about_ca_system_score_codex":0.00018803196,"about_ca_system_score_gemma":0.0004582249,"threshold_uncertainty_score":0.011663437},"labels":[],"label_agreement":null},{"id":"W3165747128","doi":"10.1007/s10827-021-00791-8","title":"Legacy of Lance M Optican: from math to medical science and back","year":2021,"lang":"en","type":"editorial","venue":"Journal of Computational Neuroscience","topic":"History and Developments in Astronomy","field":"Physics and Astronomy","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":"Theory of computation; Mathematics education; Cognitive science; Mathematics; Psychology; Algorithm","score_opus":0.010078932349845518,"score_gpt":0.28316911949282425,"score_spread":0.2730901871429787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165747128","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":[0.000016575901,0.011396869,0.00014338839,0.05648651,0.9310366,0.0000054314614,0.00002406564,0.000028256965,0.0008623281],"genre_scores_gemma":[0.00032057014,0.004565348,0.000090230584,0.016485823,0.973525,0.00001072016,0.0000085323445,0.000044254226,0.004949541],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99394625,0.0013805663,0.0005785212,0.0007303533,0.003004801,0.00035938193],"domain_scores_gemma":[0.9618093,0.016646313,0.0016030382,0.000995462,0.013242501,0.0057034832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011419098,0.0032755707,0.0031860403,0.005540267,0.0037328233,0.011708432,0.0026872186,0.014448071,0.012479594],"category_scores_gemma":[0.044284604,0.0010526954,0.0019984362,0.001856618,0.0046387296,0.0062568765,0.0029739987,0.021525353,0.007708847],"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":[0.000011989293,0.0000027348804,0.0000073044716,0.00008690749,0.0000071856803,0.00004019978,0.000014414577,0.00001069769,0.000016764745,0.000464034,0.9962968,0.0030408697],"study_design_scores_gemma":[0.00003357444,0.000008730821,0.00010988171,0.00045935283,0.000024958235,0.00018551057,0.00004529622,0.0000798435,0.000054860564,0.0028428575,0.9961337,0.000021566824],"about_ca_topic_score_codex":0.0030831557,"about_ca_topic_score_gemma":0.010275476,"teacher_disagreement_score":0.014448071,"about_ca_system_score_codex":0.004404033,"about_ca_system_score_gemma":0.0059815403,"threshold_uncertainty_score":0.06039071},"labels":[],"label_agreement":null},{"id":"W3202066411","doi":"10.1007/s10827-021-00798-1","title":"Active sensing in a dynamic olfactory world","year":2021,"lang":"en","type":"editorial","venue":"Journal of Computational Neuroscience","topic":"Insect Pheromone Research and Control","field":"Agricultural and Biological Sciences","cited_by":41,"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":"Medical Research Council; National Science Foundation of Sri Lanka; Canadian Institutes of Health Research; UK Research and Innovation; Deutsche Forschungsgemeinschaft; Wellcome Trust; Francis Crick Institute; National Science Foundation","keywords":"Olfaction; Neuroscience; Odor; Context (archaeology); Computer science; Olfactory system; Cognitive science; Perspective (graphical); Psychology; Artificial intelligence; Biology","score_opus":0.01830103149235222,"score_gpt":0.269324203827774,"score_spread":0.25102317233542176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202066411","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":[0.000055892964,0.0058807586,0.0004946931,0.03221013,0.9599646,0.000010597881,0.000045163724,0.000055426433,0.0012827606],"genre_scores_gemma":[0.0008098993,0.003401857,0.00026445853,0.009583358,0.9789514,0.000015611784,0.000027524615,0.000038092836,0.006907846],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99646384,0.00066433,0.00040977023,0.00042592542,0.0018287362,0.00020733097],"domain_scores_gemma":[0.9824239,0.008995718,0.0006415066,0.00048690237,0.005128352,0.0023236542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075356564,0.0034263867,0.002682155,0.0023388525,0.0025734885,0.007382802,0.0036612134,0.014505358,0.009409211],"category_scores_gemma":[0.016087946,0.0013985691,0.002159695,0.0010065692,0.0033505221,0.0048576295,0.0019598652,0.020467244,0.005084804],"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":[0.000053868396,0.000009002552,0.00002005993,0.0001848456,0.000030231871,0.00010814253,0.000009893652,0.00007839517,0.00009148462,0.00095963053,0.9925867,0.00586771],"study_design_scores_gemma":[0.00012791954,0.000034680248,0.00030738846,0.00035468722,0.00011606722,0.00034033952,0.000034670593,0.0011687645,0.00025838657,0.0066845957,0.9905335,0.000039059116],"about_ca_topic_score_codex":0.0018882927,"about_ca_topic_score_gemma":0.006097071,"teacher_disagreement_score":0.014505358,"about_ca_system_score_codex":0.002904214,"about_ca_system_score_gemma":0.00241494,"threshold_uncertainty_score":0.039852798},"labels":[],"label_agreement":null},{"id":"W3207087726","doi":"10.1007/s10827-021-00799-0","title":"Fast-slow analysis as a technique for understanding the neuronal response to current ramps","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"National Institute on Deafness and Other Communication Disorders; National Institute on Drug Abuse; Foundation for the National Institutes of Health; National Science Foundation","keywords":"Current (fluid); Neuroscience; Theory of computation; Computer science; Psychology; Physics; Algorithm","score_opus":0.06885374689320604,"score_gpt":0.33646033854295027,"score_spread":0.2676065916497442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207087726","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":[0.012045844,0.00013742309,0.98428,0.000098199394,0.00005874041,0.000028885192,0.00010488711,0.0014195091,0.0018264903],"genre_scores_gemma":[0.42924055,0.0007789368,0.56151104,0.00018382796,0.00016651241,0.00021374032,0.00028151608,0.0016951642,0.00592873],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999199,0.000014097611,0.0000057449834,0.000016471171,0.000028864004,0.00001498279],"domain_scores_gemma":[0.9994312,0.0002785195,0.00004771045,0.00010667722,0.00008976818,0.000046198744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048613673,0.0009776792,0.0004113745,0.0012502719,0.0006058808,0.0009484808,0.0007317007,0.00059592456,0.0054546394],"category_scores_gemma":[0.0017092297,0.0003593505,0.00060809444,0.0005569901,0.00059859833,0.0015609381,0.00069711835,0.0016282713,0.0009785388],"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":[0.0005514424,0.00014171215,0.0016385736,0.00036761232,0.00014711988,0.00048109933,0.00045956075,0.05214019,0.64618874,0.095268235,0.004677291,0.19793835],"study_design_scores_gemma":[0.00003102352,0.00009119998,0.0021768839,0.000024458843,0.00006238646,0.0003688499,0.00010076114,0.85437447,0.057564825,0.07613651,0.009013748,0.000054941414],"about_ca_topic_score_codex":0.0020446982,"about_ca_topic_score_gemma":0.0022359628,"teacher_disagreement_score":0.0054546394,"about_ca_system_score_codex":0.0002474654,"about_ca_system_score_gemma":0.00059683656,"threshold_uncertainty_score":0.018247545},"labels":[],"label_agreement":null},{"id":"W4226382742","doi":"10.1007/s10827-022-00825-9","title":"Exact mean-field models for spiking neural networks with adaptation","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"Moment closure; Artificial neural network; Ansatz; Statistical physics; Bursting; Mean field theory; Spike (software development); Computer science; Physics; Artificial intelligence; Neuroscience","score_opus":0.04896618141639022,"score_gpt":0.2678971265825815,"score_spread":0.2189309451661913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226382742","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":[0.046985924,0.0012541068,0.94141346,0.0015503862,0.00023620318,0.00004770168,0.0002329758,0.00035848,0.0079207085],"genre_scores_gemma":[0.9240383,0.0010502912,0.053567033,0.0005853039,0.0002463904,0.00022082882,0.00027590472,0.00029909497,0.019716945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944216,0.00023440624,0.000030701816,0.00010495059,0.000100200625,0.00008747509],"domain_scores_gemma":[0.99744874,0.0016069948,0.0002214338,0.0002277665,0.000307024,0.00018810111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002173192,0.0009661921,0.0023785888,0.00095073227,0.0007239249,0.0016237297,0.0032386251,0.0039727394,0.0030250638],"category_scores_gemma":[0.0086836,0.0010802874,0.0014139754,0.0010830592,0.0023382585,0.0033175712,0.0019372893,0.0025908793,0.0005017333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000310377,0.000020298608,0.00018009392,0.000034486544,0.000031231946,0.00004398198,0.000038851296,0.8949355,0.00043463754,0.10071461,0.0006956429,0.0028395604],"study_design_scores_gemma":[0.0000035727858,0.0000028556267,0.000025624797,0.0000027596873,0.0000028491957,0.0000065436175,0.000002655469,0.971809,0.000029482233,0.028015882,0.00009367228,0.0000050408003],"about_ca_topic_score_codex":0.011050398,"about_ca_topic_score_gemma":0.009195878,"teacher_disagreement_score":0.011050398,"about_ca_system_score_codex":0.0020308297,"about_ca_system_score_gemma":0.0013934107,"threshold_uncertainty_score":0.02197218},"labels":[],"label_agreement":null},{"id":"W4313453474","doi":"10.1007/s10827-022-00841-9","title":"31st Annual Computational Neuroscience Meeting: CNS*2022","year":2023,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","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":"York University","funders":"Agencia Estatal de Investigación","keywords":"Neuroscience; Computational neuroscience; Theory of computation; Cognitive science; Cognitive neuroscience; Psychology; Computer science; Cognition","score_opus":0.013976664763214387,"score_gpt":0.297714940832495,"score_spread":0.2837382760692806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313453474","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":[0.015344678,0.023129791,0.18312149,0.07329318,0.14526096,0.00087721896,0.015906887,0.017565586,0.5255002],"genre_scores_gemma":[0.030135037,0.010285147,0.048147164,0.0035680027,0.011970737,0.0007867682,0.021265222,0.0040126,0.8698294],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950457,0.000108396096,0.000027972546,0.00010700397,0.00018509515,0.00006695256],"domain_scores_gemma":[0.9982413,0.00019562084,0.00002955628,0.00017086405,0.00083261327,0.00053008477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017569059,0.0021286984,0.00094526104,0.001144154,0.00087612914,0.0024973894,0.0014973611,0.0018258457,0.21653762],"category_scores_gemma":[0.0029962354,0.00048607812,0.000578413,0.0008025855,0.0005427641,0.0017544675,0.0027407347,0.001991666,0.114921585],"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":[0.00013167191,0.000028144748,0.00013511747,0.00007170835,0.000016840315,0.00005603185,0.000010557894,0.0008669445,0.00099016,0.0016273652,0.93075293,0.06531251],"study_design_scores_gemma":[0.00007144139,0.000069073794,0.00068992795,0.00015844178,0.000024206984,0.00012517112,0.000039209084,0.018462004,0.0011105613,0.0062977346,0.97292715,0.000025073818],"about_ca_topic_score_codex":0.0032561957,"about_ca_topic_score_gemma":0.011135667,"teacher_disagreement_score":0.21653762,"about_ca_system_score_codex":0.00077212567,"about_ca_system_score_gemma":0.0017289831,"threshold_uncertainty_score":0.7243905},"labels":[],"label_agreement":null},{"id":"W4322720140","doi":"10.1007/s10827-023-00845-z","title":"Adaptive unscented Kalman filter for neuronal state and parameter estimation","year":2023,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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 Toronto; University Health Network","funders":"","keywords":"Kalman filter; Ensemble Kalman filter; Computer science; Extended Kalman filter; Control theory (sociology); Invariant extended Kalman filter; Robustness (evolution); Unscented transform; Alpha beta filter; Adaptive filter; Fast Kalman filter; Kernel adaptive filter; Residual; Benchmark (surveying); Covariance; Filter (signal processing); Algorithm; Mathematics; Filter design; Artificial intelligence; Moving horizon estimation; Statistics; Computer vision","score_opus":0.05339354321204616,"score_gpt":0.29773682576778077,"score_spread":0.24434328255573462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322720140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002314149,0.00013685293,0.99695766,0.00004992754,0.000037615828,0.000007795723,0.000039197956,0.00020499282,0.0002517663],"genre_scores_gemma":[0.3908189,0.0006688661,0.59904367,0.00017398955,0.00014442574,0.00025127572,0.00071551895,0.00019075676,0.007992532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952674,0.00010412917,0.000038377988,0.00013596659,0.00014308124,0.000051661704],"domain_scores_gemma":[0.99860626,0.0006503078,0.00010603457,0.00014676501,0.0004596984,0.0000309313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008978837,0.00054453884,0.0010283127,0.0004330608,0.00046943585,0.00072494906,0.001240813,0.0011240939,0.0021948447],"category_scores_gemma":[0.005448745,0.0005886225,0.0006921666,0.0007385422,0.00056192896,0.0010973796,0.0009995693,0.0017953739,0.0009003815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001996787,0.0000636254,0.0009408728,0.0001531562,0.00013415296,0.00006159009,0.00014974267,0.75561225,0.008872527,0.018761482,0.0026208737,0.21242997],"study_design_scores_gemma":[0.000006271839,0.0000107066635,0.00017276713,0.0000054456964,0.000007705376,0.000009885873,0.0000039181896,0.99571747,0.00087912055,0.0024978565,0.0006808663,0.000008004273],"about_ca_topic_score_codex":0.026402306,"about_ca_topic_score_gemma":0.02523661,"teacher_disagreement_score":0.026402306,"about_ca_system_score_codex":0.00080505345,"about_ca_system_score_gemma":0.0023371642,"threshold_uncertainty_score":0.052497208},"labels":[],"label_agreement":null},{"id":"W4405673820","doi":"10.1007/s10827-024-00887-x","title":"Modelling the effect of allopregnanolone on the resolution of spike-wave discharges","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","cited_by":2,"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","keywords":"Allopregnanolone; Neuroscience; Childhood absence epilepsy; Afterhyperpolarization; GABAA receptor; Psychology; Epilepsy; Neuroactive steroid; Juvenile myoclonic epilepsy; Medicine; Internal medicine; Receptor; Electrophysiology","score_opus":0.08974441314179153,"score_gpt":0.3496161124794093,"score_spread":0.2598716993376178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405673820","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":[0.910035,0.0005367554,0.07400835,0.0006666957,0.000093874514,0.000036067635,0.0006304123,0.00038236732,0.01361061],"genre_scores_gemma":[0.9953295,0.00008561826,0.0032023902,0.000023595328,0.000005809927,0.000012695846,0.00009560119,0.000035356927,0.0012094942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999275,0.000015223293,0.0000040249092,0.000011988747,0.000016320144,0.000024873952],"domain_scores_gemma":[0.99926573,0.0005280005,0.00006135583,0.000026234942,0.00007619198,0.000042481057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019508656,0.00035593894,0.000364322,0.00025231758,0.00021933368,0.0005598163,0.00084660685,0.0012758899,0.0025648372],"category_scores_gemma":[0.002247271,0.00022839254,0.0004917954,0.0002608541,0.00031592898,0.0004338676,0.00027997143,0.0006763145,0.00017922917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057021232,0.000021469763,0.0005583284,0.000038369122,0.000012320749,0.00005779327,0.000023304498,0.9924378,0.003534138,0.0015563078,0.00014675042,0.001556399],"study_design_scores_gemma":[0.0000044454177,0.0000073175984,0.00018908925,0.000002289117,0.000003051229,0.0000067274473,0.0000039359397,0.99895954,0.00048024027,0.00027131208,0.000069475995,0.0000025513966],"about_ca_topic_score_codex":0.020308105,"about_ca_topic_score_gemma":0.011407552,"teacher_disagreement_score":0.020308105,"about_ca_system_score_codex":0.00055166805,"about_ca_system_score_gemma":0.0007745104,"threshold_uncertainty_score":0.040379822},"labels":[],"label_agreement":null},{"id":"W4408039506","doi":"10.1007/s10827-025-00895-5","title":"Network effects of traumatic brain injury: from infra slow to high frequency oscillations and seizures","year":2025,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","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":"Université Laval","funders":"National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research; U.S. Department of Veterans Affairs; National Institutes of Health; National Science Foundation","keywords":"Traumatic brain injury; Neuroscience; Epilepsy; Homeostatic plasticity; Neuroplasticity; Nerve net; Biological neural network; Electroencephalography; Psychology; Synaptic plasticity; Medicine; Physics; Psychiatry; Internal medicine; Receptor; Metaplasticity","score_opus":0.014836842993108655,"score_gpt":0.2883452193965924,"score_spread":0.27350837640348374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408039506","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":[0.84111583,0.00092914887,0.15038772,0.00064654567,0.000042121912,0.00004863897,0.0002587949,0.00013603008,0.0064352374],"genre_scores_gemma":[0.9941128,0.00055944733,0.004271366,0.0000350746,0.000012248282,0.00002632584,0.00006439183,0.000013801695,0.00090466917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999961,0.0000092795635,0.000002430506,0.000010008419,0.000010194008,0.0000070098145],"domain_scores_gemma":[0.99990225,0.000028983814,0.000033661916,0.000011359852,0.000013371512,0.000010408979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008918046,0.00030648988,0.00015676711,0.00018668747,0.00012565524,0.00026790702,0.00024461898,0.00023317877,0.0006480625],"category_scores_gemma":[0.00049861457,0.00010984912,0.00029206314,0.0001614862,0.0003967764,0.00048656395,0.0003004785,0.00024843015,0.000050230206],"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":[0.0002041667,0.00010841722,0.015026914,0.00016950925,0.00009804017,0.0006893473,0.00019205715,0.8696182,0.064936385,0.02666778,0.00070483715,0.021584373],"study_design_scores_gemma":[0.0000121727735,0.00010729499,0.01062978,0.000014935659,0.000028055367,0.00024784467,0.00006265639,0.9703249,0.003008365,0.014812429,0.0007366787,0.000014853699],"about_ca_topic_score_codex":0.0022606624,"about_ca_topic_score_gemma":0.0018313208,"teacher_disagreement_score":0.0022606624,"about_ca_system_score_codex":0.0002182845,"about_ca_system_score_gemma":0.00022120221,"threshold_uncertainty_score":0.0044950247},"labels":[],"label_agreement":null},{"id":"W94795508","doi":"10.1007/s10827-010-0307-7","title":"Automatic classification and robust identification of vestibulo-ocular reflex responses: from theory to practice","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Vestibular and auditory disorders","field":"Neuroscience","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":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Reflex; Vestibulo–ocular reflex; Computer science; A priori and a posteriori; Identification (biology); Nonlinear system; Nystagmus; Algorithm; Least-squares function approximation; Artificial intelligence; Control theory (sociology); Pattern recognition (psychology); Eye movement; Mathematics; Psychology; Audiology; Statistics; Neuroscience; Medicine; Control (management); Physics","score_opus":0.0762994923599674,"score_gpt":0.32335403440543437,"score_spread":0.24705454204546695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W94795508","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":[0.019984454,0.00041957665,0.97782487,0.00034448985,0.000044564888,0.000030251349,0.000028382492,0.0004885106,0.00083499064],"genre_scores_gemma":[0.5919883,0.00047371705,0.40499276,0.00019091943,0.00013272882,0.00008643899,0.00013903534,0.00014998276,0.0018461493],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99903226,0.0002981987,0.00006687933,0.00027924543,0.00024866674,0.00007467833],"domain_scores_gemma":[0.99506515,0.0030021335,0.00032730517,0.00083818485,0.0006663494,0.00010095846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014978845,0.00070989464,0.00082351407,0.000703841,0.0003167786,0.0016522935,0.0012778649,0.0014614809,0.001922384],"category_scores_gemma":[0.009106685,0.000582393,0.00043498923,0.00046543995,0.00082784763,0.001435336,0.0012333356,0.0015437484,0.0010413016],"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":[0.00044478505,0.00027343637,0.0024206485,0.00023448553,0.0001412353,0.00010406394,0.00019860922,0.12818417,0.052243043,0.017335603,0.0025568472,0.79586303],"study_design_scores_gemma":[0.000018843812,0.00006653739,0.0008742351,0.000017401668,0.000016276897,0.000060164686,0.000020781179,0.9790556,0.007979749,0.011023537,0.00085223146,0.000014665963],"about_ca_topic_score_codex":0.0026706096,"about_ca_topic_score_gemma":0.0024893438,"teacher_disagreement_score":0.0026706096,"about_ca_system_score_codex":0.00045410034,"about_ca_system_score_gemma":0.0011658048,"threshold_uncertainty_score":0.007921636},"labels":[],"label_agreement":null}]}