{"meta":{"query_hash":"d1b99ec5682c","filters":{"venue":"Frontiers in Computational Neuroscience"},"cohort_total":123,"direct_labels_cover":0,"predictions_cover":123,"exported":123,"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/d1b99ec5682c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Frontiers+in+Computational+Neuroscience"},"results":[{"id":"W1541048112","doi":"10.3389/fncom.2015.00082","title":"Quantifying effects of stochasticity in reference frame transformations on posterior distributions","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reference frame; Measure (data warehouse); Computer science; Transformation (genetics); Frame (networking); Noise (video); Rotation (mathematics); Distribution (mathematics); Scaling; Empirical distribution function; Statistical physics; Mathematics; Statistics; Artificial intelligence; Data mining; Physics; Mathematical analysis; Geometry","score_opus":0.07540799151512628,"score_gpt":0.3021660221137673,"score_spread":0.22675803059864102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541048112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16959748,0.00019577448,0.82773703,0.0003041749,0.000019729858,0.000033583514,0.000119368386,0.00023114294,0.0017616452],"genre_scores_gemma":[0.9284318,0.00031471974,0.070130505,0.0000843945,0.000031737192,0.000060379978,0.00025336212,0.00018895297,0.00050409953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774903,0.0007770101,0.00015634792,0.00043662766,0.00073109486,0.00014983413],"domain_scores_gemma":[0.966957,0.027268965,0.0017806264,0.0026654177,0.0010052089,0.00032290502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006218695,0.0006583052,0.0006114018,0.00093191024,0.00037620225,0.0015150482,0.0009595504,0.001002832,0.0013342522],"category_scores_gemma":[0.04902359,0.0005556981,0.0005300112,0.0007489288,0.002269836,0.0028780291,0.0019789631,0.0016600997,0.00020047666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002019514,0.000070905335,0.008501499,0.00017001732,0.00011601762,0.00028226583,0.00041285672,0.80916166,0.028545916,0.1065921,0.00038562424,0.045559138],"study_design_scores_gemma":[0.000018005132,0.000100082856,0.009462024,0.000043967157,0.000027216225,0.0002320483,0.00006261431,0.9200848,0.009315515,0.060023442,0.0005714393,0.00005878778],"about_ca_topic_score_codex":0.0018716475,"about_ca_topic_score_gemma":0.0018477893,"teacher_disagreement_score":0.006218695,"about_ca_system_score_codex":0.0010772347,"about_ca_system_score_gemma":0.00093088753,"threshold_uncertainty_score":0.032888055},"labels":[],"label_agreement":null},{"id":"W1549069064","doi":"10.3389/fncom.2014.00020","title":"Muscle synergies evoked by microstimulation are preferentially encoded during behavior","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Emerging Frontiers in Research and Innovation; Canadian Institutes of Health Research; National Science Foundation","keywords":"Microstimulation; Neuroscience; Motor unit; Biology; Stimulation","score_opus":0.007394416643573236,"score_gpt":0.20505309525128482,"score_spread":0.19765867860771158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1549069064","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":[0.9870144,0.00017426099,0.011195509,0.000038626014,0.000009101055,0.000027781056,0.00007668518,0.00008106061,0.0013826382],"genre_scores_gemma":[0.9961377,0.000094020084,0.0033133472,0.000018411372,0.0000044319136,0.0000234518,0.00007196303,0.000019546,0.00031716292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986506,0.000019425754,0.000011113752,0.00003873465,0.000041109382,0.000024560106],"domain_scores_gemma":[0.9996141,0.00013165064,0.00010699774,0.000048150177,0.000042936972,0.000056109046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002016036,0.00022533597,0.00020142969,0.00034819872,0.00012111336,0.00017557206,0.00013051293,0.00020081719,0.0007928219],"category_scores_gemma":[0.0011432345,0.00014251324,0.0001533457,0.00022823231,0.00030564747,0.00023679572,0.00036276004,0.00020080016,0.000092863025],"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":[0.00007089813,0.000011510861,0.0025815326,0.000061144536,0.000014043476,0.000077570636,0.000078671306,0.0003617231,0.99054223,0.00016622474,0.000024349118,0.0060101533],"study_design_scores_gemma":[0.000017635544,0.00054517866,0.7364814,0.00002911608,0.000059380607,0.0010153056,0.00023653776,0.009161471,0.24999116,0.001309159,0.0011252475,0.000028431194],"about_ca_topic_score_codex":0.00035747056,"about_ca_topic_score_gemma":0.0007217753,"teacher_disagreement_score":0.0007928219,"about_ca_system_score_codex":0.00015608681,"about_ca_system_score_gemma":0.00016998031,"threshold_uncertainty_score":0.0026522875},"labels":[],"label_agreement":null},{"id":"W1843757395","doi":"10.3389/fncom.2015.00112","title":"Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences","keywords":"Synapse; Computer science; Nonlinear system; Representation (politics); Complex dynamics; Transmission (telecommunications); Biological system; Neuroscience; Physics; Mathematics; Telecommunications; Biology","score_opus":0.07023290850396992,"score_gpt":0.30490209511965083,"score_spread":0.23466918661568092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843757395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08653271,0.00025810732,0.90101343,0.00038322288,0.000092198265,0.000052099127,0.0001583416,0.0007753495,0.0107345125],"genre_scores_gemma":[0.8761316,0.00041163593,0.1192547,0.00009334681,0.000047039895,0.00016854891,0.00016375424,0.00030847857,0.0034208894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998871,0.000035805737,0.000006958491,0.000013003869,0.00004274414,0.000014327574],"domain_scores_gemma":[0.99956554,0.00024195053,0.000040140727,0.0000711298,0.000048604477,0.00003255901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041673915,0.000497172,0.0005921041,0.0003289391,0.00048516694,0.0006963037,0.00092898746,0.0010789267,0.0014159328],"category_scores_gemma":[0.0021150007,0.00033466602,0.0006356936,0.0003506371,0.0006019667,0.0009949465,0.0006774973,0.0011941134,0.00037387348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011497614,0.0000158808,0.00026011394,0.00002138361,0.000013862025,0.00005547322,0.000036239362,0.96714103,0.004354135,0.025172539,0.00024952844,0.0026682448],"study_design_scores_gemma":[0.0000010622573,0.0000017394748,0.00002146166,0.0000012946196,9.646199e-7,0.000003529365,0.0000015089734,0.997384,0.00018165536,0.0022746227,0.00012675217,0.0000014893564],"about_ca_topic_score_codex":0.004376054,"about_ca_topic_score_gemma":0.0030604692,"teacher_disagreement_score":0.004376054,"about_ca_system_score_codex":0.00055382913,"about_ca_system_score_gemma":0.0006876427,"threshold_uncertainty_score":0.008701146},"labels":[],"label_agreement":null},{"id":"W1915506765","doi":"10.3389/fncom.2015.00121","title":"A model-based approach to predict muscle synergies using optimization: application to feedback control","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Task (project management); Motor control; Control (management); Space (punctuation); Control engineering; Control theory (sociology); Artificial intelligence; Engineering; Systems engineering","score_opus":0.04687904838927408,"score_gpt":0.2600881108697171,"score_spread":0.21320906248044302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1915506765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016439344,0.000093024035,0.9977417,0.0000447815,0.000008749327,0.000009837001,0.000011102151,0.0001509372,0.0002959988],"genre_scores_gemma":[0.43302712,0.00044374226,0.5636539,0.000093617695,0.00007360537,0.0003178592,0.00009904553,0.00020977018,0.0020814291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974185,0.000079461664,0.000017006121,0.00006554022,0.0000722655,0.000023906478],"domain_scores_gemma":[0.9994609,0.00034449066,0.00006137964,0.00003327238,0.000079145546,0.000020920645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081413076,0.00093366427,0.0011994113,0.00069612486,0.00042955927,0.000655512,0.000782722,0.0009685059,0.0016402386],"category_scores_gemma":[0.0021923785,0.0006851919,0.000843213,0.00053074496,0.000530209,0.0008152875,0.0007420127,0.0009655043,0.00031250916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019677285,0.000023533563,0.00015804096,0.000049032675,0.000033029515,0.000031656276,0.00002832883,0.95442843,0.0019379838,0.0058805496,0.00032589422,0.03708387],"study_design_scores_gemma":[0.0000021070757,0.0000079687525,0.0000396717,0.0000025264733,0.0000021160367,0.0000060294938,0.0000010378518,0.9981041,0.00015848325,0.0015172987,0.00015529078,0.0000033354888],"about_ca_topic_score_codex":0.0067064646,"about_ca_topic_score_gemma":0.0043956866,"teacher_disagreement_score":0.0067064646,"about_ca_system_score_codex":0.00055278314,"about_ca_system_score_gemma":0.0010961597,"threshold_uncertainty_score":0.01333487},"labels":[],"label_agreement":null},{"id":"W1928324897","doi":"10.3389/fncom.2015.00077","title":"Editorial: State-dependent brain computation","year":2015,"lang":"en","type":"editorial","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"James S. McDonnell Foundation","keywords":"Computation; State (computer science); Volume (thermodynamics); Computer science; Front (military); Brain size; Neuroscience; Geology; Psychology; Physics; Algorithm; Medicine; Oceanography; Magnetic resonance imaging","score_opus":0.01221894680928565,"score_gpt":0.2672095081521213,"score_spread":0.25499056134283565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1928324897","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":[0.000030981064,0.0054477896,0.0003094296,0.033767086,0.95854646,0.000014655242,0.0000615563,0.00006472286,0.0017572948],"genre_scores_gemma":[0.000616115,0.0028799626,0.00012315797,0.012930994,0.97173786,0.000023629353,0.000042295724,0.00005408803,0.01159197],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963676,0.0005801075,0.00043028765,0.0005917395,0.0017396165,0.00029059712],"domain_scores_gemma":[0.9882287,0.00502722,0.000925131,0.00040035511,0.0037527601,0.0016658761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005621313,0.003940596,0.0034613821,0.0034042154,0.0028263046,0.0075432668,0.0031021982,0.016475607,0.02186931],"category_scores_gemma":[0.021622691,0.0012408993,0.0028826087,0.0011333697,0.002758762,0.004073572,0.0024609366,0.016912138,0.011857713],"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":[0.000028351522,0.0000060270568,0.000012589313,0.00010844695,0.000017264376,0.000087700486,0.0000046364535,0.000052230193,0.000059134578,0.00049133605,0.9956495,0.003482677],"study_design_scores_gemma":[0.00010330771,0.000029614024,0.00025089787,0.0004156646,0.00006573756,0.0002819963,0.000016845472,0.00055276946,0.00022225139,0.004254346,0.99377656,0.000030005425],"about_ca_topic_score_codex":0.0012898522,"about_ca_topic_score_gemma":0.0032895568,"teacher_disagreement_score":0.02186931,"about_ca_system_score_codex":0.0031682146,"about_ca_system_score_gemma":0.0025313052,"threshold_uncertainty_score":0.07316011},"labels":[],"label_agreement":null},{"id":"W1938263175","doi":"10.3389/fncom.2015.00072","title":"A kinematic model for 3-D head-free gaze-shifts","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Saccade; Head (geology); Kinematics; Computer science; Orientation (vector space); Eye movement; Fixation (population genetics); Rotation (mathematics); Computer vision; Artificial intelligence; Physics; Mathematics; Geometry; Geology","score_opus":0.07020397183885306,"score_gpt":0.3021781047203559,"score_spread":0.23197413288150281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1938263175","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":[0.02873863,0.00020497771,0.9562517,0.0002937834,0.000058724687,0.00004911491,0.00039610072,0.0005903465,0.013416551],"genre_scores_gemma":[0.8917051,0.0007833548,0.0852059,0.000149259,0.00006021552,0.00050401513,0.0006038044,0.00015827968,0.02083011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999192,0.000012262853,0.0000057316643,0.000026338892,0.000024416708,0.000012033171],"domain_scores_gemma":[0.9998857,0.000026417474,0.000026864318,0.000015532789,0.000033925113,0.0000116262445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001410163,0.0004660364,0.00038079332,0.00032596898,0.00034498208,0.00073475944,0.0008584813,0.000903991,0.0042323046],"category_scores_gemma":[0.00044051054,0.00032504296,0.0007129554,0.00029498411,0.00050596875,0.0006578749,0.0006388619,0.00053388515,0.0011677475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034764184,0.000017709757,0.00087859953,0.000055896577,0.000021461674,0.00015275116,0.00013142382,0.9409042,0.008663156,0.04036439,0.00076328404,0.00801246],"study_design_scores_gemma":[0.000014355396,0.000029260467,0.0003822446,0.000008341643,0.000010811681,0.00005094767,0.000017207987,0.9898511,0.0005718733,0.0068728835,0.00218054,0.000010420124],"about_ca_topic_score_codex":0.009526458,"about_ca_topic_score_gemma":0.0061482526,"teacher_disagreement_score":0.009526458,"about_ca_system_score_codex":0.00060563715,"about_ca_system_score_gemma":0.0009942221,"threshold_uncertainty_score":0.018941998},"labels":[],"label_agreement":null},{"id":"W1965743050","doi":"10.3389/fncom.2015.00006","title":"Hybrid model of the context dependent vestibulo-ocular reflex: implications for vergence-version interactions","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Vestibular and auditory disorders","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Saccadic masking; Vestibulo–ocular reflex; Computer science; Eye movement; Context (archaeology); Nystagmus; Reflex; Vestibular system; Fixation (population genetics); Neuroscience; Saccade; Psychology; Artificial intelligence; Geology; Medicine","score_opus":0.06646344269240435,"score_gpt":0.3118247617838188,"score_spread":0.24536131909141445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965743050","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4095563,0.0008661617,0.5571518,0.00068698573,0.00013696699,0.00007927184,0.00041270375,0.0003363636,0.030773478],"genre_scores_gemma":[0.98809475,0.000210444,0.0057501663,0.000051855342,0.000020898033,0.000066199886,0.000059822865,0.000032674827,0.0057133213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992895,0.00001433952,0.0000037649381,0.000020014755,0.000018661782,0.000014128062],"domain_scores_gemma":[0.999882,0.00003926018,0.000021823633,0.000010649651,0.000023157168,0.000023187973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012128649,0.00047299714,0.0005082856,0.0002099912,0.00033900805,0.00063533476,0.0009156762,0.00087964017,0.0025180182],"category_scores_gemma":[0.00040457826,0.00022493393,0.000607867,0.00014789909,0.0004785497,0.0007150764,0.0007465699,0.00059033866,0.00025713365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006447617,0.000028895454,0.0009876558,0.000043959193,0.000040219646,0.00022284119,0.00007611929,0.97053266,0.012044412,0.012093007,0.00020259545,0.0036632246],"study_design_scores_gemma":[0.000006645809,0.000020075306,0.00028116364,0.000002059447,0.00000740985,0.000019900623,0.000007870282,0.997242,0.00019106628,0.0020232066,0.00019296946,0.0000057084244],"about_ca_topic_score_codex":0.0071593677,"about_ca_topic_score_gemma":0.005256522,"teacher_disagreement_score":0.0071593677,"about_ca_system_score_codex":0.00050071394,"about_ca_system_score_gemma":0.0004884928,"threshold_uncertainty_score":0.014235437},"labels":[],"label_agreement":null},{"id":"W1976150695","doi":"10.3389/fncom.2014.00107","title":"Differential effects of excitatory and inhibitory heterogeneity on the gain and asynchronous state of sparse cortical networks","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Excitatory postsynaptic potential; Inhibitory postsynaptic potential; Neural coding; Neuroscience; Network dynamics; Synchronization (alternating current); Computer science; Population; Models of neural computation; Asynchronous communication; Biological neural network; Cortical neurons; Artificial neural network; Biological system; Biology; Artificial intelligence; Mathematics","score_opus":0.011703916704771314,"score_gpt":0.21933667846905094,"score_spread":0.20763276176427964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976150695","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":[0.9439244,0.00022576442,0.05103017,0.0003373251,0.000014079963,0.000020978083,0.000063502266,0.00013841523,0.004245359],"genre_scores_gemma":[0.9987325,0.000047112255,0.0009198025,0.00001010043,0.000005224705,0.000006984397,0.000011147249,0.00001103515,0.00025608297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998648,0.000036886944,0.0000059859685,0.0000231993,0.00003078447,0.000038376405],"domain_scores_gemma":[0.99893075,0.0006077822,0.00019692643,0.000057963494,0.000075664575,0.00013089846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037822337,0.00027621846,0.00030795264,0.00054379355,0.00032035448,0.00072501577,0.0004962779,0.0004680915,0.0010826183],"category_scores_gemma":[0.003877538,0.00021465408,0.00037496304,0.00017295242,0.00081135903,0.0009452781,0.0006799086,0.00041614485,0.000073944684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029436022,0.0000683795,0.007135995,0.00012271239,0.00006643085,0.00083393755,0.0004296185,0.8060074,0.08980154,0.08570343,0.000533525,0.009002601],"study_design_scores_gemma":[0.000014049676,0.000032010095,0.00273838,0.0000066659154,0.000011717424,0.00006915638,0.000048371014,0.9849261,0.0028673161,0.009161431,0.000109826346,0.00001502104],"about_ca_topic_score_codex":0.0021516564,"about_ca_topic_score_gemma":0.001335303,"teacher_disagreement_score":0.0021516564,"about_ca_system_score_codex":0.000719087,"about_ca_system_score_gemma":0.0003078393,"threshold_uncertainty_score":0.0052173734},"labels":[],"label_agreement":null},{"id":"W1978638316","doi":"10.3389/neuro.10.005.2008","title":"Modeling Thalamocortical Cell: Impact of Ca2+ Channel Distribution and Cell Geometry on Firing Pattern","year":2008,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":40,"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":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Soma; Tonic (physiology); Geometry; Amplitude; Bursting; Electrophysiology; Compartment (ship); Physics; Biophysics; Conductance; Chemistry; Calcium channel; Neuroscience; Calcium; Mathematics; Biology; Optics; Geology","score_opus":0.02838282309121968,"score_gpt":0.2526632734867072,"score_spread":0.22428045039548752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978638316","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":[0.8747395,0.0002248573,0.12162023,0.00012992426,0.000012762905,0.000032801978,0.00021807868,0.00015987053,0.002861967],"genre_scores_gemma":[0.9910302,0.00011606087,0.008112485,0.000012665117,0.0000033504857,0.000032688622,0.000050412265,0.000014468874,0.0006277026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999081,0.000017717199,0.0000053076833,0.000027589693,0.000022389613,0.00001886657],"domain_scores_gemma":[0.9997687,0.000091543596,0.000042142292,0.000024634865,0.000045252153,0.000027646209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012444577,0.0003725741,0.0005265651,0.00018339892,0.00021925321,0.00056938664,0.0010345791,0.0007660741,0.00063833577],"category_scores_gemma":[0.00059354916,0.0002191884,0.00056148757,0.00016044681,0.00034820833,0.0004080822,0.00030361186,0.00028067033,0.00013720515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047045538,0.0000126037885,0.0016233979,0.000027898957,0.000016772989,0.00014238915,0.000047052585,0.97960967,0.015866973,0.0012648354,0.0000429708,0.0012984205],"study_design_scores_gemma":[0.0000051519437,0.000017743661,0.00045829068,0.0000016146384,0.000009864012,0.00004572449,0.000008945913,0.9978543,0.0011733607,0.00033588763,0.00008499083,0.0000041408916],"about_ca_topic_score_codex":0.01711624,"about_ca_topic_score_gemma":0.010236106,"teacher_disagreement_score":0.01711624,"about_ca_system_score_codex":0.0008781701,"about_ca_system_score_gemma":0.00083198876,"threshold_uncertainty_score":0.03403324},"labels":[],"label_agreement":null},{"id":"W1979018752","doi":"10.3389/fncom.2012.00007","title":"Cellular and Circuit Mechanisms Maintain Low Spike Co-Variability and Enhance Population Coding in Somatosensory Cortex","year":2012,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Benter Foundation; National Science Foundation","keywords":"Somatosensory system; Neuroscience; Coding (social sciences); Spike (software development); Population; Psychology; Computer science; Medicine; Mathematics; Statistics","score_opus":0.01717830172789925,"score_gpt":0.24854666665904634,"score_spread":0.2313683649311471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979018752","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":[0.91912276,0.00013465168,0.07915523,0.00018171208,0.000009194973,0.000012279921,0.00005914188,0.0002498665,0.0010751387],"genre_scores_gemma":[0.99752516,0.00003921264,0.0022762162,0.000007924808,0.0000025528554,0.000007648993,0.000012328371,0.000008897602,0.000120102406],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99993765,0.000013419087,0.0000041000244,0.000015270996,0.0000129277,0.000016698412],"domain_scores_gemma":[0.9998048,0.00007350895,0.00006104954,0.000015245452,0.000015919575,0.000029454672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015206115,0.00020818162,0.00020214525,0.00018706417,0.00018142865,0.00042079538,0.00038707352,0.0003223809,0.0004185277],"category_scores_gemma":[0.0006566519,0.0001656163,0.00026569626,0.00011381135,0.00040316532,0.00047782288,0.0003574031,0.00029275034,0.000054642038],"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":[0.00018953024,0.00012506235,0.0072247745,0.00012494955,0.000078461766,0.00025463372,0.00014288753,0.3497541,0.6091601,0.016225742,0.00035711646,0.016362596],"study_design_scores_gemma":[0.000015282674,0.000048056078,0.010752811,0.000004241304,0.000015552443,0.000056644963,0.00002670406,0.955673,0.023732807,0.009515758,0.00014175747,0.000017397158],"about_ca_topic_score_codex":0.001119957,"about_ca_topic_score_gemma":0.0013387429,"teacher_disagreement_score":0.001119957,"about_ca_system_score_codex":0.00061033777,"about_ca_system_score_gemma":0.00034938744,"threshold_uncertainty_score":0.004428327},"labels":[],"label_agreement":null},{"id":"W1980262550","doi":"10.3389/fncom.2013.00143","title":"Detecting functional connectivity change points for single-subject fMRI data","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Functional magnetic resonance imaging; False positive paradox; Resting state fMRI; Set (abstract data type); Data set; Artificial intelligence; Pattern recognition (psychology); Graph; A priori and a posteriori; Data mining; Machine learning; Psychology; Theoretical computer science; Neuroscience","score_opus":0.16661103837891456,"score_gpt":0.2958512091305903,"score_spread":0.12924017075167576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980262550","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":[0.33657146,0.00043488198,0.6585773,0.0002583338,0.000044395034,0.00030094822,0.0014356254,0.0014020514,0.000975029],"genre_scores_gemma":[0.7275916,0.00028200875,0.2670088,0.000082387334,0.000051831226,0.0004914827,0.003660938,0.00032628325,0.00050463644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983134,0.00062882324,0.000110126566,0.0005906479,0.00028468278,0.00007233947],"domain_scores_gemma":[0.99001175,0.007380951,0.00088457664,0.0011302185,0.00045586025,0.00013667527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004221972,0.0005547495,0.00078289397,0.0029963427,0.0004691491,0.0005953259,0.0006905447,0.0009805037,0.0012014129],"category_scores_gemma":[0.02247034,0.0002839743,0.0007259339,0.0018166829,0.0008811216,0.0010863023,0.0005524626,0.0007341733,0.00030656412],"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":[0.0016035954,0.0005655403,0.117732674,0.0014268985,0.0015227307,0.0032935352,0.001955431,0.18058638,0.20775165,0.011663471,0.0070651327,0.46483296],"study_design_scores_gemma":[0.00006894126,0.0006176485,0.1925702,0.00006483966,0.00024153266,0.0030345588,0.0003747387,0.7274439,0.027877726,0.04226544,0.0053000646,0.00014042319],"about_ca_topic_score_codex":0.001976594,"about_ca_topic_score_gemma":0.003916458,"teacher_disagreement_score":0.004221972,"about_ca_system_score_codex":0.0004017067,"about_ca_system_score_gemma":0.000390583,"threshold_uncertainty_score":0.022328198},"labels":[],"label_agreement":null},{"id":"W1987433044","doi":"10.3389/fncom.2013.00120","title":"Robustness of muscle synergies during visuomotor adaptation","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Isometric exercise; Electromyography; Robustness (evolution); Computer science; Physical medicine and rehabilitation; Motor control; Elbow; Artificial intelligence; Mathematics; Psychology; Anatomy; Neuroscience; Medicine; Physical therapy; Biology","score_opus":0.021911254216408115,"score_gpt":0.2299287101392388,"score_spread":0.2080174559228307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987433044","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":[0.99650407,0.00007357955,0.0030197792,0.000011210283,0.0000033862052,0.000013607122,0.00004784836,0.000057092704,0.00026942324],"genre_scores_gemma":[0.99885726,0.000030304627,0.000812655,0.000005615188,0.0000024121714,0.000010905578,0.00008947935,0.00001207165,0.000179342],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99981135,0.00003674664,0.000014521626,0.00006467751,0.00003941158,0.000033253524],"domain_scores_gemma":[0.99933964,0.000311767,0.00010687649,0.00011634605,0.00006298585,0.00006232469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038828052,0.0002604309,0.0003053737,0.00026948095,0.00010135207,0.00019533823,0.00011101515,0.00022882762,0.0008442014],"category_scores_gemma":[0.0030577993,0.00023295412,0.00017280159,0.000116687566,0.0002408095,0.00018420334,0.00032563042,0.0002124688,0.00016479997],"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":[0.0009653305,0.00006018056,0.007899112,0.00005190234,0.00006533903,0.000099779594,0.00017192276,0.003239048,0.96192497,0.000030440315,0.000044790602,0.02544728],"study_design_scores_gemma":[0.000056673558,0.0013875255,0.9176335,0.0000121595585,0.00006215783,0.0004668801,0.000114855124,0.020926997,0.058682725,0.00028883357,0.0003352817,0.00003250642],"about_ca_topic_score_codex":0.00082572945,"about_ca_topic_score_gemma":0.0007072328,"teacher_disagreement_score":0.0008442014,"about_ca_system_score_codex":0.00009655343,"about_ca_system_score_gemma":0.000086001884,"threshold_uncertainty_score":0.0028241873},"labels":[],"label_agreement":null},{"id":"W1989797211","doi":"10.3389/fncom.2011.00033","title":"Visual Representation Determines Search Difficulty: Explaining Visual Search Asymmetries","year":2011,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Visual search; Visual cortex; Computer science; Stimulus (psychology); Variety (cybernetics); Coding (social sciences); Set (abstract data type); Cognitive psychology; Artificial intelligence; Psychology; Neuroscience; Mathematics","score_opus":0.138951362097745,"score_gpt":0.37563130305658876,"score_spread":0.23667994095884376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989797211","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":[0.86130965,0.0004729785,0.12977383,0.00054808706,0.00001777216,0.00004510897,0.00017454704,0.00017440695,0.0074836263],"genre_scores_gemma":[0.9951249,0.000084646716,0.004490917,0.00003130176,0.000011405227,0.000015247974,0.000044409164,0.000029991112,0.00016729486],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99959415,0.00010130091,0.000037998245,0.00008477168,0.00010895346,0.000072865085],"domain_scores_gemma":[0.99341875,0.004276786,0.0010232683,0.00084868295,0.00022915719,0.00020323723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011828493,0.000318776,0.0005341585,0.0006771284,0.00017045326,0.0009887199,0.0006400366,0.00076171407,0.0021901962],"category_scores_gemma":[0.010640547,0.00023609912,0.00031370614,0.00047845254,0.0013150509,0.0028742221,0.0010350222,0.00068825774,0.00025721677],"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":[0.0014200854,0.00023475349,0.07185894,0.00077278615,0.00010097787,0.0013441213,0.0016059473,0.044087943,0.51014256,0.23724785,0.0012006215,0.12998344],"study_design_scores_gemma":[0.00013203327,0.00027182355,0.115678005,0.00006326265,0.00006610505,0.00163701,0.00046126306,0.36926067,0.045714326,0.4657295,0.0008924957,0.000093523326],"about_ca_topic_score_codex":0.00030791946,"about_ca_topic_score_gemma":0.0002570138,"teacher_disagreement_score":0.0021901962,"about_ca_system_score_codex":0.00035756736,"about_ca_system_score_gemma":0.00017176422,"threshold_uncertainty_score":0.0073269606},"labels":[],"label_agreement":null},{"id":"W2006299824","doi":"10.3389/fncom.2010.00135","title":"Measuring neuronal branching patterns using model-based approach","year":2010,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research","keywords":"Branching (polymer chemistry); Computer science; Neuroscience; Psychology; Chemistry","score_opus":0.05839547521002736,"score_gpt":0.25720619068823886,"score_spread":0.1988107154782115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006299824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092041954,0.00025721823,0.9049107,0.00010688678,0.0000148805375,0.000034785746,0.00015794596,0.000565293,0.001910391],"genre_scores_gemma":[0.81331855,0.0003893796,0.18522021,0.000041649957,0.00001933358,0.000108429784,0.00029615432,0.00008080195,0.00052558904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.00012025688,0.000026769472,0.000097820004,0.00013048682,0.00003136143],"domain_scores_gemma":[0.99859506,0.0006922483,0.00022392852,0.00025875753,0.00016173527,0.00006821037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007838189,0.00055371993,0.0007166492,0.0015609158,0.00036095976,0.0013404194,0.001132411,0.0011903709,0.00070205634],"category_scores_gemma":[0.0039776145,0.00052982173,0.00093969365,0.0011615255,0.0006058661,0.0016027221,0.0009539974,0.00067800016,0.00018422531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028798193,0.000026738982,0.0025804862,0.000053161602,0.000050509923,0.000060619655,0.00004974895,0.9602009,0.009125879,0.013358245,0.00017690833,0.01428801],"study_design_scores_gemma":[0.0000014421424,0.000006712066,0.0004178794,0.0000025790055,0.0000043520818,0.000024000983,0.000004525415,0.9933164,0.0008215051,0.0052779033,0.000116042305,0.0000065573126],"about_ca_topic_score_codex":0.0038733045,"about_ca_topic_score_gemma":0.0030349437,"teacher_disagreement_score":0.0038733045,"about_ca_system_score_codex":0.0015164063,"about_ca_system_score_gemma":0.00067458575,"threshold_uncertainty_score":0.011002302},"labels":[],"label_agreement":null},{"id":"W2007671282","doi":"10.3389/fncom.2014.00132","title":"Modeling the shape hierarchy for visually guided grasping","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Deutscher Akademischer Austauschdienst","keywords":"Isomap; Artificial intelligence; Pattern recognition (psychology); Dimensionality reduction; GRASP; Computer vision; Computer science; Curvature; Dimension (graph theory); Mathematics; Nonlinear dimensionality reduction; Geometry","score_opus":0.05578643785609798,"score_gpt":0.3161753461833234,"score_spread":0.2603889083272254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007671282","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":[0.5428671,0.00040351937,0.44076306,0.0007268483,0.00002672369,0.00005855535,0.00023249387,0.0006289307,0.014292783],"genre_scores_gemma":[0.9634406,0.0001788498,0.033590484,0.000053221964,0.000010851178,0.000052886106,0.000084225234,0.00009716804,0.002491792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993515,0.00001837562,0.00000263955,0.000013818678,0.000014656191,0.000015415451],"domain_scores_gemma":[0.9998191,0.00008207393,0.00002612996,0.000020505395,0.000020156343,0.000032155574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022028267,0.000304496,0.00035994314,0.00045009024,0.000279933,0.00059448474,0.0007476007,0.00090822874,0.002052493],"category_scores_gemma":[0.00089326344,0.00046764355,0.0006204945,0.00032460634,0.00054244394,0.00091565656,0.00052751915,0.0005656214,0.000308331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021481213,0.000009885727,0.00044926125,0.000014916898,0.000009609128,0.000038203685,0.000032950033,0.9826658,0.0036825193,0.009008575,0.00015363994,0.0039131395],"study_design_scores_gemma":[0.0000025384984,0.0000048159604,0.00015234033,0.0000013682292,0.0000015943539,0.0000053516255,0.0000039974784,0.9956104,0.00009810893,0.0040225256,0.00009445451,0.0000024039748],"about_ca_topic_score_codex":0.013085224,"about_ca_topic_score_gemma":0.013793153,"teacher_disagreement_score":0.013085224,"about_ca_system_score_codex":0.001523016,"about_ca_system_score_gemma":0.000907171,"threshold_uncertainty_score":0.026018083},"labels":[],"label_agreement":null},{"id":"W2010655017","doi":"10.3389/fncom.2013.00075","title":"Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"Engineering and Physical Sciences Research Council; Fundação para a Ciência e a Tecnologia; Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Synaptic plasticity; Computer science; Plasticity; Probabilistic logic; Term (time); Neuroscience; Bayesian probability; Bayesian inference; Artificial intelligence; Physics; Psychology; Biology","score_opus":0.029198002783778463,"score_gpt":0.2627331309902007,"score_spread":0.23353512820642222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010655017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17753664,0.00030052208,0.82072115,0.00028419096,0.000013064096,0.000024890416,0.0001568407,0.0002725011,0.0006901922],"genre_scores_gemma":[0.95335406,0.00035161164,0.045331646,0.00004756804,0.000018879979,0.000058670157,0.000258192,0.00006352027,0.00051584415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963045,0.00012948236,0.000024468483,0.000088139444,0.000091523434,0.00003593823],"domain_scores_gemma":[0.99725914,0.0019862182,0.00028070147,0.00019166234,0.00019228242,0.00009002974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017004836,0.0003777299,0.00067568733,0.00071702804,0.0003462348,0.0008101866,0.0013384656,0.0008914558,0.00061732007],"category_scores_gemma":[0.009170441,0.00079609547,0.00063917134,0.00043653496,0.0010877609,0.0014798767,0.0008303263,0.0010999846,0.00012331341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000957044,0.000035912366,0.003648079,0.000086011205,0.00007246207,0.00008719539,0.00008610445,0.9331641,0.012200742,0.031767976,0.0003444288,0.018411268],"study_design_scores_gemma":[0.0000028862962,0.0000040867635,0.0007310664,0.0000026387086,0.0000024465096,0.000010140741,0.0000036080942,0.9871154,0.0007668926,0.011308377,0.000045423214,0.000007020891],"about_ca_topic_score_codex":0.004275536,"about_ca_topic_score_gemma":0.005246683,"teacher_disagreement_score":0.004275536,"about_ca_system_score_codex":0.00094067305,"about_ca_system_score_gemma":0.0006224995,"threshold_uncertainty_score":0.008993149},"labels":[],"label_agreement":null},{"id":"W2013702864","doi":"10.3389/fncom.2013.00179","title":"Does the entorhinal cortex use the Fourier transform?","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Innovation Trust","keywords":"Entorhinal cortex; Position (finance); Perspective (graphical); Computer science; Fourier transform; Precession; Spatial frequency; Physics; Topology (electrical circuits); Mathematics; Hippocampus; Neuroscience; Artificial intelligence; Optics; Psychology; Quantum mechanics","score_opus":0.049182954367350956,"score_gpt":0.275283495453806,"score_spread":0.22610054108645503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013702864","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":[0.42068776,0.034716554,0.36119276,0.046797372,0.0019906468,0.000081502905,0.00085685594,0.0019745517,0.13170198],"genre_scores_gemma":[0.9592764,0.0069899764,0.022117475,0.0017909277,0.0004838113,0.000024133766,0.00018243313,0.0001666674,0.008968202],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99984396,0.000030754258,0.000009631826,0.000050596216,0.000039796872,0.000025197522],"domain_scores_gemma":[0.9995326,0.00014725054,0.000076219956,0.00012994405,0.00008058598,0.000033492117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005157986,0.00028404148,0.0003135396,0.0004531155,0.00020296528,0.0011700278,0.000666885,0.0007939394,0.0023965177],"category_scores_gemma":[0.0028587256,0.00024705852,0.0005019239,0.000398522,0.0012379044,0.0036805088,0.00044900243,0.00052005035,0.0007420672],"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":[0.00038603248,0.0001409198,0.035974484,0.00062842126,0.00049023255,0.0012703992,0.0015793818,0.017453376,0.0367896,0.49790016,0.019526556,0.38786042],"study_design_scores_gemma":[0.00015009602,0.00016913645,0.051944118,0.00030547954,0.00019571134,0.0027622,0.0008341965,0.0676659,0.012372547,0.7933999,0.07003884,0.0001619474],"about_ca_topic_score_codex":0.0036994007,"about_ca_topic_score_gemma":0.001674087,"teacher_disagreement_score":0.0036994007,"about_ca_system_score_codex":0.0003704851,"about_ca_system_score_gemma":0.00034901046,"threshold_uncertainty_score":0.008017182},"labels":[],"label_agreement":null},{"id":"W2018429176","doi":"10.3389/fncom.2015.00044","title":"The CNP signal is able to silence a supra threshold neuronal model","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute","funders":"","keywords":"Neuroscience; SIGNAL (programming language); Premovement neuronal activity; Endogeny; Pulse (music); Waveform; Inhibitory postsynaptic potential; Central nervous system; Biological neural network; Noise (video); Physics; Computer science; Biology; Telecommunications; Endocrinology","score_opus":0.05528691126706343,"score_gpt":0.27593838212511534,"score_spread":0.22065147085805192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018429176","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":[0.94833726,0.00014591767,0.046130896,0.00017083569,0.000040885607,0.000019939856,0.000108724686,0.00009396191,0.0049515846],"genre_scores_gemma":[0.99753004,0.00006062534,0.0017627568,0.000013281245,0.0000016813863,0.000012912683,0.000030178446,0.000006568639,0.00058205466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999621,0.0000053208837,0.0000017800136,0.000008560214,0.0000110882675,0.000011133619],"domain_scores_gemma":[0.9998913,0.000050160008,0.00001686462,0.000009141535,0.000019810941,0.000012758024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008053718,0.00014462981,0.00016865967,0.00010382164,0.00010610905,0.00019701663,0.00034020096,0.0003094834,0.0017338472],"category_scores_gemma":[0.00046473063,0.000066554734,0.0001722257,0.000071745846,0.00026174425,0.00023618039,0.00015419714,0.0002255206,0.0001069308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023042252,0.00008563141,0.0020865963,0.00025144074,0.00006368419,0.00044896718,0.00012944381,0.7475462,0.23270962,0.00863708,0.00047183078,0.0073391935],"study_design_scores_gemma":[0.000021295224,0.0001983305,0.0014345895,0.0000067913325,0.000022247503,0.00007004317,0.000034917186,0.9658183,0.030223044,0.0015861674,0.0005761771,0.000008110939],"about_ca_topic_score_codex":0.0017641649,"about_ca_topic_score_gemma":0.001238323,"teacher_disagreement_score":0.0017641649,"about_ca_system_score_codex":0.00027459784,"about_ca_system_score_gemma":0.00026950252,"threshold_uncertainty_score":0.005800247},"labels":[],"label_agreement":null},{"id":"W2019432841","doi":"10.3389/fncom.2014.00054","title":"Philosophical reflections on therapeutic brain stimulation","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Stimulation; Neuroscience; Front (military); Deep brain stimulation; Brain stimulation; Psychology; Cognitive science; Medicine; Physics; Internal medicine","score_opus":0.061416447183100784,"score_gpt":0.35004214400606404,"score_spread":0.28862569682296324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019432841","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015251068,0.03721572,0.019995766,0.8724841,0.011254821,0.000033035067,0.000092496404,0.000058414353,0.057340533],"genre_scores_gemma":[0.36006087,0.057000328,0.026419688,0.48295787,0.048694443,0.00088816445,0.00011450579,0.00039958826,0.023464493],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.992833,0.0040425532,0.00044291574,0.00074714667,0.001616578,0.00031779587],"domain_scores_gemma":[0.98516536,0.01229953,0.00032477215,0.00063668494,0.0012541203,0.0003195892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018109528,0.0008549771,0.0010918296,0.0011096124,0.0025515726,0.0046580825,0.0022927218,0.012181256,0.006779316],"category_scores_gemma":[0.023227343,0.00033104976,0.0011288751,0.00047879588,0.04432751,0.007875723,0.00356103,0.02226606,0.0023400187],"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":[0.0000652304,0.000021376183,0.000036345784,0.0001848164,0.000024087103,0.000105671694,0.0005270644,0.0006122733,0.00016125214,0.956571,0.028806847,0.012884017],"study_design_scores_gemma":[0.00006255794,0.000026468977,0.000060546266,0.0003377868,0.000011761218,0.00025276476,0.0002358841,0.000504148,0.00020272173,0.9054001,0.09288269,0.000022610551],"about_ca_topic_score_codex":0.0016387843,"about_ca_topic_score_gemma":0.0012959612,"teacher_disagreement_score":0.018109528,"about_ca_system_score_codex":0.005416129,"about_ca_system_score_gemma":0.0030567078,"threshold_uncertainty_score":0.09577352},"labels":[],"label_agreement":null},{"id":"W2029301766","doi":"10.3389/fncom.2013.00184","title":"Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Moment closure; Bursting; Statistical physics; Parameter space; Moment (physics); Population; Mean field theory; Bifurcation; Closure (psychology); Quartic function; Computer science; Field (mathematics); Mathematics; Nonlinear system; Physics; Statistics; Mechanics; Classical mechanics","score_opus":0.023344906206822346,"score_gpt":0.24881139782419304,"score_spread":0.2254664916173707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029301766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24161151,0.0006899196,0.7449005,0.00094011106,0.000066555265,0.00006263,0.00019491198,0.00020177766,0.011331981],"genre_scores_gemma":[0.96377414,0.00048333354,0.026992615,0.00017443321,0.000047942245,0.0001348359,0.00010239224,0.000056009285,0.008234335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998461,0.00003662986,0.000007730364,0.000034514887,0.000042233387,0.000032793552],"domain_scores_gemma":[0.99942696,0.00025654837,0.00011905352,0.000033134776,0.00010117208,0.00006318497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006410476,0.0005555069,0.0007583289,0.0007657172,0.0005270545,0.00076406077,0.0013935852,0.0013172149,0.0014664829],"category_scores_gemma":[0.0020331678,0.00040629334,0.0010065545,0.0004975392,0.0009727839,0.0014340695,0.0007968483,0.0009022356,0.00021062624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021069,0.000017514221,0.0006189802,0.000022110149,0.000022727683,0.00013046987,0.000094124414,0.9326717,0.0029780522,0.06098411,0.00030955888,0.0021296346],"study_design_scores_gemma":[0.0000043375608,0.0000053452372,0.000109108216,0.0000022622369,0.0000036139413,0.000013533985,0.000007647241,0.98868585,0.00009748512,0.010955512,0.00011023822,0.0000050652884],"about_ca_topic_score_codex":0.0073406687,"about_ca_topic_score_gemma":0.0046169776,"teacher_disagreement_score":0.0073406687,"about_ca_system_score_codex":0.001253246,"about_ca_system_score_gemma":0.0005732799,"threshold_uncertainty_score":0.014595866},"labels":[],"label_agreement":null},{"id":"W2031650785","doi":"10.3389/fncom.2012.00030","title":"Fluctuating Inhibitory Inputs Promote Reliable Spiking at Theta Frequencies in Hippocampal Interneurons","year":2012,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Government of Ontario; Compute Canada","keywords":"Inhibitory postsynaptic potential; Neuroscience; Hippocampal formation; Excitatory postsynaptic potential; Hippocampus; Population; Postsynaptic Current; Theta rhythm; Physics; Subthreshold conduction; Biology; Medicine; Voltage","score_opus":0.05198272138782128,"score_gpt":0.32267140613323525,"score_spread":0.27068868474541397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031650785","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":[0.99892175,0.0000402877,0.0007602537,0.000008623877,0.0000024259525,0.0000019522713,0.00001642566,0.000020706022,0.00022765886],"genre_scores_gemma":[0.9995009,0.000046473207,0.0003185088,0.0000071592003,0.0000010597987,0.0000031273157,0.000016108295,0.0000062544855,0.00010042617],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999486,0.0000047857743,0.000005524955,0.000007603591,0.00001646976,0.000017063106],"domain_scores_gemma":[0.9998574,0.000029574388,0.000038258524,0.000013967127,0.000014598278,0.000046189365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007809115,0.0002363419,0.00017552785,0.00012506412,0.000085744185,0.00025456722,0.00023760197,0.00012210227,0.00041644368],"category_scores_gemma":[0.00033359413,0.00012667192,0.00016778163,0.00007819763,0.00015485741,0.00012667554,0.0003078267,0.0003055557,0.00008464444],"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":[0.000083121675,0.00002736105,0.0014303499,0.000024981371,0.0000095482,0.000060999802,0.00001938515,0.0015386299,0.99514955,0.00007727042,0.000021770093,0.0015571284],"study_design_scores_gemma":[0.000058633177,0.0010318053,0.05379142,0.000022528659,0.00009203908,0.00025549374,0.00018475146,0.0472941,0.89612883,0.00044704336,0.0006723728,0.000020966443],"about_ca_topic_score_codex":0.0007226188,"about_ca_topic_score_gemma":0.0013525267,"teacher_disagreement_score":0.0007226188,"about_ca_system_score_codex":0.00023459368,"about_ca_system_score_gemma":0.00016051627,"threshold_uncertainty_score":0.0017020702},"labels":[],"label_agreement":null},{"id":"W2036846283","doi":"10.3389/fncom.2013.00034","title":"Neural mass modeling of power-line magnetic fields effects on brain activity","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"Canadian Institutes of Health Research; Mitacs; Electricité de France; Hydro-Québec","keywords":"Front line; Front (military); Neuroscience; Power (physics); Computer science; Physics; Psychology; Political science; Meteorology","score_opus":0.017895628589547515,"score_gpt":0.24555325280616785,"score_spread":0.22765762421662034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036846283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22924592,0.00080171484,0.743846,0.0005411674,0.000090584974,0.00010599803,0.00024248709,0.0003453978,0.02478065],"genre_scores_gemma":[0.97462875,0.00055494567,0.013791708,0.00010014571,0.000043048232,0.00018762052,0.00008433999,0.000046573594,0.010562878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999275,0.000022977827,0.0000033518913,0.000016248287,0.000018136368,0.000011747203],"domain_scores_gemma":[0.9998288,0.00008070447,0.00003757648,0.000011743612,0.000023621025,0.000017519738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017894604,0.00058091054,0.00040925894,0.00024748026,0.00019794644,0.000457229,0.0007700848,0.0010221924,0.0017515286],"category_scores_gemma":[0.0007446645,0.00022983977,0.0004928314,0.00019000561,0.0005368937,0.0006922161,0.00048301983,0.00036803118,0.0002661704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004002792,0.000025517777,0.00040923012,0.00004171433,0.000020992293,0.00015226778,0.000078473924,0.96644896,0.010994843,0.017945888,0.000266333,0.0035757637],"study_design_scores_gemma":[0.0000047260014,0.000018576702,0.00017745743,0.0000025989573,0.000005945942,0.000019082456,0.0000062734857,0.9960587,0.00037485783,0.0030783077,0.0002508917,0.0000025857357],"about_ca_topic_score_codex":0.0023836168,"about_ca_topic_score_gemma":0.0012965803,"teacher_disagreement_score":0.0023836168,"about_ca_system_score_codex":0.00047820562,"about_ca_system_score_gemma":0.00029710942,"threshold_uncertainty_score":0.005859494},"labels":[],"label_agreement":null},{"id":"W2046944613","doi":"10.3389/fncom.2013.00186","title":"Effort minimization and synergistic muscle recruitment for three-dimensional force generation","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"FP7 Information and Communication Technologies; National Institutes of Health; European Commission; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institutes of Health Research","keywords":"Isometric exercise; Redundancy (engineering); Computer science; Motor unit recruitment; Electromyography; Neuroscience; Biology; Medicine; Physical therapy","score_opus":0.06377728789224475,"score_gpt":0.2692965170242793,"score_spread":0.20551922913203458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046944613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071182445,0.00020458414,0.92679346,0.00016938426,0.000014351382,0.00003162392,0.000024421837,0.00011957859,0.0014601642],"genre_scores_gemma":[0.7895392,0.00012649375,0.2089861,0.000049822538,0.00001292724,0.00012441145,0.0000430696,0.00007737262,0.0010405539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996145,0.00014649946,0.000028746996,0.00008639711,0.00008459928,0.000039246235],"domain_scores_gemma":[0.9992434,0.0004617916,0.00012249991,0.00005624223,0.000075192125,0.000041038234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011921846,0.00061878975,0.00050947495,0.00036385277,0.00022388014,0.000487426,0.00052406406,0.0007740935,0.001014111],"category_scores_gemma":[0.0033275082,0.00041203148,0.0004970926,0.00028497292,0.0006808197,0.00075674284,0.0007028941,0.00044070624,0.00020336841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014379164,0.0000764347,0.0018461909,0.00018187282,0.0000649126,0.00015792738,0.00017544029,0.8711305,0.052161336,0.021927414,0.00031850726,0.051815715],"study_design_scores_gemma":[0.0000056919234,0.000036066984,0.0011596738,0.000009386938,0.000005465466,0.00003572333,0.000008698196,0.99109113,0.0021268425,0.005308531,0.00020149017,0.000011246839],"about_ca_topic_score_codex":0.0017894309,"about_ca_topic_score_gemma":0.002406131,"teacher_disagreement_score":0.0017894309,"about_ca_system_score_codex":0.0004902032,"about_ca_system_score_gemma":0.00057674665,"threshold_uncertainty_score":0.0063049197},"labels":[],"label_agreement":null},{"id":"W2063561386","doi":"10.3389/fncom.2014.00147","title":"A neuromorphic system for video object recognition","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; U.S. Department of Defense","keywords":"Computer science; Artificial intelligence; Cognitive neuroscience of visual object recognition; 3D single-object recognition; Object detection; Computer vision; Neuromorphic engineering; Convolutional neural network; Object (grammar); Pattern recognition (psychology); Artificial neural network","score_opus":0.035327534850967256,"score_gpt":0.2571146966102463,"score_spread":0.221787161759279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063561386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070118636,0.0020496582,0.8673062,0.00094469805,0.00090574677,0.0005063457,0.0012212204,0.021259641,0.03568778],"genre_scores_gemma":[0.65950614,0.0010511313,0.30191812,0.0016155072,0.00019228435,0.00050632405,0.001049833,0.0003327954,0.033827826],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973947,0.000024688661,0.000015303829,0.000076924356,0.00011805089,0.000025532267],"domain_scores_gemma":[0.99981195,0.000030086974,0.000021218348,0.000040438983,0.000077275305,0.000019059933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022908761,0.000379562,0.00029142672,0.0004986235,0.0002626364,0.00062479026,0.0013014105,0.000620527,0.0068686325],"category_scores_gemma":[0.0006757685,0.0001587839,0.00023540725,0.00043696293,0.00018884534,0.0007055303,0.00055784715,0.000450544,0.0027586757],"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":[0.0002504245,0.00024472023,0.0011355997,0.00033764372,0.000090280824,0.0003401199,0.00009126022,0.005548494,0.29127514,0.0076858206,0.017379252,0.6756212],"study_design_scores_gemma":[0.0001897786,0.0014702455,0.0070032272,0.00019082923,0.00019078537,0.0034673878,0.0001501928,0.35236496,0.4257341,0.017845832,0.19125015,0.0001424517],"about_ca_topic_score_codex":0.0008361987,"about_ca_topic_score_gemma":0.0016938782,"teacher_disagreement_score":0.0068686325,"about_ca_system_score_codex":0.00044256452,"about_ca_system_score_gemma":0.0004138758,"threshold_uncertainty_score":0.022977829},"labels":[],"label_agreement":null},{"id":"W2065956212","doi":"10.3389/fncom.2014.00023","title":"TMS-induced neural noise in sensory cortex interferes with short-term memory storage in prefrontal cortex","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Wilfrid Laurier University","keywords":"Sensory system; Prefrontal cortex; Neuroscience; Interference theory; Sensory memory; Working memory; Transcranial magnetic stimulation; Sensory cortex; Somatosensory system; Long-term memory; Cortex (anatomy); Sensory stimulation therapy; Psychology; Stimulation; Cognition","score_opus":0.02366917416599984,"score_gpt":0.25140553758798173,"score_spread":0.2277363634219819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065956212","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":[0.95310664,0.000110935325,0.045708653,0.000085329484,0.0000266572,0.00001605731,0.00006038309,0.000101493075,0.000783696],"genre_scores_gemma":[0.996673,0.000049216884,0.0031444628,0.0000079021365,0.0000017741739,0.0000072591024,0.000023264885,0.000005755493,0.00008736344],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999523,0.000007363455,0.000004464373,0.000011522926,0.000013092972,0.000011296474],"domain_scores_gemma":[0.9998628,0.00006825139,0.000025197876,0.0000195759,0.000011701238,0.000012550148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017114557,0.00020872203,0.00019638926,0.000097838754,0.0001205888,0.0002816619,0.00045124633,0.00018101343,0.000579094],"category_scores_gemma":[0.0007945316,0.0000944084,0.00033892997,0.00008590311,0.00026195447,0.00027788387,0.00025381835,0.00021758217,0.000054993063],"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":[0.00037203688,0.000083075414,0.0037616969,0.00014495534,0.00009678547,0.00016981584,0.00007740393,0.025462475,0.95157474,0.0024792172,0.0000973366,0.015680367],"study_design_scores_gemma":[0.00010643464,0.00068189413,0.07357675,0.000020385034,0.00015977351,0.0005068448,0.00009933363,0.5756704,0.3386051,0.009642705,0.00089042296,0.000039920113],"about_ca_topic_score_codex":0.0014690021,"about_ca_topic_score_gemma":0.001370349,"teacher_disagreement_score":0.0014690021,"about_ca_system_score_codex":0.00029621625,"about_ca_system_score_gemma":0.00029846738,"threshold_uncertainty_score":0.0029209256},"labels":[],"label_agreement":null},{"id":"W2066477421","doi":"10.3389/fncom.2011.00007","title":"Gain Modulation by an Urgency Signal Controls the Speed–Accuracy Trade-Off in a Network Model of a Cortical Decision Circuit","year":2011,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Canadian Institutes of Health Research","funders":"Canadian Institutes of Health Research; National Natural Science Foundation of China","keywords":"SIGNAL (programming language); Modulation (music); Computer science; Neuroscience; Psychology; Physics; Acoustics","score_opus":0.05370845856673114,"score_gpt":0.26534904714213803,"score_spread":0.21164058857540688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066477421","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":[0.75202036,0.00014932877,0.23849945,0.00094861,0.00004280871,0.00002923089,0.00008922833,0.00019819196,0.008022731],"genre_scores_gemma":[0.99112767,0.000060139348,0.0075928126,0.000026093923,0.0000064445658,0.000020929436,0.000015221292,0.000014723452,0.0011359302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987197,0.00003570709,0.00000493672,0.000039571816,0.000020539126,0.000027353539],"domain_scores_gemma":[0.99948287,0.00028205916,0.00007786414,0.00003812308,0.000051353272,0.000067595945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035003872,0.00029956893,0.00031880275,0.00025918594,0.0003459418,0.0009789529,0.0007402915,0.0005726088,0.0019871122],"category_scores_gemma":[0.0021553144,0.0002788518,0.0003807195,0.00018548095,0.0008226043,0.001288118,0.00056514534,0.0005955112,0.00014796412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026287892,0.000088212684,0.0034444875,0.00006336554,0.000065999855,0.00023631334,0.0002478052,0.83667505,0.07378659,0.07375935,0.00052264315,0.010847289],"study_design_scores_gemma":[0.000016676699,0.000032408516,0.0008723982,0.000002552007,0.000010958981,0.000026824564,0.000013928909,0.9802069,0.001578639,0.017078782,0.00015209403,0.0000078922085],"about_ca_topic_score_codex":0.0033988648,"about_ca_topic_score_gemma":0.0032038474,"teacher_disagreement_score":0.0033988648,"about_ca_system_score_codex":0.0009816226,"about_ca_system_score_gemma":0.0005000786,"threshold_uncertainty_score":0.007122278},"labels":[],"label_agreement":null},{"id":"W2068635075","doi":"10.3389/fncom.2014.00019","title":"Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Feed forward; Automatic gain control; Control theory (sociology); Monotonic function; Noise (video); Computer science; Feedforward neural network; Artificial neural network; Electric fish; Biological system; Control (management); Mathematics; Artificial intelligence; Telecommunications; Biology; Fish <Actinopterygii>; Engineering; Amplifier","score_opus":0.006781784069197439,"score_gpt":0.2215741857965064,"score_spread":0.21479240172730896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068635075","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":[0.6311572,0.00033088346,0.3622335,0.0001853408,0.000026503953,0.000025798574,0.000048426104,0.000246603,0.0057458687],"genre_scores_gemma":[0.99441046,0.00006106359,0.0041982825,0.000014162637,0.0000042769357,0.0000134972615,0.000008760742,0.000013761213,0.001275592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990535,0.000024392328,0.0000051440843,0.000022591423,0.000022510561,0.000019946143],"domain_scores_gemma":[0.9995388,0.0002540657,0.00009514507,0.000025131596,0.000044796572,0.00004207081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003324068,0.00042993922,0.0002563337,0.00023835916,0.0002156147,0.0005252005,0.0004788253,0.00035702117,0.00066903385],"category_scores_gemma":[0.0016322698,0.0002102376,0.00032462613,0.00013644557,0.0008595289,0.00052362593,0.00051845243,0.0004146828,0.00007611605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016201551,0.00003190478,0.0010162325,0.000059585524,0.000030792075,0.0002052383,0.0001546647,0.9117938,0.03752495,0.037097223,0.00014430558,0.011779311],"study_design_scores_gemma":[0.000004895096,0.00002251613,0.00023218544,0.0000025559839,0.0000038390804,0.000014688237,0.0000069977586,0.9925096,0.0016208253,0.00551584,0.000059770042,0.00000617185],"about_ca_topic_score_codex":0.0029212725,"about_ca_topic_score_gemma":0.0031674283,"teacher_disagreement_score":0.0029212725,"about_ca_system_score_codex":0.0006406805,"about_ca_system_score_gemma":0.0003442439,"threshold_uncertainty_score":0.0058085322},"labels":[],"label_agreement":null},{"id":"W2071050489","doi":"10.3389/fncom.2014.00084","title":"Feature integration and object representations along the dorsal stream visual hierarchy","year":2014,"lang":"en","type":"review","venue":"Frontiers in Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hierarchy; Dorsum; Feature (linguistics); Computer science; Object (grammar); Artificial intelligence; Pattern recognition (psychology); Linguistics; Biology; Anatomy","score_opus":0.05861830442173529,"score_gpt":0.37951486174797144,"score_spread":0.3208965573262362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071050489","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066462316,0.988035,0.002642693,0.000540782,0.0003230309,0.000014190605,0.000045625893,0.000031094045,0.0077029876],"genre_scores_gemma":[0.00583702,0.98694885,0.0018340348,0.00035546737,0.0003389205,0.000023708551,0.0000789772,0.0000111261315,0.0045718937],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99987566,0.000016063485,0.000014187796,0.000036186953,0.000043732343,0.000014112202],"domain_scores_gemma":[0.9998741,0.000039516653,0.00001909986,0.0000062407903,0.000048183098,0.000012835103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003367014,0.0007573824,0.00075008126,0.001865995,0.00026387835,0.0009929716,0.001229119,0.0012927854,0.0023794093],"category_scores_gemma":[0.0004853103,0.00028938617,0.00035339885,0.0013692061,0.0013578914,0.0020357042,0.0006396073,0.001372008,0.0017361977],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077355035,0.00002524584,0.0002950433,0.00723666,0.00006460797,0.00035906906,0.00024753754,0.00087418285,0.0038195974,0.06394894,0.020094555,0.90295714],"study_design_scores_gemma":[0.000012678569,0.00005256314,0.0029297213,0.0020418596,0.000052347626,0.002791839,0.000098243014,0.0003348464,0.0014938478,0.036356855,0.9538038,0.000031348533],"about_ca_topic_score_codex":0.0019318848,"about_ca_topic_score_gemma":0.0018593231,"teacher_disagreement_score":0.0023794093,"about_ca_system_score_codex":0.0011853764,"about_ca_system_score_gemma":0.0009453087,"threshold_uncertainty_score":0.008600533},"labels":[],"label_agreement":null},{"id":"W2074686798","doi":"10.3389/fncom.2014.00162","title":"The next move in neuromodulation therapy: a question of timing","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Lawson Health Research Institute","funders":"","keywords":"Neuromodulation; Neuroscience; Psychology; Front (military); Computer science; Geology; Oceanography","score_opus":0.07877395055377479,"score_gpt":0.3183321963602418,"score_spread":0.239558245806467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074686798","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046036956,0.071127765,0.031297725,0.7679456,0.022532057,0.00007769199,0.00040465064,0.00025520302,0.10175563],"genre_scores_gemma":[0.49616617,0.13094717,0.051683675,0.2325605,0.043385617,0.0006786603,0.0004528518,0.0011662963,0.042959042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99619776,0.0013011174,0.00030943233,0.0006614873,0.0011080342,0.0004221774],"domain_scores_gemma":[0.98597103,0.0076595717,0.0009367098,0.0006441271,0.002411658,0.0023769853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012462288,0.00068499474,0.0018361493,0.0010184044,0.0029655064,0.008831701,0.0024603396,0.008706474,0.029948168],"category_scores_gemma":[0.02831233,0.00037635223,0.0008229939,0.0007142499,0.009929934,0.017178535,0.004039449,0.01719715,0.005063701],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004673513,0.000245582,0.0012668633,0.0010807607,0.0000861269,0.00041753254,0.0015385374,0.001169643,0.001391113,0.7177298,0.10630421,0.16830248],"study_design_scores_gemma":[0.00014786472,0.00025975384,0.0011488146,0.0024404197,0.00006192672,0.0006123585,0.0024261402,0.0010584769,0.0007183451,0.70124954,0.2897604,0.00011604154],"about_ca_topic_score_codex":0.0028359392,"about_ca_topic_score_gemma":0.0034080497,"teacher_disagreement_score":0.029948168,"about_ca_system_score_codex":0.0038867684,"about_ca_system_score_gemma":0.007879177,"threshold_uncertainty_score":0.100186646},"labels":[],"label_agreement":null},{"id":"W2076160518","doi":"10.3389/fncom.2014.00156","title":"Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Ministerio de Ciencia e Innovación; Eisai; BioClinica; Synarc; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense; Meso Scale Diagnostics; Medpace; Biogen; Novartis Pharmaceuticals Corporation; Pfizer; Bristol-Myers Squibb; Eli Lilly and Company; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Foundation for the National Institutes of Health","keywords":"Bayesian probability; Component (thermodynamics); Computer science; Bayesian network; Artificial intelligence; Pattern recognition (psychology); Physics","score_opus":0.04591037542138848,"score_gpt":0.28374769144247247,"score_spread":0.237837316021084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076160518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076278266,0.00064465724,0.9904647,0.0003749077,0.00001896094,0.000045198845,0.00014126775,0.00015434841,0.0005281563],"genre_scores_gemma":[0.45652634,0.0027264494,0.53675336,0.00029967714,0.0003199542,0.00035644433,0.0010915734,0.00009317207,0.001832981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822444,0.0010714721,0.0000885647,0.00027010514,0.00028478258,0.00006068062],"domain_scores_gemma":[0.9932092,0.005819368,0.00028492804,0.00020658561,0.00040052325,0.00007939135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004698814,0.0008965937,0.0009905451,0.00373676,0.00061966054,0.0012173644,0.0014603582,0.0010971768,0.0013160827],"category_scores_gemma":[0.014533666,0.0006190384,0.00134898,0.0019424658,0.00075517705,0.0013467054,0.00094767904,0.0015179254,0.00032785238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024956107,0.00014952237,0.007847535,0.00023351154,0.0006548031,0.000178728,0.00017906615,0.7356659,0.0020728295,0.050747134,0.002640309,0.1993811],"study_design_scores_gemma":[0.000011692823,0.00001990576,0.0008055654,0.000022409886,0.000058268684,0.00004137673,0.000008981647,0.9653125,0.00026265145,0.032808542,0.00063229795,0.000015782383],"about_ca_topic_score_codex":0.01572824,"about_ca_topic_score_gemma":0.015624328,"teacher_disagreement_score":0.01572824,"about_ca_system_score_codex":0.0011831687,"about_ca_system_score_gemma":0.0015263313,"threshold_uncertainty_score":0.031273365},"labels":[],"label_agreement":null},{"id":"W2082159103","doi":"10.3389/fncom.2012.00082","title":"A model of food reward learning with dynamic reward exposure","year":2012,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McGill University; Montreal Neurological Institute and Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development","keywords":"Reward system; Psychology; Cognitive psychology; Neuroscience; Computer science","score_opus":0.029669939781906816,"score_gpt":0.24177972029732173,"score_spread":0.2121097805154149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082159103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27705064,0.0008449393,0.67086047,0.0067906673,0.00022042863,0.0001293856,0.0015908374,0.0004434021,0.04206924],"genre_scores_gemma":[0.92494357,0.0006254909,0.030777402,0.00048151976,0.00012364722,0.00033370443,0.0003261564,0.000087631604,0.04230099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996685,0.00009957368,0.000014246296,0.000094443814,0.000047931135,0.000075360396],"domain_scores_gemma":[0.999064,0.0004895166,0.00013827386,0.000065536646,0.00008324658,0.00015946582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083155476,0.00058172,0.00122362,0.00046546967,0.0005406703,0.0013798815,0.0027596713,0.0027689687,0.010758857],"category_scores_gemma":[0.0026245248,0.00046867997,0.0012930669,0.00072309194,0.001534831,0.001872044,0.0015271206,0.0021443123,0.0008296232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015924394,0.000106318344,0.0012796294,0.00008277555,0.00005096391,0.00026431674,0.00013237604,0.74548435,0.00257805,0.24289548,0.0013478566,0.005618604],"study_design_scores_gemma":[0.00005923354,0.000051055886,0.00047802879,0.0000088639445,0.000015021357,0.000049954717,0.0000133727635,0.9361324,0.000106591,0.062438995,0.000628499,0.000018064176],"about_ca_topic_score_codex":0.008475328,"about_ca_topic_score_gemma":0.0051191323,"teacher_disagreement_score":0.010758857,"about_ca_system_score_codex":0.0010911656,"about_ca_system_score_gemma":0.0011672387,"threshold_uncertainty_score":0.035991907},"labels":[],"label_agreement":null},{"id":"W2083863866","doi":"10.3389/fncom.2012.00069","title":"Using “Smart Stimulators” to Treat Parkinson’s Disease: Re-Engineering Neurostimulation Devices","year":2012,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Princess Margaret Cancer Centre; University of Toronto; Lawson Health Research Institute; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Mitacs","keywords":"Neurostimulation; Parkinson's disease; Medicine; Deep brain stimulation; Disease; Neuroscience; Physical medicine and rehabilitation; Psychology; Internal medicine; Stimulation","score_opus":0.04689149561771412,"score_gpt":0.3110989888850539,"score_spread":0.2642074932673398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083863866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24495287,0.062617995,0.63198113,0.0115761105,0.0016774762,0.0002679518,0.0001772644,0.0012009911,0.045548167],"genre_scores_gemma":[0.7170385,0.028532108,0.23560856,0.0027037258,0.0003303079,0.00016725947,0.0001398365,0.0001328011,0.015346879],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9999362,0.000013454088,0.000005808592,0.000013935045,0.000023059252,0.000007575392],"domain_scores_gemma":[0.9999616,0.000015737025,0.0000047507856,0.0000068954246,0.0000073943743,0.0000035458977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017023123,0.0002731742,0.00018395603,0.00015092992,0.000096499854,0.00033558297,0.00027714862,0.00048313013,0.0013324496],"category_scores_gemma":[0.0002324376,0.000107900676,0.0002576765,0.000099003264,0.00039786266,0.00075571163,0.00026690098,0.00040033867,0.0005174791],"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":[0.0003418959,0.00013687012,0.0009683153,0.0011706178,0.00010875273,0.0010849525,0.00043352018,0.011357975,0.37923807,0.023945866,0.008657915,0.5725552],"study_design_scores_gemma":[0.0004736997,0.003212516,0.0066679525,0.0007250118,0.0004752538,0.0141997365,0.0007143044,0.10423843,0.34081638,0.09895444,0.42924148,0.00028078054],"about_ca_topic_score_codex":0.00021790482,"about_ca_topic_score_gemma":0.00063550944,"teacher_disagreement_score":0.0013324496,"about_ca_system_score_codex":0.00012877127,"about_ca_system_score_gemma":0.000113276175,"threshold_uncertainty_score":0.0044575334},"labels":[],"label_agreement":null},{"id":"W2087076820","doi":"10.3389/fncom.2014.00090","title":"Spike-timing prediction in cortical neurons with active dendrites","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Wilfrid Laurier University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; European Commission","keywords":"Soma; Apical dendrite; Tuft; Compartment (ship); Neuroscience; Dendrite (mathematics); Electrophysiology; Spike (software development); Pyramidal cell; Impulse (physics); Physics; Biological system; Computer science; Biology; Mathematics; Geology","score_opus":0.020884619104409054,"score_gpt":0.24366228604403428,"score_spread":0.22277766693962522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087076820","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":[0.8940252,0.00009179395,0.10447638,0.00012898071,0.000010119946,0.000012147317,0.00010715662,0.00013129078,0.0010169956],"genre_scores_gemma":[0.9961063,0.000037512877,0.0033945697,0.000008807888,0.0000027310086,0.000010653237,0.00003459337,0.000010471143,0.00039430909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99995685,0.000010066318,0.000003337661,0.000010234907,0.000009404745,0.0000100931475],"domain_scores_gemma":[0.99972874,0.00013759291,0.00003933623,0.000025128127,0.00003861998,0.00003060711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000190389,0.00029876316,0.0005264236,0.00014288374,0.00017131947,0.00044815795,0.0005689258,0.0006484313,0.0002908932],"category_scores_gemma":[0.0010235504,0.00027195126,0.00034151148,0.00015569736,0.00032439752,0.00053656707,0.00022451761,0.00037891575,0.00007669485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030122412,0.0000104269775,0.0010143715,0.000013394123,0.000008761191,0.000063707266,0.000020613466,0.9912089,0.005302339,0.0013834661,0.000041203566,0.00090263446],"study_design_scores_gemma":[0.0000021669894,0.0000036605854,0.00017863579,4.340013e-7,0.0000011594534,0.0000054251846,0.0000016820807,0.99900657,0.00035666933,0.0004309871,0.000011398141,0.000001176576],"about_ca_topic_score_codex":0.007286152,"about_ca_topic_score_gemma":0.004672273,"teacher_disagreement_score":0.007286152,"about_ca_system_score_codex":0.0006814286,"about_ca_system_score_gemma":0.0006106511,"threshold_uncertainty_score":0.014487505},"labels":[],"label_agreement":null},{"id":"W2097247951","doi":"10.3389/fncom.2012.00086","title":"Extracting functionally feedforward networks from a population of spiking neurons","year":2012,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Institute for Biological Sciences; University of Ottawa","funders":"","keywords":"DNQX; Feed forward; AMPA receptor; Neuroscience; Antagonist; Physics; Population; Computer science; Biological system; NMDA receptor; Topology (electrical circuits); Biology; Receptor; Mathematics","score_opus":0.029168135767791644,"score_gpt":0.2582836131411848,"score_spread":0.22911547737339316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097247951","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":[0.6765597,0.000120253964,0.3216397,0.000071125374,0.000009398918,0.000050520743,0.00023638629,0.00033693816,0.0009759554],"genre_scores_gemma":[0.92442256,0.00009251305,0.07452506,0.000014794339,0.000008765983,0.000045926834,0.0004116325,0.000019898907,0.0004587321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998909,0.000023940687,0.0000076076267,0.000029484838,0.00002801022,0.000019980733],"domain_scores_gemma":[0.99944645,0.00027382674,0.00009760659,0.000049430382,0.00009840563,0.0000342618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036585136,0.0007032536,0.0003409843,0.0010759906,0.00020261145,0.00041056605,0.00041417216,0.00050801894,0.00043241627],"category_scores_gemma":[0.0019290468,0.00037391804,0.0005508313,0.0005174599,0.00028369963,0.00045017523,0.00033717407,0.00039976343,0.00012374535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103126156,0.00006513421,0.009109588,0.000106086045,0.00008173946,0.00042296096,0.00016883753,0.882999,0.049319003,0.0032251002,0.0002640702,0.054135315],"study_design_scores_gemma":[0.0000023806115,0.000015998554,0.0017787396,0.0000026338053,0.000006255613,0.000024358915,0.000014106654,0.9948249,0.0018911712,0.0013603894,0.000075003205,0.0000040924647],"about_ca_topic_score_codex":0.0042188056,"about_ca_topic_score_gemma":0.006302647,"teacher_disagreement_score":0.0042188056,"about_ca_system_score_codex":0.0005037409,"about_ca_system_score_gemma":0.0005497747,"threshold_uncertainty_score":0.008388519},"labels":[],"label_agreement":null},{"id":"W2103536139","doi":"10.3389/fncom.2012.00083","title":"An investigation of dendritic delay in octopus cells of the mammalian cochlear nucleus","year":2012,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Health and Medical Research Council; Medical Research Council; State Government of Victoria; Australian Government","keywords":"octopus (software); Nucleus; Cochlear nucleus; Neuroscience; Biology; Physics","score_opus":0.021131673404641164,"score_gpt":0.24666700895272387,"score_spread":0.2255353355480827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103536139","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":[0.9943189,0.00015648529,0.004864263,0.000012686912,0.000005646661,0.0000051277943,0.00006784047,0.000018493054,0.0005505137],"genre_scores_gemma":[0.99817693,0.0001382664,0.0013581794,0.000006476749,0.0000015366223,0.0000039893725,0.00006276886,0.000004261262,0.00024747793],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999696,0.0000016553822,0.000002238513,0.000009657468,0.000009034741,0.000007742045],"domain_scores_gemma":[0.99987626,0.000032206546,0.000025585561,0.000012464699,0.000027610187,0.000025813544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006100437,0.00010407014,0.00016229524,0.00016118944,0.00016554982,0.00027286937,0.0002474451,0.00024118174,0.0004902794],"category_scores_gemma":[0.0003039151,0.00007527469,0.00015607104,0.00017195233,0.00010892039,0.00030722067,0.00015637596,0.00025137607,0.00008464477],"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":[0.00010774696,0.000016717444,0.0036387169,0.000044230987,0.000006022155,0.00017760861,0.000050912513,0.0018558853,0.9900232,0.00070574804,0.000015876392,0.0033574156],"study_design_scores_gemma":[0.00005405703,0.00076797674,0.1618011,0.000025764473,0.000106685315,0.0020153285,0.000441518,0.11722847,0.7130428,0.0020154363,0.002442983,0.000057931822],"about_ca_topic_score_codex":0.0015659163,"about_ca_topic_score_gemma":0.0014307172,"teacher_disagreement_score":0.0015659163,"about_ca_system_score_codex":0.00039836724,"about_ca_system_score_gemma":0.00024731443,"threshold_uncertainty_score":0.0031136274},"labels":[],"label_agreement":null},{"id":"W2104733366","doi":"10.3389/fncom.2012.00023","title":"Population coding in sparsely connected networks of noisy neurons","year":2012,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coding (social sciences); Computer science; Population; Statistics; Mathematics; Medicine","score_opus":0.026744361757778227,"score_gpt":0.22992514976630685,"score_spread":0.20318078800852862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104733366","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":[0.5128552,0.0002583897,0.48124853,0.00050694373,0.00003211759,0.000030025469,0.000130257,0.00016665511,0.004772086],"genre_scores_gemma":[0.98950374,0.00009810572,0.0089063775,0.00004255822,0.0000133624,0.000028966742,0.000043781296,0.000014605649,0.0013484462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997029,0.0001008274,0.000012586134,0.000071296876,0.00006784949,0.00004457408],"domain_scores_gemma":[0.99813545,0.0011513325,0.00033813657,0.00011600609,0.00016985218,0.000089248984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070658536,0.00025789032,0.00044486395,0.0004799979,0.00034214664,0.00075527927,0.000732407,0.0006860233,0.0007613403],"category_scores_gemma":[0.0043847593,0.0003741953,0.0004028167,0.0003231816,0.001179406,0.0014637616,0.0006091447,0.00053070777,0.00008800081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006027628,0.000015262236,0.0009468051,0.000023483753,0.000020872785,0.00010506116,0.00008842633,0.94505525,0.004177596,0.046072192,0.000153126,0.0032817011],"study_design_scores_gemma":[0.000004493231,0.0000070284505,0.00022712351,0.000002110589,0.0000030013318,0.000011784075,0.0000071527757,0.9836247,0.00029593182,0.015747504,0.000065306354,0.000003886813],"about_ca_topic_score_codex":0.002976954,"about_ca_topic_score_gemma":0.0024115327,"teacher_disagreement_score":0.002976954,"about_ca_system_score_codex":0.000936253,"about_ca_system_score_gemma":0.00030647995,"threshold_uncertainty_score":0.006793022},"labels":[],"label_agreement":null},{"id":"W2121102898","doi":"10.3389/fncom.2011.00001","title":"Mechanisms Gating the Flow of Information in the Cortex: What They Might Look Like and What Their Uses may be","year":2011,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Gating; Neuroscience; Bistability; Thalamus; Cortex (anatomy); Electrophysiology; Biological neural network; Computer science; Physics; Psychology","score_opus":0.030034955881698293,"score_gpt":0.22825193641538555,"score_spread":0.19821698053368725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121102898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15194096,0.15875238,0.5071608,0.108942606,0.004205853,0.0002825019,0.0013559788,0.0026086164,0.06475021],"genre_scores_gemma":[0.8439039,0.05762522,0.08131122,0.0064150933,0.0019053273,0.0007129353,0.00047940802,0.00033518346,0.0073116967],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994623,0.0001257258,0.00003376669,0.00014176719,0.00009544841,0.00014108552],"domain_scores_gemma":[0.9990434,0.00025779195,0.00013796121,0.00020643347,0.00018329202,0.00017120678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014557525,0.0008561858,0.0012450573,0.0011214579,0.0009637309,0.005835566,0.002761646,0.0043443325,0.0036057779],"category_scores_gemma":[0.0030773703,0.0007048576,0.0012322641,0.00069689454,0.008545187,0.01444959,0.0013928269,0.0025489016,0.0011575277],"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":[0.00027100978,0.00007421342,0.0046625254,0.0014454599,0.00023098338,0.0004196857,0.0016712591,0.010205996,0.02207211,0.87891847,0.005664169,0.07436414],"study_design_scores_gemma":[0.00006517686,0.000112687696,0.0031004935,0.0004864222,0.00009618451,0.00061192113,0.00086013024,0.021747064,0.005396283,0.93671376,0.030656423,0.0001534966],"about_ca_topic_score_codex":0.0013438943,"about_ca_topic_score_gemma":0.00076444575,"teacher_disagreement_score":0.005835566,"about_ca_system_score_codex":0.001308144,"about_ca_system_score_gemma":0.0011271574,"threshold_uncertainty_score":0.01206255},"labels":[],"label_agreement":null},{"id":"W2123232591","doi":"10.3389/fncom.2013.00166","title":"Computational modeling of the negative priming effect based on inhibition patterns and working memory","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; Jewish General Hospital","funders":"Korea Advanced Institute of Science and Technology; Korea Science and Engineering Foundation; National Research Foundation of Korea; National Research Foundation; Brown University","keywords":"Negative priming; Stroop effect; Working memory; Disinhibition; Priming (agriculture); Psychology; Task (project management); Differential effects; Cognitive psychology; Neuroscience; Selective attention; Cognition; Medicine; Biology","score_opus":0.06886659756476075,"score_gpt":0.30643656355375326,"score_spread":0.2375699659889925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123232591","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":[0.5844167,0.0006798843,0.3931299,0.0008586808,0.000096697004,0.00011097006,0.00045587757,0.0004983698,0.0197529],"genre_scores_gemma":[0.96895516,0.0002527358,0.027533328,0.00006318377,0.000027855065,0.00020298443,0.0001205755,0.000046164027,0.0027980213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999274,0.00002297375,0.0000040205973,0.000013243643,0.000012351768,0.0000198851],"domain_scores_gemma":[0.99963343,0.0002190656,0.00004740839,0.00002505579,0.00003850667,0.000036566893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027705877,0.00048029743,0.00070239126,0.0003774684,0.0003583017,0.0006280937,0.001597491,0.0010010579,0.0019671267],"category_scores_gemma":[0.00089959253,0.00035967285,0.0007555327,0.00030680417,0.0004849757,0.00070934114,0.0004981281,0.0005073601,0.00018793768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045394776,0.000033447886,0.0007522377,0.00002778306,0.000025286536,0.00013016029,0.00002352096,0.98742175,0.0015240894,0.007881761,0.0002296069,0.0019050646],"study_design_scores_gemma":[0.0000049382156,0.0000045125735,0.00007159463,8.101894e-7,0.0000031132613,0.000005911071,0.0000013868276,0.99863285,0.000067430854,0.0011700735,0.0000359001,0.0000014362669],"about_ca_topic_score_codex":0.008158409,"about_ca_topic_score_gemma":0.0060590836,"teacher_disagreement_score":0.008158409,"about_ca_system_score_codex":0.0005481184,"about_ca_system_score_gemma":0.000841839,"threshold_uncertainty_score":0.01622188},"labels":[],"label_agreement":null},{"id":"W2137058429","doi":"10.3389/fncom.2014.00123","title":"Structured chaos shapes spike-response noise entropy in balanced neural networks","year":2014,"lang":"en","type":"preprint","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Washington; Burroughs Wellcome Fund; National Institutes of Health; National Science Foundation","keywords":"Chaotic; Computer science; Spike train; Artificial neural network; Entropy (arrow of time); Statistical physics; Spike (software development); ENCODE; Stimulus (psychology); Pattern recognition (psychology); Physics; Artificial intelligence; Biology","score_opus":0.015427865304241544,"score_gpt":0.251185794075846,"score_spread":0.23575792877160448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137058429","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":[0.90583235,0.00017098524,0.090746485,0.00021053516,0.000013721509,0.000012991053,0.00007283857,0.00010889015,0.0028311955],"genre_scores_gemma":[0.9984775,0.000031912245,0.0012712176,0.000008616927,0.0000045907577,0.0000065853324,0.000017801234,0.000010237139,0.00017140213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998661,0.000041190306,0.000007314418,0.00002496913,0.000034043063,0.000026343749],"domain_scores_gemma":[0.9989355,0.00061074784,0.0002261973,0.00005603466,0.00007916636,0.00009221266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040885436,0.00024050318,0.00032328698,0.0004779582,0.0002445432,0.0006772439,0.00035283243,0.00039697668,0.0007558287],"category_scores_gemma":[0.0038593432,0.00022138114,0.00022822189,0.00018125177,0.00083641824,0.0010860903,0.0005887681,0.00029437672,0.00008668379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034752156,0.00005828097,0.010787032,0.00012923517,0.00008432445,0.0005416941,0.00036177385,0.7342814,0.09302965,0.1494453,0.0005489685,0.010384774],"study_design_scores_gemma":[0.000010935138,0.000027242057,0.0031684234,0.000007541371,0.0000062097192,0.000048742717,0.0000185118,0.9461567,0.001927946,0.048516642,0.00010120213,0.000009948421],"about_ca_topic_score_codex":0.0006683093,"about_ca_topic_score_gemma":0.0006991549,"teacher_disagreement_score":0.0007558287,"about_ca_system_score_codex":0.00053041015,"about_ca_system_score_gemma":0.00018913475,"threshold_uncertainty_score":0.003848374},"labels":[],"label_agreement":null},{"id":"W2137513148","doi":"10.3389/fncom.2013.00109","title":"Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian mixture Kalman filtering","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"RIKEN Brain Science Institute; RIKEN; Natural Sciences and Engineering Research Council of Canada","keywords":"Inhibitory postsynaptic potential; Excitatory postsynaptic potential; Kalman filter; Gaussian; Neuroscience; Computer science; Artificial intelligence; Chemistry; Biology; Computational chemistry","score_opus":0.023498745126186205,"score_gpt":0.24860614291712108,"score_spread":0.22510739779093486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137513148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012753351,0.00016233228,0.98614675,0.00003450701,0.000015818134,0.000020115805,0.000025429392,0.00060871965,0.00023301085],"genre_scores_gemma":[0.4460028,0.00051816145,0.55131423,0.00008884095,0.000038761915,0.00014312116,0.00026498534,0.00019732474,0.0014318307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995179,0.00006922401,0.000039710092,0.00017533034,0.00013436175,0.00006344239],"domain_scores_gemma":[0.99892384,0.0006171876,0.00012787813,0.000098988035,0.000193909,0.00003825847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011979151,0.0012877452,0.0012839137,0.0006978738,0.00041174423,0.00083390507,0.0013686802,0.001187539,0.0006929134],"category_scores_gemma":[0.0053325924,0.00075427594,0.0009711818,0.00067317736,0.0006187022,0.0014207534,0.0008585483,0.0016593041,0.0004361311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016139196,0.00007378929,0.004710559,0.00015067104,0.00018414058,0.00012340021,0.00022396736,0.74920946,0.027016768,0.0072018304,0.0009072837,0.21003668],"study_design_scores_gemma":[0.000005368664,0.000011625259,0.0007238175,0.000006392969,0.000012303148,0.000026139127,0.0000065755985,0.99368495,0.003314828,0.0019300382,0.0002622186,0.000015730007],"about_ca_topic_score_codex":0.013212953,"about_ca_topic_score_gemma":0.013725056,"teacher_disagreement_score":0.013212953,"about_ca_system_score_codex":0.0006933061,"about_ca_system_score_gemma":0.001980923,"threshold_uncertainty_score":0.026272058},"labels":[],"label_agreement":null},{"id":"W2141985218","doi":"10.3389/fncom.2015.00116","title":"Leg mechanics contribute to establishing swing phase trajectories during memory-guided stepping movements in walking cats: a computational analysis","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Swing; Torque; Trajectory; Inverse dynamics; Hindlimb; Knee flexion; Position (finance); Biomechanics; Physics; Computer science; Physical medicine and rehabilitation; Anatomy; Medicine; Acoustics; Classical mechanics; Kinematics","score_opus":0.021027769516465546,"score_gpt":0.2643418404448394,"score_spread":0.24331407092837384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141985218","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":[0.98906904,0.000038713806,0.009892557,0.000050462906,0.0000025284012,0.000018216055,0.000086082706,0.000047673548,0.0007947838],"genre_scores_gemma":[0.9967405,0.00004268772,0.002831137,0.000007692842,0.000001716243,0.000020890739,0.0000723872,0.00001106481,0.00027201578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999974,0.000004734336,0.00000221076,0.000006709381,0.000006265968,0.0000059701224],"domain_scores_gemma":[0.9998122,0.000102004204,0.000026663774,0.000015566136,0.000021494283,0.000022155655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012136817,0.00024562087,0.00034569734,0.0003408663,0.0003531247,0.00044622752,0.00045312932,0.0007180178,0.0009080768],"category_scores_gemma":[0.00049559213,0.00034860196,0.00047850976,0.00021822896,0.0003290636,0.00025812254,0.0002780946,0.00026437722,0.00009712337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051786225,0.000059153794,0.0074740048,0.000043479016,0.000042813634,0.00020318755,0.000053804233,0.9823674,0.0063207233,0.0004420625,0.00005537992,0.0028861694],"study_design_scores_gemma":[0.00000538833,0.000015390131,0.001929491,0.0000016038275,0.000007818754,0.000012955947,0.000009757461,0.99766076,0.00023775437,0.000085123174,0.00003076704,0.0000031754546],"about_ca_topic_score_codex":0.01899059,"about_ca_topic_score_gemma":0.014415881,"teacher_disagreement_score":0.01899059,"about_ca_system_score_codex":0.00042105519,"about_ca_system_score_gemma":0.00085017557,"threshold_uncertainty_score":0.03776008},"labels":[],"label_agreement":null},{"id":"W2150060182","doi":"10.3389/fncom.2013.00021","title":"Local field potentials reflect multiple spatial scales in V4","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Local field potential; Field (mathematics); Neuroscience; Psychology; Mathematics","score_opus":0.007648711472084798,"score_gpt":0.21722046716460935,"score_spread":0.20957175569252456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150060182","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":[0.99413586,0.00013377289,0.004679477,0.000034071785,0.0000025520985,0.0000072652065,0.00008375846,0.00007272822,0.00085039745],"genre_scores_gemma":[0.99849784,0.000029424193,0.0011999399,0.000008325476,0.000002376894,0.0000035343985,0.000061306346,0.000013198099,0.00018400243],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99988866,0.000016198175,0.000004528882,0.00003515607,0.00002441026,0.000030969633],"domain_scores_gemma":[0.99978787,0.000063242696,0.000042552772,0.000023485423,0.00005477141,0.000028085193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016653587,0.00025681785,0.00023850916,0.0010387547,0.00027527806,0.0006621482,0.00037884878,0.0003066568,0.00095994625],"category_scores_gemma":[0.0013363252,0.00023653082,0.00029279946,0.00035125637,0.00046365513,0.0006132024,0.0006560142,0.0002530473,0.00012735196],"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":[0.00038321296,0.00005647924,0.105091095,0.00015788822,0.00011952722,0.00037138286,0.00058585074,0.01813292,0.8234212,0.0017083742,0.00042759956,0.04954443],"study_design_scores_gemma":[0.00003776345,0.00016737744,0.8525122,0.000030183659,0.00009458477,0.00062163686,0.00045231037,0.090219386,0.049764305,0.004844121,0.0012008807,0.000055208544],"about_ca_topic_score_codex":0.011910621,"about_ca_topic_score_gemma":0.009922796,"teacher_disagreement_score":0.011910621,"about_ca_system_score_codex":0.00066564046,"about_ca_system_score_gemma":0.00031797195,"threshold_uncertainty_score":0.023682594},"labels":[],"label_agreement":null},{"id":"W2157667978","doi":"10.3389/fncom.2013.00144","title":"Experimentally constrained CA1 fast-firing parvalbumin-positive interneuron network models exhibit sharp transitions into coherent high frequency rhythms","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Douglas Mental Health University Institute; McGill University; University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Institutes of Health Research; Government of Ontario; Compute Canada","keywords":"Neuroscience; Excitatory postsynaptic potential; Inhibitory postsynaptic potential; Interneuron; Hippocampal formation; Gating; Physics; Population; Network model; Coherence (philosophical gambling strategy); Coupling (piping); Biological system; Computer science; Biology; Artificial intelligence; Materials science","score_opus":0.03084576498156336,"score_gpt":0.2952296654546723,"score_spread":0.2643839004731089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157667978","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":[0.94381267,0.00015329088,0.048339583,0.00020838971,0.000013492022,0.000041270312,0.00027607757,0.000093767405,0.007061451],"genre_scores_gemma":[0.9938314,0.000098949226,0.004892562,0.000019848512,0.0000037784637,0.00006720046,0.0001197409,0.000016734857,0.0009497766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999931,0.00001672876,0.0000033041626,0.000018222981,0.000013107158,0.000017635895],"domain_scores_gemma":[0.99968326,0.0001453346,0.00008880959,0.000019711302,0.000031478838,0.00003151311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019102287,0.0005032245,0.00034494494,0.00037871135,0.00024043508,0.00039464992,0.0005787304,0.0006368701,0.0010673297],"category_scores_gemma":[0.0009071032,0.0002901819,0.0004920545,0.00016102538,0.0005188801,0.0004787043,0.0004351207,0.0003879812,0.00009072866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001655144,0.000012303177,0.00059624837,0.000013876252,0.00001097951,0.0000472233,0.000019694236,0.99361527,0.0027101433,0.0025148115,0.000052556643,0.0003902199],"study_design_scores_gemma":[0.000008623572,0.000013512875,0.00035436364,0.000002414,0.0000054873235,0.000010284289,0.000009324359,0.99778396,0.00037968863,0.0013414399,0.00008750203,0.0000034546058],"about_ca_topic_score_codex":0.0069258953,"about_ca_topic_score_gemma":0.007105341,"teacher_disagreement_score":0.0069258953,"about_ca_system_score_codex":0.0008284604,"about_ca_system_score_gemma":0.00042999067,"threshold_uncertainty_score":0.013771117},"labels":[],"label_agreement":null},{"id":"W2164670619","doi":"10.3389/neuro.10.020.2009","title":"Geometrical tile design for complex neighborhoods","year":2009,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Tile; Rectangle; Square (algebra); Simple (philosophy); Cuboid; Computer science; Geometric shape; Geometry; Von Neumann architecture; Mathematics; Algorithm; Pure mathematics; Geography","score_opus":0.04911912229643655,"score_gpt":0.2919502913458633,"score_spread":0.24283116904942675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164670619","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":[0.11735562,0.00025852135,0.87063676,0.0001262497,0.000060476155,0.000052005293,0.00008053224,0.0002484237,0.011181349],"genre_scores_gemma":[0.72858113,0.00025741785,0.26661098,0.000060042763,0.00002303118,0.00023726637,0.00014292647,0.00010482004,0.0039823945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971384,0.00009858585,0.000020149473,0.000061142804,0.00007151707,0.000034729277],"domain_scores_gemma":[0.9995778,0.00016159457,0.000061795865,0.00009058308,0.0000710523,0.000037153462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034314246,0.0002885341,0.00046135602,0.0003835742,0.0004384732,0.00076379435,0.0005498902,0.0005267589,0.0020270003],"category_scores_gemma":[0.001580937,0.00027401882,0.00054657285,0.00028071075,0.0011123832,0.00071186165,0.0008335619,0.00031056342,0.00035939127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010414863,0.000017253964,0.0008190065,0.0001166089,0.000018802355,0.00015763348,0.00019866021,0.56988263,0.012876064,0.39137998,0.0010355425,0.023393666],"study_design_scores_gemma":[0.000032395936,0.00008215614,0.00025566912,0.000016201637,0.000011940165,0.00014932232,0.000055610148,0.86778754,0.004121884,0.119285524,0.008182777,0.000018884868],"about_ca_topic_score_codex":0.0006857272,"about_ca_topic_score_gemma":0.00067123625,"teacher_disagreement_score":0.0020270003,"about_ca_system_score_codex":0.00052320707,"about_ca_system_score_gemma":0.0003592469,"threshold_uncertainty_score":0.006780982},"labels":[],"label_agreement":null},{"id":"W2165919804","doi":"10.3389/fncom.2013.00083","title":"Motor cortical regulation of sparse synergies provides a framework for the flexible control of precision walking","year":2013,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Control (management); Motor control; Front (military); Neuroscience; Psychology; Artificial intelligence; Geology","score_opus":0.029767602344099236,"score_gpt":0.26868107179519773,"score_spread":0.2389134694510985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165919804","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":[0.8692908,0.00065420143,0.124122635,0.00016950587,0.000031198124,0.000093445735,0.00023110933,0.00036684604,0.0050401064],"genre_scores_gemma":[0.9882608,0.00020050455,0.010699504,0.000030767347,0.000012812108,0.000046598947,0.0000867432,0.000025883786,0.0006364366],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998456,0.000018108063,0.000011988508,0.000040834246,0.000044472847,0.000038901006],"domain_scores_gemma":[0.9997621,0.000063466,0.00005378482,0.000048929018,0.000031611922,0.000040178264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002508931,0.00031001086,0.00034098103,0.00043394702,0.00017548687,0.0005969805,0.00032976925,0.00025152348,0.0012408947],"category_scores_gemma":[0.0007777724,0.0002495345,0.00032774362,0.00020689084,0.00050979876,0.0005427558,0.00076113973,0.00034457125,0.00016986203],"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":[0.00013662067,0.00003788273,0.0024488233,0.00009085455,0.00003864154,0.00018654324,0.000100544654,0.0044168346,0.9679676,0.0017978696,0.00010013042,0.022677623],"study_design_scores_gemma":[0.00014794058,0.001777797,0.6644895,0.00011629665,0.00015636685,0.0011878741,0.00034992245,0.1163621,0.18755946,0.023383591,0.0043540434,0.0001150872],"about_ca_topic_score_codex":0.001005016,"about_ca_topic_score_gemma":0.0017287575,"teacher_disagreement_score":0.0012408947,"about_ca_system_score_codex":0.00024841074,"about_ca_system_score_gemma":0.00036801206,"threshold_uncertainty_score":0.004151225},"labels":[],"label_agreement":null},{"id":"W2169964703","doi":"10.3389/fncom.2014.00028","title":"A hypothesis on the role of perturbation size on the human sensorimotor adaptation","year":2014,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Adaptation (eye); Cognitive psychology; Perturbation (astronomy); Psychology; Neuroscience; Computer science; Physics","score_opus":0.03402615314115705,"score_gpt":0.2269771812578156,"score_spread":0.19295102811665854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169964703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41019136,0.0025319792,0.50254977,0.007172036,0.0017546271,0.00025408156,0.0010695758,0.0022931807,0.072183564],"genre_scores_gemma":[0.977242,0.00043914968,0.018192278,0.0004351186,0.00021510631,0.0000948636,0.00012043745,0.00017748396,0.0030835352],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99972683,0.000044917648,0.000014718262,0.00012798092,0.000047251448,0.00003844121],"domain_scores_gemma":[0.99844104,0.0008461371,0.00014960582,0.00024780887,0.00016451588,0.00015090895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007753729,0.00072817534,0.0005624046,0.0005423299,0.00030929138,0.0014049902,0.0010313322,0.001289626,0.013415741],"category_scores_gemma":[0.0058056363,0.0002972993,0.0005937905,0.00024364695,0.0016726094,0.0023726567,0.0009855392,0.00076507067,0.0016001748],"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":[0.0017329679,0.00038017132,0.009118745,0.00084062316,0.0002499001,0.0011589708,0.0009792651,0.02506418,0.60057276,0.22464457,0.0063211815,0.12893662],"study_design_scores_gemma":[0.0003830749,0.0018578968,0.080330335,0.0002580954,0.0003117348,0.0021854152,0.00075784983,0.34792468,0.060937442,0.49665028,0.008155069,0.00024809525],"about_ca_topic_score_codex":0.00035070698,"about_ca_topic_score_gemma":0.00019309364,"teacher_disagreement_score":0.013415741,"about_ca_system_score_codex":0.00025199083,"about_ca_system_score_gemma":0.00041195154,"threshold_uncertainty_score":0.04488015},"labels":[],"label_agreement":null},{"id":"W2177943730","doi":"10.3389/fncom.2015.00133","title":"Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Montreal Cognitive Assessment; Resting state fMRI; Dementia; Electroencephalography; Cognition; Correlation; Audiology; Psychology; Disconnection; Neuropsychology; Cognitive impairment; Cardiology; Internal medicine; Medicine; Neuroscience; Mathematics; Disease","score_opus":0.042498492898198195,"score_gpt":0.2710015920681219,"score_spread":0.22850309916992373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177943730","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":[0.9974935,0.0002308114,0.0016185851,0.000028720684,0.0000037017428,0.000010304333,0.00025233199,0.000010532289,0.0003514416],"genre_scores_gemma":[0.9989961,0.00010620695,0.00052661484,0.0000049437635,0.0000053681756,0.00000915896,0.00026143197,0.0000018003717,0.000088454406],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989355,0.000032880787,0.000010996588,0.000031675656,0.000016889782,0.000014041583],"domain_scores_gemma":[0.99965155,0.00011575022,0.00013013961,0.000025594256,0.000046035053,0.000031028354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020737076,0.00027303514,0.00015985964,0.0006610845,0.00011874166,0.00030182162,0.00014539932,0.00014096573,0.0007419597],"category_scores_gemma":[0.0013022232,0.00006466666,0.00016805592,0.00050843245,0.00009691798,0.00023244889,0.00017383463,0.00012748573,0.00007570445],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013526214,0.00013687373,0.91678584,0.00012091534,0.00044638634,0.00068873836,0.00047152405,0.0035415858,0.015348981,0.00017490296,0.0004905819,0.060441118],"study_design_scores_gemma":[0.000011987113,0.00013038726,0.9902631,0.000007253729,0.00006413814,0.00051375997,0.0001237379,0.0077341944,0.0006463116,0.0002911663,0.0002070592,0.000006882911],"about_ca_topic_score_codex":0.0019384172,"about_ca_topic_score_gemma":0.0025146455,"teacher_disagreement_score":0.0019384172,"about_ca_system_score_codex":0.00014075123,"about_ca_system_score_gemma":0.00007901927,"threshold_uncertainty_score":0.0038542151},"labels":[],"label_agreement":null},{"id":"W2197068329","doi":"10.3389/fncom.2015.00148","title":"The Compression Flow as a Measure to Estimate the Brain Connectivity Changes in Resting State fMRI and 18FDG-PET Alzheimer's Disease Connectomes","year":2015,"lang":"en","type":"article","venue":"Frontiers in 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":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Meso Scale Diagnostics; Regione Lombardia; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Connectome; Resting state fMRI; Neuroscience; Connectomics; Functional connectivity; Measure (data warehouse); Psychology; Computer science; Data mining","score_opus":0.05313598224133688,"score_gpt":0.32115879373403305,"score_spread":0.2680228114926962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2197068329","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":[0.59461474,0.0012058885,0.40125155,0.0001635623,0.000039025468,0.00016853883,0.0007908225,0.0007023449,0.0010634868],"genre_scores_gemma":[0.917951,0.00040862313,0.08018131,0.000029889134,0.00006744073,0.00013161471,0.00075125054,0.000055957167,0.00042306512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997348,0.00008494636,0.00002074607,0.00005264685,0.00007815401,0.000028706758],"domain_scores_gemma":[0.9982346,0.0011865756,0.00028403956,0.00009926259,0.00014180684,0.000053599342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012371759,0.0005466622,0.00031010868,0.0043046763,0.00024759473,0.0005689364,0.0003114399,0.00055690674,0.0006715341],"category_scores_gemma":[0.0052603334,0.00019384167,0.0004144931,0.0013257533,0.0006028144,0.00096193474,0.00040282437,0.00032525035,0.000083242594],"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":[0.0016472383,0.00027810087,0.18325157,0.0006427159,0.0005622058,0.00069534563,0.0008219012,0.27367905,0.0843649,0.016697856,0.0027640522,0.43459508],"study_design_scores_gemma":[0.000027642835,0.00032091522,0.1770944,0.000051461953,0.00011817441,0.0012685164,0.0001039914,0.7934144,0.01731482,0.008784754,0.0014256996,0.00007522075],"about_ca_topic_score_codex":0.0022855808,"about_ca_topic_score_gemma":0.0016863099,"teacher_disagreement_score":0.0043046763,"about_ca_system_score_codex":0.00044276787,"about_ca_system_score_gemma":0.00030730417,"threshold_uncertainty_score":0.0065428615},"labels":[],"label_agreement":null},{"id":"W2234885283","doi":"10.3389/fncom.2015.00154","title":"An Assessment of Six Muscle Spindle Models for Predicting Sensory Information during Human Wrist Movements","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Institute of Neurosciences, Mental Health and Addiction; Alberta Heritage Foundation for Medical Research; Fondation pour la Recherche Médicale; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions; Whitaker International Program","keywords":"Wrist; Sensory system; Computer science; Physical medicine and rehabilitation; Neuroscience; Artificial intelligence; Psychology; Medicine; Anatomy","score_opus":0.0337492200779009,"score_gpt":0.3059877376995621,"score_spread":0.27223851762166124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2234885283","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":[0.9206147,0.00016093621,0.07668411,0.0001902877,0.000016315948,0.00017718827,0.00016260547,0.0002455375,0.0017483203],"genre_scores_gemma":[0.9888515,0.000046911944,0.010732262,0.000014734086,0.0000037403547,0.000087897264,0.000083480576,0.000013670394,0.0001657305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973387,0.00009137068,0.000026381593,0.000058866837,0.000064673295,0.000024789075],"domain_scores_gemma":[0.9966174,0.0022632836,0.00032166258,0.00017523233,0.00049212755,0.00013044658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019460355,0.00085299375,0.0004976279,0.0007410767,0.00023539446,0.00069293287,0.00072041195,0.0008500281,0.0008090871],"category_scores_gemma":[0.006437346,0.00050024217,0.00095381576,0.00027519566,0.0003067167,0.0004537998,0.00045544657,0.00043015325,0.00015882798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023205226,0.000083043706,0.009923025,0.000059536527,0.00008942504,0.00005405566,0.00008802235,0.9772315,0.0019476886,0.00023495023,0.00007756722,0.009979112],"study_design_scores_gemma":[0.000022392062,0.00015650221,0.0015870674,0.000009087906,0.000020759695,0.000021534499,0.000012367792,0.99735826,0.00053091114,0.00022875433,0.000045235796,0.00000724571],"about_ca_topic_score_codex":0.006794571,"about_ca_topic_score_gemma":0.0038325419,"teacher_disagreement_score":0.006794571,"about_ca_system_score_codex":0.0011969319,"about_ca_system_score_gemma":0.0008265116,"threshold_uncertainty_score":0.013510048},"labels":[],"label_agreement":null},{"id":"W2241635779","doi":"10.3389/fncom.2015.00142","title":"Editorial: Hierarchical Object Representations in the Visual Cortex and Computer Vision","year":2015,"lang":"en","type":"editorial","venue":"Frontiers in Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Volume (thermodynamics); Object (grammar); Front (military); Visual cortex; Neuroscience; Artificial intelligence; Vision science; Cognitive science; Computer vision; Psychology; Geography; Physics","score_opus":0.02849550142663408,"score_gpt":0.3579804058989189,"score_spread":0.3294849044722848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241635779","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":[0.00004532539,0.0040144967,0.00023727119,0.028752727,0.9653377,0.000019532488,0.00009305685,0.00007411268,0.0014257719],"genre_scores_gemma":[0.0006982837,0.0034723338,0.00016001175,0.012257763,0.97280663,0.000023446379,0.000057485693,0.00004108513,0.010482876],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99747145,0.00035934316,0.00032955987,0.00035735845,0.0012751636,0.00020713764],"domain_scores_gemma":[0.9886332,0.003866292,0.0008230815,0.00028760397,0.004413156,0.0019766244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004680194,0.0036790892,0.0034244887,0.0035429397,0.002521971,0.0059052464,0.0033110532,0.012077675,0.017347794],"category_scores_gemma":[0.015796728,0.0009530435,0.002380145,0.0011206821,0.002223098,0.0036737062,0.0014216107,0.0132125225,0.012469508],"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":[0.00005362585,0.000008601887,0.000021583894,0.00013567592,0.000015068762,0.000107452004,0.0000054614025,0.000036459678,0.00006103996,0.0003253777,0.9950376,0.0041920478],"study_design_scores_gemma":[0.00010557963,0.000037746016,0.00039364077,0.000435603,0.000066970155,0.00047822724,0.00003276455,0.00044407806,0.00027253255,0.0027349712,0.99496484,0.00003298731],"about_ca_topic_score_codex":0.0013507259,"about_ca_topic_score_gemma":0.004772524,"teacher_disagreement_score":0.017347794,"about_ca_system_score_codex":0.0024741008,"about_ca_system_score_gemma":0.0021917152,"threshold_uncertainty_score":0.058034122},"labels":[],"label_agreement":null},{"id":"W2298974907","doi":"10.3389/fncom.2016.00015","title":"Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Invariant (physics); Mean field theory; Computer science; Artificial neural network; Type (biology); Mathematics; Topology (electrical circuits); Algorithm; Physics; Combinatorics; Artificial intelligence","score_opus":0.014513276073952058,"score_gpt":0.24175385490024254,"score_spread":0.22724057882629048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298974907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22220582,0.00011280743,0.77077657,0.00024327556,0.000022955097,0.000026156707,0.00005130787,0.00016666499,0.006394472],"genre_scores_gemma":[0.9163332,0.00017195671,0.079504445,0.00007029998,0.000013767671,0.00007549694,0.00007491699,0.00011352655,0.0036424175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999819,0.00005173485,0.00001062046,0.000036351656,0.000055269393,0.000027063506],"domain_scores_gemma":[0.99936944,0.00030258019,0.00012463116,0.000059837876,0.00009497348,0.000048577054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006589417,0.00059005094,0.0004871018,0.000493212,0.0003306154,0.00068182737,0.0006850094,0.0008385178,0.0009926411],"category_scores_gemma":[0.00395602,0.00042811866,0.00055627525,0.00021474743,0.0012769501,0.0013050764,0.00089504494,0.00081902154,0.0002088379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029010847,0.000027705213,0.00074447354,0.0000612029,0.000026524509,0.00017021004,0.00015956788,0.6860457,0.016299961,0.28706422,0.00038307486,0.008988316],"study_design_scores_gemma":[0.0000051624766,0.000010099429,0.00007447012,0.000004191016,0.0000025956679,0.000023173869,0.000010925388,0.94284266,0.001428332,0.055435576,0.00015630528,0.000006448058],"about_ca_topic_score_codex":0.0016567404,"about_ca_topic_score_gemma":0.0021000507,"teacher_disagreement_score":0.0016567404,"about_ca_system_score_codex":0.001258532,"about_ca_system_score_gemma":0.00069662655,"threshold_uncertainty_score":0.009131372},"labels":[],"label_agreement":null},{"id":"W2314387497","doi":"10.3389/fncom.2016.00026","title":"Vestibular Compensation in Unilateral Patients Often Causes Both Gain and Time Constant Asymmetries in the VOR","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Vestibular and auditory disorders","field":"Neuroscience","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":"McGill University","funders":"","keywords":"Vestibular system; Vestibulo–ocular reflex; Reflex; Compensation (psychology); Vestibular nuclei; Audiology; Neuroscience; Psychology; Medicine","score_opus":0.014019596439201252,"score_gpt":0.23275826317864315,"score_spread":0.2187386667394419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314387497","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":[0.9739174,0.0012570517,0.020599097,0.00031347718,0.00007573556,0.000041293188,0.0002942729,0.0003032012,0.0031984157],"genre_scores_gemma":[0.99810165,0.00015133519,0.0012352885,0.00004373534,0.0000117443415,0.000009702217,0.000083094805,0.00001569817,0.00034773714],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99985623,0.000019778514,0.000015450867,0.000034505032,0.000045410845,0.000028594604],"domain_scores_gemma":[0.99977034,0.00008400094,0.00006188668,0.000028899733,0.000023098299,0.000031761254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017058139,0.00043785607,0.0005056375,0.0003822684,0.0002975006,0.00033010897,0.00014163456,0.00046128078,0.0039710044],"category_scores_gemma":[0.001028864,0.00010113765,0.0002960127,0.00034064558,0.0005543032,0.0003231483,0.00046055607,0.00026447754,0.0004512248],"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":[0.0022079097,0.00027050186,0.12447663,0.00077224814,0.00021607646,0.027171614,0.00080649526,0.007887846,0.56710607,0.0067080706,0.002548691,0.25982788],"study_design_scores_gemma":[0.0003814068,0.0028307536,0.55213267,0.0001846007,0.00040913315,0.14173523,0.0022977307,0.04757611,0.20976116,0.031487968,0.010955113,0.0002480897],"about_ca_topic_score_codex":0.00089682615,"about_ca_topic_score_gemma":0.001216793,"teacher_disagreement_score":0.0039710044,"about_ca_system_score_codex":0.00025380455,"about_ca_system_score_gemma":0.00034242123,"threshold_uncertainty_score":0.013284326},"labels":[],"label_agreement":null},{"id":"W2316097698","doi":"10.3389/fncom.2016.00029","title":"Statistical Evaluation of Waveform Collapse Reveals Scale-Free Properties of Neuronal Avalanches","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Ottawa","keywords":"Statistical physics; Power law; Physics; Mathematics; Statistics","score_opus":0.04995736473168048,"score_gpt":0.2696762551242278,"score_spread":0.21971889039254736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316097698","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":[0.95415086,0.00017665379,0.043933567,0.000041119227,0.000015578116,0.000050776343,0.0002647839,0.00039483607,0.00097178674],"genre_scores_gemma":[0.9931978,0.000051921972,0.006140818,0.000009690948,0.000008698159,0.00003426852,0.00036491262,0.000046627807,0.00014524125],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995253,0.00006648184,0.000046742105,0.000090249116,0.00020570234,0.000065649554],"domain_scores_gemma":[0.9937092,0.0033263585,0.0010104022,0.0006320117,0.00082173944,0.0005002252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014349992,0.00033251487,0.0003892146,0.002370271,0.00036014034,0.0006333979,0.00039493878,0.0003555591,0.0012433301],"category_scores_gemma":[0.0100592375,0.00018034085,0.00046850715,0.0011494193,0.00070687494,0.0007549695,0.0007117317,0.0008474628,0.00016923461],"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":[0.0013563079,0.0004578216,0.12804131,0.0006046228,0.00039495414,0.0022259967,0.0024668635,0.07070691,0.6095345,0.013177015,0.001436188,0.16959758],"study_design_scores_gemma":[0.000023409122,0.00085923343,0.40885726,0.00004396848,0.00007635022,0.0014367964,0.00059023564,0.50583875,0.07176964,0.009035778,0.0013318275,0.00013674586],"about_ca_topic_score_codex":0.000617213,"about_ca_topic_score_gemma":0.00039754758,"teacher_disagreement_score":0.002370271,"about_ca_system_score_codex":0.00029221742,"about_ca_system_score_gemma":0.00023031647,"threshold_uncertainty_score":0.007589102},"labels":[],"label_agreement":null},{"id":"W2342059485","doi":"10.3389/fncom.2016.00037","title":"An Eye in the Palm of Your Hand: Alterations in Visual Processing Near the Hand, a Mini-Review","year":2016,"lang":"en","type":"review","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Efference copy; Visual processing; Eye–hand coordination; Computer science; Posterior parietal cortex; Motor system; Eye movement; Visual space; Artificial intelligence; Psychology; Computer vision; Neuroscience; Perception","score_opus":0.0668305156970293,"score_gpt":0.3706910334858109,"score_spread":0.3038605177887816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342059485","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011038773,0.99880743,0.00006989309,0.00028796814,0.00037111502,0.0000050754243,0.00002795922,0.0000054202255,0.0003147174],"genre_scores_gemma":[0.0008598301,0.9978283,0.00013072022,0.0003928856,0.00045780983,0.000013211544,0.00005439427,0.0000035569683,0.00025929572],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961734,0.00005189072,0.00011494972,0.00009347699,0.00008585026,0.000036531066],"domain_scores_gemma":[0.9974866,0.0015565917,0.00033043348,0.00004388825,0.00045408227,0.00012838356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010499441,0.0010052818,0.001752818,0.0038979982,0.00032378695,0.0016321167,0.0017551909,0.0018662979,0.0057360064],"category_scores_gemma":[0.003333217,0.00043191266,0.0011724052,0.0025906824,0.0006840044,0.0026942294,0.0007915843,0.0015730701,0.0019402207],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022179812,0.00009078424,0.00076478295,0.11656859,0.00036967327,0.00038177348,0.00017120902,0.00039802073,0.0015111241,0.0029355583,0.057644796,0.81894195],"study_design_scores_gemma":[0.000033085176,0.00029413757,0.0044976384,0.03591331,0.0009766873,0.0042903526,0.00023771764,0.00017586854,0.0009037501,0.0024099597,0.95019853,0.00006903215],"about_ca_topic_score_codex":0.0017685604,"about_ca_topic_score_gemma":0.001895707,"teacher_disagreement_score":0.0057360064,"about_ca_system_score_codex":0.00079059304,"about_ca_system_score_gemma":0.0022945995,"threshold_uncertainty_score":0.019188821},"labels":[],"label_agreement":null},{"id":"W2439110883","doi":"10.3389/fncom.2016.00051","title":"Modeling Interactions between Speech Production and Perception: Speech Error Detection at Semantic and Phonological Levels and the Inner Speech Loop","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; McGill University","funders":"","keywords":"Speech production; Computer science; Speech recognition; Speech error; Neurocomputational speech processing; Speech perception; Task (project management); Word error rate; Perception; Word (group theory); Natural language processing; Artificial intelligence; Psychology; Linguistics","score_opus":0.05492240830478599,"score_gpt":0.299645215364153,"score_spread":0.24472280705936703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2439110883","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":[0.5767807,0.00021246666,0.41905245,0.00031098927,0.00003342919,0.000036794314,0.000070126436,0.00020969147,0.0032933715],"genre_scores_gemma":[0.98477995,0.00008965359,0.014076391,0.000023227143,0.000007977374,0.000044676966,0.000025588532,0.000011401262,0.0009411399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990785,0.000025643458,0.0000048835236,0.000029850917,0.000015689156,0.000016004382],"domain_scores_gemma":[0.9996735,0.0002039697,0.000044560235,0.000028933113,0.000030710504,0.000018284816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023892023,0.00030037455,0.00027069973,0.00017194019,0.00013058612,0.00043413092,0.0006301392,0.0008562266,0.0006700497],"category_scores_gemma":[0.0014304046,0.00021889812,0.00039316402,0.000102138045,0.00037827168,0.0009583827,0.00030983117,0.00046526737,0.0001050865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000611286,0.000055077453,0.001735388,0.00003057589,0.000030377267,0.00007946091,0.00007058474,0.96964866,0.016008131,0.004137599,0.0000676481,0.008075366],"study_design_scores_gemma":[0.0000024007197,0.000014132207,0.00031188206,6.9651236e-7,0.0000029702492,0.0000080899745,0.0000025855754,0.99792993,0.00046211004,0.001227536,0.000035687834,0.0000020786224],"about_ca_topic_score_codex":0.0036797186,"about_ca_topic_score_gemma":0.0022631062,"teacher_disagreement_score":0.0036797186,"about_ca_system_score_codex":0.00047427503,"about_ca_system_score_gemma":0.00046237675,"threshold_uncertainty_score":0.0073165894},"labels":[],"label_agreement":null},{"id":"W2468260813","doi":"10.3389/fncom.2016.00062","title":"Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Multisensory perception and integration","field":"Psychology","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":"Natural Sciences and Engineering Research Council; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; York University","keywords":"Modal; Action selection; Inference; Causal inference; Computer science; Selection (genetic algorithm); Action (physics); Artificial intelligence; Machine learning; Econometrics; Psychology; Mathematics; Neuroscience; Chemistry","score_opus":0.09540157421083385,"score_gpt":0.4626625349271278,"score_spread":0.36726096071629394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468260813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13948004,0.00032575664,0.8524219,0.0019406857,0.000046143334,0.00006486131,0.00009751928,0.00013068116,0.00549243],"genre_scores_gemma":[0.88723695,0.00017785352,0.111028634,0.000113720256,0.000063736676,0.00011648525,0.00007031819,0.00004064697,0.0011516883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822754,0.00085329794,0.00009985967,0.0003425848,0.0002988082,0.00017793494],"domain_scores_gemma":[0.98189086,0.015653418,0.00084916333,0.0005922461,0.0004908012,0.0005234071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004744891,0.00057905924,0.0015705348,0.0013952311,0.0013296474,0.003162395,0.0032235305,0.0017798473,0.0045851413],"category_scores_gemma":[0.01899843,0.00069021876,0.0024929603,0.0012789591,0.0042976695,0.0051965513,0.0021598183,0.0028970165,0.00020372997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016266089,0.00009659266,0.002833031,0.00013483132,0.00018046968,0.00032746338,0.00047338987,0.62535435,0.0012553876,0.3533525,0.00035659946,0.015472685],"study_design_scores_gemma":[0.000014032655,0.000013411549,0.00017148556,0.000006089532,0.000015347425,0.000022352038,0.000022811573,0.8915529,0.00014940013,0.107919835,0.000103377155,0.000008977123],"about_ca_topic_score_codex":0.0069108806,"about_ca_topic_score_gemma":0.0048003765,"teacher_disagreement_score":0.0069108806,"about_ca_system_score_codex":0.0021962037,"about_ca_system_score_gemma":0.001953521,"threshold_uncertainty_score":0.025093675},"labels":[],"label_agreement":null},{"id":"W2506959184","doi":"10.3389/fncom.2016.00081","title":"Burst Firing in the Electrosensory System of Gymnotiform Weakly Electric Fish: Mechanisms and Functional Roles","year":2016,"lang":"en","type":"review","venue":"Frontiers in Computational Neuroscience","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Electric fish; Neuroscience; Sensory system; Stimulus (psychology); Bursting; Biology; Action (physics); Fish <Actinopterygii>; Physics; Psychology; Cognitive psychology","score_opus":0.02276022632780545,"score_gpt":0.24975372498896184,"score_spread":0.2269934986611564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506959184","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033007725,0.9977738,0.0002480023,0.00023534932,0.000084527615,0.0000039721767,0.00003840331,0.000010074983,0.0012758434],"genre_scores_gemma":[0.001487323,0.9974632,0.0002912937,0.00008997244,0.00008725161,0.000004898437,0.00004153146,0.0000020611656,0.0005324333],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99990726,0.000010095072,0.000016489068,0.000025709853,0.00003214608,0.000008288078],"domain_scores_gemma":[0.9998311,0.00005702137,0.000037655267,0.000005839461,0.000044824334,0.000023603403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035338302,0.00069702865,0.00084094785,0.0024867188,0.0002789478,0.0007759886,0.0006702339,0.0007508343,0.0021143106],"category_scores_gemma":[0.00043510826,0.00023638654,0.00032862872,0.0020875013,0.00051373633,0.0012549387,0.00067647663,0.0007799679,0.0010432289],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005128429,0.000028771281,0.0003921544,0.024129355,0.00008292694,0.0002446352,0.00014931733,0.0006447156,0.004250788,0.005200774,0.013447958,0.9513772],"study_design_scores_gemma":[0.000008507881,0.0000937672,0.0036181002,0.003824673,0.00014793451,0.0018144416,0.000104067956,0.00012940429,0.00087456213,0.0039614993,0.9853888,0.00003415859],"about_ca_topic_score_codex":0.0017934905,"about_ca_topic_score_gemma":0.002553691,"teacher_disagreement_score":0.0024867188,"about_ca_system_score_codex":0.0004989875,"about_ca_system_score_gemma":0.00096815807,"threshold_uncertainty_score":0.007073045},"labels":[],"label_agreement":null},{"id":"W2523494305","doi":"10.3389/fncom.2016.00101","title":"Stress Assessment by Prefrontal Relative Gamma","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad; European Regional Development Fund; Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía","keywords":"Prefrontal cortex; Electroencephalography; Audiology; Brain activity and meditation; Psychology; Neuroscience; Functional magnetic resonance imaging; Stress (linguistics); Medicine; Cognition","score_opus":0.023636091496112475,"score_gpt":0.2758520328229041,"score_spread":0.25221594132679165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523494305","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":[0.94736147,0.0047570765,0.037615642,0.00016490932,0.00012862886,0.0001370535,0.0009225492,0.0002345169,0.008678218],"genre_scores_gemma":[0.9900013,0.0018164567,0.0066014715,0.00007270567,0.0001224474,0.00008393061,0.00032143836,0.000032282427,0.0009479299],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997842,0.00005182944,0.000016214135,0.00006543662,0.000054593533,0.000027746877],"domain_scores_gemma":[0.99944943,0.00014448342,0.00016952067,0.000038622704,0.00014548143,0.000052448886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043438553,0.0007730232,0.00027740715,0.0012326714,0.00016218847,0.00052061304,0.00021611359,0.00032143624,0.0025108592],"category_scores_gemma":[0.0017240281,0.00013917268,0.00026221882,0.00068898784,0.00024212108,0.0004100777,0.0004490046,0.00028064358,0.00037973645],"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":[0.0030924077,0.00023485567,0.20132129,0.0012255526,0.0005820034,0.0011010903,0.0015242521,0.0027631125,0.31133014,0.0011244051,0.002073386,0.47362757],"study_design_scores_gemma":[0.00007016438,0.0013622913,0.9646547,0.00013115234,0.00033276915,0.0027600676,0.0008868941,0.0063367747,0.017890058,0.0022121335,0.0032721583,0.00009086035],"about_ca_topic_score_codex":0.0009316346,"about_ca_topic_score_gemma":0.0009798039,"teacher_disagreement_score":0.0025108592,"about_ca_system_score_codex":0.00013682326,"about_ca_system_score_gemma":0.00014765038,"threshold_uncertainty_score":0.008399606},"labels":[],"label_agreement":null},{"id":"W2527798464","doi":"10.3389/fncom.2017.00024","title":"Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":460,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Concordia University; Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Samsung; Université de Montréal; Compute Canada; Canadian Institute for Advanced Research","keywords":"Backpropagation; Computer science; Artificial neural network; Propagation of uncertainty; Algorithm; Computation; Hebbian theory; Error function; Artificial intelligence","score_opus":0.04478735180542688,"score_gpt":0.26324462530636716,"score_spread":0.2184572735009403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527798464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030997207,0.00084677344,0.9918937,0.00067764876,0.00010770703,0.000018929717,0.000028854378,0.00023425548,0.003092283],"genre_scores_gemma":[0.5101894,0.005464619,0.46643838,0.0014308593,0.00082732295,0.00032896016,0.0002271394,0.0009158879,0.014177391],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904627,0.0002715717,0.000053685348,0.00017498857,0.00036100947,0.000092524686],"domain_scores_gemma":[0.99718934,0.0017908514,0.00019317359,0.0003625295,0.00033405298,0.00013004261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019504543,0.0015767389,0.001495728,0.0010148624,0.00057612144,0.0024589812,0.003864237,0.0030654564,0.003727709],"category_scores_gemma":[0.0075371577,0.0009656119,0.001175317,0.0009278163,0.0027517201,0.006825112,0.0035202878,0.0045112614,0.0010092465],"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":[0.000072378374,0.000056827776,0.00035251083,0.00016535154,0.00008563432,0.00010629997,0.0001478672,0.38642594,0.001489885,0.5598796,0.0017719002,0.049445797],"study_design_scores_gemma":[0.000009323543,0.000022789774,0.00003637287,0.000023582683,0.000008636142,0.000027066728,0.000006416172,0.76574373,0.0004940661,0.23127802,0.0023345656,0.000015408306],"about_ca_topic_score_codex":0.004056208,"about_ca_topic_score_gemma":0.0027732514,"teacher_disagreement_score":0.004056208,"about_ca_system_score_codex":0.0016131365,"about_ca_system_score_gemma":0.001571784,"threshold_uncertainty_score":0.012470424},"labels":[],"label_agreement":null},{"id":"W2546688631","doi":"10.3389/fncom.2016.00110","title":"Simultaneous Bayesian Estimation of Excitatory and Inhibitory Synaptic Conductances by Exploiting Multiple Recorded Trials","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; SickKids Foundation; University of Toronto","funders":"RIKEN Brain Science Institute; RIKEN; Fonds de Recherche du Québec - Santé; Japan Agency for Medical Research and Development","keywords":"Excitatory postsynaptic potential; Computer science; Inhibitory postsynaptic potential; Bayesian probability; Bayes' theorem; Inference; Artificial intelligence; Bayesian inference; Machine learning; Neuroscience; Biology","score_opus":0.03419592386738719,"score_gpt":0.271176603537964,"score_spread":0.23698067967057684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546688631","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":[0.015357896,0.00007993333,0.9838573,0.000056648845,0.000010619517,0.00002834738,0.000024850242,0.00017450322,0.000409995],"genre_scores_gemma":[0.5401562,0.00037255132,0.45703807,0.00010498736,0.00003715325,0.00023664333,0.00017558968,0.00014709159,0.0017317028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993038,0.00017521255,0.00005718021,0.0001955605,0.00021459344,0.00005371845],"domain_scores_gemma":[0.99759907,0.0014661124,0.00034929873,0.00025390802,0.00026691225,0.00006483438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019035699,0.00077111396,0.0010301134,0.0004800471,0.00036853115,0.0008219618,0.0011246502,0.0009943529,0.0008876375],"category_scores_gemma":[0.009709132,0.00062836794,0.00085264834,0.00056728267,0.00084682874,0.0019088928,0.0012578892,0.0015258895,0.0002562697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020242056,0.000093284936,0.005056546,0.00020000448,0.00015525555,0.00016677867,0.0002166645,0.79243267,0.024142798,0.020296203,0.000554782,0.1564826],"study_design_scores_gemma":[0.000011603078,0.000029599503,0.0010994672,0.00001268617,0.000014466577,0.000063002866,0.0000099118415,0.9855736,0.0057051713,0.0070387544,0.00041820272,0.000023461316],"about_ca_topic_score_codex":0.0032760128,"about_ca_topic_score_gemma":0.0044916496,"teacher_disagreement_score":0.0032760128,"about_ca_system_score_codex":0.00058148557,"about_ca_system_score_gemma":0.0016268371,"threshold_uncertainty_score":0.010067105},"labels":[],"label_agreement":null},{"id":"W2561161270","doi":"10.3389/fncom.2016.00128","title":"Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Context (archaeology); Adaptation (eye); Abstraction; Artificial intelligence; Representation (politics); Mechanism (biology); Computational model; Machine learning; Neuroscience; Psychology","score_opus":0.03990773293564276,"score_gpt":0.2678596043738336,"score_spread":0.22795187143819085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561161270","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":[0.8078063,0.0001459639,0.18539704,0.00048706657,0.00003256047,0.000017044293,0.000057791123,0.0007610513,0.0052951057],"genre_scores_gemma":[0.9849513,0.00004050972,0.014744309,0.000018769386,0.000004120045,0.0000074189384,0.000021834378,0.00001758938,0.00019408372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999373,0.000014482488,0.0000039947085,0.000013271159,0.00001547455,0.000015377382],"domain_scores_gemma":[0.99964964,0.00012315073,0.00006877104,0.00009591547,0.000024656609,0.000037888247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021179528,0.00027089647,0.00023730018,0.00014713108,0.00014329785,0.0005668476,0.000633772,0.00031049887,0.0012486189],"category_scores_gemma":[0.0013179868,0.00019919017,0.00034267138,0.00011396241,0.0005659053,0.0011434904,0.0007060694,0.0005280685,0.000114942246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033535666,0.00015752163,0.007079714,0.00016824345,0.00013925172,0.00026397448,0.00017875551,0.7253328,0.1313955,0.081216656,0.0013114295,0.05242083],"study_design_scores_gemma":[0.000045910278,0.00007197644,0.0026521923,0.000006590662,0.000030599673,0.00005137742,0.000022754606,0.9383517,0.012044146,0.046139415,0.0005711641,0.000012113369],"about_ca_topic_score_codex":0.0013159072,"about_ca_topic_score_gemma":0.0014459786,"teacher_disagreement_score":0.0013159072,"about_ca_system_score_codex":0.00039746688,"about_ca_system_score_gemma":0.00037989413,"threshold_uncertainty_score":0.0041770935},"labels":[],"label_agreement":null},{"id":"W2585792070","doi":"10.3389/fncom.2016.00143","title":"Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Trajectory; Model predictive control; Control theory (sociology); Controller (irrigation); Nonlinear system; Computer science; Tracking (education); Task (project management); Nonlinear model; Trajectory optimization; Control (management); Artificial intelligence; Engineering; Physics; Psychology","score_opus":0.04311960094348084,"score_gpt":0.29608963789744014,"score_spread":0.2529700369539593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585792070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42691493,0.0002895205,0.5556139,0.0005076327,0.00006457525,0.00010294522,0.0001804052,0.0006969031,0.015629187],"genre_scores_gemma":[0.98988426,0.000060978407,0.0087253805,0.000012230754,0.0000035049984,0.00007080295,0.00003827445,0.000012719596,0.0011917955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999038,0.000028851073,0.000004897916,0.000013694009,0.000033661072,0.00001511807],"domain_scores_gemma":[0.9993698,0.00045064389,0.00005658265,0.000026757616,0.000079396035,0.000016834763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003498156,0.00040208467,0.0004170088,0.00026373673,0.0002601016,0.00044897827,0.0004277799,0.0005648736,0.0013897199],"category_scores_gemma":[0.0015193454,0.00023556351,0.00027412386,0.0002290507,0.00053138885,0.0003031144,0.00035880198,0.00047358405,0.00011622926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014102304,0.0000036818592,0.00007106035,0.000007886278,0.0000019340778,0.000009534028,0.000009686098,0.9982468,0.00026770032,0.0005161396,0.000025539675,0.0008260395],"study_design_scores_gemma":[0.0000019287495,0.0000034725936,0.00002621335,4.6693194e-7,4.972508e-7,6.4134053e-7,0.0000010331446,0.99973816,0.0000761695,0.00013045981,0.000020361877,5.703434e-7],"about_ca_topic_score_codex":0.023115862,"about_ca_topic_score_gemma":0.01081578,"teacher_disagreement_score":0.023115862,"about_ca_system_score_codex":0.00064088625,"about_ca_system_score_gemma":0.0006893372,"threshold_uncertainty_score":0.04596263},"labels":[],"label_agreement":null},{"id":"W2588899242","doi":"10.3389/fncom.2017.00008","title":"Contributions of EEG-fMRI to Assessing the Epileptogenicity of Focal Cortical Dysplasia","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Cortical dysplasia; EEG-fMRI; Medicine; Concordance; Fluid-attenuated inversion recovery; Lesion; Neuroradiology; Magnetic resonance imaging; Epilepsy; Electroencephalography; Electrocorticography; Radiology; Pathology; Neurology; Internal medicine","score_opus":0.032421009797613976,"score_gpt":0.37708785289384766,"score_spread":0.3446668430962337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588899242","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":[0.9972951,0.0009260125,0.00079907745,0.00007002801,0.000009750361,0.000009611616,0.00007048166,0.000012977074,0.0008070269],"genre_scores_gemma":[0.99887127,0.00025030703,0.0006716923,0.000023872999,0.000018811506,0.00000537685,0.00005466257,0.0000031800444,0.0001008937],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99984765,0.000054580603,0.000015989552,0.00003283406,0.000030927164,0.000018113178],"domain_scores_gemma":[0.99916244,0.00035246328,0.00022787164,0.000039710812,0.00013261741,0.000085018044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051407726,0.0003700755,0.00018288955,0.00058528996,0.00009387534,0.00029338207,0.0001496532,0.00029702333,0.000569286],"category_scores_gemma":[0.0030803063,0.00009449126,0.00013659592,0.00014670115,0.00016511441,0.00023541442,0.00020517527,0.0001501101,0.00012949345],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012265507,0.00006488741,0.8758938,0.00014169051,0.00014807872,0.0022543494,0.00014061804,0.00069228123,0.044397183,0.0000510424,0.00025425217,0.074735224],"study_design_scores_gemma":[0.00001567953,0.00038442345,0.9819047,0.00003028177,0.00006399967,0.009376111,0.00011319913,0.0017167028,0.005900702,0.00013378041,0.00034945816,0.000011057165],"about_ca_topic_score_codex":0.00071885996,"about_ca_topic_score_gemma":0.0019204124,"teacher_disagreement_score":0.00071885996,"about_ca_system_score_codex":0.00012288263,"about_ca_system_score_gemma":0.000110289206,"threshold_uncertainty_score":0.0027187467},"labels":[],"label_agreement":null},{"id":"W2614985414","doi":"10.3389/fncom.2017.00035","title":"Linear Parameter Varying Identification of Dynamic Joint Stiffness during Time-Varying Voluntary Contractions","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Qatar National Research Fund; Fonds Québécois de la Recherche sur la Nature et les Technologies; McGill University; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Control theory (sociology); Joint stiffness; Stretch reflex; Torque; Ankle; Isometric exercise; Stiffness; Reflex; Nonlinear system; Mathematics; Computer science; Physics; Engineering; Structural engineering; Medicine; Anatomy; Physical therapy","score_opus":0.014627919078248907,"score_gpt":0.24642169646853643,"score_spread":0.23179377739028753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614985414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3880849,0.0001113603,0.61017746,0.000040717103,0.000010105071,0.000075923024,0.000046482925,0.00038011794,0.0010729765],"genre_scores_gemma":[0.9717935,0.000047908645,0.027501669,0.00000748443,0.000002999669,0.00004068929,0.000043127915,0.00001996482,0.0005425844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979573,0.000049954917,0.0000096599515,0.000067709574,0.00005917427,0.000017878072],"domain_scores_gemma":[0.99948275,0.00031114064,0.00006914425,0.000047061614,0.00007461965,0.000015279473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050427776,0.00046673923,0.00034731335,0.00026088022,0.00022580657,0.00032906546,0.00037688698,0.00032546252,0.00050174835],"category_scores_gemma":[0.0019119205,0.00033436433,0.0002753169,0.00015916924,0.00022126509,0.00036520057,0.0003464397,0.00036148698,0.00012103797],"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":[0.00035373995,0.00014386848,0.008006712,0.00016429934,0.00008653444,0.00021592542,0.0002928809,0.75828636,0.109949395,0.0013552624,0.00016527591,0.12097967],"study_design_scores_gemma":[0.0000056467793,0.00009212956,0.005463657,0.0000038681897,0.000008468157,0.0000445458,0.000012749605,0.9873226,0.0066592363,0.00026444354,0.00011510068,0.0000075264225],"about_ca_topic_score_codex":0.0057509826,"about_ca_topic_score_gemma":0.0056821774,"teacher_disagreement_score":0.0057509826,"about_ca_system_score_codex":0.00028417015,"about_ca_system_score_gemma":0.0005132371,"threshold_uncertainty_score":0.011435032},"labels":[],"label_agreement":null},{"id":"W2622275264","doi":"10.3389/fncom.2017.00051","title":"Estimation of Time-Varying, Intrinsic and Reflex Dynamic Joint Stiffness during Movement. Application to the Ankle Joint","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Joint stiffness; Torque; Joint (building); Control theory (sociology); Stiffness; Ankle; Computer science; Position (finance); Simulation; Engineering; Structural engineering; Physics; Artificial intelligence","score_opus":0.010424438338136803,"score_gpt":0.2382638515379389,"score_spread":0.22783941319980208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622275264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18397544,0.0002923591,0.81456643,0.000039019782,0.000024041867,0.00005438656,0.00007420729,0.00029359153,0.0006805252],"genre_scores_gemma":[0.81656706,0.00026785408,0.18170805,0.000018864886,0.000015595959,0.00006342211,0.00011911121,0.000035752724,0.0012043007],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982697,0.000028800941,0.000010001877,0.000061003233,0.00006305804,0.000010198708],"domain_scores_gemma":[0.9994585,0.0002631359,0.00009792656,0.00004790745,0.00010396925,0.000028496976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003949973,0.00045031623,0.0003342598,0.00050017756,0.00019348806,0.0002491401,0.00032994663,0.00041716284,0.00040592422],"category_scores_gemma":[0.0019403315,0.00029994588,0.00023453978,0.0002969622,0.00018986128,0.00042502597,0.00031037873,0.0002928296,0.00016067078],"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":[0.0003682733,0.00018062464,0.017209359,0.00040981645,0.00016003325,0.00022391173,0.0004578718,0.108946085,0.47628057,0.0010612755,0.00039418385,0.3943079],"study_design_scores_gemma":[0.000016073958,0.00029388012,0.040975247,0.000016023008,0.00004939351,0.00036591015,0.000058608777,0.90685165,0.049916223,0.0005504575,0.0008602359,0.00004633361],"about_ca_topic_score_codex":0.0026428653,"about_ca_topic_score_gemma":0.004903467,"teacher_disagreement_score":0.0026428653,"about_ca_system_score_codex":0.00017567335,"about_ca_system_score_gemma":0.000267752,"threshold_uncertainty_score":0.005254984},"labels":[],"label_agreement":null},{"id":"W2650517833","doi":"10.3389/fncom.2017.00054","title":"Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Studienstiftung des Deutschen Volkes; Deutsche Forschungsgemeinschaft","keywords":"Hebbian theory; Neuroscience; Artificial neural network; Stimulus (psychology); Biological neural network; Computer science; Acetylcholine; Neurotransmission; Artificial intelligence; Psychology; Biology; Cognitive psychology","score_opus":0.040117037011026965,"score_gpt":0.2919727516661788,"score_spread":0.25185571465515183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2650517833","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":[0.7041213,0.0009797692,0.27820596,0.0016344163,0.0001227619,0.00008677306,0.00018218544,0.0006775391,0.013989357],"genre_scores_gemma":[0.97718155,0.00018530243,0.019646548,0.00008505523,0.0000143730695,0.000064657506,0.00005627483,0.00003653968,0.0027296855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982136,0.000058364178,0.000010377401,0.00004106691,0.000032154054,0.00003663454],"domain_scores_gemma":[0.9990638,0.00060635665,0.00010781724,0.000066975714,0.0000982266,0.000056864803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006468167,0.00069339824,0.0005722983,0.00031187042,0.00025904633,0.0007760522,0.0009980339,0.0012464289,0.0016971835],"category_scores_gemma":[0.0034666457,0.0003665036,0.0007349318,0.0002013358,0.00068139477,0.0013644699,0.0007324451,0.0011881234,0.00022336355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042825566,0.000029794512,0.00032085838,0.000025669635,0.000016870703,0.000029406086,0.000019037632,0.990607,0.0020881968,0.0027691484,0.00012666997,0.003924431],"study_design_scores_gemma":[0.0000065500917,0.0000192158,0.0000902571,0.000002404463,0.00000482601,0.000004725126,0.0000020714683,0.9979678,0.00041091052,0.0014309144,0.000057572328,0.0000026812315],"about_ca_topic_score_codex":0.003814878,"about_ca_topic_score_gemma":0.003289435,"teacher_disagreement_score":0.003814878,"about_ca_system_score_codex":0.00092247454,"about_ca_system_score_gemma":0.0006438724,"threshold_uncertainty_score":0.0075853467},"labels":[],"label_agreement":null},{"id":"W2735817772","doi":"10.3389/fncom.2017.00063","title":"Characterization of Spatial Frequency Channels Underlying Disparity Sensitivity by Factor Analysis of Population Data","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spatial frequency; Sensitivity (control systems); Population; Physics; Optics","score_opus":0.11551301512950479,"score_gpt":0.284707680524483,"score_spread":0.16919466539497818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735817772","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":[0.8814224,0.0001665813,0.11469875,0.000042133175,0.00001286084,0.00007906203,0.0014453973,0.0003048158,0.0018279245],"genre_scores_gemma":[0.98221856,0.000049170318,0.016453426,0.000008893813,0.000010480073,0.00007684548,0.0010044109,0.000052170646,0.00012596523],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99904436,0.00019724428,0.000080986814,0.00037421004,0.00020427453,0.00009893353],"domain_scores_gemma":[0.99451965,0.002948716,0.00052460073,0.0010120921,0.0008728296,0.00012211117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014770059,0.0003709313,0.00046115744,0.001622746,0.00022197209,0.0005374083,0.00025837024,0.0001972832,0.0018108857],"category_scores_gemma":[0.009315401,0.00012668954,0.0005567375,0.001063335,0.00054196926,0.00056189985,0.0004435591,0.00042343,0.00026320745],"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":[0.0009491924,0.00024147559,0.2925268,0.00037705165,0.00067400886,0.0002960407,0.0016394098,0.00825232,0.47927743,0.005465953,0.0014454667,0.20885484],"study_design_scores_gemma":[0.00002402673,0.00027995277,0.9048073,0.000017888504,0.00020714007,0.0006279103,0.00025754818,0.049594514,0.034741506,0.007442372,0.0019186498,0.00008112348],"about_ca_topic_score_codex":0.0017405901,"about_ca_topic_score_gemma":0.0014786532,"teacher_disagreement_score":0.0018108857,"about_ca_system_score_codex":0.00023048803,"about_ca_system_score_gemma":0.00030234607,"threshold_uncertainty_score":0.0078112483},"labels":[],"label_agreement":null},{"id":"W2766396684","doi":"10.3389/fncom.2017.00098","title":"Neural Synchronization from the Perspective of Non-linear Dynamics","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; SickKids Foundation; University of Toronto","funders":"","keywords":"Perspective (graphical); Dynamics (music); Synchronization (alternating current); Computer science; Front (military); Neuroscience; Psychology; Artificial intelligence; Telecommunications; Physics; Meteorology","score_opus":0.02162702358826665,"score_gpt":0.2803060223216605,"score_spread":0.2586789987333939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766396684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0497748,0.012015884,0.8586322,0.011257615,0.0017603625,0.000025995874,0.00030626872,0.00021322542,0.06601366],"genre_scores_gemma":[0.9191736,0.011472801,0.04876072,0.0006911555,0.002876971,0.00007315134,0.0001446591,0.00015845009,0.016648503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99980325,0.00006309288,0.000008821073,0.00004489821,0.00006438724,0.00001555641],"domain_scores_gemma":[0.9996414,0.00016616202,0.00005516396,0.000040369363,0.00005936029,0.000037572012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047251946,0.0005368707,0.0005596858,0.0006127423,0.00038044195,0.0015338172,0.00068136683,0.0007319409,0.003882285],"category_scores_gemma":[0.0014333022,0.0002287168,0.0005545103,0.0003589944,0.0015596665,0.0021944751,0.0011571225,0.0015592389,0.00063401094],"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":[0.000041333893,0.000013094476,0.0005054947,0.00018077264,0.00007887384,0.00022219727,0.00018316746,0.06765621,0.006509848,0.904332,0.0021252923,0.018151755],"study_design_scores_gemma":[0.000014943403,0.00003123685,0.00068421883,0.000043909877,0.000025929148,0.00014615076,0.00007099937,0.30491534,0.00047558473,0.6878386,0.0057331193,0.000019975952],"about_ca_topic_score_codex":0.00080676866,"about_ca_topic_score_gemma":0.00070555886,"teacher_disagreement_score":0.003882285,"about_ca_system_score_codex":0.00058250595,"about_ca_system_score_gemma":0.00031497653,"threshold_uncertainty_score":0.012987554},"labels":[],"label_agreement":null},{"id":"W2782856978","doi":"10.3389/fncom.2017.00115","title":"Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips","year":2018,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Universidade Federal do Rio Grande do Norte","keywords":"Electroencephalography; Computer science; Brain–computer interface; Speech recognition; Arousal; Valence (chemistry); Affective computing; Artificial intelligence; Pattern recognition (psychology); Psychology; Neuroscience","score_opus":0.02581616531417099,"score_gpt":0.29280976397812314,"score_spread":0.26699359866395217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782856978","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":[0.52731276,0.006649647,0.44540593,0.00046861998,0.0003660305,0.0003730845,0.006079643,0.0032845214,0.010059707],"genre_scores_gemma":[0.914703,0.0022201857,0.07660819,0.00010870769,0.00025116274,0.00019843254,0.0035864664,0.00007718232,0.002246586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986136,0.000027033904,0.000011928549,0.000034462,0.000049422237,0.000015693771],"domain_scores_gemma":[0.99980897,0.000063103835,0.00003503078,0.000020632968,0.0000598065,0.000012409586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020252992,0.00045827945,0.00028324613,0.0011955007,0.00009365002,0.0003757757,0.00020444528,0.0002766665,0.0013956585],"category_scores_gemma":[0.000975818,0.00008189019,0.0002939381,0.00078779424,0.00009285328,0.00026412224,0.000262517,0.00032617507,0.000706061],"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":[0.00076530565,0.0001827637,0.01835099,0.0004518829,0.00016112636,0.00037493772,0.000084460204,0.007152268,0.19069332,0.00072271825,0.0071832677,0.77387697],"study_design_scores_gemma":[0.00018458976,0.0006760357,0.3558096,0.0001701335,0.0003731455,0.0019783666,0.00028133372,0.5095898,0.1104323,0.0039196475,0.016470823,0.000114231916],"about_ca_topic_score_codex":0.0011703272,"about_ca_topic_score_gemma":0.0021180392,"teacher_disagreement_score":0.0013956585,"about_ca_system_score_codex":0.00010780483,"about_ca_system_score_gemma":0.00014015712,"threshold_uncertainty_score":0.0046690106},"labels":[],"label_agreement":null},{"id":"W2788953943","doi":"10.3389/fncom.2018.00011","title":"Temporal Dissociation of Neocortical and Hippocampal Contributions to Mental Time Travel Using Intracranial Recordings in Humans","year":2018,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Azrieli Foundation; Israel Science Foundation","keywords":"Temporal lobe; Dissociation (chemistry); Hippocampal formation; Chronesthesia; Neuroscience; Hippocampus; Psychology; Temporal cortex; Neocortex; Cognitive psychology; Cognition; Episodic memory; Chemistry; Epilepsy","score_opus":0.041920730783500684,"score_gpt":0.3333059876477337,"score_spread":0.29138525686423306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788953943","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":[0.98291826,0.0013048729,0.010115741,0.00014000678,0.00003113237,0.00004417845,0.00070997304,0.00007987455,0.0046559237],"genre_scores_gemma":[0.99547595,0.0006303113,0.0031453548,0.000043457563,0.000021470278,0.000027712256,0.0002173527,0.000013926705,0.000424347],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99994123,0.00000863426,0.000003394743,0.00001895584,0.000019097135,0.000008779233],"domain_scores_gemma":[0.9998209,0.000080123,0.000037898244,0.000021169382,0.000024932782,0.000015075986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000088905006,0.00017394737,0.0001064717,0.0003142177,0.00012021326,0.0004099313,0.00010306899,0.0001931732,0.0009782419],"category_scores_gemma":[0.0008529453,0.00012549944,0.000093466246,0.00027431446,0.00049558433,0.00027235603,0.0002058097,0.00025361805,0.00016331201],"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":[0.002883538,0.00010724739,0.09109089,0.00051420974,0.00016218616,0.0025998002,0.005094802,0.0008405218,0.6997983,0.0025390733,0.0019862188,0.19238327],"study_design_scores_gemma":[0.00006454587,0.00033748124,0.9275303,0.0000618374,0.000122042526,0.0052884873,0.0019337,0.003452454,0.0532038,0.0038472414,0.0040974906,0.00006066826],"about_ca_topic_score_codex":0.0015483473,"about_ca_topic_score_gemma":0.0030954164,"teacher_disagreement_score":0.0015483473,"about_ca_system_score_codex":0.00009796367,"about_ca_system_score_gemma":0.0001263744,"threshold_uncertainty_score":0.0032725334},"labels":[],"label_agreement":null},{"id":"W2796785929","doi":"10.3389/fncom.2018.00026","title":"Commentary: Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach","year":2018,"lang":"en","type":"letter","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Volkswagen Foundation","keywords":"Resting state fMRI; Amplitude; Phase (matter); Coupling (piping); Magnetoencephalography; Physics; Nuclear magnetic resonance; Computer science; Neuroscience; Psychology; Materials science; Electroencephalography; Quantum mechanics","score_opus":0.0542321616311541,"score_gpt":0.3241460233331606,"score_spread":0.2699138617020065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796785929","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016045067,0.001001208,0.00036838689,0.9179423,0.078894146,0.00003284643,0.000354333,0.00008897746,0.0011573416],"genre_scores_gemma":[0.002129273,0.0007738127,0.00049380807,0.8681564,0.12355612,0.00011072693,0.00008242262,0.00005329705,0.0046440833],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99491984,0.0014414574,0.0008352001,0.0008130499,0.0016632398,0.00032716186],"domain_scores_gemma":[0.9607574,0.025180843,0.0015885448,0.0008685158,0.010032021,0.0015727008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076539717,0.0018076674,0.0020457883,0.0013683587,0.0035285736,0.0027710856,0.004105133,0.07235049,0.00880812],"category_scores_gemma":[0.07210538,0.0008463491,0.002430974,0.0008966065,0.004187297,0.002878003,0.0018136901,0.0462015,0.013754496],"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":[0.00005775569,0.000010260518,0.0000819924,0.000075835494,0.000011043047,0.00026332523,0.00003841309,0.00006591304,0.000096447264,0.0010333086,0.9952166,0.0030491122],"study_design_scores_gemma":[0.00032251194,0.00009729692,0.0010228775,0.000924007,0.00008582297,0.0015575973,0.0001975383,0.0011832669,0.0006804022,0.020459747,0.97332674,0.00014226508],"about_ca_topic_score_codex":0.009423929,"about_ca_topic_score_gemma":0.008867042,"teacher_disagreement_score":0.07235049,"about_ca_system_score_codex":0.00534679,"about_ca_system_score_gemma":0.0047581187,"threshold_uncertainty_score":0.040478528},"labels":[],"label_agreement":null},{"id":"W2804432531","doi":"10.3389/fncom.2018.00040","title":"Modeling Current Sources for Neural Stimulation in COMSOL","year":2018,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Duke University","keywords":"Multiphysics; Electrode; Computer science; Substrate (aquarium); Materials science; Current source; Current (fluid); Finite element method; Electrode array; Silicone; Biomedical engineering; Electronic engineering; Optoelectronics; Biological system; Electrical engineering; Chemistry; Physics; Engineering; Composite material","score_opus":0.06464651854647363,"score_gpt":0.32037467484393334,"score_spread":0.2557281562974597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804432531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004527427,0.0005036848,0.96828866,0.0006093431,0.00016902089,0.00016734801,0.0006778913,0.0033583557,0.021698268],"genre_scores_gemma":[0.20322025,0.0022242984,0.7439537,0.00079525734,0.0001705153,0.0023375305,0.0022376683,0.0026887485,0.042372055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995198,0.00007667721,0.000025909732,0.00003957739,0.00030619124,0.0000319054],"domain_scores_gemma":[0.9989604,0.0004923945,0.000103803824,0.00010167528,0.00030422013,0.00003749203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060265214,0.0010058446,0.0005864995,0.00078842696,0.0005240696,0.0013516974,0.0018441606,0.0020071608,0.022216825],"category_scores_gemma":[0.0019517434,0.0006446897,0.0013575255,0.0006010308,0.0005533625,0.0010403671,0.0011934885,0.0012972177,0.005237778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040423405,0.000044666354,0.0006167382,0.0006634163,0.00006888507,0.00017349493,0.0002257369,0.9025005,0.010237154,0.030060388,0.010865421,0.044503108],"study_design_scores_gemma":[0.000027101847,0.000023686762,0.000088105255,0.00009302557,0.000015004521,0.00011745759,0.000036891743,0.9456734,0.0040064207,0.009386085,0.040511694,0.000021132095],"about_ca_topic_score_codex":0.002493438,"about_ca_topic_score_gemma":0.0035197674,"teacher_disagreement_score":0.022216825,"about_ca_system_score_codex":0.000812794,"about_ca_system_score_gemma":0.0014767211,"threshold_uncertainty_score":0.0743227},"labels":[],"label_agreement":null},{"id":"W2806555232","doi":"10.3389/fncom.2018.00041","title":"Inhibiting Basal Ganglia Regions Reduces Syllable Sequencing Errors in Parkinson's Disease: A Computer Simulation Study","year":2018,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"RWTH Aachen University","keywords":"Subthalamic nucleus; Basal ganglia; Deep brain stimulation; Striatum; Neuroscience; Pars compacta; Substantia nigra; Globus pallidus; Parkinson's disease; Dopamine; Psychology; Medicine; Dopaminergic; Disease; Central nervous system; Internal medicine","score_opus":0.04080110931886733,"score_gpt":0.30914034647596483,"score_spread":0.2683392371570975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806555232","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":[0.99134797,0.00013478857,0.0054431236,0.00012519625,0.000011375937,0.00004853143,0.0002197971,0.000070064234,0.0025991946],"genre_scores_gemma":[0.9964059,0.000109915934,0.0025772327,0.00003024176,0.0000032020887,0.00007002478,0.0001718944,0.000009694124,0.0006217667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999397,0.000024582654,0.000003964296,0.000010086508,0.00000841285,0.000013192776],"domain_scores_gemma":[0.998417,0.0013329317,0.0000846649,0.000032636493,0.000079717414,0.000052996398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026732424,0.00054966356,0.0005091058,0.00031768138,0.00020970407,0.00040894267,0.000638798,0.001008637,0.0024707983],"category_scores_gemma":[0.0014461599,0.00026326493,0.00067629997,0.00023824032,0.0003105279,0.00027423262,0.00026281376,0.00055100094,0.0000976475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033189394,0.0003042431,0.0038072208,0.00009277385,0.00009041454,0.0001484136,0.00007120705,0.9905109,0.0014124513,0.0005620095,0.00020121709,0.0024673052],"study_design_scores_gemma":[0.000112805144,0.00030415968,0.0011111158,0.0000085886195,0.000051252842,0.000026068574,0.000027374712,0.99714786,0.0006526156,0.00040462395,0.00014668335,0.0000068985046],"about_ca_topic_score_codex":0.016788881,"about_ca_topic_score_gemma":0.012956804,"teacher_disagreement_score":0.016788881,"about_ca_system_score_codex":0.0006149594,"about_ca_system_score_gemma":0.00055435067,"threshold_uncertainty_score":0.033382297},"labels":[],"label_agreement":null},{"id":"W2917408107","doi":"10.3389/fncom.2019.00004","title":"Retooling Computational Techniques for EEG-Based Neurocognitive Modeling of Children's Data, Validity and Prospects for Learning and Education","year":2019,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neurocognitive; Electroencephalography; Computer science; Artificial intelligence; Machine learning; Psychology; Cognition; Neuroscience","score_opus":0.0377693922625317,"score_gpt":0.3113370543928734,"score_spread":0.2735676621303417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917408107","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":[0.0049276953,0.00006580893,0.99097496,0.0001917116,0.000020697391,0.000036073834,0.00011878639,0.0028425674,0.00082161353],"genre_scores_gemma":[0.12578519,0.00030340182,0.8711227,0.00009867666,0.000028791903,0.00043316325,0.000345739,0.0007906558,0.0010917159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993113,0.00025909135,0.000067810244,0.00014220762,0.00018732956,0.000032229236],"domain_scores_gemma":[0.9943737,0.0039552487,0.000231486,0.0010059759,0.00034161628,0.000092015565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023813082,0.0010866579,0.0007268292,0.0015191669,0.00051942567,0.002718003,0.0024116188,0.0008001358,0.0056768344],"category_scores_gemma":[0.0141039565,0.00079829583,0.001923223,0.000811608,0.001246217,0.002752415,0.0029106017,0.0023277502,0.001097767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012954975,0.00014660743,0.004713241,0.00038282695,0.00048426638,0.00029476176,0.0014712474,0.6082679,0.006583645,0.15736404,0.003983212,0.21617867],"study_design_scores_gemma":[0.000010398506,0.000012313112,0.00033821687,0.00003990133,0.000013959743,0.0000387686,0.000063986496,0.94709706,0.0014351096,0.04629393,0.0046395585,0.000016730428],"about_ca_topic_score_codex":0.007038419,"about_ca_topic_score_gemma":0.006766229,"teacher_disagreement_score":0.007038419,"about_ca_system_score_codex":0.0010641473,"about_ca_system_score_gemma":0.0015435661,"threshold_uncertainty_score":0.018990934},"labels":[],"label_agreement":null},{"id":"W2937762196","doi":"10.3389/fncom.2019.00023","title":"On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems","year":2019,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Orthogonality; Task (project management); Degrees of freedom (physics and chemistry); Kinematics; Computer science; Motor control; Representation (politics); Controller (irrigation); Control theory (sociology); Motion (physics); Control (management); Mechatronics; Space (punctuation); Artificial intelligence; Mathematics; Engineering; Psychology; Neuroscience","score_opus":0.03817077050890802,"score_gpt":0.2597789269811506,"score_spread":0.2216081564722426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937762196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1993247,0.0017150973,0.78846467,0.0011159931,0.00007608769,0.00004851876,0.00008205202,0.00019155002,0.008981305],"genre_scores_gemma":[0.956464,0.00088976114,0.040996976,0.00009695377,0.000043854,0.000058143396,0.000050746134,0.000044203138,0.0013552818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963236,0.00011927005,0.00001910209,0.00009559055,0.000099688936,0.000033932338],"domain_scores_gemma":[0.9984175,0.0010957185,0.00019310998,0.00012964767,0.00009423311,0.00006986381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011657625,0.0005397275,0.0004124327,0.00044066092,0.00028929542,0.00068000786,0.00046855112,0.0004908665,0.0019692932],"category_scores_gemma":[0.004822663,0.00038238775,0.00046796704,0.00036398647,0.0012046966,0.0013643645,0.001159544,0.00064034964,0.00019040442],"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":[0.00023395641,0.00017500464,0.004675299,0.00044649403,0.00016288582,0.00039872775,0.0004804623,0.7470705,0.04670699,0.11254077,0.000928868,0.08618004],"study_design_scores_gemma":[0.000011568528,0.00011046039,0.0022801238,0.000028009259,0.000021514927,0.00008765262,0.000035544956,0.9548163,0.001688631,0.04029469,0.0006059418,0.0000197069],"about_ca_topic_score_codex":0.0017472384,"about_ca_topic_score_gemma":0.0014454782,"teacher_disagreement_score":0.0019692932,"about_ca_system_score_codex":0.00029255307,"about_ca_system_score_gemma":0.00046476515,"threshold_uncertainty_score":0.006587982},"labels":[],"label_agreement":null},{"id":"W2963839688","doi":"10.3389/fncom.2019.00054","title":"Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease","year":2019,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"National Institute on Aging","keywords":"Neuroinformatics; Neuroscience; Dementia; Connectome; Population; Electroencephalography; Virtual screening; Disease; Positron emission tomography; Local field potential; Neuroimaging; Neurology; Psychology; Medicine; Bioinformatics; Biology; Drug discovery; Internal medicine; Functional connectivity","score_opus":0.030776421676026674,"score_gpt":0.27384438108925396,"score_spread":0.24306795941322729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963839688","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":[0.61680675,0.002010168,0.36664632,0.002528233,0.00014310416,0.00016923348,0.0013754602,0.00078344386,0.009537299],"genre_scores_gemma":[0.96253866,0.0007662453,0.035030838,0.00013777685,0.000031738455,0.00019251202,0.00026555106,0.00006375499,0.00097289553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989736,0.000049544637,0.0000047438616,0.000019029541,0.000019134397,0.000010062907],"domain_scores_gemma":[0.9995388,0.00031573695,0.0000554562,0.000035477144,0.000024848423,0.000029665396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039989944,0.00066546636,0.00047462774,0.00047193829,0.00029265322,0.00075256854,0.0005728517,0.00082899776,0.0015922891],"category_scores_gemma":[0.0016989504,0.00032488714,0.00070032786,0.00034302895,0.00048637265,0.00059040566,0.0005635212,0.0006309782,0.000145194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023487877,0.000025640371,0.0015368592,0.000028720784,0.000059368675,0.000028396682,0.000015232095,0.9918869,0.0006206787,0.003053385,0.00016049537,0.0025607357],"study_design_scores_gemma":[0.0000077957275,0.000012642495,0.0004118706,0.0000029905725,0.000009648634,0.000010455259,0.000005751811,0.9939904,0.00016712709,0.0051709265,0.000206783,0.0000035951853],"about_ca_topic_score_codex":0.007239156,"about_ca_topic_score_gemma":0.0061653825,"teacher_disagreement_score":0.007239156,"about_ca_system_score_codex":0.0006892643,"about_ca_system_score_gemma":0.0006973044,"threshold_uncertainty_score":0.014394045},"labels":[],"label_agreement":null},{"id":"W2983431646","doi":"10.3389/fncom.2019.00072","title":"Prediction and Classification of Alzheimer’s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers","year":2019,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Meso Scale Diagnostics; National Research Foundation of Korea; National Research Foundation; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Ministry of Science and ICT, South Korea; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Biomarker; Apolipoprotein E; Dementia; Medicine; Disease; Neuroimaging; Cerebrospinal fluid; Internal medicine; Imaging biomarker; Oncology; Artificial intelligence; Magnetic resonance imaging; Radiology; Computer science; Psychiatry; Biology","score_opus":0.01662354629166961,"score_gpt":0.2745878401972464,"score_spread":0.2579642939055768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983431646","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":[0.98699236,0.0010911383,0.010296005,0.00018866874,0.000051855513,0.000044090953,0.00071919354,0.000108002605,0.00050868856],"genre_scores_gemma":[0.99267274,0.00024809228,0.0055653756,0.000039778402,0.000060894654,0.00002851088,0.0011439803,0.0000035210385,0.0002369816],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996313,0.000088305336,0.000055888857,0.000096572345,0.000065400665,0.00006249342],"domain_scores_gemma":[0.9992849,0.00022655452,0.00014041772,0.000044117995,0.0001755101,0.0001285434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011297496,0.0008492444,0.0010648323,0.0021054929,0.00019766354,0.0007506708,0.00041909041,0.0005683852,0.00035904098],"category_scores_gemma":[0.001548671,0.00012891526,0.00066263555,0.00055346533,0.00018927023,0.00039335838,0.0004787915,0.00045847104,0.00018566071],"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":[0.0020828662,0.00094009703,0.7799316,0.00012430744,0.00053568283,0.0005712621,0.00010625878,0.027010683,0.008872896,0.0002736158,0.0031184424,0.1764323],"study_design_scores_gemma":[0.00011313608,0.000546146,0.32059687,0.00005643239,0.00028394273,0.00036248425,0.00019367183,0.67310154,0.002602651,0.001257239,0.0008324116,0.000053523254],"about_ca_topic_score_codex":0.0042023496,"about_ca_topic_score_gemma":0.003941977,"teacher_disagreement_score":0.0042023496,"about_ca_system_score_codex":0.00027714542,"about_ca_system_score_gemma":0.00047997342,"threshold_uncertainty_score":0.008355737},"labels":[],"label_agreement":null},{"id":"W3018013674","doi":"10.3389/fncom.2020.00034","title":"Topological View of Flows Inside the BOLD Spontaneous Activity of the Human Brain","year":2020,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Topology (electrical circuits); Functional magnetic resonance imaging; Resting state fMRI; Blood-oxygen-level dependent; Computer science; Persistent homology; Brain activity and meditation; Topological data analysis; Betti number; Artificial intelligence; Computer vision; Mathematics; Psychology; Algorithm; Electroencephalography; Neuroscience; Pure mathematics","score_opus":0.03227489643239082,"score_gpt":0.2631923044446767,"score_spread":0.23091740801228586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018013674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19911721,0.000752574,0.7867295,0.00095458445,0.00011191718,0.00006108658,0.0015191028,0.0010622717,0.009691732],"genre_scores_gemma":[0.87215495,0.0008360362,0.123549394,0.00008280485,0.0001425182,0.000090987756,0.0009059794,0.0001669488,0.002070303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9998586,0.000046503585,0.00000855666,0.00003562722,0.00003071692,0.000019852185],"domain_scores_gemma":[0.9990527,0.00039413953,0.00018072588,0.0001276743,0.00013229591,0.000112522044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003566191,0.00046636412,0.0001917468,0.003643308,0.00041880406,0.0019281666,0.00040865914,0.0004732535,0.0033793512],"category_scores_gemma":[0.0018284494,0.00023821829,0.00031846494,0.0010647781,0.0015018507,0.0020120803,0.00076832,0.00054978987,0.00032144116],"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":[0.0003846451,0.00008015871,0.013234702,0.00059993577,0.000107022744,0.0010167273,0.00214677,0.17456989,0.08648399,0.6204041,0.0054738508,0.09549814],"study_design_scores_gemma":[0.000033055898,0.0002867194,0.033131417,0.00012382865,0.00006872137,0.0013867543,0.0013698682,0.5653659,0.012382982,0.36376512,0.021948207,0.00013742955],"about_ca_topic_score_codex":0.0012533041,"about_ca_topic_score_gemma":0.00098862,"teacher_disagreement_score":0.003643308,"about_ca_system_score_codex":0.0003965338,"about_ca_system_score_gemma":0.0003196025,"threshold_uncertainty_score":0.011305034},"labels":[],"label_agreement":null},{"id":"W3045753504","doi":"10.3389/fncom.2020.00063","title":"The Neuroscience of Spatial Navigation and the Relationship to Artificial Intelligence","year":2020,"lang":"en","type":"review","venue":"Frontiers in Computational Neuroscience","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"National Institute on Alcohol Abuse and Alcoholism; Florida Department of Health; National Institute on Aging; Alzheimer's Association","keywords":"Computer science; Cognitive neuroscience; Neuroinformatics; Systems neuroscience; Computational neuroscience; Neuroscience; Artificial intelligence; Cognitive science; Spatial cognition; Cognition; Data science; Psychology","score_opus":0.20067278851567966,"score_gpt":0.37970795554503706,"score_spread":0.1790351670293574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045753504","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003057687,0.92680883,0.021026723,0.017307138,0.0017469678,0.000017139904,0.000052357147,0.00006390477,0.029919349],"genre_scores_gemma":[0.065697275,0.91515946,0.0085002445,0.0039291508,0.0026959481,0.0000697708,0.00006521315,0.000025064533,0.0038578806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99943763,0.00023107254,0.00003866452,0.000098385244,0.00013508003,0.000059076458],"domain_scores_gemma":[0.99892694,0.0007362337,0.00007288669,0.000050768766,0.00015182968,0.00006128071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010415496,0.0005772689,0.00079686125,0.0015080551,0.0005255435,0.0025406603,0.0011014601,0.0019650008,0.0017405587],"category_scores_gemma":[0.0019786383,0.0001941191,0.00045082773,0.0014494489,0.006620772,0.002992897,0.0010687796,0.0028948933,0.0006050028],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049469156,0.000037906033,0.0006614956,0.0022466185,0.00006697915,0.00021859376,0.0008055093,0.003363319,0.0011842225,0.7277859,0.014572206,0.24900772],"study_design_scores_gemma":[0.000008604613,0.00006763871,0.0015957654,0.0013135956,0.000023873525,0.0005044281,0.00037460867,0.0015074658,0.0005571503,0.7208556,0.27314502,0.000046286747],"about_ca_topic_score_codex":0.0025576307,"about_ca_topic_score_gemma":0.0019190757,"teacher_disagreement_score":0.0025576307,"about_ca_system_score_codex":0.0018582756,"about_ca_system_score_gemma":0.0017184952,"threshold_uncertainty_score":0.013482809},"labels":[],"label_agreement":null},{"id":"W3045775993","doi":"10.3389/fncom.2020.00064","title":"Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate","year":2020,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Asynchronous communication; Computer science; Feed forward; Backpropagation; Artificial neural network; Artificial intelligence; Telecommunications","score_opus":0.03153565497985524,"score_gpt":0.26196487940680163,"score_spread":0.23042922442694638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045775993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3548317,0.00070982677,0.63148814,0.0014111005,0.00016414581,0.00021200215,0.0011633572,0.0016380061,0.008381722],"genre_scores_gemma":[0.9698313,0.0004596457,0.027178558,0.00009816376,0.00008940171,0.0003122108,0.0004250434,0.00035292894,0.001252779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983918,0.00028679037,0.00012881342,0.0003927417,0.0004237012,0.00037610333],"domain_scores_gemma":[0.9508457,0.031772736,0.007597854,0.0014721556,0.005021492,0.003290111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024519174,0.0016004407,0.0015706522,0.0016546051,0.0008812798,0.0014181304,0.0013698068,0.002139517,0.0025677239],"category_scores_gemma":[0.047477383,0.0011707067,0.0011218416,0.00047741542,0.0021522227,0.0031057233,0.002120179,0.0026730695,0.00078087335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014234041,0.00046367262,0.009969406,0.001397069,0.00028563075,0.00647233,0.0015222899,0.4403918,0.3145717,0.19618097,0.0047737695,0.022547847],"study_design_scores_gemma":[0.00019913814,0.0003787892,0.0045412113,0.00009693535,0.00005851742,0.0007467378,0.00016827844,0.8853473,0.03250503,0.07510823,0.0007194469,0.00013040668],"about_ca_topic_score_codex":0.0018303319,"about_ca_topic_score_gemma":0.0017098597,"teacher_disagreement_score":0.0025677239,"about_ca_system_score_codex":0.00079919666,"about_ca_system_score_gemma":0.0018031106,"threshold_uncertainty_score":0.01296711},"labels":[],"label_agreement":null},{"id":"W3083624247","doi":"10.3389/fncom.2020.00078","title":"Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations","year":2020,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spiking neural network; Multiplexing; Neuroscience; Artificial neural network; Artificial intelligence; Psychology; Telecommunications","score_opus":0.032122361152030265,"score_gpt":0.2572273482475795,"score_spread":0.2251049870955492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083624247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4025027,0.00019956894,0.59460264,0.00025468552,0.000031901778,0.00002282543,0.000046757308,0.00039982915,0.0019392164],"genre_scores_gemma":[0.9835856,0.0000931184,0.015461927,0.000022180906,0.000012290732,0.000024223027,0.000030107198,0.000024865374,0.00074576674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998846,0.000034752444,0.000007301729,0.00003307643,0.00002119459,0.00001905943],"domain_scores_gemma":[0.99955124,0.00022988477,0.00009729869,0.000048469916,0.000035568984,0.000037355127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040030965,0.0003802289,0.0003129677,0.00018550323,0.0001657316,0.00037231686,0.0005872253,0.00044145822,0.00050486694],"category_scores_gemma":[0.0023097028,0.00035831897,0.00039502443,0.00017091206,0.00054570276,0.00084450963,0.00046176254,0.00047041144,0.000092630245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055950135,0.000021739424,0.0012519001,0.000026773427,0.000034454104,0.00010532213,0.00007648877,0.9567475,0.014422033,0.01472894,0.00015760533,0.012371317],"study_design_scores_gemma":[0.0000018649632,0.000008309338,0.00007943945,9.834914e-7,0.0000022486527,0.000008221497,0.0000020728485,0.9961671,0.00045478044,0.003235827,0.000037245238,0.0000018235448],"about_ca_topic_score_codex":0.0017510233,"about_ca_topic_score_gemma":0.0027234512,"teacher_disagreement_score":0.0017510233,"about_ca_system_score_codex":0.000497327,"about_ca_system_score_gemma":0.0002462554,"threshold_uncertainty_score":0.003608346},"labels":[],"label_agreement":null},{"id":"W3099527391","doi":"10.3389/fncom.2020.573554","title":"Hierarchical Sequencing and Feedforward and Feedback Control Mechanisms in Speech Production: A Preliminary Approach for Modeling Normal and Disordered Speech","year":2020,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"RWTH Aachen University","keywords":"Utterance; Speech production; Computer science; Production (economics); Feed forward; Speech recognition; Hierarchy; Selection (genetic algorithm); Artificial intelligence","score_opus":0.034015068926246866,"score_gpt":0.25462170423568065,"score_spread":0.22060663530943378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099527391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037257034,0.0005553521,0.9503439,0.0004624138,0.00005921417,0.00006368039,0.00018622696,0.00025640472,0.010815708],"genre_scores_gemma":[0.7670825,0.0010188599,0.22145225,0.00010932027,0.00010087426,0.00037570018,0.00019525987,0.00007319901,0.009591923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999156,0.000025041954,0.000006238574,0.000021580701,0.000019877984,0.000011677866],"domain_scores_gemma":[0.9998673,0.000055596025,0.000018320636,0.000018885405,0.000021030473,0.000018882092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032185082,0.00052464433,0.00033807082,0.00042512952,0.00031786953,0.0006248528,0.000770852,0.000731419,0.0020147325],"category_scores_gemma":[0.0005077999,0.00020931833,0.00056061003,0.00021360813,0.00080500473,0.0011084466,0.00046812548,0.00059312215,0.00034616073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006952607,0.00005009652,0.0013967962,0.00013520052,0.00003152466,0.00031209446,0.00049141515,0.5219818,0.016764984,0.43337741,0.000685272,0.024703968],"study_design_scores_gemma":[0.000007448127,0.000050548002,0.00059905514,0.0000142112785,0.000012762389,0.00008544594,0.000033824657,0.8676382,0.00072689645,0.12818885,0.002630519,0.000012256754],"about_ca_topic_score_codex":0.0025623366,"about_ca_topic_score_gemma":0.002821785,"teacher_disagreement_score":0.0025623366,"about_ca_system_score_codex":0.000405632,"about_ca_system_score_gemma":0.000711022,"threshold_uncertainty_score":0.0067399144},"labels":[],"label_agreement":null},{"id":"W3115234771","doi":"10.3389/fncom.2020.575143","title":"A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity","year":2020,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University Health Network; Baycrest Hospital; University of Toronto; Centre for Addiction and Mental Health","funders":"Natural Sciences and Engineering Research Council of Canada; Krembil Foundation","keywords":"Neuroscience; Rhythm; Thalamus; Modulation (music); Connectome; Electroencephalography; Resting state fMRI; Human Connectome Project; Computer science; Physics; Functional connectivity; Psychology; Acoustics","score_opus":0.041777732478642596,"score_gpt":0.2601218669358954,"score_spread":0.21834413445725281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115234771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27556622,0.0009244735,0.67216814,0.0025098408,0.00013703061,0.00011120342,0.0009858004,0.00061048893,0.046986736],"genre_scores_gemma":[0.9641537,0.0006314426,0.021306826,0.00023386149,0.000072079936,0.00028668917,0.00022497725,0.00008126539,0.013009186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992776,0.000022695802,0.000002520015,0.000021870012,0.000014332598,0.000010825397],"domain_scores_gemma":[0.9998808,0.000038645565,0.000025893793,0.000014078761,0.000016955542,0.000023737402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018937908,0.0005301049,0.00036861986,0.00046673793,0.0003550852,0.000512242,0.001165882,0.0010867397,0.0030145524],"category_scores_gemma":[0.00045447832,0.00026655817,0.00053320243,0.0003589566,0.0008035138,0.0012004188,0.0005124029,0.000586618,0.00040926828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005904844,0.000044115503,0.0008830281,0.00007452579,0.0000620571,0.0003685259,0.000119510005,0.8698304,0.01972898,0.1030537,0.0012439085,0.004532155],"study_design_scores_gemma":[0.000012464601,0.00003799113,0.0006011202,0.000004476963,0.000013133365,0.00007983528,0.000015674721,0.9748156,0.00028118075,0.023365228,0.00076559174,0.000007727905],"about_ca_topic_score_codex":0.0035334863,"about_ca_topic_score_gemma":0.0033857264,"teacher_disagreement_score":0.0035334863,"about_ca_system_score_codex":0.0006313551,"about_ca_system_score_gemma":0.00046311252,"threshold_uncertainty_score":0.010084748},"labels":[],"label_agreement":null},{"id":"W3165679058","doi":"10.3389/fncom.2021.659838","title":"Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature","year":2021,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Chinese Academy of Sciences; Institute of Biophysics, Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Betweenness centrality; Centrality; Diffusion MRI; Neurology; Neuroimaging; Computer science; Feature (linguistics); Connectome; Graph; Connectomics; Artificial intelligence; Medicine; Neuroscience; Pattern recognition (psychology); Psychology; Functional connectivity; Magnetic resonance imaging; Mathematics; Theoretical computer science; Statistics","score_opus":0.03708595069693046,"score_gpt":0.34776959948722636,"score_spread":0.3106836487902959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165679058","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":[0.97592133,0.0017376128,0.018890593,0.0002605173,0.000045939327,0.000065170025,0.0012580006,0.00014374254,0.0016770719],"genre_scores_gemma":[0.99290365,0.00044006892,0.0052676112,0.000034316312,0.000044542423,0.000024864285,0.0008719121,0.000007951886,0.00040516196],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985003,0.000021630487,0.000017029337,0.000057366462,0.000026487016,0.000027465783],"domain_scores_gemma":[0.99943835,0.00015667209,0.00015551927,0.00004293302,0.00012246506,0.00008408412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005263416,0.0007816044,0.00046632567,0.003279013,0.000259569,0.0006358478,0.00028743612,0.00059715606,0.0009726019],"category_scores_gemma":[0.0015930223,0.00013541745,0.00075162,0.0010998772,0.00025356436,0.00071976375,0.00043013558,0.00038808977,0.00027573563],"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":[0.00064973254,0.0002186102,0.87882215,0.00020200081,0.0006845366,0.0008671838,0.00022777265,0.0106253885,0.009163774,0.0010082554,0.003884516,0.09364601],"study_design_scores_gemma":[0.000066184686,0.00059251045,0.84538573,0.00010125064,0.00093089615,0.0023680872,0.0005686025,0.13554879,0.004851265,0.007269212,0.0022431784,0.00007426527],"about_ca_topic_score_codex":0.0035884052,"about_ca_topic_score_gemma":0.006487762,"teacher_disagreement_score":0.0035884052,"about_ca_system_score_codex":0.00026548203,"about_ca_system_score_gemma":0.0002874232,"threshold_uncertainty_score":0.0071350336},"labels":[],"label_agreement":null},{"id":"W3181242778","doi":"10.3389/fncom.2021.684423","title":"Quantitative Assessment of Stress Through EEG During a Virtual Reality Stress-Relax Session","year":2021,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministerio de Ciencia, Innovación y Universidades","keywords":"Electroencephalography; Stress (linguistics); Computer science; Session (web analytics); Artificial intelligence; Correlation; Psychology; Pattern recognition (psychology); Audiology; Speech recognition; Mathematics; Medicine","score_opus":0.0332588278509646,"score_gpt":0.34389480029113745,"score_spread":0.3106359724401728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181242778","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":[0.9822838,0.00011697758,0.01629736,0.000037495152,0.00001790409,0.00008419049,0.00034336536,0.00010844876,0.0007105728],"genre_scores_gemma":[0.9899631,0.00018290228,0.008559613,0.0000303278,0.00003562446,0.0001504176,0.00036734206,0.000023046967,0.0006875926],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997366,0.000082843755,0.00001757624,0.00007022428,0.00006394347,0.000028855658],"domain_scores_gemma":[0.9993623,0.00022650574,0.0001305083,0.00005166779,0.00014366418,0.00008538489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043883675,0.0006058135,0.00034569355,0.00034553575,0.00014808326,0.00041721493,0.0002133244,0.00039084916,0.0013638657],"category_scores_gemma":[0.0020791385,0.00010997395,0.00018895666,0.00019838952,0.00021958753,0.00025942153,0.00038587302,0.0002893702,0.00029727095],"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":[0.0038286846,0.00082018896,0.16428123,0.0009194059,0.00044985954,0.00075787003,0.004492377,0.0065942034,0.6037204,0.00036869172,0.0012503173,0.21251675],"study_design_scores_gemma":[0.000067940426,0.0059562325,0.9384252,0.000050099472,0.00017191198,0.00085566886,0.0016695438,0.013327543,0.03749753,0.0004333745,0.0014469173,0.000098006225],"about_ca_topic_score_codex":0.0004920473,"about_ca_topic_score_gemma":0.0008433573,"teacher_disagreement_score":0.0013638657,"about_ca_system_score_codex":0.00006785069,"about_ca_system_score_gemma":0.00009826811,"threshold_uncertainty_score":0.0045626163},"labels":[],"label_agreement":null},{"id":"W3194814824","doi":"10.3389/fncom.2021.678688","title":"Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds","year":2021,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; State Key Laboratory of Cognitive Neuroscience and Learning; York University","keywords":"Statistical physics; Dimensionality reduction; Computer science; Visual cortex; Physics; Artificial intelligence; Neuroscience","score_opus":0.030745914564574185,"score_gpt":0.2568477329237681,"score_spread":0.22610181835919393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194814824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2851998,0.00030907255,0.70536387,0.0006629247,0.000060828603,0.000067840825,0.00025899347,0.00042774988,0.0076489765],"genre_scores_gemma":[0.9556349,0.0002389776,0.039829124,0.000095732765,0.00006047131,0.00015683404,0.00034795734,0.00013483036,0.0035011931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997981,0.00008170267,0.000010401892,0.000039026647,0.00003886245,0.000031934258],"domain_scores_gemma":[0.99933237,0.00033135954,0.00011958957,0.00009487853,0.0000758022,0.000045993813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005121639,0.00080085895,0.0010493451,0.0006390632,0.0004918702,0.00093829614,0.00079094566,0.0008936171,0.0013372222],"category_scores_gemma":[0.0021155162,0.00037071054,0.001312742,0.0002764975,0.0011243701,0.00089354103,0.0009936644,0.0010922025,0.00023654931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013161943,0.000017253707,0.0004398758,0.000018770386,0.000020694599,0.000044964992,0.00004763075,0.96496683,0.0010342413,0.031282563,0.00026969478,0.0018443005],"study_design_scores_gemma":[0.0000015827243,0.0000030517615,0.00005084106,8.468869e-7,0.0000014259842,0.0000030813717,0.0000025346503,0.9939458,0.000045196775,0.0058796653,0.00006422587,0.0000019198453],"about_ca_topic_score_codex":0.0070350147,"about_ca_topic_score_gemma":0.0043015685,"teacher_disagreement_score":0.0070350147,"about_ca_system_score_codex":0.0008203298,"about_ca_system_score_gemma":0.0009049575,"threshold_uncertainty_score":0.013988137},"labels":[],"label_agreement":null},{"id":"W3199458823","doi":"10.3389/fncom.2021.653097","title":"A Neuron-Glial Model of Exosomal Release in the Onset and Progression of Alzheimer's Disease","year":2021,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Agencia Estatal de Investigación; Basque Center for Applied Mathematics; Eusko Jaurlaritza; Ministerio de Ciencia, Innovación y Universidades","keywords":"Exosome; Microvesicles; Neuroscience; Neurotoxicity; Extracellular vesicles; Extracellular; Chemistry; Biology; Cell biology; Medicine; Biochemistry; Internal medicine; microRNA","score_opus":0.016637843609241174,"score_gpt":0.2738969429127723,"score_spread":0.2572590993035311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199458823","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":[0.65440434,0.0088303685,0.26900846,0.0058020423,0.0008716997,0.00018481597,0.0012629,0.0005112343,0.059124064],"genre_scores_gemma":[0.9558576,0.0024888716,0.023958053,0.00029359484,0.00011347104,0.00014394952,0.00021422046,0.000033084307,0.016897142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999229,0.00002158088,0.0000068376175,0.000022640888,0.0000117731115,0.000014339159],"domain_scores_gemma":[0.9999325,0.0000106402,0.000017722381,0.0000057341354,0.000010855141,0.000022491817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017627221,0.0006153255,0.0006888834,0.00047634897,0.00039116858,0.00056554243,0.0009956112,0.0014256618,0.0021853829],"category_scores_gemma":[0.00018768967,0.00022601162,0.0008453666,0.00032212833,0.0005915507,0.0010016393,0.0009673145,0.0005456097,0.00037909785],"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":[0.00084859226,0.0004171046,0.0035533453,0.0006021439,0.00019263955,0.004957342,0.00044145723,0.20204495,0.24099928,0.5314908,0.0026470441,0.011805254],"study_design_scores_gemma":[0.00022524346,0.00045303203,0.0020355117,0.000051693718,0.00011613695,0.0012148736,0.00021926116,0.827126,0.007585007,0.15580684,0.0051021306,0.000064305954],"about_ca_topic_score_codex":0.002674327,"about_ca_topic_score_gemma":0.0022596775,"teacher_disagreement_score":0.002674327,"about_ca_system_score_codex":0.000574269,"about_ca_system_score_gemma":0.0005356968,"threshold_uncertainty_score":0.0073108077},"labels":[],"label_agreement":null},{"id":"W4200267796","doi":"10.3389/fncom.2021.759489","title":"InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling","year":2021,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Inverse dynamics; Torque; Computer science; Acceleration; Joint (building); Control theory (sociology); Electromyography; Redundancy (engineering); Recurrent neural network; Artificial intelligence; Simulation; Artificial neural network; Engineering; Physical medicine and rehabilitation; Kinematics","score_opus":0.0166356676743056,"score_gpt":0.21980454696679705,"score_spread":0.20316887929249144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200267796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003495077,0.0001335339,0.9941425,0.000100098245,0.000042966236,0.000020377873,0.00003268325,0.0006423328,0.0013905319],"genre_scores_gemma":[0.30081844,0.00031160857,0.6877881,0.0003234531,0.00015674799,0.00038259395,0.0004385997,0.00050627446,0.009274178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965954,0.00008122664,0.000019155003,0.00009343539,0.000107760825,0.000038877555],"domain_scores_gemma":[0.9995492,0.00019568465,0.000054841847,0.000056741694,0.00012166384,0.000021828275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008333919,0.0011944682,0.0012172728,0.0006529814,0.00034052916,0.00089639716,0.0016942307,0.0015756314,0.003385419],"category_scores_gemma":[0.0017336385,0.00065678457,0.0009874499,0.00068587426,0.00052882987,0.0010309692,0.0011094407,0.0011668152,0.00079053466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048937505,0.00005247655,0.00037259082,0.00010291698,0.0000731108,0.00009386144,0.00004025927,0.8924129,0.003025154,0.011120751,0.0014457671,0.09121128],"study_design_scores_gemma":[0.0000024568997,0.000011539802,0.000030055135,0.000003146126,0.0000027744743,0.000011181697,0.0000016475256,0.99768484,0.0002606442,0.0015016185,0.00048740703,0.0000026559212],"about_ca_topic_score_codex":0.006191787,"about_ca_topic_score_gemma":0.007103202,"teacher_disagreement_score":0.006191787,"about_ca_system_score_codex":0.00053493754,"about_ca_system_score_gemma":0.0013241513,"threshold_uncertainty_score":0.012311518},"labels":[],"label_agreement":null},{"id":"W4205521696","doi":"10.3389/fncom.2021.678232","title":"Neural Substrates of the Drift-Diffusion Model in Brain Disorders","year":2022,"lang":"en","type":"review","venue":"Frontiers in Computational Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"United Arab Emirates University","keywords":"Basal ganglia; Schizophrenia (object-oriented programming); Neuroscience; Autism; Psychology; Autism spectrum disorder; Functional connectivity; Cognitive psychology; Computer science; Psychiatry; Central nervous system","score_opus":0.06633983332001732,"score_gpt":0.3071321492991946,"score_spread":0.24079231597917727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205521696","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014070963,0.9870662,0.005373668,0.0016646641,0.00017023121,0.000014486732,0.000062352694,0.000025229143,0.00421597],"genre_scores_gemma":[0.018224644,0.97869164,0.0019184733,0.00028128296,0.00018029289,0.000026588055,0.000058227324,0.0000054281772,0.0006134739],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999089,0.000029192783,0.000012088141,0.00002387067,0.00001635221,0.000009636165],"domain_scores_gemma":[0.9997427,0.00018601336,0.000025033136,0.00000832279,0.000026230948,0.000011691556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048913877,0.0008056321,0.0007879632,0.0010726644,0.00021229201,0.0008833905,0.0006306358,0.0010282825,0.0016818357],"category_scores_gemma":[0.00095918926,0.00021448244,0.0005472547,0.0008550146,0.0009119401,0.0009715035,0.00060646574,0.0011391117,0.00044678658],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105884035,0.000054385782,0.0016420964,0.015552378,0.00045777165,0.0009964873,0.0003908785,0.007822503,0.0025916444,0.139352,0.012097805,0.81893617],"study_design_scores_gemma":[0.00005996886,0.00022260481,0.0129076,0.0137702,0.0007864703,0.010535378,0.00049361604,0.01039432,0.0025988878,0.45080996,0.4972429,0.00017810304],"about_ca_topic_score_codex":0.0022096816,"about_ca_topic_score_gemma":0.0020331764,"teacher_disagreement_score":0.0022096816,"about_ca_system_score_codex":0.0008141039,"about_ca_system_score_gemma":0.00095234776,"threshold_uncertainty_score":0.0059068203},"labels":[],"label_agreement":null},{"id":"W4205884244","doi":"10.3389/fncom.2021.769982","title":"Prediction and Modeling of Neuropsychological Scores in Alzheimer’s Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; European Commission; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Neuroimaging; Neuropsychology; Entorhinal cortex; Psychology; Clinical Dementia Rating; Superior frontal gyrus; Middle temporal gyrus; Angular gyrus; Dementia; Cognition; Neuroscience; Alzheimer's Disease Neuroimaging Initiative; Superior temporal gyrus; Alzheimer's disease; Medicine; Functional magnetic resonance imaging; Hippocampus; Disease; Internal medicine; Cognitive impairment","score_opus":0.12568575309943075,"score_gpt":0.3669456649051377,"score_spread":0.24125991180570694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205884244","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":[0.8237469,0.0012064959,0.17183289,0.0005794735,0.000054577966,0.000095273484,0.0010039195,0.00036614825,0.0011142397],"genre_scores_gemma":[0.98492,0.00018919432,0.013849822,0.000039740124,0.000031682925,0.00007993973,0.00045215557,0.000009476666,0.00042797247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996468,0.00013974891,0.000022928216,0.00011481133,0.00003958536,0.000036207595],"domain_scores_gemma":[0.9985405,0.0010349086,0.0001983866,0.000041510626,0.00014022578,0.000044386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017640708,0.0009100582,0.00056255626,0.0011953276,0.00020782325,0.0007329992,0.00055477855,0.0005987421,0.00068664935],"category_scores_gemma":[0.0035696067,0.00027648086,0.0007665554,0.00047521695,0.00030676587,0.0004626025,0.0004349225,0.0006851818,0.00017927839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000383352,0.0002809463,0.06522859,0.000068114714,0.00023177991,0.00018468061,0.000057431083,0.8845539,0.0015895205,0.00047461034,0.00064027996,0.046306744],"study_design_scores_gemma":[0.0000029473301,0.00001944511,0.0031193446,0.000003528034,0.000011766856,0.000013754329,0.000005069943,0.9962817,0.00012860788,0.0003774562,0.000032386626,0.000003955418],"about_ca_topic_score_codex":0.0090179145,"about_ca_topic_score_gemma":0.006406112,"teacher_disagreement_score":0.0090179145,"about_ca_system_score_codex":0.0006563829,"about_ca_system_score_gemma":0.00047289746,"threshold_uncertainty_score":0.017930865},"labels":[],"label_agreement":null},{"id":"W4226227343","doi":"10.3389/fncom.2022.757244","title":"Forgetting Enhances Episodic Control With Structured Memories","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital; The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Canada First Research Excellence Fund; University of Toronto; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Forgetting; Mnemonic; Episodic memory; Reinforcement learning; Cognitive psychology; Control (management); Psychology; Reinforcement; Computer science; Encoding (memory); Artificial intelligence; Neuroscience; Social psychology; Cognition","score_opus":0.027876757506142432,"score_gpt":0.26615232706326336,"score_spread":0.23827556955712093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226227343","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":[0.9595319,0.00019063067,0.037993137,0.0000770554,0.00002556757,0.000021764841,0.000036459576,0.0002322338,0.001891112],"genre_scores_gemma":[0.9940399,0.00006958402,0.005259887,0.000019481917,0.000008176228,0.000009677351,0.000049421953,0.000014794849,0.0005290966],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997961,0.000030361012,0.00002332128,0.00005568588,0.000054281965,0.00004029721],"domain_scores_gemma":[0.99732625,0.00086569734,0.0005866504,0.00077011366,0.00020561634,0.00024577274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045692112,0.0003404753,0.00032150335,0.0001977512,0.00014785552,0.000693575,0.0006567026,0.00035837688,0.0021537247],"category_scores_gemma":[0.0039155674,0.00017551213,0.00029758769,0.00010349781,0.0005126382,0.0011099429,0.0007006587,0.00057391083,0.00018764618],"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":[0.0030704015,0.0015596249,0.04409126,0.00081484416,0.0005940863,0.0015349525,0.0011510877,0.15626371,0.4889882,0.0276131,0.0011763425,0.27314234],"study_design_scores_gemma":[0.00022680085,0.0033372908,0.055163052,0.000080305,0.0003030026,0.0010647916,0.00030396463,0.6517498,0.22069268,0.061664067,0.005327529,0.00008672737],"about_ca_topic_score_codex":0.0005114341,"about_ca_topic_score_gemma":0.000528879,"teacher_disagreement_score":0.0021537247,"about_ca_system_score_codex":0.0002732294,"about_ca_system_score_gemma":0.0003169753,"threshold_uncertainty_score":0.00720495},"labels":[],"label_agreement":null},{"id":"W4283018032","doi":"10.3389/fncom.2022.875282","title":"Recent Trends in Non-invasive Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review","year":2022,"lang":"en","type":"review","venue":"Frontiers in Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Coherence (philosophical gambling strategy); Brain activity and meditation; Multidisciplinary approach; Cognitive neuroscience; Brain–computer interface; Electroencephalography; Cognition; Neural engineering; Neuroscience; Psychology; Artificial intelligence","score_opus":0.11011544373397729,"score_gpt":0.3901034502464072,"score_spread":0.2799880065124299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283018032","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002074822,0.9985505,0.00024430154,0.00044145627,0.00014212386,0.00008797078,0.00013063182,0.000007046261,0.00018850992],"genre_scores_gemma":[0.0020931142,0.99604625,0.0007221323,0.0005718156,0.000114087736,0.0002463402,0.000116010844,0.0000058514524,0.00008436299],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.991154,0.0025412065,0.003717077,0.0007211991,0.0016282429,0.00023833163],"domain_scores_gemma":[0.92239106,0.061089773,0.008003407,0.0008451562,0.0071232812,0.0005473354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012762485,0.0016068361,0.005985364,0.021585401,0.0007657074,0.003896995,0.0022562344,0.0024182252,0.00514592],"category_scores_gemma":[0.055965357,0.0010079935,0.0060826223,0.015659142,0.0012149318,0.0037493836,0.00231259,0.0016417252,0.00069264695],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095436946,0.0000233179,0.00040504785,0.8765233,0.001929327,0.00012890816,0.0003921583,0.00014048727,0.00028932036,0.000931383,0.0035860555,0.11555518],"study_design_scores_gemma":[0.00005074157,0.000102844184,0.0014416485,0.92884356,0.0121718645,0.0003441926,0.00034774773,0.000091724396,0.00016286249,0.0008952483,0.055508155,0.00003952979],"about_ca_topic_score_codex":0.0065659224,"about_ca_topic_score_gemma":0.02107664,"teacher_disagreement_score":0.021585401,"about_ca_system_score_codex":0.0033680222,"about_ca_system_score_gemma":0.018444236,"threshold_uncertainty_score":0.06749529},"labels":[],"label_agreement":null},{"id":"W4283360602","doi":"10.3389/fncom.2022.892354","title":"Augmenting Human Selves Through Artificial Agents – Lessons From the Brain","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Embodied and Extended Cognition","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto; Mila - Quebec Artificial Intelligence Institute; Centre for Addiction and Mental Health; University of Ottawa","funders":"Economic and Social Research Council; Canadian Institutes of Health Research; European Commission; Natural Sciences and Engineering Research Council of Canada; Wellcome Trust","keywords":"Artificial general intelligence; Consciousness; Computer science; Cognitive science; Nexus (standard); Realm; Artificial intelligence; Adaptive behavior; Cognition; Morality; Psychology; Cognitive psychology; Social psychology; Epistemology; Neuroscience","score_opus":0.10242229973351163,"score_gpt":0.33868190159528705,"score_spread":0.23625960186177541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283360602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15268713,0.00970504,0.6517415,0.02749093,0.0006445727,0.00011062467,0.00018408727,0.0011189554,0.15631713],"genre_scores_gemma":[0.86217904,0.0042025195,0.12262006,0.0010413821,0.00016149443,0.0001243909,0.00007811215,0.00012724144,0.009465842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997626,0.00010547196,0.000008893101,0.00005569846,0.000048799957,0.000018546167],"domain_scores_gemma":[0.9995022,0.00025067638,0.00003713078,0.00009986797,0.000052623112,0.000057445304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005302833,0.0003853964,0.00023199123,0.00024666238,0.00042922035,0.0020304113,0.00090201857,0.000944953,0.0028030733],"category_scores_gemma":[0.001700792,0.00018924623,0.00035681704,0.00014020658,0.0034738025,0.0029885976,0.0014461207,0.0014728802,0.0005641408],"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":[0.00006226231,0.00007331741,0.0016970343,0.00031488153,0.00009276457,0.00020746033,0.0015667247,0.055638645,0.009582486,0.8155673,0.0049181688,0.11027899],"study_design_scores_gemma":[0.00003144404,0.00008735714,0.0010220836,0.000116489915,0.000037952417,0.00016059761,0.00036778976,0.07111136,0.002731278,0.8750591,0.049229477,0.00004510401],"about_ca_topic_score_codex":0.0008175611,"about_ca_topic_score_gemma":0.0008201513,"teacher_disagreement_score":0.0028030733,"about_ca_system_score_codex":0.00042537882,"about_ca_system_score_gemma":0.00044983273,"threshold_uncertainty_score":0.009377241},"labels":[],"label_agreement":null},{"id":"W4285729441","doi":"10.3389/fncom.2022.849323","title":"A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Attention Deficit Hyperactivity Disorder","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Children's Hospital of Eastern Ontario; University of Ottawa; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Tonic (physiology); Dopamine; Dopaminergic; Psychology; Neuroscience; Reinforcement; Reinforcement learning; Basal ganglia; Computer science; Machine learning; Central nervous system","score_opus":0.04144501949219342,"score_gpt":0.30757143998633263,"score_spread":0.2661264204941392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285729441","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":[0.8068799,0.00025426247,0.1850988,0.0006582085,0.000040563136,0.000095334784,0.00025719684,0.00022135877,0.006494365],"genre_scores_gemma":[0.9916232,0.000112498674,0.006927522,0.000044861303,0.0000054776283,0.000073304465,0.00005884974,0.000008070002,0.0011461532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999018,0.000028457162,0.0000064103606,0.000027055088,0.00001781439,0.000018465988],"domain_scores_gemma":[0.9998567,0.00004602536,0.000044449847,0.000014672955,0.000014572397,0.000023571345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002139108,0.00049272686,0.00032737202,0.0002291798,0.0001765038,0.00040093256,0.0006273492,0.00058081344,0.0014860753],"category_scores_gemma":[0.0004474986,0.00018877171,0.00050543126,0.00008601484,0.00043898402,0.0004338175,0.0004251206,0.0004947098,0.00012317169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006163396,0.0005637807,0.012272004,0.0002262503,0.00020162555,0.0010812331,0.00025986668,0.78562033,0.14686455,0.035933856,0.00046886722,0.015891403],"study_design_scores_gemma":[0.000072845076,0.00046583315,0.005331532,0.000011448739,0.000060354763,0.00027240824,0.000032070748,0.9792157,0.0034309458,0.010531997,0.00055393146,0.000020965319],"about_ca_topic_score_codex":0.0018395948,"about_ca_topic_score_gemma":0.001456837,"teacher_disagreement_score":0.0018395948,"about_ca_system_score_codex":0.00040330004,"about_ca_system_score_gemma":0.00042950848,"threshold_uncertainty_score":0.004971385},"labels":[],"label_agreement":null},{"id":"W4291003108","doi":"10.3389/fncom.2022.851485","title":"Motor cortex outputs evoked by long-duration microstimulation encode synergistic muscle activation patterns not controlled movement trajectories","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Forelimb; Microstimulation; Neuroscience; Motor cortex; Stimulus (psychology); Anatomy; Evoked potential; Bicuculline; Physics; Chemistry; Stimulation; Medicine; GABAA receptor; Biology; Psychology; Receptor; Internal medicine","score_opus":0.01762864605533403,"score_gpt":0.24467116556505925,"score_spread":0.2270425195097252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291003108","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":[0.9953759,0.00021302914,0.003575104,0.000011363011,0.0000066200887,0.000015682,0.00005773855,0.000043991582,0.00070060045],"genre_scores_gemma":[0.99730325,0.00010233553,0.0020296029,0.00001401067,0.0000026361147,0.000027318938,0.000070203765,0.000011808569,0.00043877546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999589,0.0000047727654,0.0000033906488,0.000008906093,0.000011850587,0.000012360606],"domain_scores_gemma":[0.9999063,0.000035024237,0.000021900138,0.000009894012,0.000009867438,0.000016991426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076497716,0.00021540734,0.0001366432,0.000086291046,0.000042192416,0.00010092205,0.000087968925,0.00012556696,0.00060298684],"category_scores_gemma":[0.00025775586,0.0000679543,0.000108400855,0.00007795751,0.00011469142,0.00009568817,0.00014140541,0.00015225253,0.00008911763],"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":[0.000056986733,0.0000044327653,0.00021556424,0.000019014798,0.0000024979813,0.000025664092,0.0000075431067,0.00008512874,0.9980009,0.00001880008,0.0000063905277,0.0015571032],"study_design_scores_gemma":[0.000026361284,0.00064324983,0.13543089,0.000018683628,0.00004342798,0.00044697383,0.000063647225,0.00439017,0.85781264,0.00012570713,0.0009896721,0.000008585977],"about_ca_topic_score_codex":0.00039998046,"about_ca_topic_score_gemma":0.0014475922,"teacher_disagreement_score":0.00060298684,"about_ca_system_score_codex":0.00015343653,"about_ca_system_score_gemma":0.0000894769,"threshold_uncertainty_score":0.0020172},"labels":[],"label_agreement":null},{"id":"W4292707814","doi":"10.3389/fncom.2022.980613","title":"Combining backpropagation with Equilibrium Propagation to improve an Actor-Critic reinforcement learning framework","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University; University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Compute Canada","keywords":"Backpropagation; Reinforcement learning; Computer science; Artificial intelligence; Variety (cybernetics); Artificial neural network; Task (project management); Machine learning; Propagation of uncertainty; Algorithm; Engineering","score_opus":0.018713650393969508,"score_gpt":0.2571294174646655,"score_spread":0.238415767070696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292707814","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":[0.013659471,0.00030975434,0.9818055,0.0002971881,0.000102285725,0.000038270515,0.000020456053,0.00070548116,0.0030615975],"genre_scores_gemma":[0.79293454,0.0003591627,0.2002133,0.00025648085,0.00013662525,0.00017640655,0.00007803419,0.00017400352,0.0056715454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962556,0.00011238425,0.000022336033,0.000072978924,0.00011925515,0.00004752934],"domain_scores_gemma":[0.99884987,0.00061307807,0.00009218646,0.0000806629,0.00029766213,0.00006648357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016348556,0.0014494143,0.0010899875,0.00047946264,0.00033706124,0.0007931099,0.0015830472,0.0012840678,0.0018594143],"category_scores_gemma":[0.0041920817,0.0004984007,0.00042821027,0.00032487846,0.0009083884,0.0010216433,0.0010137646,0.0017626977,0.00052907487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004412214,0.00003682526,0.0003507143,0.00004107172,0.000050424536,0.00006940122,0.000029146297,0.9577005,0.0018290412,0.010284148,0.00086646114,0.028698176],"study_design_scores_gemma":[0.000003841915,0.000008960313,0.000014448039,0.0000019178722,0.0000034402528,0.0000043703612,6.0434803e-7,0.99810696,0.00017767759,0.0015434269,0.0001324503,0.0000019390957],"about_ca_topic_score_codex":0.0050683664,"about_ca_topic_score_gemma":0.004522793,"teacher_disagreement_score":0.0050683664,"about_ca_system_score_codex":0.00070438685,"about_ca_system_score_gemma":0.0010777679,"threshold_uncertainty_score":0.010077715},"labels":[],"label_agreement":null},{"id":"W4293063570","doi":"10.3389/fncom.2022.903883","title":"Patterns of synchronization in 2D networks of inhibitory neurons","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Diagonal; Synchronization (alternating current); Coupling (piping); Inhibitory postsynaptic potential; Computer science; Stability (learning theory); Artificial neural network; Torus; Network model; Topology (electrical circuits); Symmetry (geometry); Biological system; Statistical physics; Physics; Neuroscience; Mathematics; Artificial intelligence; Biology; Machine learning; Combinatorics; Geometry","score_opus":0.01420734766405686,"score_gpt":0.2289128659102113,"score_spread":0.21470551824615441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293063570","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":[0.9226114,0.0002064783,0.07046823,0.0003753718,0.0000282422,0.000017101882,0.00012775736,0.00011541851,0.006049992],"genre_scores_gemma":[0.99565166,0.00005444924,0.0035396013,0.000024980964,0.000005578569,0.000016213558,0.000036396377,0.000011887177,0.0006591887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998621,0.000042490967,0.000007292358,0.000038535098,0.000024804174,0.000024801715],"domain_scores_gemma":[0.9995596,0.00017234376,0.000116888084,0.000035907382,0.000050592444,0.00006461178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029319277,0.00022436737,0.0003317872,0.0004424164,0.0003349897,0.00070041104,0.0005480207,0.0005774042,0.0010482995],"category_scores_gemma":[0.0014291886,0.00032484782,0.0003520907,0.00025421943,0.0009914034,0.0007436058,0.000518535,0.00032221648,0.00012364239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015562752,0.000039790317,0.00437028,0.000066123845,0.000049800878,0.0003332098,0.00027033966,0.9281822,0.020142369,0.04247475,0.0006259815,0.0032894926],"study_design_scores_gemma":[0.000016248672,0.000019809579,0.0013438135,0.0000045582806,0.0000057845386,0.00003700648,0.000043803233,0.9885815,0.00080497237,0.008973435,0.00015871706,0.0000103740695],"about_ca_topic_score_codex":0.0027430034,"about_ca_topic_score_gemma":0.0023254657,"teacher_disagreement_score":0.0027430034,"about_ca_system_score_codex":0.00072458613,"about_ca_system_score_gemma":0.0002658318,"threshold_uncertainty_score":0.0054540634},"labels":[],"label_agreement":null},{"id":"W4297548862","doi":"10.3389/fncom.2022.969119","title":"Computational modeling of trans-synaptic nanocolumns, a modulator of synaptic transmission","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Photoreceptor and optogenetics research","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Neurotransmission; Postsynaptic potential; Neuroscience; Neurotransmitter receptor; Neurotransmitter; Synaptic vesicle; Synapse; Active zone; Postsynaptic density; Synaptic cleft; Ribbon synapse; Biophysics; Chemistry; Receptor; Vesicle; Biology; Central nervous system; Biochemistry","score_opus":0.03818403796402725,"score_gpt":0.28888896431141664,"score_spread":0.2507049263473894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297548862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47523952,0.0024599219,0.48486602,0.002522194,0.00030888175,0.00014509159,0.0016905684,0.0007755037,0.031992357],"genre_scores_gemma":[0.9321152,0.0012248956,0.059547823,0.00029813347,0.00005883094,0.00034606818,0.00062459486,0.0001548098,0.0056297453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998754,0.000030038109,0.0000070115143,0.000026389756,0.000032870783,0.000028140876],"domain_scores_gemma":[0.99951017,0.00032376958,0.000045667257,0.000027108712,0.000056129327,0.00003717902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020946626,0.00052008184,0.00078770495,0.00036617808,0.000479382,0.00089898246,0.0013553139,0.0016870213,0.0018888382],"category_scores_gemma":[0.0012410051,0.00043414914,0.0008335758,0.0003762399,0.00064929895,0.00074423885,0.00071316026,0.000760012,0.00023211226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002184814,0.000017682265,0.00054591085,0.0000405766,0.000019384366,0.000063588894,0.000020527697,0.99054927,0.0018440391,0.005302917,0.00021233466,0.0013618603],"study_design_scores_gemma":[0.0000034027514,0.0000036228491,0.00007381374,0.000002527078,0.0000036667113,0.00000659796,0.000004433579,0.9983725,0.00018694041,0.0011285497,0.00021190256,0.0000020625566],"about_ca_topic_score_codex":0.010463061,"about_ca_topic_score_gemma":0.0069184713,"teacher_disagreement_score":0.010463061,"about_ca_system_score_codex":0.0007521761,"about_ca_system_score_gemma":0.0011201169,"threshold_uncertainty_score":0.020804286},"labels":[],"label_agreement":null},{"id":"W4303415323","doi":"10.3389/fncom.2022.964686","title":"Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Convolutional neural network; Deep learning; Computer science; Artificial intelligence; Linear discriminant analysis; Machine learning; Heart disease; Medical diagnosis; Artificial neural network; Graph; Pattern recognition (psychology); Medicine; Cardiology; Pathology","score_opus":0.13417359072821405,"score_gpt":0.4182797198974728,"score_spread":0.28410612916925876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303415323","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":[0.6524668,0.0010615864,0.3299965,0.0014121385,0.00031426048,0.00023237496,0.005141652,0.0023814377,0.0069933967],"genre_scores_gemma":[0.9366974,0.0003112234,0.055473812,0.00023555508,0.00007234115,0.00008391808,0.004484724,0.00003145014,0.0026094115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980336,0.000028996485,0.0000122856845,0.000066129476,0.00005296815,0.0000363403],"domain_scores_gemma":[0.99977165,0.0000667902,0.00003406287,0.000022953878,0.00007757174,0.000026964484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002586194,0.00053516374,0.00038554153,0.0011170141,0.00020516058,0.0003273926,0.0004160933,0.00042022174,0.0007452914],"category_scores_gemma":[0.0008005225,0.00014675249,0.00047548706,0.00072959164,0.00014950146,0.00036943654,0.0004680295,0.00045151525,0.00037643363],"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":[0.0011472824,0.0013244685,0.11717682,0.00030520817,0.00036514533,0.0014121933,0.00021180607,0.16358694,0.034684412,0.0033414534,0.022860836,0.6535835],"study_design_scores_gemma":[0.000015445012,0.00011747038,0.01763515,0.000017688651,0.00003481477,0.00017760637,0.000058907157,0.9743878,0.0044643404,0.0015789099,0.0014949011,0.000017058415],"about_ca_topic_score_codex":0.0074371565,"about_ca_topic_score_gemma":0.013529191,"teacher_disagreement_score":0.0074371565,"about_ca_system_score_codex":0.00041515112,"about_ca_system_score_gemma":0.00037384193,"threshold_uncertainty_score":0.014787734},"labels":[],"label_agreement":null},{"id":"W4310984249","doi":"10.3389/fncom.2022.1037550","title":"Quasicriticality explains variability of human neural dynamics across life span","year":2022,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Valorisation des Données; National Science Foundation","keywords":"Life span; Dynamics (music); Span (engineering); Neural system; Computer science; Neuroscience; Psychology; Biology; Evolutionary biology; Engineering","score_opus":0.034137656052064036,"score_gpt":0.3017602178160157,"score_spread":0.26762256176395166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310984249","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":[0.93790877,0.0010183983,0.05760785,0.00041811712,0.000024492627,0.00001941155,0.001537315,0.00017283154,0.0012927861],"genre_scores_gemma":[0.99690175,0.00019279678,0.0018601497,0.000023526014,0.000024982339,0.000012141723,0.00078435824,0.000025562244,0.0001746769],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982846,0.0000459698,0.000009129896,0.00007892595,0.0000154506,0.000021997248],"domain_scores_gemma":[0.9986394,0.0006603552,0.00025577965,0.00026588837,0.000094083865,0.00008446357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007004574,0.00028227805,0.00036796855,0.0009777779,0.00026514963,0.0006565383,0.0004150983,0.00033776957,0.0011240499],"category_scores_gemma":[0.004094444,0.00015327084,0.00043474528,0.0005367612,0.0006621832,0.0006991133,0.00057528575,0.00037097532,0.00024039038],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009644141,0.00017580137,0.5688585,0.0005694922,0.0008787378,0.001669074,0.0021423853,0.17482066,0.05554762,0.03790557,0.00955914,0.14690857],"study_design_scores_gemma":[0.000019858135,0.00014910044,0.50801504,0.00005038717,0.00009363324,0.0008586523,0.00023713271,0.39574748,0.0024059555,0.08933689,0.0030250675,0.000060898456],"about_ca_topic_score_codex":0.0036548332,"about_ca_topic_score_gemma":0.003981406,"teacher_disagreement_score":0.0036548332,"about_ca_system_score_codex":0.00025854076,"about_ca_system_score_gemma":0.00023876331,"threshold_uncertainty_score":0.0072671175},"labels":[],"label_agreement":null},{"id":"W4320495208","doi":"10.3389/fncom.2023.1108889","title":"Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training","year":2023,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Brain–computer interface; Computer science; Classifier (UML); Inefficiency; User interface; Human–computer interaction; Artificial intelligence; Machine learning; Electroencephalography","score_opus":0.04374206960422777,"score_gpt":0.27463308767788747,"score_spread":0.2308910180736597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320495208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49443397,0.00075521285,0.49805737,0.00032263537,0.00011060838,0.00025354972,0.0004990746,0.0018777592,0.0036898023],"genre_scores_gemma":[0.9194681,0.00021721306,0.07887077,0.000059162805,0.000025784888,0.00011141234,0.00028150238,0.00016866955,0.0007973676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998941,0.00038468643,0.000083435494,0.00017153649,0.00035700816,0.00006225878],"domain_scores_gemma":[0.9961747,0.0016556849,0.00068216777,0.0004232504,0.0008995958,0.00016470005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013101328,0.0006901135,0.00044664647,0.0008808786,0.00020593553,0.00061196665,0.00050784566,0.0004352325,0.0013213331],"category_scores_gemma":[0.013451307,0.0001762413,0.00030799198,0.0006313009,0.00058881246,0.00075090484,0.0008335333,0.00056404644,0.00032043355],"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":[0.0010237104,0.00039974425,0.0469811,0.0008373515,0.00037625484,0.00030724434,0.0012621193,0.16276091,0.18354952,0.006769143,0.0045170155,0.59121585],"study_design_scores_gemma":[0.000036012887,0.0021405842,0.16725484,0.00007274879,0.00014939702,0.00060440163,0.00026123325,0.7438442,0.07526288,0.005783947,0.0044048615,0.00018480682],"about_ca_topic_score_codex":0.0019943994,"about_ca_topic_score_gemma":0.0030874044,"teacher_disagreement_score":0.0019943994,"about_ca_system_score_codex":0.0004993808,"about_ca_system_score_gemma":0.00044522478,"threshold_uncertainty_score":0.006928742},"labels":[],"label_agreement":null},{"id":"W4324077718","doi":"10.3389/fncom.2023.1040629","title":"A survey of neurophysiological differentiation across mouse visual brain areas and timescales","year":2023,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; Tiny Blue Dot Foundation","keywords":"Visual cortex; Neuroscience; Stimulus (psychology); Neurophysiology; Perception; Visual perception; Psychology; Population; Brain activity and meditation; Thalamus; Electroencephalography; Cognitive psychology; Medicine","score_opus":0.039300097659260014,"score_gpt":0.30253601343584857,"score_spread":0.26323591577658856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324077718","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":[0.9593364,0.0026103065,0.033364847,0.00008820339,0.000019593885,0.000021537184,0.0017554602,0.00041582403,0.0023878901],"genre_scores_gemma":[0.9780467,0.0016709282,0.016381642,0.000110176974,0.00001197469,0.00008961683,0.0014886892,0.00017206946,0.0020280646],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998635,0.000006517161,0.000014048969,0.000060390947,0.00003679056,0.000018730718],"domain_scores_gemma":[0.9995041,0.00016125845,0.00013673173,0.000046226352,0.00007524459,0.00007631124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002781837,0.00027431865,0.00023918509,0.0011878387,0.0001543435,0.00039172254,0.00022803503,0.00025448375,0.0009079592],"category_scores_gemma":[0.00058774656,0.00018636769,0.00025582686,0.0004981445,0.00028624566,0.00041108616,0.00032346422,0.00040874662,0.00023523379],"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":[0.00004560816,0.000007000144,0.0033354573,0.00006796491,0.000012984809,0.000027960417,0.00004542862,0.00011209752,0.989481,0.000119658114,0.000041806885,0.0067030266],"study_design_scores_gemma":[0.000013105365,0.00032135687,0.5108689,0.000064162,0.00009526299,0.0010672048,0.00022463828,0.005125368,0.47699755,0.0010040423,0.004183066,0.000035419023],"about_ca_topic_score_codex":0.000625198,"about_ca_topic_score_gemma":0.0011125109,"teacher_disagreement_score":0.0011878387,"about_ca_system_score_codex":0.00025826582,"about_ca_system_score_gemma":0.00011836779,"threshold_uncertainty_score":0.003037393},"labels":[],"label_agreement":null},{"id":"W4376875909","doi":"10.3389/fncom.2023.1151895","title":"Dynamic models for musical rhythm perception and coordination","year":2023,"lang":"en","type":"review","venue":"Frontiers in Computational Neuroscience","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Rhythm; Computer science; Perception; Synchronization (alternating current); Context (archaeology); Adaptation (eye); Cognitive science; Artificial intelligence; Cognitive psychology; Neuroscience; Psychology; Physics","score_opus":0.10224448397412318,"score_gpt":0.36149291756791047,"score_spread":0.2592484335937873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376875909","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014877178,0.8298676,0.121611744,0.007231905,0.0014643471,0.00003157557,0.00024489287,0.00014921198,0.037910983],"genre_scores_gemma":[0.04975966,0.9140798,0.018966854,0.0012816618,0.0019870861,0.00013844679,0.00032536846,0.000059765393,0.013401363],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998524,0.000041405565,0.000011507783,0.000038122555,0.00004429385,0.00001209703],"domain_scores_gemma":[0.99968743,0.00020792158,0.000024713241,0.000019180801,0.000044060736,0.000016763586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005343981,0.0011691026,0.00087279326,0.001280299,0.000306992,0.0010893408,0.0015397274,0.0024465513,0.004341077],"category_scores_gemma":[0.0014200378,0.0003699714,0.0007281628,0.0011346499,0.0017919316,0.0019724926,0.0007091475,0.0019950496,0.0017667522],"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":[0.000016768447,0.000021497177,0.00017628819,0.0019375223,0.00007882863,0.00014314889,0.000091524635,0.030101571,0.0007227433,0.7824339,0.025771419,0.15850471],"study_design_scores_gemma":[0.000012434519,0.000025399191,0.0004883505,0.0009450094,0.00004335854,0.00037984658,0.000041769774,0.02251001,0.00024146371,0.6795493,0.29572767,0.000035250643],"about_ca_topic_score_codex":0.0027788628,"about_ca_topic_score_gemma":0.0022372685,"teacher_disagreement_score":0.004341077,"about_ca_system_score_codex":0.001722737,"about_ca_system_score_gemma":0.0010453471,"threshold_uncertainty_score":0.014522314},"labels":[],"label_agreement":null},{"id":"W4379876064","doi":"10.3389/fncom.2023.1148284","title":"Reservoir based spiking models for univariate Time Series Classification","year":2023,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Division of Electrical, Communications and Cyber Systems; National Science Foundation","keywords":"Neuromorphic engineering; Computer science; Spiking neural network; Overhead (engineering); Artificial intelligence; MNIST database; Parallel computing; Deep learning; Artificial neural network","score_opus":0.045745039503774655,"score_gpt":0.27156810896356554,"score_spread":0.2258230694597909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379876064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09191338,0.0012613004,0.89754087,0.0008363418,0.0001452384,0.000060293092,0.0005431002,0.0017271923,0.0059722797],"genre_scores_gemma":[0.9070756,0.00090845145,0.082916215,0.0002141384,0.000053976455,0.00014914823,0.0005582465,0.00015289894,0.007971382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998599,0.000026920665,0.000010241288,0.000036126836,0.000042004453,0.000024848086],"domain_scores_gemma":[0.9996394,0.00018864605,0.000041797408,0.000040208135,0.00007084232,0.000019117533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041416686,0.0004526499,0.0005831381,0.0003618656,0.0002126644,0.00078655704,0.0010983292,0.0006568212,0.0024540292],"category_scores_gemma":[0.0014492066,0.0002587479,0.0006993981,0.0006093764,0.00033196056,0.0010091523,0.00050557754,0.001291856,0.00049927225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009558919,0.00005412359,0.0012272876,0.00007685994,0.000060023332,0.00006279368,0.000041050687,0.9321722,0.00598466,0.012728201,0.0016658899,0.04583137],"study_design_scores_gemma":[9.0141265e-7,0.0000051078096,0.000042496315,0.0000015908488,0.0000022184488,0.0000049049986,0.0000014617076,0.9980767,0.00041249732,0.0013055807,0.00014485922,0.000001722352],"about_ca_topic_score_codex":0.003582947,"about_ca_topic_score_gemma":0.004641368,"teacher_disagreement_score":0.003582947,"about_ca_system_score_codex":0.0006969632,"about_ca_system_score_gemma":0.0006837288,"threshold_uncertainty_score":0.008209586},"labels":[],"label_agreement":null},{"id":"W4385703793","doi":"10.3389/fncom.2023.1223258","title":"Excitatory/inhibitory balance emerges as a key factor for RBN performance, overriding attractor dynamics","year":2023,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Attractor; Computer science; Memorization; Binary number; Key (lock); Dynamics (music); Balance (ability); Edge of chaos; Reservoir computing; Network dynamics; Inhibitory postsynaptic potential; Artificial neural network; Artificial intelligence; Neuroscience; Mathematics; Recurrent neural network; Physics; Cognitive psychology; Psychology","score_opus":0.02264753235638384,"score_gpt":0.264253555771474,"score_spread":0.2416060234150902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385703793","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":[0.9052008,0.00096437667,0.0814669,0.0006457986,0.000070539936,0.000037003538,0.00015234694,0.0008676895,0.01059443],"genre_scores_gemma":[0.9958633,0.00011794495,0.0034896696,0.000030061206,0.0000056126732,0.000009946097,0.00004364733,0.000031330605,0.00040850753],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997857,0.00003344942,0.00002381925,0.00004565628,0.000056449375,0.00005489978],"domain_scores_gemma":[0.9985843,0.00068921194,0.00023847132,0.00014389156,0.00019812924,0.00014606975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007185387,0.00049855706,0.0004299597,0.0003289808,0.0003892909,0.00097094086,0.0004938483,0.0005190003,0.002429453],"category_scores_gemma":[0.0053773583,0.00016394282,0.0001588142,0.00020902675,0.00059003825,0.0013903824,0.000602004,0.0006452777,0.0005607373],"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":[0.00079789775,0.0003072921,0.02217322,0.00077315135,0.00018645693,0.0005713515,0.00041368973,0.27809685,0.4817643,0.025171436,0.0027335207,0.18701085],"study_design_scores_gemma":[0.000039321192,0.0006741687,0.0114695765,0.000108697655,0.00011047037,0.00043908713,0.00025716797,0.79958385,0.15620238,0.028642654,0.0024096812,0.00006286583],"about_ca_topic_score_codex":0.0010594074,"about_ca_topic_score_gemma":0.0013645588,"teacher_disagreement_score":0.002429453,"about_ca_system_score_codex":0.00041598323,"about_ca_system_score_gemma":0.00056972133,"threshold_uncertainty_score":0.008127332},"labels":[],"label_agreement":null},{"id":"W4387903762","doi":"10.3389/fncom.2023.1241455","title":"Spatial frequency channels depend on stimulus bandwidth in normal and amblyopic vision: an exploratory factor analysis","year":2023,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"McGill University Health Centre; McGill University","keywords":"Spatial frequency; Contrast (vision); Stimulus (psychology); Optics; Sensitivity (control systems); Audiology; Psychology; Physics; Medicine","score_opus":0.05662195444972379,"score_gpt":0.3305233374853136,"score_spread":0.2739013830355898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387903762","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":[0.99667454,0.000051062383,0.0026416068,0.000026230926,0.0000066446883,0.00003917834,0.00030622244,0.000039001305,0.00021543157],"genre_scores_gemma":[0.9967033,0.000016875569,0.0024342427,0.000009224227,0.0000064407523,0.00008102799,0.00059338054,0.000022034883,0.00013347605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977914,0.0005388049,0.0002028684,0.00060730096,0.0006097869,0.0002497588],"domain_scores_gemma":[0.9886086,0.008392563,0.0007186222,0.00081931124,0.0009683009,0.00049254077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029026472,0.0009263191,0.0007373885,0.0018210019,0.0005207076,0.0010365084,0.00037706518,0.00048624031,0.0018352955],"category_scores_gemma":[0.010277654,0.00020138688,0.0015581605,0.0010401128,0.0008303718,0.0005783629,0.00093007373,0.0007526822,0.00029306588],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004484292,0.00075449835,0.8387447,0.00022022174,0.0017848792,0.0006653458,0.0052619255,0.002689654,0.071301006,0.00073582056,0.0013275018,0.072030045],"study_design_scores_gemma":[0.000040250874,0.00086247904,0.9846727,0.000019248519,0.00022331261,0.00044701874,0.0011910984,0.008428514,0.0030939067,0.00045349525,0.00052075414,0.00004725975],"about_ca_topic_score_codex":0.0036878583,"about_ca_topic_score_gemma":0.001716899,"teacher_disagreement_score":0.0036878583,"about_ca_system_score_codex":0.00040114342,"about_ca_system_score_gemma":0.0006151631,"threshold_uncertainty_score":0.015350819},"labels":[],"label_agreement":null},{"id":"W4388797179","doi":"10.3389/fncom.2023.1286681","title":"Corrigendum: Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training","year":2023,"lang":"en","type":"erratum","venue":"Frontiers in Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Holland Bloorview Kids Rehabilitation Hospital","funders":"","keywords":"Measure (data warehouse); Interface (matter); Computer science; Riemannian geometry; Training (meteorology); Human–computer interaction; User interface; Geometry; Mathematics; Data mining; Physics; Operating system","score_opus":0.05315288270077352,"score_gpt":0.27505281859029207,"score_spread":0.22189993588951856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388797179","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012248136,0.0013587803,0.0033200507,0.032035787,0.9578751,0.00003161409,0.0012377141,0.00092374196,0.0030947276],"genre_scores_gemma":[0.01729706,0.0108756535,0.01795565,0.09434753,0.5113338,0.0005953272,0.0065359306,0.006378796,0.33468023],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99417853,0.0009828485,0.00082700816,0.00093505764,0.002725496,0.0003510257],"domain_scores_gemma":[0.95222044,0.008070445,0.0010530518,0.002545983,0.034843944,0.0012660263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047687883,0.004041007,0.0032226203,0.0047042267,0.0037394825,0.0043129325,0.0050344747,0.008909519,0.08268335],"category_scores_gemma":[0.06857406,0.0016030433,0.0027491734,0.0028841111,0.0036117982,0.0035459641,0.0028048267,0.0084068775,0.052745484],"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":[0.000018342536,0.0000052800165,0.00004186825,0.00007954721,0.000009619912,0.00010887818,0.0000144317,0.00007025408,0.000041580563,0.00082599383,0.99357915,0.00520515],"study_design_scores_gemma":[0.000073419884,0.00006064369,0.0013316881,0.00043873664,0.00008997609,0.0010406865,0.00008803088,0.0018804312,0.0009942283,0.0064353906,0.98742175,0.00014499313],"about_ca_topic_score_codex":0.03831044,"about_ca_topic_score_gemma":0.043060523,"teacher_disagreement_score":0.08268335,"about_ca_system_score_codex":0.005479202,"about_ca_system_score_gemma":0.004964046,"threshold_uncertainty_score":0.2766034},"labels":[],"label_agreement":null},{"id":"W4389236503","doi":"10.3389/fncom.2023.1274824","title":"Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system","year":2023,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","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":"Alberta Children's Hospital; Hotchkiss Brain Institute; University of Calgary","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutsche Forschungsgemeinschaft","keywords":"Retraining; Computer science; Convolutional neural network; Neuroplasticity; Artificial intelligence; Neurodegeneration; In silico; Neuroscience; Network model; Cognitive neuroscience of visual object recognition; Rehabilitation; Machine learning; Object (grammar); Psychology; Medicine; Disease; Biology; Pathology","score_opus":0.013250194326974874,"score_gpt":0.2709069216283352,"score_spread":0.2576567273013603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389236503","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":[0.82907534,0.00036728274,0.16023956,0.00033030755,0.000098012184,0.0000842991,0.00063369627,0.00042287874,0.008748596],"genre_scores_gemma":[0.98728,0.00016247606,0.010427848,0.000037198748,0.000004129762,0.000075620526,0.00012761288,0.000023703167,0.0018615763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999527,0.000009716095,0.0000025429333,0.000010909014,0.000012168851,0.000011989403],"domain_scores_gemma":[0.9998362,0.00007926524,0.000030473739,0.000015130577,0.00002175396,0.000017041773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014501855,0.00043752915,0.00027015107,0.00019254614,0.00013795923,0.00028975643,0.00066890917,0.0008464673,0.0010502858],"category_scores_gemma":[0.0005181261,0.00022183183,0.00047012433,0.00012630982,0.0003735468,0.00027425197,0.00032913228,0.00042045795,0.000105950014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031736232,0.000019117351,0.00039859582,0.000038319995,0.000013679426,0.0000751723,0.000019742702,0.9888869,0.008932779,0.0007914138,0.000053461637,0.0007391544],"study_design_scores_gemma":[0.000004865239,0.000016227646,0.00024132713,0.0000042722336,0.000005698064,0.00001531274,0.000004548203,0.99734193,0.0019561169,0.0002633031,0.00014370366,0.0000027073327],"about_ca_topic_score_codex":0.0073428852,"about_ca_topic_score_gemma":0.0038729839,"teacher_disagreement_score":0.0073428852,"about_ca_system_score_codex":0.0006220858,"about_ca_system_score_gemma":0.00050943805,"threshold_uncertainty_score":0.014600277},"labels":[],"label_agreement":null},{"id":"W4390584412","doi":"10.3389/fncom.2023.1199736","title":"Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Amyotrophic Lateral Sclerosis Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Roche (Canada); University of Toronto; University Health Network; Queen's University","funders":"","keywords":"Stratification (seeds); Open science; Computer science; Psychology; Artificial intelligence; Data science; Biology; Physics; Astronomy","score_opus":0.05050674291766184,"score_gpt":0.32904521511542123,"score_spread":0.2785384721977594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390584412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25535706,0.0065416847,0.7021598,0.021532923,0.00033130782,0.000848243,0.0021465267,0.0035509146,0.007531487],"genre_scores_gemma":[0.5516354,0.0017315608,0.44033474,0.0019880917,0.00029229856,0.0003672404,0.0021895315,0.0002823881,0.0011787667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98489326,0.011435108,0.00065664767,0.0011869686,0.0015301894,0.00029777663],"domain_scores_gemma":[0.8622752,0.1254843,0.0017191737,0.0044612945,0.0035583675,0.002501746],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.036011036,0.0008239072,0.00083838595,0.0023270915,0.0010463239,0.0025110657,0.0028112275,0.0017102284,0.00324435],"category_scores_gemma":[0.07228208,0.0003148273,0.0012108375,0.0017983655,0.0021952572,0.0023802875,0.003157371,0.003596086,0.00083654176],"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":[0.001239354,0.0016280154,0.06443934,0.0011192061,0.0004567049,0.0021307871,0.004645991,0.13502274,0.002585118,0.0141547145,0.017374093,0.7552039],"study_design_scores_gemma":[0.000461891,0.0011860611,0.015664835,0.0005085084,0.00022115227,0.0019289734,0.0024900748,0.7846264,0.012189173,0.14055207,0.039977472,0.00019349178],"about_ca_topic_score_codex":0.0027181813,"about_ca_topic_score_gemma":0.0023307332,"teacher_disagreement_score":0.99718875,"about_ca_system_score_codex":0.0015362414,"about_ca_system_score_gemma":0.0031205765,"threshold_uncertainty_score":0.19044685},"labels":[],"label_agreement":null},{"id":"W4392123086","doi":"10.3389/fncom.2024.1347748","title":"Noise-induced synchrony of two-neuron motifs with asymmetric noise and uneven coupling","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coupling (piping); Noise (video); Physics; Asymmetry; Neuron; Biological neuron model; Synchronization (alternating current); Stimulus (psychology); Bifurcation; Biological system; Statistical physics; Computer science; Neuroscience; Telecommunications; Nonlinear system; Quantum mechanics; Artificial intelligence; Biology; Psychology; Engineering","score_opus":0.019516658056797986,"score_gpt":0.2596833616625866,"score_spread":0.24016670360578862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392123086","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":[0.8918,0.0002650652,0.10277728,0.0001597145,0.00003863009,0.00003669802,0.00009464269,0.00018163228,0.0046464545],"genre_scores_gemma":[0.99588877,0.0000576198,0.0035304169,0.000023857212,0.000006198352,0.000022279588,0.0000374912,0.000012992248,0.00042054942],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998004,0.00003576236,0.000014485395,0.00005673506,0.00005284428,0.00003977226],"domain_scores_gemma":[0.999537,0.0001583446,0.00013237506,0.000041088446,0.000059324826,0.00007193752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002813951,0.0002529824,0.0003829859,0.00036827984,0.0003329469,0.00041384954,0.00045950362,0.00039358065,0.0012758116],"category_scores_gemma":[0.0018106884,0.00020040594,0.00031702514,0.00018168539,0.00067994365,0.00058337604,0.0007056486,0.00039916552,0.0001563296],"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":[0.00045973758,0.00015520176,0.015733737,0.00036632337,0.00017541873,0.002214919,0.00064300455,0.34886974,0.5251383,0.07867458,0.0011011222,0.026467858],"study_design_scores_gemma":[0.000036115856,0.00013241572,0.0077527296,0.000020487396,0.000032608983,0.00034307234,0.00011239734,0.9417915,0.02257333,0.02631361,0.0008577741,0.0000340108],"about_ca_topic_score_codex":0.00066806405,"about_ca_topic_score_gemma":0.0008526784,"teacher_disagreement_score":0.0012758116,"about_ca_system_score_codex":0.0003871892,"about_ca_system_score_gemma":0.00033337218,"threshold_uncertainty_score":0.0042679906},"labels":[],"label_agreement":null},{"id":"W4392816447","doi":"10.3389/fncom.2024.1348138","title":"The connectivity degree controls the difficulty in reservoir design of random boolean networks","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Reservoir computing; Excitatory postsynaptic potential; Computer science; Inhibitory postsynaptic potential; Degree (music); Balance (ability); Computation; Artificial neural network; Boolean data type; Value (mathematics); Recurrent neural network; Theoretical computer science; Algorithm; Neuroscience; Artificial intelligence; Physics; Machine learning","score_opus":0.030337803638791728,"score_gpt":0.2584488936362918,"score_spread":0.22811108999750007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392816447","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":[0.5894608,0.0008232293,0.39626187,0.0012825378,0.00007373756,0.00007889594,0.00009836396,0.00027313878,0.011647446],"genre_scores_gemma":[0.98976094,0.00015704027,0.0092771975,0.00003413411,0.000008576254,0.000034231583,0.00002528905,0.000026696729,0.00067580905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99982905,0.00005153726,0.000010662562,0.00003401516,0.000026169197,0.00004854562],"domain_scores_gemma":[0.9983011,0.0012008772,0.00020766175,0.00006521219,0.00010656309,0.000118684875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060854526,0.00045580423,0.00053185987,0.0003527739,0.00032959544,0.0009374638,0.00069964613,0.0008102881,0.0017093696],"category_scores_gemma":[0.004905038,0.00042610307,0.0003035291,0.00015530769,0.0007424746,0.0013610356,0.000852667,0.00064347155,0.00014088296],"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":[0.00018539296,0.000057877318,0.0024033885,0.00015020899,0.00003636167,0.00021089877,0.00008571613,0.9273165,0.012905293,0.042222235,0.000589976,0.013836277],"study_design_scores_gemma":[0.000007731585,0.000031864325,0.00017225886,0.000009089493,0.0000066474727,0.000026840666,0.0000112858515,0.9913038,0.0009602548,0.007336701,0.00012789636,0.000005710703],"about_ca_topic_score_codex":0.0012315613,"about_ca_topic_score_gemma":0.0015202798,"teacher_disagreement_score":0.0017093696,"about_ca_system_score_codex":0.00052856473,"about_ca_system_score_gemma":0.0005524313,"threshold_uncertainty_score":0.00571841},"labels":[],"label_agreement":null},{"id":"W4396777893","doi":"10.3389/fncom.2024.1327986","title":"Computational modeling to study the impact of changes in Nav1.8 sodium channel on neuropathic pain","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Pain Mechanisms and Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Redeemer University; McMaster University","funders":"","keywords":"Neuropathic pain; NAV1; Sodium channel; Sodium channel blocker; Medicine; Neuroscience; Sodium; Anesthesia; Chemistry; Psychology","score_opus":0.04203107603486972,"score_gpt":0.3295087302837788,"score_spread":0.2874776542489091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396777893","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":[0.7192138,0.002223961,0.23750183,0.002305328,0.0003484123,0.00023123695,0.003211248,0.0010727996,0.033891376],"genre_scores_gemma":[0.9579818,0.00072738307,0.036261864,0.00026759246,0.00005412705,0.00038140398,0.0010846512,0.00012947999,0.0031116984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988914,0.00003440083,0.0000073788065,0.000018951254,0.000027020042,0.000023030803],"domain_scores_gemma":[0.9992211,0.0005753399,0.000049989238,0.000027961787,0.00009157167,0.00003401332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000273113,0.0006333792,0.0009097995,0.0004058567,0.0004848179,0.00058484974,0.0009662059,0.0012753597,0.0026694885],"category_scores_gemma":[0.0016595165,0.00040189078,0.00084617373,0.0004764625,0.00035576982,0.00049461005,0.0005133154,0.0007860752,0.00020324685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025831348,0.000021730237,0.0006224568,0.000046245415,0.000023594812,0.000039151524,0.000012496275,0.99585176,0.0004665987,0.0014268731,0.0002642843,0.0011989921],"study_design_scores_gemma":[0.000007353023,0.00000768059,0.000083286235,0.0000034079358,0.000005540374,0.0000055545734,0.000005568018,0.9991148,0.000097168966,0.0005042158,0.0001631174,0.0000021362735],"about_ca_topic_score_codex":0.01587786,"about_ca_topic_score_gemma":0.010568154,"teacher_disagreement_score":0.01587786,"about_ca_system_score_codex":0.0005747544,"about_ca_system_score_gemma":0.0013340794,"threshold_uncertainty_score":0.03157091},"labels":[],"label_agreement":null},{"id":"W4401868171","doi":"10.3389/fncom.2024.1434421","title":"Deep learning for detecting prenatal alcohol exposure in pediatric brain MRI: a transfer learning approach with explainability insights","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Children's Hospital Research Institute; Kids Brain Health Network; Canadian Institutes of Health Research; Alberta Innovates","keywords":"Prenatal alcohol exposure; Transfer of learning; Prenatal exposure; Deep learning; Computer science; Artificial intelligence; Neuroimaging; Neuroscience; Psychology; Alcohol; Pregnancy; Chemistry; Biology","score_opus":0.014740167940082143,"score_gpt":0.2517821544881344,"score_spread":0.23704198654805225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401868171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2372164,0.0020526722,0.7548665,0.0015926796,0.0000814058,0.00011176573,0.000526343,0.0013765937,0.0021756473],"genre_scores_gemma":[0.9219462,0.00070971187,0.07354178,0.0003277397,0.00007818331,0.00013286763,0.0009011025,0.000062507286,0.002300007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99976426,0.0000783292,0.000012910121,0.00006975658,0.000035192952,0.00003956918],"domain_scores_gemma":[0.99933213,0.0004037495,0.000060591043,0.00005686804,0.00010950944,0.000037164846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089112076,0.000897829,0.0005100342,0.0006136233,0.00020682202,0.0004902114,0.0008578872,0.00087344466,0.00100505],"category_scores_gemma":[0.0024096144,0.0002492273,0.0006689226,0.0003712366,0.00035336684,0.0007329202,0.0009832376,0.0014139977,0.0002453896],"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":[0.0003045573,0.00041273853,0.0220083,0.00015127564,0.00029658072,0.0005751259,0.0002302646,0.53827643,0.010813039,0.005903738,0.004357104,0.41667083],"study_design_scores_gemma":[0.0000054081675,0.000049163446,0.00103018,0.0000096144,0.000019752488,0.000032453187,0.000016785012,0.9942958,0.0011520388,0.003029397,0.00035385363,0.0000055673927],"about_ca_topic_score_codex":0.004133768,"about_ca_topic_score_gemma":0.0037838412,"teacher_disagreement_score":0.004133768,"about_ca_system_score_codex":0.0005229229,"about_ca_system_score_gemma":0.00078905874,"threshold_uncertainty_score":0.008219421},"labels":[],"label_agreement":null},{"id":"W4402677794","doi":"10.3389/fncom.2024.1360095","title":"Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Wake Forest School of Medicine; University of California, San Diego; Jewish General Hospital; York University; University of South Florida; Rush University; Vanderbilt University Medical Center; Georgetown University; Medical School, University of Michigan; Northwestern University; University of Pittsburgh; University of California, Los Angeles; University of Texas Southwestern Medical Center; Johns Hopkins University; Houston Methodist Research Institute; NYU Langone Medical Center; Ohio State University; University of Rochester; Emory University; Cleveland Clinic; University of Pennsylvania; Vanderbilt University; Yale University","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Affine transformation; Machine learning; Deep learning; Set (abstract data type); Heuristics; Data mining","score_opus":0.014287803839939215,"score_gpt":0.2856154726462086,"score_spread":0.2713276688062694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402677794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71814364,0.010042638,0.25545722,0.0043846974,0.000637402,0.00037413227,0.0022791831,0.0032395297,0.0054415916],"genre_scores_gemma":[0.96817243,0.00075745257,0.02744726,0.0004065353,0.00011740262,0.00015298233,0.0018509479,0.00043751288,0.0006575016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96776307,0.018681942,0.0027879626,0.0063519487,0.0038294129,0.0005856208],"domain_scores_gemma":[0.8368805,0.10772141,0.0092928065,0.03644006,0.008799889,0.0008653843],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06236301,0.0017036744,0.0013066629,0.0011725622,0.0010995708,0.0035975762,0.0024075692,0.001660525,0.0009945296],"category_scores_gemma":[0.15133731,0.0008824937,0.0018115565,0.0011073063,0.0024597056,0.0033059635,0.0037408294,0.002662481,0.00059463916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046573803,0.00046413025,0.21855897,0.001455811,0.0035606264,0.00055567984,0.0015063506,0.4864485,0.009651209,0.007961488,0.008055986,0.2571239],"study_design_scores_gemma":[0.000299058,0.0013081939,0.05857696,0.0010478244,0.0009217954,0.0006040383,0.00074050965,0.8570838,0.036505748,0.032949764,0.009678033,0.00028434553],"about_ca_topic_score_codex":0.008137654,"about_ca_topic_score_gemma":0.0062181093,"teacher_disagreement_score":0.937637,"about_ca_system_score_codex":0.0013823537,"about_ca_system_score_gemma":0.0019537571,"threshold_uncertainty_score":0.3298111},"labels":[],"label_agreement":null},{"id":"W4403659763","doi":"10.3389/fncom.2024.1487877","title":"Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Foothills Medical Centre; Women and Children’s Health Research Institute; University of Alberta; University of Calgary","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Canada Foundation for Innovation; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Fluid-attenuated inversion recovery; Segmentation; Hyperintensity; Artificial intelligence; Computer science; Ground truth; Deep learning; Pattern recognition (psychology); Dementia; Gold standard (test); Cognitive decline; Magnetic resonance imaging; Machine learning; Medicine; Radiology; Disease; Pathology","score_opus":0.032667383489255304,"score_gpt":0.33060699446134956,"score_spread":0.29793961097209426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403659763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20493197,0.00083282695,0.78439957,0.00035870133,0.00011614206,0.00019372458,0.00034188927,0.0074237976,0.0014014464],"genre_scores_gemma":[0.7603446,0.00014159686,0.23535565,0.0002689732,0.00005902962,0.00018349072,0.0013093653,0.00034326775,0.00199403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985971,0.00048740997,0.00010144656,0.0004417713,0.00022902756,0.00014324853],"domain_scores_gemma":[0.99484384,0.0028931384,0.00043669943,0.00056227966,0.0010654534,0.00019860691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035562667,0.0014053903,0.0011806255,0.000955158,0.00052124914,0.0009782955,0.0020709038,0.0020260427,0.0017595241],"category_scores_gemma":[0.0075309468,0.0006378957,0.0015176573,0.00066059886,0.00083150563,0.0013820009,0.0014930214,0.0017610019,0.00071256823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006724331,0.00057577423,0.005069056,0.00027314606,0.0003198561,0.00015286783,0.00020682583,0.72423464,0.01338387,0.0010977411,0.0034295414,0.25058421],"study_design_scores_gemma":[0.000007865673,0.00005056392,0.00038439975,0.000005852378,0.000009047698,0.00001559328,0.0000065614445,0.9965192,0.0022871788,0.0005738841,0.00013410243,0.000005686298],"about_ca_topic_score_codex":0.007270008,"about_ca_topic_score_gemma":0.007745533,"teacher_disagreement_score":0.007270008,"about_ca_system_score_codex":0.0009997074,"about_ca_system_score_gemma":0.0015089852,"threshold_uncertainty_score":0.01880753},"labels":[],"label_agreement":null},{"id":"W4404121121","doi":"10.3389/fncom.2024.1466364","title":"Simulated synapse loss induces depression-like behaviors in deep reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Treatment of Major Depression","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge; Mount Royal University","funders":"","keywords":"Reinforcement learning; Major depressive disorder; Neuroscience; Monoamine neurotransmitter; Psychology; Context (archaeology); Anhedonia; Reinforcement; Artificial intelligence; Computer science; Cognitive psychology; Dopamine; Cognition; Medicine; Biology; Serotonin","score_opus":0.01524363339065636,"score_gpt":0.29562015595491853,"score_spread":0.28037652256426215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404121121","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":[0.97728467,0.00003970003,0.020904198,0.00014214618,0.000024006531,0.000019443101,0.000080635546,0.00008596372,0.0014191965],"genre_scores_gemma":[0.9968894,0.0000141863975,0.0026941563,0.000020181269,9.943947e-7,0.0000151995055,0.000031738484,0.000004285615,0.0003299467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999424,0.000016685995,0.0000035828675,0.000009116719,0.000011434666,0.000016771715],"domain_scores_gemma":[0.99974126,0.00012590166,0.000054737942,0.000022852253,0.000026269063,0.000029086437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023408227,0.00025492595,0.00022978612,0.000106941494,0.00009549319,0.00019853392,0.00038263627,0.0003078534,0.0009930771],"category_scores_gemma":[0.0008243694,0.0001149303,0.00028188137,0.000050972445,0.000306002,0.0001870483,0.00028544446,0.00050284405,0.000049182065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038180812,0.00025596254,0.0041568303,0.00008069472,0.00007236444,0.00020199045,0.0000433744,0.96089834,0.02258301,0.0038850058,0.00039087975,0.0070497463],"study_design_scores_gemma":[0.0000269744,0.00011180865,0.0006744092,0.0000030644321,0.000009317303,0.000014675156,0.000005620519,0.9946926,0.003147637,0.0012028952,0.00010746013,0.000003554034],"about_ca_topic_score_codex":0.002729318,"about_ca_topic_score_gemma":0.0020488824,"teacher_disagreement_score":0.002729318,"about_ca_system_score_codex":0.00040345712,"about_ca_system_score_gemma":0.00030983117,"threshold_uncertainty_score":0.005426824},"labels":[],"label_agreement":null},{"id":"W4404280349","doi":"10.3389/fncom.2024.1452457","title":"Sex differences in brain MRI using deep learning toward fairer healthcare outcomes","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"National Institute on Aging","keywords":"Neuroimaging; Deep learning; Convolutional neural network; Preprocessor; Artificial intelligence; Computer science; Scope (computer science); Set (abstract data type); Transgender; Health care; Machine learning; Brain morphometry; Psychology; Magnetic resonance imaging; Medicine; Neuroscience","score_opus":0.03344110352143549,"score_gpt":0.29675576450812224,"score_spread":0.26331466098668677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404280349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.486602,0.0013312461,0.4947185,0.0046745,0.0002431358,0.00016819629,0.0015478937,0.0012270906,0.009487426],"genre_scores_gemma":[0.96186274,0.00015495918,0.03517292,0.00039232482,0.000057649893,0.000046124886,0.0004539171,0.000067798734,0.0017914398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995259,0.00017898878,0.000015823514,0.00012252238,0.00007951407,0.00007727876],"domain_scores_gemma":[0.9988098,0.0006376201,0.00015549739,0.00015302823,0.0001589625,0.00008503434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020703971,0.00049385446,0.000338187,0.00070365384,0.00032141263,0.001036144,0.00065127626,0.00060938974,0.0022395945],"category_scores_gemma":[0.008099371,0.00015865997,0.00045918772,0.0003410898,0.00050483545,0.000789516,0.0011132138,0.0009201584,0.00034984123],"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":[0.0010152081,0.00036015632,0.13873447,0.00020404703,0.00032268232,0.0006948537,0.00097540114,0.31698048,0.010907428,0.025923733,0.011742681,0.49213883],"study_design_scores_gemma":[0.000034182805,0.00010537734,0.016383495,0.000054287284,0.00004217846,0.00016173853,0.00017112133,0.9275552,0.004756615,0.047702327,0.0030039072,0.0000296586],"about_ca_topic_score_codex":0.005664966,"about_ca_topic_score_gemma":0.0071664653,"teacher_disagreement_score":0.005664966,"about_ca_system_score_codex":0.0008566432,"about_ca_system_score_gemma":0.0010325689,"threshold_uncertainty_score":0.011264026},"labels":[],"label_agreement":null},{"id":"W4410453527","doi":"10.3389/fncom.2025.1560064","title":"Interpretable machine learning for precision cognitive aging","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fraser Health; Royal Columbian Hospital; Simon Fraser University","funders":"Mitacs","keywords":"Interpretability; Machine learning; Artificial intelligence; Computer science; Cognition; Support vector machine; Random forest; Logistic regression; Regression; Artificial neural network; Gradient boosting; Predictive modelling; Psychology","score_opus":0.018632827392424087,"score_gpt":0.30121367618407924,"score_spread":0.2825808487916551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410453527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043758217,0.01726792,0.9160866,0.008967466,0.00066362286,0.00019841909,0.0014983437,0.0029958559,0.008563579],"genre_scores_gemma":[0.8190569,0.0055520907,0.16777791,0.00088358263,0.0010046253,0.00032707417,0.0017782595,0.00023748429,0.0033821617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980965,0.00095602154,0.0001110638,0.00039437434,0.00035530623,0.000086841574],"domain_scores_gemma":[0.98645383,0.009748589,0.0011482893,0.0011878533,0.0012473922,0.00021395466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060772547,0.001472782,0.0011303282,0.0020750803,0.00042560554,0.002667155,0.001325574,0.0013364633,0.0039874045],"category_scores_gemma":[0.029428482,0.00033435063,0.0010733743,0.0015299129,0.0013626944,0.0020523896,0.0017465925,0.0027503306,0.0012383261],"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":[0.00031758644,0.00024866578,0.022797149,0.0010899273,0.0005927994,0.0002417594,0.00054799864,0.29656145,0.0016246244,0.08562462,0.015568856,0.5747846],"study_design_scores_gemma":[0.000018837613,0.00010133972,0.004974179,0.00028889946,0.000081204176,0.00009701458,0.00008883442,0.7650255,0.0010052861,0.22060972,0.00766687,0.000042323718],"about_ca_topic_score_codex":0.0035852357,"about_ca_topic_score_gemma":0.002425385,"teacher_disagreement_score":0.0060772547,"about_ca_system_score_codex":0.001431126,"about_ca_system_score_gemma":0.0015998755,"threshold_uncertainty_score":0.032140017},"labels":[],"label_agreement":null},{"id":"W4410617336","doi":"10.3389/fncom.2025.1569374","title":"Reinforced liquid state machines—new training strategies for spiking neural networks based on reinforcements","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Bundesministerium für Bildung und Forschung","keywords":"Reinforcement learning; Computer science; Artificial neural network; Adaptability; Artificial intelligence; Adaptation (eye); Reinforcement; State (computer science); Spiking neural network; Reservoir computing; Machine learning; Recurrent neural network; Engineering; Neuroscience","score_opus":0.021097433617318744,"score_gpt":0.2702712606263056,"score_spread":0.24917382700898683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410617336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080370076,0.00029954998,0.91490746,0.00031769325,0.000063365915,0.000054433724,0.00003745473,0.0009171603,0.0030328024],"genre_scores_gemma":[0.93293154,0.000118008495,0.06496017,0.00010744458,0.000018925635,0.000086709835,0.000035990422,0.00006800282,0.0016732346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998406,0.000050988503,0.0000127412795,0.000031862968,0.000042719254,0.000021079404],"domain_scores_gemma":[0.99926394,0.00036533742,0.00011153356,0.00007812778,0.00013344696,0.000047643774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060894236,0.00044218267,0.00036346618,0.00025212974,0.0001971136,0.00045733512,0.0009611717,0.0006067372,0.0020738107],"category_scores_gemma":[0.0025993118,0.00023035468,0.00026686257,0.00016745336,0.00066278427,0.0008352686,0.00090339733,0.00080634095,0.00027643293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014001183,0.00008249039,0.0010981195,0.00008698848,0.000040695166,0.000084824984,0.000106371444,0.8448641,0.02025246,0.019538963,0.0010255121,0.112679444],"study_design_scores_gemma":[0.0000048049674,0.000025719128,0.000041905736,0.0000046756472,0.0000027424367,0.000008641818,0.0000019626918,0.99578214,0.001467461,0.0024293368,0.00022759964,0.00000293095],"about_ca_topic_score_codex":0.0011029805,"about_ca_topic_score_gemma":0.0016632576,"teacher_disagreement_score":0.0020738107,"about_ca_system_score_codex":0.0005058284,"about_ca_system_score_gemma":0.00046837673,"threshold_uncertainty_score":0.006937623},"labels":[],"label_agreement":null},{"id":"W4411041924","doi":"10.3389/fncom.2025.1564932","title":"A new method for community-based intelligent screening of early Alzheimer’s disease populations based on digital biomarkers of the writing process","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cognition; Neuropsychology; Montreal Cognitive Assessment; Handwriting; Neuropsychological assessment; Confidence interval; Medicine; Computer science; Cognitive impairment; Psychology; Artificial intelligence; Internal medicine; Psychiatry","score_opus":0.07507535491470957,"score_gpt":0.4089832215212051,"score_spread":0.3339078666064955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411041924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39677703,0.0038270273,0.57642466,0.0006251521,0.0005649998,0.0017960382,0.0054232613,0.0039546266,0.010607223],"genre_scores_gemma":[0.69584227,0.0011002086,0.29374117,0.0004189501,0.00025313668,0.0017010984,0.0021072626,0.00010322762,0.0047326945],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99799335,0.00051163975,0.0001620527,0.000620596,0.0006173926,0.000095007475],"domain_scores_gemma":[0.9970311,0.00088923145,0.00042249207,0.00024671433,0.0012247256,0.00018571717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025928002,0.0009960671,0.00095651945,0.0055003986,0.0004340229,0.0014293833,0.0006724483,0.0008883232,0.0027001963],"category_scores_gemma":[0.006581892,0.00030811707,0.0007066995,0.002126798,0.0003169886,0.0011466629,0.0011060286,0.0006029751,0.0009484423],"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":[0.0015808971,0.0006924505,0.3320452,0.00067866367,0.0005684251,0.0006465249,0.00047342657,0.0035139108,0.042470895,0.0014824785,0.0058050677,0.6100421],"study_design_scores_gemma":[0.00062349084,0.0032118289,0.55361354,0.0003780859,0.0020382875,0.009634568,0.0014773074,0.33308288,0.05738292,0.00873061,0.029314563,0.0005118113],"about_ca_topic_score_codex":0.0014884718,"about_ca_topic_score_gemma":0.0025093644,"teacher_disagreement_score":0.0055003986,"about_ca_system_score_codex":0.00033560983,"about_ca_system_score_gemma":0.00060336164,"threshold_uncertainty_score":0.013712227},"labels":[],"label_agreement":null},{"id":"W4413178502","doi":"10.3389/fncom.2025.1662598","title":"The improved thalamo-cortical spiking network model of deep brain stimulation","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Deep brain stimulation; Neuroscience; Computer science; Psychology; Medicine; Parkinson's disease; Internal medicine","score_opus":0.01978349511822709,"score_gpt":0.2707494109254489,"score_spread":0.25096591580722183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413178502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048975945,0.001395204,0.9052489,0.0008580058,0.00021558364,0.000074009906,0.00049434026,0.00035575038,0.04238235],"genre_scores_gemma":[0.9238141,0.0015139629,0.04993533,0.00025216836,0.00011900531,0.0002434234,0.00029216605,0.00009920013,0.023730626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988055,0.00003682524,0.00000501231,0.000026370975,0.00003260806,0.000018607387],"domain_scores_gemma":[0.9998988,0.00003111219,0.000017973809,0.000008748005,0.00002553032,0.00001778731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001977791,0.0006694213,0.00047981425,0.00035040584,0.00019985218,0.0005752383,0.00095447886,0.0008511093,0.002706667],"category_scores_gemma":[0.00046196263,0.00020318448,0.000782788,0.00033188998,0.00055539416,0.0006247441,0.0007284873,0.00086606725,0.00037879747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091973285,0.000037362046,0.0005474069,0.00010765714,0.000061441715,0.00025170908,0.00009239906,0.8325173,0.015490808,0.13604625,0.0015482913,0.013207362],"study_design_scores_gemma":[0.0000068213653,0.000017313567,0.0000907855,0.000003637645,0.000007686117,0.000029735966,0.000004637044,0.98655826,0.00023338904,0.012241774,0.0008011162,0.0000048465754],"about_ca_topic_score_codex":0.00520142,"about_ca_topic_score_gemma":0.0038260028,"teacher_disagreement_score":0.00520142,"about_ca_system_score_codex":0.0005732152,"about_ca_system_score_gemma":0.00053486996,"threshold_uncertainty_score":0.0103423},"labels":[],"label_agreement":null},{"id":"W4413181046","doi":"10.3389/fncom.2025.1597914","title":"Super special relativity","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Mathematical Theories and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Time dilation; Perception; Time perception; Information processing; Computer science; Inertial frame of reference; Theory of relativity; Cognitive science; Cognitive psychology; Psychology; Artificial intelligence; Neuroscience; Physics; Theoretical physics","score_opus":0.007806560426526715,"score_gpt":0.2766637024803802,"score_spread":0.26885714205385347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413181046","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07886678,0.0077834073,0.32088184,0.012001733,0.002344445,0.00009365009,0.0004991086,0.00066598575,0.5768631],"genre_scores_gemma":[0.9092654,0.004599384,0.045605816,0.0023409168,0.003729379,0.00016574,0.00049551605,0.00017926183,0.033618603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99932885,0.00019098377,0.00002507308,0.00019064327,0.00018134617,0.00008323784],"domain_scores_gemma":[0.9992742,0.00019137442,0.00008887387,0.00023795862,0.000113757524,0.00009380401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010456836,0.00048710342,0.0005708533,0.0010264413,0.0016060392,0.0016006588,0.0005648645,0.0007123917,0.00643145],"category_scores_gemma":[0.0017862286,0.00022192895,0.0008383771,0.00035442875,0.0033319043,0.0031449758,0.002105066,0.0023260997,0.00078843266],"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":[0.0000020538944,0.0000012291622,0.00003626117,0.000010888554,0.000003527338,0.000015712749,0.00005832808,0.00020562974,0.00010578927,0.9976386,0.00066008704,0.0012619529],"study_design_scores_gemma":[0.000003687602,0.000009207219,0.00012916473,0.000007719803,0.000003853911,0.000087277214,0.000032785996,0.0009233889,0.00007336202,0.983094,0.015629807,0.0000057470115],"about_ca_topic_score_codex":0.0012529884,"about_ca_topic_score_gemma":0.0007793375,"teacher_disagreement_score":0.00643145,"about_ca_system_score_codex":0.0013082506,"about_ca_system_score_gemma":0.00062582485,"threshold_uncertainty_score":0.02151537},"labels":[],"label_agreement":null},{"id":"W4413181688","doi":"10.3389/fncom.2025.1639829","title":"Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spike (software development); Evoked activity; Poisson distribution; Spatial ecology; Spike train; Scale (ratio); Cortical neurons; Artificial intelligence; Neuroscience; Mathematics; Biology; Cartography; Geography","score_opus":0.024784617512466932,"score_gpt":0.3018134070359115,"score_spread":0.27702878952344456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413181688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07691506,0.00017331947,0.9214811,0.00029953805,0.0000073827837,0.00002367536,0.00009636944,0.00027343928,0.00073019264],"genre_scores_gemma":[0.8834235,0.00018245213,0.11432747,0.00010264928,0.000035219768,0.0001425949,0.00036947773,0.00011744984,0.001299271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954504,0.00022878434,0.00002125452,0.00009169873,0.00007827812,0.000035008743],"domain_scores_gemma":[0.9961397,0.0032031024,0.00028820787,0.00015212566,0.00014203343,0.00007485449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017344038,0.00053761376,0.00063923164,0.00059905404,0.00035226275,0.0007767603,0.0010368613,0.0010784437,0.0007584087],"category_scores_gemma":[0.010282709,0.00055780535,0.0005075354,0.00054298865,0.0008809192,0.0012531948,0.0010968919,0.0010316898,0.00022963519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000732138,0.000032722917,0.0028042032,0.00007258395,0.00007643061,0.00016348544,0.000115060284,0.95423096,0.009157057,0.014963731,0.00046836372,0.01784229],"study_design_scores_gemma":[0.0000049765495,0.000006237808,0.00055749755,0.0000028507934,0.0000022722502,0.000022706527,0.0000070744113,0.9919017,0.00063158054,0.0067747682,0.00008210489,0.000006094826],"about_ca_topic_score_codex":0.0015545097,"about_ca_topic_score_gemma":0.0018137255,"teacher_disagreement_score":0.0017344038,"about_ca_system_score_codex":0.00047770742,"about_ca_system_score_gemma":0.00060301786,"threshold_uncertainty_score":0.009172499},"labels":[],"label_agreement":null}]}