{"meta":{"query_hash":"7209f8944236","filters":{"venue":"IEEE Transactions on Information Technology in Biomedicine"},"cohort_total":68,"direct_labels_cover":0,"predictions_cover":68,"exported":68,"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/7209f8944236","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Information+Technology+in+Biomedicine"},"results":[{"id":"W1980784430","doi":"10.1109/titb.2012.2205011","title":"GPU-Based Visualization and Synchronization of 4-D Cardiac MR and Ultrasound Images","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","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":"Robarts Clinical Trials; Western University; National Research Council Institute for Biodiagnostics","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Computer vision; Visualization; Artificial intelligence; Cardiac imaging; Image quality; Rendering (computer graphics); Image registration; Context (archaeology); Image processing; Multispectral image; Modality (human–computer interaction); Image (mathematics); Radiology; Medicine","score_opus":0.006742944448989097,"score_gpt":0.2597989387092543,"score_spread":0.2530559942602652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980784430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019965006,0.00022041531,0.9786595,0.00038184423,0.00022386256,0.00024190248,0.000011280888,0.0002668339,0.000029327777],"genre_scores_gemma":[0.99259835,0.0005951252,0.0065853796,0.00015524332,0.0000091166385,0.000035006844,0.000013946442,0.0000050795297,0.000002770977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991052,0.000033267406,0.00040872168,0.000120848046,0.00017063043,0.00016133914],"domain_scores_gemma":[0.9993446,0.00008633865,0.00016229518,0.00023030737,0.00012644383,0.0000500548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032908385,0.00012310912,0.00018324444,0.0019454873,0.000085148014,0.000030348334,0.00014951483,0.00016338551,0.00000415563],"category_scores_gemma":[0.000017404831,0.00011822151,0.000020238713,0.0016173925,0.00021019099,0.0010673068,0.0000043000964,0.00011216898,0.0000014402822],"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":[0.00004342409,0.0006564215,0.021230584,0.0006254692,0.00009768074,7.2577416e-7,0.0052693244,0.00028022227,0.018055575,0.48883143,0.0005264366,0.4643827],"study_design_scores_gemma":[0.001993889,0.0011172274,0.011230307,0.00040808116,0.00005605622,0.000048227248,0.0006162736,0.20116596,0.7746808,0.005693239,0.0023484118,0.0006415181],"about_ca_topic_score_codex":0.000016531703,"about_ca_topic_score_gemma":9.68171e-7,"teacher_disagreement_score":0.9726333,"about_ca_system_score_codex":0.00003517312,"about_ca_system_score_gemma":0.000025685924,"threshold_uncertainty_score":0.48209333},"labels":[],"label_agreement":null},{"id":"W1995725266","doi":"10.1109/titb.2003.821334","title":"DICOM structured report document type definition","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"DICOM; Computer science; XML; Document type definition; Information retrieval; Markup language; ENCODE; Medical imaging; SGML; Document Structure Description; World Wide Web; Artificial intelligence","score_opus":0.01297331531851464,"score_gpt":0.24824126855452322,"score_spread":0.23526795323600858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995725266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021252578,0.000038063514,0.9704868,0.0042645778,0.0015284219,0.00021131909,0.0000017507554,0.00047508304,0.0017413839],"genre_scores_gemma":[0.9740875,0.000068360016,0.025256608,0.00050160737,0.000005488326,0.000036399353,0.000006776544,0.0000031866814,0.000034084973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99887705,0.000024337822,0.0004999741,0.00016170341,0.00022143396,0.00021550267],"domain_scores_gemma":[0.99918944,0.000043170196,0.00014443176,0.0004943734,0.000092479466,0.000036115616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026140906,0.00012956552,0.00017117203,0.0013201121,0.000100660065,0.000030510892,0.00030705502,0.00021193054,0.00004486639],"category_scores_gemma":[0.000048983875,0.000109773326,0.00002792705,0.0016014649,0.00013408391,0.00069492136,0.0000023915275,0.00025155372,0.00008644936],"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":[0.000098243865,0.00039100135,0.0013148177,0.00012645168,0.00014268328,0.0002914115,0.0025151838,0.0015135274,0.0045455866,0.5675146,0.0025171721,0.41902933],"study_design_scores_gemma":[0.00914935,0.0027638315,0.0054620556,0.0003858549,0.00008603677,0.005653105,0.003882874,0.006395961,0.3940588,0.49776077,0.07288515,0.0015162115],"about_ca_topic_score_codex":0.000018875638,"about_ca_topic_score_gemma":0.000011765425,"teacher_disagreement_score":0.9528349,"about_ca_system_score_codex":0.00009717403,"about_ca_system_score_gemma":0.000067795925,"threshold_uncertainty_score":0.44764265},"labels":[],"label_agreement":null},{"id":"W2005627454","doi":"10.1109/titb.2012.2204067","title":"System Identification and Closed-Loop Control of End-Tidal CO<formula formulatype=\"inline\"><tex Notation=\"TeX\">$_{\\bf 2}$</tex></formula> in Mechanically Ventilated Patients","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":13,"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 British Columbia; University of Alberta","funders":"","keywords":"Notation; Closed loop; Loop (graph theory); Mathematics; Control (management); Control theory (sociology); Chemistry; Computer science; Arithmetic; Combinatorics; Engineering; Control engineering; Artificial intelligence","score_opus":0.008503584104980033,"score_gpt":0.24912605160525828,"score_spread":0.24062246750027824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005627454","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82144034,0.00020845547,0.17197731,0.00082386745,0.0013417208,0.0029936952,0.00028248542,0.00044428738,0.0004878342],"genre_scores_gemma":[0.99754816,0.00013875254,0.00074927404,0.00032684297,0.00006747843,0.00035063212,0.00060762966,0.000050784012,0.00016045703],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9948338,0.00008402479,0.002842664,0.0004303298,0.0009891826,0.00081999094],"domain_scores_gemma":[0.9970495,0.00013290174,0.001064509,0.0007871149,0.00065521174,0.00031076168],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013883031,0.0005568838,0.0010933381,0.00396575,0.00021986845,0.00003439765,0.0003345682,0.0010923347,0.00014408509],"category_scores_gemma":[0.00012538536,0.00050769496,0.00018251811,0.0020629105,0.00031839364,0.0014732423,0.000012061079,0.0007112283,0.0001532326],"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":[0.0040887995,0.0037496537,0.008377772,0.002852411,0.0008520915,0.000043840137,0.0037773342,0.00040534,0.7239015,0.03599576,0.00057294994,0.21538252],"study_design_scores_gemma":[0.07198417,0.009109242,0.04237791,0.003660962,0.0018512824,0.00057097274,0.0042617624,0.06694335,0.7583808,0.0011217765,0.037176307,0.0025615029],"about_ca_topic_score_codex":0.000020924723,"about_ca_topic_score_gemma":0.00004832046,"teacher_disagreement_score":0.212821,"about_ca_system_score_codex":0.00052093517,"about_ca_system_score_gemma":0.00020304059,"threshold_uncertainty_score":0.99973744},"labels":[],"label_agreement":null},{"id":"W2015485633","doi":"10.1109/titb.2012.2191295","title":"Link Technologies and BlackBerry Mobile Health (mHealth) Solutions: A Review","year":2012,"lang":"en","type":"review","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"mHealth; eHealth; Telemedicine; Wearable computer; Computer science; Mobile technology; Telecommunications; Mobile computing; Mobile phone; Mobile device; Mobile telephony; Health care; Internet privacy; Multimedia; Computer security; World Wide Web; Mobile radio; Embedded system","score_opus":0.02681010094206518,"score_gpt":0.2964534463159351,"score_spread":0.2696433453738699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015485633","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.3004524e-7,0.94334215,0.04879191,0.001438456,0.0008171015,0.0027297293,0.000114382274,0.0026764015,0.0000892464],"genre_scores_gemma":[0.00038942025,0.99487317,0.0006549457,0.00018262598,0.000047492926,0.0036626228,0.00012441738,0.000053826498,0.0000114547465],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99689454,0.000055024695,0.0018020724,0.00024795212,0.00023407649,0.00076632295],"domain_scores_gemma":[0.9985923,0.00012268974,0.0003987642,0.00072186644,0.000054901997,0.00010947037],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006908376,0.00058413815,0.0018462746,0.0038348709,0.00017671273,0.000015971857,0.00040768585,0.0013324902,0.000030962874],"category_scores_gemma":[0.000015234313,0.0005160402,0.00013695136,0.0031806112,0.00046414862,0.00050653866,0.000009503214,0.0020105273,0.00017517577],"study_design_candidate":"design_other","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":[0.0000011149648,0.00002554453,9.657438e-8,0.042581018,0.000062103216,0.0000011094934,0.00004859214,0.00011596873,8.918576e-8,0.00010622207,0.0009741989,0.95608395],"study_design_scores_gemma":[0.0002917423,0.00023052178,2.7253196e-7,0.052838147,0.0002463553,0.00021620136,0.00020464533,0.00064055016,0.000003704998,0.0000926545,0.94483745,0.00039777672],"about_ca_topic_score_codex":0.000012699566,"about_ca_topic_score_gemma":0.000016304986,"teacher_disagreement_score":0.95568615,"about_ca_system_score_codex":0.00066757033,"about_ca_system_score_gemma":0.00014761105,"threshold_uncertainty_score":0.999964},"labels":[],"label_agreement":null},{"id":"W2024476192","doi":"10.1109/titb.2011.2157829","title":"Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":136,"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":"Sichuan University of Science and Engineering","keywords":"Segmentation; Artificial intelligence; Computer science; Skin lesion; Pattern recognition (psychology); Image segmentation; Pixel; Computer vision; Iterative method; Convergence (economics); Image (mathematics); Market segmentation; Algorithm; Medicine; Pathology","score_opus":0.017422190079370216,"score_gpt":0.2550228283851951,"score_spread":0.23760063830582487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024476192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10727173,0.000011641049,0.8883199,0.0014790795,0.0005721068,0.0009098777,0.0000030338088,0.0004883069,0.0009443169],"genre_scores_gemma":[0.9960133,0.000041335283,0.0030093936,0.00053083466,0.000017921191,0.00018862134,0.000019870571,0.000012992982,0.00016577596],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869406,0.000025176038,0.00062100013,0.00017541162,0.00025564223,0.0002286842],"domain_scores_gemma":[0.9993279,0.000030275074,0.00019311577,0.00028132988,0.00009488044,0.00007250202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015006251,0.00019432632,0.00026626175,0.0029918605,0.00013028951,0.000009698444,0.000086294545,0.00021171878,0.0002803162],"category_scores_gemma":[0.000014781976,0.00017319532,0.00005642757,0.0012541924,0.00015500617,0.000373628,0.000002474746,0.000339089,0.0001899726],"study_design_candidate":"design_other","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":[0.00046347477,0.000467594,0.000048332506,0.00020205382,0.00012500629,0.00007304514,0.0057870774,0.000417334,0.0059772464,0.00031662354,0.0002724734,0.98584974],"study_design_scores_gemma":[0.030369977,0.013917477,0.005541065,0.0034835504,0.0011057882,0.008247217,0.038806487,0.36211765,0.5258155,0.0051362882,0.0034745275,0.0019845066],"about_ca_topic_score_codex":0.000048632795,"about_ca_topic_score_gemma":0.000014373311,"teacher_disagreement_score":0.98386526,"about_ca_system_score_codex":0.0003262512,"about_ca_system_score_gemma":0.00003283027,"threshold_uncertainty_score":0.70627004},"labels":[],"label_agreement":null},{"id":"W2024630350","doi":"10.1109/titb.2012.2206116","title":"3-D Streaming Supplying Partner Protocols for Mobile Collaborative Exergaming for Health","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Multimedia Communication and Technology","field":"Social Sciences","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 Ottawa","funders":"","keywords":"Loneliness; Phobias; Mental health; Physical activity; Anxiety; Bottleneck; Obesity; Internet privacy; Computer science; Multimedia; Psychology; Medicine; Physical therapy; Social psychology; Psychotherapist; Psychiatry","score_opus":0.03338676290065261,"score_gpt":0.4052884479872503,"score_spread":0.37190168508659766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024630350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044494737,0.00011100775,0.9301413,0.029627226,0.0006481963,0.033393133,0.00017356548,0.0007915296,0.00066453335],"genre_scores_gemma":[0.9042609,0.00014131425,0.021332389,0.0005528831,0.00005089421,0.073489994,0.000049372513,0.000012223074,0.00011006126],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985616,0.00005743512,0.00058506645,0.00011436091,0.00017006912,0.00051142107],"domain_scores_gemma":[0.99880123,0.00032747045,0.0002798134,0.00029093435,0.00020786251,0.000092694005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011082886,0.00013215128,0.0002639311,0.0013446497,0.0006814014,0.000018942743,0.00029917463,0.00034073176,0.000048533915],"category_scores_gemma":[0.0001246177,0.00012966289,0.000043071283,0.0013810558,0.00044801313,0.00066064816,0.0000027614253,0.00024722423,0.000015274447],"study_design_candidate":"design_other","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":[0.00016353038,0.0004970657,0.00045328244,0.0001798342,0.000047626236,8.9107374e-8,0.034409877,0.00009875464,0.0006523901,0.037597377,0.001833562,0.9240666],"study_design_scores_gemma":[0.004247725,0.00095837493,0.0000783447,0.0002463968,0.000017782506,0.0000030026065,0.064443134,0.0018480147,0.016784798,0.001761725,0.90928775,0.0003229529],"about_ca_topic_score_codex":0.00009122491,"about_ca_topic_score_gemma":0.00023781872,"teacher_disagreement_score":0.92374367,"about_ca_system_score_codex":0.00032862596,"about_ca_system_score_gemma":0.00022168532,"threshold_uncertainty_score":0.52874994},"labels":[],"label_agreement":null},{"id":"W2040520897","doi":"10.1109/titb.2011.2177097","title":"An Infobutton For Web 2.0 Clinical Discussions: The Knowledge Linkage Framework","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Information retrieval; Lexicon; Linkage (software); Recall; Keyword search; Set (abstract data type); World Wide Web; Natural language processing; Psychology","score_opus":0.034026762428731226,"score_gpt":0.3437569239337854,"score_spread":0.30973016150505417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040520897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11417132,0.00022831763,0.8788895,0.003997877,0.0013564912,0.0006228345,0.00006788732,0.00020326735,0.00046247395],"genre_scores_gemma":[0.9874723,0.00034546244,0.010924714,0.0007492529,0.00010479224,0.00027459481,0.000046824724,0.000010899962,0.0000711843],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99872655,0.000060372055,0.00064006547,0.000206135,0.000099898425,0.00026696202],"domain_scores_gemma":[0.99902564,0.00010131181,0.0001395691,0.00056288595,0.00009234091,0.00007827854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006572628,0.00016707118,0.00020836882,0.00044725643,0.00017442007,0.00001009885,0.0004543425,0.0008588329,0.000034258734],"category_scores_gemma":[0.00017925401,0.00010297506,0.00008818017,0.0005519886,0.0007958438,0.000020862115,0.0000059087724,0.00047176945,0.000035889607],"study_design_candidate":"design_other","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":[0.00051146536,0.00080335594,0.00092144305,0.00005833927,0.00010785697,0.0000015432222,0.0014458274,0.00001863317,0.0052558095,0.0017216768,0.002988658,0.9861654],"study_design_scores_gemma":[0.012959292,0.023345305,0.016253265,0.000900097,0.00029221017,0.0001441376,0.020548023,0.007655853,0.15475996,0.02033938,0.7410361,0.0017663628],"about_ca_topic_score_codex":0.000007689752,"about_ca_topic_score_gemma":0.000027274344,"teacher_disagreement_score":0.984399,"about_ca_system_score_codex":0.000018372386,"about_ca_system_score_gemma":0.00008824329,"threshold_uncertainty_score":0.6624106},"labels":[],"label_agreement":null},{"id":"W2057458116","doi":"10.1109/titb.2011.2154384","title":"Cross-Layer Ultrasound Video Streaming Over Mobile WiMAX and HSUPA Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":45,"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":"Information Technology Research Centre; Cardiff University; Kingston University","keywords":"WiMAX; Mobile broadband; IMT Advanced; Computer science; Computer network; Interoperability; Network packet; Quality of service; Broadband; Wireless broadband; Mobile computing; Telecommunications; Mobile technology; Wireless; Wireless network; Mobile Web","score_opus":0.00615366433738879,"score_gpt":0.2235796573165212,"score_spread":0.21742599297913243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057458116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19348042,0.00014488773,0.8045875,0.000019385432,0.0004883003,0.0002876101,0.000013345457,0.00062144274,0.00035710348],"genre_scores_gemma":[0.9949196,0.0009487531,0.0038202994,0.000057475285,0.000022111835,0.00016730562,0.00002216521,0.000022686403,0.000019599898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989689,0.0000076601655,0.000488068,0.00013373316,0.00011632688,0.00028531445],"domain_scores_gemma":[0.9995116,0.000067165885,0.00007368365,0.0002447303,0.000049043683,0.000053759468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010308564,0.00020008837,0.00020282614,0.0010180881,0.00009672306,0.000017929173,0.0001214006,0.00034608252,0.00015403546],"category_scores_gemma":[0.000007275569,0.0002022534,0.000023075616,0.0010479665,0.00025313263,0.0009344673,0.0000018769398,0.000415735,0.000017580745],"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":[0.00003776824,0.000040063474,0.0012217445,0.000043148248,0.000035097004,0.0000027542467,0.00073054957,0.8839522,0.0008000627,0.00010162073,0.00005757826,0.112977415],"study_design_scores_gemma":[0.0037540465,0.00051497953,0.0059917034,0.0003171809,0.000054364504,0.000105661944,0.0013983516,0.94382846,0.040794242,0.00080094027,0.001685285,0.0007548021],"about_ca_topic_score_codex":0.000015667561,"about_ca_topic_score_gemma":0.000014545819,"teacher_disagreement_score":0.80143917,"about_ca_system_score_codex":0.00011398105,"about_ca_system_score_gemma":0.000007658818,"threshold_uncertainty_score":0.82476544},"labels":[],"label_agreement":null},{"id":"W2095082252","doi":"10.1109/titb.2011.2177668","title":"Subject-Specific Estimation of Central Aortic Blood Pressure Using an Individualized Transfer Function: A Preliminary Feasibility Study","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","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 Alberta","funders":"","keywords":"Aortic pressure; Blood pressure; Subject (documents); Medicine; Estimation; Cardiology; Function (biology); Internal medicine; Computer science; Engineering; Biology; World Wide Web","score_opus":0.034413257763449744,"score_gpt":0.2926452344870852,"score_spread":0.25823197672363546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095082252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6593738,0.00017426162,0.3382799,0.00005181886,0.00023846744,0.00165451,0.000031959986,0.00015354193,0.00004170682],"genre_scores_gemma":[0.9982621,0.00005009424,0.0013873605,0.00005578101,0.000018496305,0.00014454988,0.00006394609,0.000012978844,0.0000047054],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978933,0.00010893795,0.0009963027,0.00025500226,0.00044647127,0.00029998328],"domain_scores_gemma":[0.99890846,0.000020839196,0.00013954598,0.00059634604,0.0001695449,0.00016526588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005105184,0.00020812365,0.0004692156,0.0017308453,0.00010945265,0.000006575485,0.00010833612,0.00030167034,0.00021326558],"category_scores_gemma":[0.000018988923,0.00019491454,0.0001491218,0.0013597286,0.00020246257,0.0006356813,0.0000020680206,0.00037134858,0.000006936183],"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":[0.06856264,0.07489908,0.074066326,0.0072643277,0.0062930384,0.00014886764,0.0508352,0.016991382,0.014805482,0.00094720366,0.00006464359,0.6851218],"study_design_scores_gemma":[0.06230333,0.021042734,0.8094871,0.001062436,0.010718153,0.00058173825,0.016165463,0.028413486,0.047725607,0.0015293064,0.00011392594,0.0008566913],"about_ca_topic_score_codex":0.0001311371,"about_ca_topic_score_gemma":0.0000117148875,"teacher_disagreement_score":0.7354208,"about_ca_system_score_codex":0.000076469514,"about_ca_system_score_gemma":0.00015978879,"threshold_uncertainty_score":0.7948384},"labels":[],"label_agreement":null},{"id":"W2096323954","doi":"10.1109/titb.2009.2025125","title":"Dynamic-Model-Based Method for Selecting Significantly Expressed Genes From Time-Course Expression Profiles","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Akaike information criterion; False discovery rate; Expression (computer science); Autoregressive model; Gene expression; Gene; Computer science; Computational biology; Bayesian information criterion; Biology; Mathematics; Statistics; Artificial intelligence; Genetics","score_opus":0.007384683362115291,"score_gpt":0.2831719455749338,"score_spread":0.2757872622128185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096323954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043084204,0.000098742814,0.9528513,0.0028964623,0.00015912326,0.0006008779,0.00012415985,0.00012901277,0.00005614039],"genre_scores_gemma":[0.9042879,0.00006732987,0.09402013,0.0006859868,0.00003042298,0.0003818521,0.0003942101,0.000015032934,0.00011716384],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987423,0.000037048034,0.0004870602,0.00031368213,0.0001681386,0.00025179796],"domain_scores_gemma":[0.99912024,0.000034596393,0.00022015252,0.00041364474,0.00015095924,0.00006039473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002298839,0.00021038357,0.00021208728,0.0006457296,0.00015209756,0.000017869923,0.00024542288,0.00044455938,0.000023798266],"category_scores_gemma":[0.000025224195,0.00019227473,0.000071087146,0.00050137535,0.000075778225,0.00003305897,0.0000019832742,0.0001871118,0.000010382804],"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":[0.00024771102,0.00011912351,0.000006016229,0.000012675155,0.000011693935,1.3909073e-7,0.00006590208,0.011377807,0.886819,0.000009100416,0.0006572098,0.10067363],"study_design_scores_gemma":[0.0015612881,0.00041176952,0.000052291012,0.00007439674,0.000022045102,0.0000015330746,0.00026558572,0.16755776,0.827369,0.00048698005,0.0020174254,0.00017994929],"about_ca_topic_score_codex":0.0000041173726,"about_ca_topic_score_gemma":0.0000030766005,"teacher_disagreement_score":0.8612037,"about_ca_system_score_codex":0.000053494892,"about_ca_system_score_gemma":0.00012930001,"threshold_uncertainty_score":0.7840736},"labels":[],"label_agreement":null},{"id":"W2096465810","doi":"10.1109/titb.2005.847187","title":"Conceptual Framework of Knowledge Management for Ethical Decision-Making Support in Neonatal Intensive Care","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Palliative Care and End-of-Life Issues","field":"Medicine","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":"Carleton University","funders":"","keywords":"Intensive care; Context (archaeology); Variety (cybernetics); Knowledge management; Adaptation (eye); Quality (philosophy); Clinical decision support system; Psychology; Value (mathematics); Information exchange; Decision support system; Medicine; Nursing; Computer science; Intensive care medicine; Artificial intelligence","score_opus":0.034953511333685246,"score_gpt":0.396863514808849,"score_spread":0.3619100034751638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096465810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13455485,0.00070122635,0.8581802,0.004119664,0.00046102225,0.0010138034,0.00009302075,0.00009281474,0.0007833652],"genre_scores_gemma":[0.9714289,0.00038072714,0.027178694,0.00083159556,0.00002326666,0.000107064356,0.000030315065,0.000008009035,0.000011454742],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9986263,0.000012225329,0.0008161189,0.00013596716,0.00021021158,0.00019921339],"domain_scores_gemma":[0.9985881,0.00040781157,0.00016002919,0.00023005568,0.0005765815,0.000037454272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019464636,0.0001431936,0.00042755328,0.002146645,0.00003152418,0.00000178196,0.0001330832,0.0005649947,0.00006285537],"category_scores_gemma":[0.00023061031,0.00012774,0.00007561765,0.0010483629,0.00049631664,0.00013771943,0.0000043837936,0.00061965163,0.000018036837],"study_design_candidate":"design_other","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":[0.0013644684,0.00013203794,0.0015304126,0.00059485144,0.00013147984,0.000015534,0.060835686,0.00048333537,0.00015386424,0.010470815,0.0005693731,0.92371815],"study_design_scores_gemma":[0.023421112,0.0060340744,0.0019016304,0.023340171,0.00039317572,0.000113212816,0.6879728,0.003753067,0.10551358,0.005278123,0.14154871,0.000730334],"about_ca_topic_score_codex":0.0000053508415,"about_ca_topic_score_gemma":0.000028793103,"teacher_disagreement_score":0.9229878,"about_ca_system_score_codex":0.00020284529,"about_ca_system_score_gemma":0.00007577012,"threshold_uncertainty_score":0.5209086},"labels":[],"label_agreement":null},{"id":"W2097070075","doi":"10.1109/titb.2010.2040394","title":"A Cusum-Based Multilevel Alerting Method for Physiological Monitoring","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","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 British Columbia","funders":"","keywords":"CUSUM; Computer science; Statistics; Heuristics; Vital signs; Data mining; Medicine; Mathematics; Anesthesia","score_opus":0.039669270952919534,"score_gpt":0.36595877999287263,"score_spread":0.3262895090399531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097070075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37699792,0.000009174227,0.6142915,0.0059053893,0.0013027447,0.000816289,0.000019302272,0.0006225223,0.00003518813],"genre_scores_gemma":[0.88018554,0.000011864406,0.118778415,0.0003641166,0.00008636246,0.0005271347,0.000018246905,0.000012829127,0.000015483398],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984639,0.000019604802,0.0006968537,0.00022485394,0.00018926943,0.0004054778],"domain_scores_gemma":[0.998927,0.00022585042,0.00017408062,0.00038187622,0.000194282,0.000096887656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000482426,0.00020861595,0.00037748262,0.0020053291,0.00019848785,0.000006110714,0.00016312422,0.0008720243,0.00002754088],"category_scores_gemma":[0.00019972253,0.00017729512,0.000084588006,0.0009065586,0.00022375761,0.00019307219,0.000002107451,0.0013313562,0.000029526027],"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":[0.0004671328,0.0003741103,0.0048512686,0.00031387687,0.000049016107,0.000007738427,0.00030377862,0.0003687937,0.18596126,0.0003467534,0.00007110554,0.8068852],"study_design_scores_gemma":[0.008730841,0.0025008298,0.007108957,0.00068080437,0.000085943335,0.00009983579,0.0010746941,0.048272725,0.92680305,0.00084506214,0.0033932568,0.00040397522],"about_ca_topic_score_codex":0.000032682376,"about_ca_topic_score_gemma":0.000005403522,"teacher_disagreement_score":0.8064812,"about_ca_system_score_codex":0.000105599225,"about_ca_system_score_gemma":0.00010283839,"threshold_uncertainty_score":0.72298855},"labels":[],"label_agreement":null},{"id":"W2097126443","doi":"10.1109/titb.2010.2040287","title":"Charge State Determination of Peptide Tandem Mass Spectra Using Support Vector Machine (SVM)","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","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":"University of British Columbia; Toronto Metropolitan University; University of Saskatchewan","funders":"Université Laval","keywords":"Support vector machine; Mass spectrum; Classifier (UML); Artificial intelligence; Spectral line; Tandem; Quadrupole ion trap; Pattern recognition (psychology); Ion; Mass spectrometry; Low resolution; Linear discriminant analysis; Computer science; Tandem mass spectrometry; Quadrupole; Chemistry; Resolution (logic); Analytical Chemistry (journal); Ion trap; Physics; High resolution; Atomic physics; Chromatography; Materials science; Remote sensing","score_opus":0.0077255928553016376,"score_gpt":0.2657943759634922,"score_spread":0.2580687831081906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097126443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16688746,0.000004220438,0.8308972,0.0006843283,0.000119698125,0.00026140822,0.00016141484,0.00027819202,0.000706036],"genre_scores_gemma":[0.9265968,0.00007023893,0.07295795,0.000051148723,0.000016855538,0.00012910401,0.000051509604,0.000015042491,0.00011132484],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876374,0.0000040283053,0.0006764329,0.00015208361,0.00017940505,0.00022433011],"domain_scores_gemma":[0.9990956,0.000030839547,0.00033105773,0.0003867908,0.000107734435,0.000047939633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015940516,0.00017175335,0.00023313024,0.0009382904,0.000096406824,0.000008104815,0.00023019781,0.00029708442,0.00055450754],"category_scores_gemma":[0.000013830995,0.00017027187,0.000048736732,0.0007215536,0.00024166545,0.00035101635,0.00000257484,0.0006650075,0.000021523996],"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":[0.000028234079,0.000090228175,0.00020230982,0.00006793215,0.000010108918,0.0000016842703,0.00013932839,0.00005976718,0.9666856,0.00068413,0.0000123633,0.0320183],"study_design_scores_gemma":[0.00076007424,0.00009094066,0.000024947825,0.000051663366,0.000017028873,0.000041661704,0.000090505826,0.009834018,0.9843088,0.003203143,0.0014199457,0.00015731408],"about_ca_topic_score_codex":0.000032222302,"about_ca_topic_score_gemma":0.00002129574,"teacher_disagreement_score":0.75970936,"about_ca_system_score_codex":0.00011077319,"about_ca_system_score_gemma":0.000051691903,"threshold_uncertainty_score":0.6943486},"labels":[],"label_agreement":null},{"id":"W2099149651","doi":"10.1109/titb.2010.2052060","title":"Automatic Segmentation of Spinal Cord MRI Using Symmetric Boundary Tracing","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Tracing; Segmentation; Spinal cord; Artificial intelligence; Computer vision; Computer science; Construct (python library); Boundary (topology); Surgical planning; Active contour model; Pattern recognition (psychology); Physical medicine and rehabilitation; Image segmentation; Medicine; Radiology; Psychology; Mathematics; Neuroscience","score_opus":0.013629298534413019,"score_gpt":0.30414722348589907,"score_spread":0.29051792495148604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099149651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08643836,0.000013026462,0.91070867,0.0011118789,0.0007532102,0.0004041227,0.000006080843,0.0004925765,0.00007206776],"genre_scores_gemma":[0.7076632,0.000021379701,0.2919951,0.00025156524,0.000009255913,0.00004587336,0.000004576646,0.0000050699787,0.0000039826214],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981783,0.00003091385,0.00092007424,0.00017866872,0.0004565024,0.00023552559],"domain_scores_gemma":[0.9988872,0.00007007642,0.0003789001,0.00044114806,0.00015332326,0.000069384725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052772573,0.0001623571,0.00025408887,0.0045319735,0.00012176163,0.000036993944,0.00052396976,0.00033560183,0.000084735104],"category_scores_gemma":[0.000048983355,0.00015508359,0.000045583507,0.003753349,0.00046848255,0.0015980566,0.0000063259276,0.00075868773,0.000023248087],"study_design_candidate":"design_other","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":[0.000022103628,0.00011928905,0.00004011346,0.00010249417,0.000015437823,0.0000034403483,0.00028405528,0.00006416773,0.07283263,0.0009525478,0.000043728705,0.92552],"study_design_scores_gemma":[0.0014261008,0.0011781707,0.0003798343,0.0002413453,0.000024046176,0.00014864601,0.00048268796,0.16292837,0.8306956,0.0022035812,0.00006395223,0.00022761323],"about_ca_topic_score_codex":0.000036560654,"about_ca_topic_score_gemma":0.0000054855773,"teacher_disagreement_score":0.9252924,"about_ca_system_score_codex":0.00012546382,"about_ca_system_score_gemma":0.000120018994,"threshold_uncertainty_score":0.6324125},"labels":[],"label_agreement":null},{"id":"W2099397584","doi":"10.1109/titb.2011.2150758","title":"Generalizing Common Tasks in Automated Skin Lesion Diagnosis","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; Canadian Institutes of Health Research; University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Leverage (statistics); Artificial intelligence; Segmentation; Computer science; Skin lesion; Probabilistic logic; Receiver operating characteristic; Pattern recognition (psychology); Lesion; Machine learning; Computer vision; Dermatology; Medicine; Pathology","score_opus":0.023585878168883256,"score_gpt":0.2709464642981784,"score_spread":0.24736058612929518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099397584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94170743,0.000059302016,0.04620613,0.0045016366,0.00095996,0.0013024823,0.000015525606,0.0017656839,0.0034818463],"genre_scores_gemma":[0.99711263,0.00029186625,0.0013489422,0.0008431381,0.0000124235785,0.00027996302,0.000020742395,0.000012501217,0.00007778613],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986382,0.000025452908,0.0006925367,0.00016638874,0.00020932118,0.00026807562],"domain_scores_gemma":[0.9994051,0.000025451298,0.00012678388,0.0003251467,0.000049915947,0.00006759121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021680984,0.0001792695,0.0003155916,0.0039915084,0.00007279546,0.000007232556,0.00011519661,0.00031127717,0.00029372514],"category_scores_gemma":[0.000012636072,0.00016432554,0.00005070496,0.001785158,0.00014187832,0.00023397022,0.0000033601723,0.0004276649,0.00015975813],"study_design_candidate":"design_other","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":[0.0013612278,0.0024348805,0.012955985,0.00047847434,0.00019462049,0.00048097907,0.005924569,0.00095059746,0.007725819,0.0011584023,0.0036504609,0.962684],"study_design_scores_gemma":[0.03231764,0.008806643,0.09625054,0.0030735855,0.0004392506,0.002712874,0.014873162,0.08982866,0.7011029,0.0019237691,0.04685232,0.0018186531],"about_ca_topic_score_codex":0.0004646952,"about_ca_topic_score_gemma":0.00021719742,"teacher_disagreement_score":0.9608653,"about_ca_system_score_codex":0.00027179357,"about_ca_system_score_gemma":0.00002702248,"threshold_uncertainty_score":0.6701001},"labels":[],"label_agreement":null},{"id":"W2099675194","doi":"10.1109/titb.2007.911307","title":"Efficient Lossless Compression of 4-D Medical Images Based on the Advanced Video Coding Scheme","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":37,"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 British Columbia","funders":"Aristotle University of Thessaloniki","keywords":"Lossless compression; Computer science; Motion compensation; Data compression; Computer vision; Lossy compression; JPEG 2000; Context-adaptive binary arithmetic coding; Coding (social sciences); Artificial intelligence; Image compression; Image processing; Mathematics; Image (mathematics)","score_opus":0.013179220568266467,"score_gpt":0.27358094016143947,"score_spread":0.260401719593173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099675194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014525436,0.000023311826,0.97101176,0.012966424,0.00029678186,0.00041580538,0.000023198685,0.00056062336,0.0001766309],"genre_scores_gemma":[0.9626073,0.000111367364,0.036287446,0.00082827406,0.0000070376564,0.00013809963,0.000006680198,0.0000071293716,0.000006623881],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976618,0.00006079663,0.00075327914,0.00024351433,0.0010112345,0.0002693504],"domain_scores_gemma":[0.998083,0.00041018147,0.00030783247,0.0009765859,0.00014796865,0.00007443523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052859506,0.0002048822,0.00029633698,0.0017426172,0.00025580323,0.000011862982,0.0014771168,0.00027946068,0.00006223936],"category_scores_gemma":[0.00011490854,0.00013984718,0.000054111988,0.0019882212,0.0006772423,0.00039488857,0.000025257907,0.0006879413,0.00002589203],"study_design_candidate":"design_other","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":[0.0006574245,0.0026927784,0.0002567138,0.0003769033,0.00007472065,0.00011863044,0.002004739,0.12797193,0.081307665,0.057563826,0.0064854464,0.7204892],"study_design_scores_gemma":[0.001572123,0.00033255861,0.00012495296,0.0007474829,0.0000031258912,0.00004387219,0.00015551448,0.5444734,0.4508941,0.0005109084,0.00096693536,0.00017503274],"about_ca_topic_score_codex":0.000009727819,"about_ca_topic_score_gemma":7.3594856e-7,"teacher_disagreement_score":0.9480819,"about_ca_system_score_codex":0.00009588662,"about_ca_system_score_gemma":0.00010106611,"threshold_uncertainty_score":0.57028025},"labels":[],"label_agreement":null},{"id":"W2100448796","doi":"10.1109/titb.2004.835539","title":"Unobtrusive Monitoring of Computer Interactions to Detect Cognitive Status in Elders","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Coast Mountain College","funders":"","keywords":"Cognition; Confusion; Psychomotor learning; Computer science; Human–computer interaction; Psychology","score_opus":0.011825259762516149,"score_gpt":0.2987754585793814,"score_spread":0.2869501988168653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100448796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6048484,0.000012723489,0.3870477,0.00606163,0.00080346904,0.00063707604,0.000024995454,0.00029241017,0.00027158807],"genre_scores_gemma":[0.9939151,0.00012485991,0.005627905,0.00015311659,0.000020505639,0.00013924841,0.0000031527152,0.00000697249,0.000009141997],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985794,0.00003372983,0.00058351434,0.00016330842,0.0002494394,0.00039063703],"domain_scores_gemma":[0.99929124,0.000131366,0.00015171655,0.00017591425,0.00017496779,0.000074806194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023649745,0.00014105513,0.00024321226,0.004616349,0.00014464257,0.0000098006485,0.000227649,0.00030640693,0.000026139976],"category_scores_gemma":[0.00009465169,0.00015126794,0.00003647133,0.003451975,0.00056167645,0.0005793581,0.0000043270547,0.00053826504,0.00006100196],"study_design_candidate":"design_other","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":[0.00033214185,0.0004562294,0.007104858,0.00006927971,0.00011189359,0.000025019403,0.075863294,0.010069375,0.0033705146,0.0025593983,0.000053060292,0.89998496],"study_design_scores_gemma":[0.02294253,0.004138569,0.043281715,0.0055938587,0.00012947136,0.00006385153,0.33043337,0.00034914023,0.56761473,0.017314717,0.006630073,0.0015079627],"about_ca_topic_score_codex":0.0017503503,"about_ca_topic_score_gemma":0.0028725849,"teacher_disagreement_score":0.89847696,"about_ca_system_score_codex":0.00057394395,"about_ca_system_score_gemma":0.00016470201,"threshold_uncertainty_score":0.61685276},"labels":[],"label_agreement":null},{"id":"W2101771332","doi":"10.1109/titb.2008.2009441","title":"Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances","year":2009,"lang":"en","type":"review","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"AI in cancer detection","field":"Computer Science","cited_by":622,"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":"Breast cancer; Mammography; Medicine; Cancer; CAD; Computer-aided diagnosis; Cancer detection; Computer science; Medical physics; Radiology; Internal medicine","score_opus":0.010718065496861382,"score_gpt":0.2711614395953258,"score_spread":0.2604433740984644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101771332","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014496661,0.3902933,0.6074388,0.0007218083,0.0005791595,0.0006392722,0.000049500984,0.0002515761,0.000012106838],"genre_scores_gemma":[0.0014722323,0.9931626,0.0045329225,0.00008199698,0.000031668256,0.0006981301,0.0000059418353,0.000012695727,0.0000018497956],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980123,0.000052929827,0.00096308254,0.00036365964,0.00033547124,0.0002725594],"domain_scores_gemma":[0.99838483,0.00009518165,0.0007462088,0.0005241259,0.00018610744,0.00006353113],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020383492,0.00038738656,0.00095564313,0.004504142,0.000111412766,0.000029660907,0.0004839973,0.00050538813,0.000011391639],"category_scores_gemma":[0.0000027198596,0.00030471635,0.000090135436,0.004949855,0.00033021552,0.001137881,0.0000064803617,0.0006502802,0.000004747214],"study_design_candidate":"design_other","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":[0.00002044337,0.00005548286,0.000005964392,0.0014665428,0.000059689686,0.0000016391408,0.00005716858,0.00014494633,0.0000011303584,0.00003771016,0.000011378545,0.9981379],"study_design_scores_gemma":[0.0026503513,0.003529567,0.0001820239,0.025383117,0.0005490342,0.0011291633,0.00011598255,0.0036745935,0.0016530592,0.0007355966,0.9592558,0.0011417046],"about_ca_topic_score_codex":0.000049007274,"about_ca_topic_score_gemma":0.00008660581,"teacher_disagreement_score":0.9969962,"about_ca_system_score_codex":0.000329531,"about_ca_system_score_gemma":0.00013178986,"threshold_uncertainty_score":0.9999405},"labels":[],"label_agreement":null},{"id":"W2104955117","doi":"10.1109/titb.2009.2021159","title":"Novel Lossless fMRI Image Compression Based on Motion Compensation and Customized Entropy Coding","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Advanced Data Compression Techniques","field":"Computer Science","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 British Columbia","funders":"","keywords":"Computer science; Lossless compression; Motion compensation; Artificial intelligence; Data compression; Computer vision; Context-adaptive binary arithmetic coding; Context-adaptive variable-length coding; Entropy encoding; Quarter-pixel motion; JPEG 2000; Image compression; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.009465778566057579,"score_gpt":0.2620009117182452,"score_spread":0.2525351331521876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104955117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031265633,0.0000073282026,0.9844774,0.010451897,0.00026033426,0.00054026395,0.000025714038,0.0009832614,0.0001272169],"genre_scores_gemma":[0.87642694,0.00004749707,0.122388706,0.0010208541,0.000010423347,0.00005613654,0.00003719995,0.000006063826,0.0000061880255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843067,0.00004390221,0.00057332637,0.0003075688,0.00037569454,0.00026883083],"domain_scores_gemma":[0.99875915,0.00013030601,0.000284388,0.0006300667,0.000121413526,0.00007470492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030713103,0.0002406148,0.00030535998,0.002571054,0.00022076211,0.000064311396,0.00050000014,0.00026121273,0.000013340312],"category_scores_gemma":[0.00002985857,0.00021350804,0.00003590395,0.0011555761,0.00019030526,0.0017281887,0.0000083594905,0.0004928759,0.000023341352],"study_design_candidate":"design_other","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":[0.00035353526,0.00062783394,0.000023932726,0.00005723711,0.000011117326,0.0000080129375,0.00028215558,0.007203044,0.19719805,0.019540815,0.00054968055,0.7741446],"study_design_scores_gemma":[0.0048211655,0.00058572344,0.0005270541,0.0004037221,0.000008997613,0.00003256086,0.00008727551,0.7318217,0.25790745,0.0028159136,0.0007129608,0.0002754734],"about_ca_topic_score_codex":0.000007504805,"about_ca_topic_score_gemma":8.8278813e-7,"teacher_disagreement_score":0.8733004,"about_ca_system_score_codex":0.00017393618,"about_ca_system_score_gemma":0.00002701846,"threshold_uncertainty_score":0.8706605},"labels":[],"label_agreement":null},{"id":"W2105169232","doi":"10.1109/titb.2008.2010552","title":"Integrating Heterogeneous Classifier Ensembles for EMG Signal Decomposition Based on Classifier Agreement","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classifier (UML); Artificial intelligence; Computer science; Random subspace method; Pattern recognition (psychology); Quadratic classifier; Cascading classifiers; Machine learning; Data mining","score_opus":0.009683146557915754,"score_gpt":0.24222695869038924,"score_spread":0.2325438121324735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105169232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025813868,0.00002212961,0.96790844,0.0038221336,0.000320659,0.00053276523,0.000025585472,0.00051219587,0.0010422118],"genre_scores_gemma":[0.9960485,0.00004395642,0.002623747,0.0009808133,0.000023181467,0.00022097159,0.000039466606,0.00000951289,0.00000981392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890757,0.000011768674,0.00049147324,0.00013049754,0.00017902518,0.00027965833],"domain_scores_gemma":[0.99955,0.00007820431,0.00007499944,0.00017899745,0.00007668457,0.00004113654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012174832,0.00020868664,0.00021440341,0.0020096716,0.00015786629,0.00001933794,0.0001102894,0.00022301536,0.00004271616],"category_scores_gemma":[0.0000065835793,0.00019057795,0.000076774304,0.0008582915,0.00006980696,0.00022215936,4.3529008e-7,0.00030195713,0.00000788739],"study_design_candidate":"design_other","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":[0.0002111405,0.00021265325,0.000022277136,0.00007641459,0.00008222,0.000001961443,0.00032539142,0.07365798,0.026906155,0.0006699376,0.001481516,0.89635235],"study_design_scores_gemma":[0.0047242646,0.0035704204,0.0010124332,0.00043489138,0.000059256385,0.000019756979,0.0009473402,0.5878544,0.3915462,0.001906746,0.0073064896,0.00061781047],"about_ca_topic_score_codex":0.0000025200727,"about_ca_topic_score_gemma":0.000010510104,"teacher_disagreement_score":0.9702347,"about_ca_system_score_codex":0.00019336857,"about_ca_system_score_gemma":0.000014825329,"threshold_uncertainty_score":0.7771543},"labels":[],"label_agreement":null},{"id":"W2106220314","doi":"10.1109/titb.2006.872054","title":"Cancer Risk Analysis in Families With Hereditary Nonpolyposis Colorectal Cancer","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Genetic factors in colorectal cancer","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":"Memorial University of Newfoundland; Carleton University","funders":"","keywords":"Colorectal cancer; Cancer; Medicine; Oncology; Internal medicine","score_opus":0.004415424126407189,"score_gpt":0.23944091219492247,"score_spread":0.23502548806851528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106220314","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9738647,0.00031983986,0.020525688,0.003316331,0.0004455665,0.0007025003,0.00021575126,0.00020975433,0.00039988247],"genre_scores_gemma":[0.99656874,0.0010426069,0.0010862444,0.00025297585,0.000044882563,0.0008815922,0.000045208926,0.00001721071,0.000060536768],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981682,0.00002671555,0.0007230015,0.00027251433,0.00041703257,0.00039255887],"domain_scores_gemma":[0.9991957,0.0000753215,0.00021447772,0.0002916387,0.00015800739,0.00006483651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001646835,0.00027904415,0.00057272264,0.005351694,0.000088692395,0.000009821165,0.00014967861,0.0003989389,0.00036375498],"category_scores_gemma":[0.000010958147,0.00022414472,0.0000885699,0.0068456316,0.00046435205,0.00024134881,0.0000025964275,0.00070239155,0.000017436732],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020097094,0.00073507946,0.73187286,0.00019904126,0.0012042045,0.00005439232,0.0012044669,0.090546854,0.005781305,0.000030679854,0.0007098686,0.16565157],"study_design_scores_gemma":[0.0105202915,0.0024320001,0.81189615,0.00059064606,0.0018258529,0.0000876989,0.0032417937,0.014914519,0.1524619,0.00020597172,0.0011245578,0.0006986109],"about_ca_topic_score_codex":0.016495518,"about_ca_topic_score_gemma":0.026588812,"teacher_disagreement_score":0.16495296,"about_ca_system_score_codex":0.0007402255,"about_ca_system_score_gemma":0.00021162775,"threshold_uncertainty_score":0.9911734},"labels":[],"label_agreement":null},{"id":"W2108774690","doi":"10.1109/titb.2011.2171703","title":"A Channel Differential EZW Coding Scheme for EEG Data Compression","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Electroencephalography; Cluster analysis; Wavelet; Data compression; Channel (broadcasting); Scalability; Coding (social sciences); Differential coding; Encoding (memory); SIGNAL (programming language); Compression (physics); Pattern recognition (psychology); Speech recognition; Algorithm; Artificial intelligence; Decoding methods; Mathematics; Statistics; Psychology; Telecommunications","score_opus":0.05245130010806143,"score_gpt":0.299531608179741,"score_spread":0.24708030807167958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108774690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063628796,0.000017448256,0.9954779,0.0011375773,0.00070390623,0.0006479976,0.0001557223,0.0011078399,0.00011531302],"genre_scores_gemma":[0.77923316,0.00009655923,0.21993099,0.00026638663,0.000016289963,0.00030183402,0.00012332416,0.000010950848,0.000020482727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984156,0.000020506492,0.0006366222,0.0003525878,0.0002448486,0.000329856],"domain_scores_gemma":[0.9979863,0.00007714892,0.0002428825,0.0015126889,0.00011178548,0.00006915566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023629575,0.00021097214,0.00027270734,0.0019119387,0.00019674787,0.00002728408,0.0020607375,0.00027470072,0.00003524945],"category_scores_gemma":[0.00003081666,0.00018316734,0.00003452252,0.0010834319,0.00018996031,0.002547774,0.000059849383,0.0003815492,0.000027386843],"study_design_candidate":"design_other","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":[0.0005088215,0.0012662968,0.000045489156,0.00034842646,0.00011876841,0.000014190868,0.0028637531,0.00015549506,0.043321807,0.038107615,0.009542712,0.9037066],"study_design_scores_gemma":[0.004461546,0.000969252,0.00020566415,0.0007011235,0.00002651789,0.000069430935,0.000469407,0.54486823,0.41539094,0.022582376,0.009581045,0.00067448185],"about_ca_topic_score_codex":0.000017301709,"about_ca_topic_score_gemma":0.0000037622767,"teacher_disagreement_score":0.9030321,"about_ca_system_score_codex":0.00006736522,"about_ca_system_score_gemma":0.00003675222,"threshold_uncertainty_score":0.7469347},"labels":[],"label_agreement":null},{"id":"W2110597623","doi":"10.1109/titb.2002.802381","title":"Behavioral avoidance dynamics in the presence of a virtual spider","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":57,"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; Université du Québec en Outaouais","funders":"National Center for Research Resources","keywords":"Fractal; Spider; Virtual reality; Dynamics (music); Tracking (education); Psychology; Cognitive psychology; Computer science; Human–computer interaction; Ecology; Mathematics; Biology","score_opus":0.020720943456189654,"score_gpt":0.22501725688734525,"score_spread":0.2042963134311556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110597623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5955104,0.00044236713,0.38534278,0.012258074,0.0006363487,0.00070508366,0.0002941309,0.00009605069,0.004714737],"genre_scores_gemma":[0.99940807,0.000117386524,0.00016078391,0.00009724149,0.000006237372,0.000059810216,0.000005621242,0.000003722477,0.00014111889],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988229,0.000010403687,0.00083964824,0.00011171416,0.00005939787,0.00015590183],"domain_scores_gemma":[0.9993394,0.000040152492,0.00025995672,0.00031926658,0.000026524915,0.000014699061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030822208,0.00009461651,0.00027444086,0.0016192452,0.0000511466,0.00001074776,0.00026455798,0.00013360615,0.00033244057],"category_scores_gemma":[0.000014148128,0.00008337377,0.000051900824,0.0017998357,0.00018761218,0.00032751032,0.0000019758913,0.00025093454,0.00011256672],"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":[0.00015426269,0.0035404311,0.03173969,0.0002880802,0.00020538761,0.000025533805,0.019906687,0.015743295,0.0001543763,0.635152,0.0019634305,0.29112685],"study_design_scores_gemma":[0.0067277444,0.0033909387,0.01050033,0.0003934587,0.00005498793,0.00011834158,0.036825307,0.8902443,0.0010360992,0.020873964,0.028834337,0.0010001718],"about_ca_topic_score_codex":0.00068300334,"about_ca_topic_score_gemma":0.0004763517,"teacher_disagreement_score":0.87450105,"about_ca_system_score_codex":0.00012194355,"about_ca_system_score_gemma":0.000003984038,"threshold_uncertainty_score":0.3639991},"labels":[],"label_agreement":null},{"id":"W2110808050","doi":"10.1109/titb.2011.2125976","title":"Store-and-Feedforward Adaptive Gaming System for Hand-Finger Motion Tracking in Telerehabilitation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":38,"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":"Canadian Institutes of Health Research","keywords":"Telerehabilitation; Computer science; Session (web analytics); Feed forward; Video tracking; Computer vision; Match moving; Tracking (education); Motion (physics); Artificial intelligence; Multimedia; Object (grammar); Telemedicine; Health care","score_opus":0.020802929407913593,"score_gpt":0.26107740852855926,"score_spread":0.24027447912064567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110808050","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44993213,0.000054383723,0.5467931,0.00089046,0.00055819476,0.001268554,0.000018382048,0.00018159402,0.00030317332],"genre_scores_gemma":[0.99199027,0.000033431505,0.007390279,0.00011384863,0.000021012707,0.00040707094,0.000011418101,0.000013422017,0.00001927274],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985806,0.000028447912,0.00075856596,0.00020395016,0.00018321669,0.0002452687],"domain_scores_gemma":[0.9991994,0.00018899098,0.00016897827,0.00019010817,0.00019156486,0.000060977254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004691235,0.00017761227,0.00036681944,0.0034856324,0.00011203048,0.000009646942,0.000062380655,0.00035640693,0.00002150519],"category_scores_gemma":[0.00008916782,0.00015759381,0.0000782022,0.0009951871,0.00029763894,0.00052264443,0.0000013406834,0.00033521137,0.000014724179],"study_design_candidate":"design_other","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":[0.0020264823,0.00057803903,0.011954249,0.0014718636,0.00011271186,0.00000707686,0.01792149,0.000543342,0.0061122836,0.0023719724,0.000055411096,0.9568451],"study_design_scores_gemma":[0.048440658,0.016943136,0.4127524,0.009092043,0.0005708241,0.0006461902,0.17769942,0.08171399,0.24415791,0.0038529388,0.0024763334,0.0016541774],"about_ca_topic_score_codex":0.00007791972,"about_ca_topic_score_gemma":0.000029326866,"teacher_disagreement_score":0.9551909,"about_ca_system_score_codex":0.0004534596,"about_ca_system_score_gemma":0.000051989387,"threshold_uncertainty_score":0.6426489},"labels":[],"label_agreement":null},{"id":"W2113296984","doi":"10.1109/4233.870035","title":"Application of an object-oriented programming paradigm in three-dimensional computer modeling of mechanically active gastrointestinal tissues","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","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":"University of Alberta; University of Calgary","funders":"","keywords":"Computer science; Visualization; Electrophysiology; Biomedical engineering; Artificial intelligence; Neuroscience; Engineering; Biology","score_opus":0.011933540950335166,"score_gpt":0.2515811738221224,"score_spread":0.23964763287178725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113296984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53947544,0.0000010750354,0.45993444,0.00020490236,0.000048525944,0.00024803675,0.00000704295,0.000073498704,0.0000070674796],"genre_scores_gemma":[0.994391,0.000007180603,0.0054604523,0.00007292589,0.0000052706173,0.00005291719,0.0000034706795,0.000005465089,0.0000013111606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880445,0.000017409258,0.00055631,0.00018786099,0.00023908203,0.0001948614],"domain_scores_gemma":[0.99957526,0.000053328677,0.00011387415,0.00018822232,0.000033596498,0.000035713572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001204839,0.00011720041,0.00019666932,0.0011067498,0.00004273883,0.0000040236414,0.00018757617,0.000080071375,0.0000119823],"category_scores_gemma":[0.000015653271,0.0001093475,0.000024425659,0.0015782579,0.000179942,0.00065704924,0.0000022978477,0.0002745722,0.000005270452],"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":[0.00024905615,0.00036728237,0.000027087928,0.000041896357,0.0000019095867,0.0000065802287,0.00020054253,0.2837312,0.3531876,0.0012290237,2.7055196e-7,0.36095756],"study_design_scores_gemma":[0.00051602063,0.0006071502,0.000058243077,0.000098072705,0.0000026537123,0.00006830044,0.000032909185,0.6737201,0.32451332,0.00031079532,0.000011913134,0.000060494196],"about_ca_topic_score_codex":0.000058081285,"about_ca_topic_score_gemma":0.000017026912,"teacher_disagreement_score":0.45491558,"about_ca_system_score_codex":0.00004130822,"about_ca_system_score_gemma":0.000023938022,"threshold_uncertainty_score":0.44590616},"labels":[],"label_agreement":null},{"id":"W2116609665","doi":"10.1109/titb.2008.2004485","title":"An Activity-Subspace Approach for Estimating the Integrated Input Function and Relative Distribution Volume in PET Parametric Imaging","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Imaging Techniques and Applications","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 British Columbia","funders":"","keywords":"Subspace topology; Parametric statistics; Partial volume; Positron emission tomography; Computer science; Intersection (aeronautics); Likelihood function; Function (biology); Linear subspace; Algorithm; Estimation theory; Mathematics; Artificial intelligence; Pattern recognition (psychology); Statistics; Nuclear medicine","score_opus":0.014400093975015854,"score_gpt":0.2798792335027416,"score_spread":0.2654791395277257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116609665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19170834,0.0000148258505,0.79635626,0.010512002,0.00006996907,0.0010122687,0.00003399406,0.00026113505,0.0000311807],"genre_scores_gemma":[0.94050354,0.00004910107,0.058192376,0.0002859642,0.000018338993,0.0007067514,0.00021122467,0.000010116722,0.000022613045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988628,0.000035860056,0.00045463265,0.00021644322,0.00019148548,0.00023877769],"domain_scores_gemma":[0.9992036,0.00011479534,0.00016550306,0.00031123415,0.00013263151,0.00007222681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048176316,0.00016980275,0.0002657332,0.001226749,0.00025338543,0.000013744082,0.00011065829,0.00015746719,0.000008029422],"category_scores_gemma":[0.00013219134,0.00012657787,0.000035682002,0.0024472843,0.00054749206,0.000580893,0.000002670526,0.0007527799,0.000003566433],"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":[0.002147345,0.0039614597,0.026321944,0.00076075335,0.00020949649,0.00003403006,0.0034922038,0.0071214596,0.020583674,0.0049780277,0.005534085,0.92485553],"study_design_scores_gemma":[0.0023750707,0.0005675861,0.007863517,0.00014312012,0.00006398696,0.00036889993,0.0009012348,0.98386043,0.0020811725,0.00081506115,0.0008025879,0.00015731383],"about_ca_topic_score_codex":0.00010141304,"about_ca_topic_score_gemma":0.0000038984676,"teacher_disagreement_score":0.976739,"about_ca_system_score_codex":0.00021986889,"about_ca_system_score_gemma":0.00007145588,"threshold_uncertainty_score":0.51616955},"labels":[],"label_agreement":null},{"id":"W2117210007","doi":"10.1109/titb.2009.2035693","title":"An Adaptive Monte Carlo Approach to Phase-Based Multimodal Image Registration","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Computer science; Monte Carlo method; Image registration; Algorithm; Sampling (signal processing); Artificial intelligence; Adaptive sampling; Importance sampling; Residual; Mathematical optimization; Image (mathematics); Computer vision; Mathematics; Statistics","score_opus":0.013091529638309354,"score_gpt":0.29712154433137034,"score_spread":0.284030014693061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117210007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002496326,0.000004358645,0.9881729,0.0066866665,0.00015908688,0.00081316376,0.000018713108,0.0011826298,0.00046615137],"genre_scores_gemma":[0.6259627,0.0000041335093,0.37166026,0.0021943462,0.000010704293,0.00014035567,0.000014293398,0.0000040969476,0.000009132455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981896,0.000052005642,0.00068724476,0.00031151265,0.00045910556,0.00030052484],"domain_scores_gemma":[0.998738,0.000033763798,0.00018403179,0.0006861588,0.00019661905,0.00016142741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041391165,0.0002139904,0.00023996037,0.0025472883,0.00012639302,0.00006539301,0.00072888884,0.00033717562,0.000012517325],"category_scores_gemma":[0.000027653616,0.00020229902,0.00004164382,0.0022710038,0.00018715671,0.0021210874,0.0000020929779,0.0005373679,0.00004078873],"study_design_candidate":"design_other","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":[0.0001694318,0.0013682535,0.0000023169882,0.000019228955,0.000012420661,0.000008271096,0.0013280142,0.004524924,0.034344774,0.0017123955,0.0009839244,0.95552605],"study_design_scores_gemma":[0.0035857216,0.0045165312,0.000104032224,0.000077071796,0.00001137855,0.000024460673,0.0005875881,0.6360336,0.35379487,0.00076215924,0.00017484889,0.00032772092],"about_ca_topic_score_codex":0.000048798734,"about_ca_topic_score_gemma":0.0000044518424,"teacher_disagreement_score":0.95519835,"about_ca_system_score_codex":0.00021113649,"about_ca_system_score_gemma":0.000092164046,"threshold_uncertainty_score":0.82495147},"labels":[],"label_agreement":null},{"id":"W2117641802","doi":"10.1109/titb.2011.2129522","title":"IEEE 802.15.4 MAC With GTS Transmission for Heterogeneous Devices With Application to Wheelchair Body-Area Sensor Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Manitoba; University of Manitoba","funders":"","keywords":"Computer science; Computer network; Body area network; Wireless sensor network; Media access control; Markov chain; Network allocation vector; IEEE 802.15; Wireless; Wireless network; Real-time computing; IEEE 802.11; Telecommunications","score_opus":0.010617646321358589,"score_gpt":0.23143000488193285,"score_spread":0.22081235856057427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117641802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041336957,0.000016828897,0.9875662,0.0021257177,0.00016016776,0.0054188473,0.000012254162,0.00049686676,0.00006946737],"genre_scores_gemma":[0.9291281,0.000024869621,0.06298957,0.00076029066,0.000026368552,0.007022933,0.00001390168,0.000017457327,0.000016523856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842477,0.000021973821,0.0005375595,0.00033960276,0.0002542811,0.0004218005],"domain_scores_gemma":[0.9988378,0.00006137308,0.00020791029,0.0005747054,0.0001829237,0.00013526354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022443738,0.00027868056,0.00030580032,0.0011119797,0.0002085118,0.000038125676,0.0005806455,0.00031782073,0.000013394993],"category_scores_gemma":[0.0000014506079,0.0002034953,0.00004197535,0.0018136228,0.00013559098,0.0006609912,0.0000032500893,0.00030203655,0.000016717782],"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":[0.0013726824,0.00036173576,0.00018595446,0.00018757116,0.00010759322,0.000013343764,0.0023623898,0.15720657,0.0008656108,0.0008776695,0.00032788157,0.83613104],"study_design_scores_gemma":[0.005619624,0.0073105595,0.0003135228,0.00091331755,0.000065776425,0.00031370338,0.00039688812,0.873095,0.09304549,0.0006638235,0.01735362,0.00090872997],"about_ca_topic_score_codex":0.00004166145,"about_ca_topic_score_gemma":0.0000918382,"teacher_disagreement_score":0.9249944,"about_ca_system_score_codex":0.00007963451,"about_ca_system_score_gemma":0.00005315024,"threshold_uncertainty_score":0.8298298},"labels":[],"label_agreement":null},{"id":"W2117647041","doi":"10.1109/titb.2009.2028883","title":"Bluetooth-Based Sensor Networks for Remotely Monitoring the Physiological Signals of a Patient","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":81,"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":"Federation for the Humanities and Social Sciences","keywords":"Bluetooth; Wireless sensor network; Computer science; Wireless; Software; Embedded system; Remote patient monitoring; Real-time computing; Computer network; Telecommunications; Medicine; Operating system","score_opus":0.01002295568208205,"score_gpt":0.22732501508795613,"score_spread":0.21730205940587408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117647041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14922951,0.000055079836,0.84825665,0.0010361086,0.00044842923,0.0005046113,0.0000121110525,0.00038845645,0.00006906396],"genre_scores_gemma":[0.9970385,0.00004593965,0.002628292,0.00013993545,0.00004235319,0.00008559264,0.000008388192,0.000008694251,0.000002340854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989639,0.0000162163,0.00054382253,0.00008998662,0.00013656798,0.00024949666],"domain_scores_gemma":[0.9993878,0.00015084891,0.000101064354,0.00025839664,0.00007283518,0.000029080733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013892543,0.00015825892,0.00023889981,0.0006113925,0.00007508133,0.0000071569884,0.00017727423,0.00030118728,0.000007707571],"category_scores_gemma":[0.000016094647,0.00011666044,0.00006033248,0.00090731337,0.00014811521,0.00014535763,7.273187e-7,0.00036398208,0.0000036906104],"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":[0.000049064252,0.000039946943,0.0000055173105,0.00002166537,0.000014736959,3.5832062e-7,0.000116103736,0.8324245,0.00514287,0.000055020173,0.00012664536,0.16200355],"study_design_scores_gemma":[0.001194035,0.00092808757,0.00046930084,0.00023661136,0.000023588753,0.0000050656763,0.00044720908,0.8802131,0.11534385,0.000505508,0.0004573457,0.00017630165],"about_ca_topic_score_codex":0.0000024860874,"about_ca_topic_score_gemma":7.2432846e-7,"teacher_disagreement_score":0.84780896,"about_ca_system_score_codex":0.000076568496,"about_ca_system_score_gemma":0.000010086883,"threshold_uncertainty_score":0.47572747},"labels":[],"label_agreement":null},{"id":"W2119380192","doi":"10.1109/titb.2006.864480","title":"A Planning System Based on Markov Decision Processes to Guide People With Dementia Through Activities of Daily Living","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":180,"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 Rehabilitation Institute; University of Waterloo; University of Toronto","funders":"Alzheimer Society; Intel Corporation; Alzheimer's Association","keywords":"Dementia; Markov decision process; Computer science; Activities of daily living; Psychology; Markov chain; Gerontology; Process management; Risk analysis (engineering); Markov process; Medicine; Engineering; Psychiatry; Machine learning; Disease","score_opus":0.006720461062384356,"score_gpt":0.27262906465960857,"score_spread":0.26590860359722424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119380192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38806313,0.000022532808,0.60754955,0.0008762611,0.000071402814,0.00066573,0.000018139282,0.00014614456,0.0025870828],"genre_scores_gemma":[0.99278206,0.0000119065235,0.0065550064,0.00025001794,0.000014114403,0.0002799474,0.00002046409,0.0000127172125,0.00007375358],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983014,0.000021256976,0.00059111306,0.00018140767,0.00061143906,0.00029342473],"domain_scores_gemma":[0.999042,0.00023400654,0.00015514743,0.00025869327,0.00026100306,0.000049162998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030974348,0.00018463266,0.00033159516,0.0024715718,0.000115133014,0.00001631074,0.00012554346,0.00014531122,0.00006544278],"category_scores_gemma":[0.00004707448,0.00014238621,0.00003393889,0.0022555296,0.00013483755,0.00036391325,0.000003273216,0.00025325152,0.000018386718],"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":[0.023698842,0.009348386,0.4586141,0.018646833,0.0013952454,0.00030251007,0.014036626,0.055932824,0.035074443,0.001110497,0.009054555,0.37278515],"study_design_scores_gemma":[0.023440618,0.026306288,0.19713712,0.037465908,0.00080071285,0.00040498463,0.060145866,0.019137258,0.6293794,0.00016833362,0.004445756,0.0011677968],"about_ca_topic_score_codex":0.00013881586,"about_ca_topic_score_gemma":0.000059960268,"teacher_disagreement_score":0.6047189,"about_ca_system_score_codex":0.0001907537,"about_ca_system_score_gemma":0.00019354829,"threshold_uncertainty_score":0.5806341},"labels":[],"label_agreement":null},{"id":"W2119562361","doi":"10.1109/titb.2005.859877","title":"Modeling of Workflow-Engaged Networks on Radiology Transfers Across a Metro Network","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":8,"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; St. Boniface Hospital","funders":"","keywords":"Workflow; Metropolitan area; Computer science; Service (business); Flow network; Computer network; Data science; Medicine; Business; Database","score_opus":0.008574095332801129,"score_gpt":0.24189667064995274,"score_spread":0.23332257531715161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119562361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29941925,0.00009827687,0.6964417,0.0019581136,0.00041861218,0.00042407506,0.00002911142,0.00026402637,0.0009468244],"genre_scores_gemma":[0.9977416,0.000095046154,0.0014480621,0.00047746435,0.00007425617,0.0000720719,0.000056380242,0.000016657887,0.000018456109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980295,0.000056861445,0.0009325202,0.00020324328,0.00025364896,0.000524282],"domain_scores_gemma":[0.9992104,0.000153709,0.00011610747,0.00034524777,0.00010433871,0.0000701937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006930806,0.00025189624,0.00055002206,0.0019271336,0.00020150836,0.0000116222045,0.00016103034,0.00040900573,0.000020403544],"category_scores_gemma":[0.000020269536,0.00022037652,0.00016487193,0.0031084039,0.00043690097,0.0003471616,0.0000014550288,0.0012543002,0.000009919438],"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":[0.0013254771,0.00045474072,0.0014328612,0.00010188759,0.00015970143,0.000020903366,0.00033115962,0.7783843,0.00039639615,0.0008108407,0.00022348041,0.21635824],"study_design_scores_gemma":[0.020529436,0.0064681834,0.0042879866,0.0023794868,0.00036570706,0.0008117698,0.0047792573,0.9430597,0.010577761,0.005115558,0.00068392465,0.00094125525],"about_ca_topic_score_codex":0.00006925733,"about_ca_topic_score_gemma":0.000026115214,"teacher_disagreement_score":0.69832236,"about_ca_system_score_codex":0.00010720852,"about_ca_system_score_gemma":0.000037138474,"threshold_uncertainty_score":0.89866936},"labels":[],"label_agreement":null},{"id":"W2119922429","doi":"10.1109/titb.2011.2164259","title":"Nonlinear Unsharp Masking for Mammogram Enhancement","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":199,"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":"University of Calgary","keywords":"Unsharp masking; Computer science; Masking (illustration); Artificial intelligence; Nonlinear system; Flexibility (engineering); Measure (data warehouse); A priori and a posteriori; Image (mathematics); Image enhancement; Computer vision; Visualization; Pattern recognition (psychology); Mathematics; Data mining; Statistics","score_opus":0.017926443399578534,"score_gpt":0.2594391597498786,"score_spread":0.24151271635030006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119922429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012546215,0.00001476445,0.9934661,0.0021663746,0.00061082485,0.00081412727,0.0000109989505,0.00093580736,0.00072638266],"genre_scores_gemma":[0.6184299,0.000064586835,0.3800947,0.00061127346,0.000014781403,0.00068559626,0.000012200616,0.000008487386,0.00007844519],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985812,0.0000122154515,0.00061169843,0.00021901618,0.00022248483,0.00035338535],"domain_scores_gemma":[0.99906766,0.000034172208,0.00018055608,0.0005403428,0.00013395726,0.000043315507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034948773,0.00018429033,0.0002031201,0.0021132382,0.00012291949,0.000026525322,0.00071208295,0.00021184189,0.00005302038],"category_scores_gemma":[0.000012808333,0.00017424054,0.000051370946,0.001441705,0.0001539538,0.001143813,0.0000074332183,0.000267925,0.00008054379],"study_design_candidate":"design_other","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":[0.00009341091,0.0006397084,0.000029172228,0.00012590065,0.00005857661,0.000007303575,0.00244056,0.000030605635,0.009283717,0.015678022,0.00094773143,0.9706653],"study_design_scores_gemma":[0.0013731713,0.0012188462,0.000024964302,0.00013741455,0.000012443585,0.000017564218,0.0002304459,0.021792464,0.96009254,0.0073545515,0.007481057,0.00026455047],"about_ca_topic_score_codex":0.000019833655,"about_ca_topic_score_gemma":0.0000050220456,"teacher_disagreement_score":0.97040075,"about_ca_system_score_codex":0.0001423004,"about_ca_system_score_gemma":0.00004261069,"threshold_uncertainty_score":0.7105323},"labels":[],"label_agreement":null},{"id":"W2123889588","doi":"10.1109/titb.2006.875665","title":"Novel Multistage Three-Dimensional Medical Image Segmentation: Methodology and Validation","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Computer science; Dilation (metric space); Artificial intelligence; Image segmentation; Centroid; Computer vision; Fast marching method; Scale-space segmentation; Pattern recognition (psychology); Mathematics","score_opus":0.02057845995460659,"score_gpt":0.3023059608971348,"score_spread":0.2817275009425282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123889588","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047131553,0.000023449273,0.9833439,0.010362343,0.00039342733,0.00042144093,0.00001593225,0.0006274866,0.00009884625],"genre_scores_gemma":[0.18290655,0.000032761476,0.8151433,0.0015765863,0.000031313313,0.00020334474,0.000056599412,0.000009736882,0.000039791885],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978944,0.000067919216,0.0008375659,0.00028460144,0.00064352143,0.00027198208],"domain_scores_gemma":[0.99891114,0.00028041395,0.00022055252,0.0003433559,0.00014653138,0.00009801326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094217766,0.0001980294,0.00026061828,0.0018747199,0.00014374749,0.000044965676,0.00041870112,0.0004911349,0.00016707894],"category_scores_gemma":[0.00008496788,0.00018199369,0.000033404966,0.0013448346,0.00052969606,0.0014044313,0.00001378265,0.00059416104,0.000049548264],"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":[0.00006760721,0.00060220656,0.00024688488,0.00011933614,0.000050273575,0.000046720084,0.00046996315,0.0004023342,0.13717724,0.01303037,0.0017989398,0.8459881],"study_design_scores_gemma":[0.006026527,0.0005900708,0.0015338038,0.00018649647,0.000027671602,0.00060035195,0.00025333505,0.09392968,0.884867,0.011080805,0.00038801797,0.0005162289],"about_ca_topic_score_codex":0.00021761339,"about_ca_topic_score_gemma":0.00006517404,"teacher_disagreement_score":0.8454719,"about_ca_system_score_codex":0.000121158046,"about_ca_system_score_gemma":0.000086516826,"threshold_uncertainty_score":0.7421487},"labels":[],"label_agreement":null},{"id":"W2124468198","doi":"10.1109/titb.2010.2103954","title":"Predicting Breast Screening Attendance Using Machine Learning Techniques","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Artificial neural network; Attendance; Algorithm; Data mining","score_opus":0.04806660619266561,"score_gpt":0.2997155179202843,"score_spread":0.25164891172761866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124468198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24147862,0.00010950461,0.7540451,0.0012979904,0.00024141399,0.0005596376,0.000033622604,0.0010287177,0.0012053301],"genre_scores_gemma":[0.9646751,0.00008083081,0.034567125,0.0005128838,0.00003821349,0.00005521139,0.00001722661,0.000017508304,0.000035876154],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830526,0.000024614199,0.00071054057,0.00021476661,0.00034942577,0.0003953687],"domain_scores_gemma":[0.9991712,0.000026017879,0.00024040042,0.00028372902,0.0001772053,0.00010144141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039598456,0.00023324045,0.00036039946,0.0020761988,0.00022165287,0.0000114955765,0.00017093465,0.00033400775,0.00015680918],"category_scores_gemma":[0.000030902054,0.00021428347,0.00007199242,0.0017229866,0.00027913012,0.0007350127,0.0000051174893,0.0009655908,0.000025058882],"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":[0.002191116,0.00044838668,0.29011628,0.0003975659,0.000283377,0.0001872643,0.003718416,0.0011546494,0.03345446,0.0003525963,0.000059890546,0.667636],"study_design_scores_gemma":[0.011946464,0.006366869,0.040175617,0.008765181,0.00064933294,0.013140133,0.015384968,0.16949804,0.72760135,0.0008326471,0.0038575009,0.001781886],"about_ca_topic_score_codex":0.0010298102,"about_ca_topic_score_gemma":0.000051799543,"teacher_disagreement_score":0.7231965,"about_ca_system_score_codex":0.00021228501,"about_ca_system_score_gemma":0.00006619919,"threshold_uncertainty_score":0.8738226},"labels":[],"label_agreement":null},{"id":"W2127927483","doi":"10.1109/titb.2006.864476","title":"High-Performance Medical Image Registration Using New Optimization Techniques","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Computer science; Image registration; Metric (unit); Implementation; Image (mathematics); Bounded function; Computation; Similarity (geometry); Parallel computing; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.00797374032554557,"score_gpt":0.2587452523931287,"score_spread":0.2507715120675831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127927483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025580341,0.000011094429,0.9877678,0.0069690426,0.00037176284,0.00045435678,0.0000041073863,0.0015639426,0.00029983072],"genre_scores_gemma":[0.280768,0.00012312416,0.7179416,0.0009096193,0.0000642578,0.000079262645,0.00003344972,0.000011419145,0.00006925622],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975791,0.000039315302,0.0010280669,0.00026282988,0.0007772984,0.00031341254],"domain_scores_gemma":[0.9988336,0.000049361577,0.00032879313,0.00051344745,0.00016815834,0.000106629035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005420585,0.0002232746,0.00025440223,0.0023859853,0.00015000807,0.000071186565,0.000694451,0.00061107497,0.00015130514],"category_scores_gemma":[0.00004730778,0.00021285469,0.000034876466,0.0025013355,0.00030780156,0.0027787113,0.000008505021,0.00065030565,0.000037177248],"study_design_candidate":"design_other","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":[0.00005007382,0.0003547148,0.00005969525,0.000109488254,0.00002336224,0.00002644255,0.00021160241,0.018677788,0.017813971,0.0056818738,0.004453759,0.95253724],"study_design_scores_gemma":[0.0012031936,0.0003474006,0.000062586354,0.00025198754,0.000011274369,0.00012115575,0.00004721453,0.31122968,0.6842616,0.0019178229,0.00025818704,0.00028787245],"about_ca_topic_score_codex":0.00037331777,"about_ca_topic_score_gemma":0.000013178978,"teacher_disagreement_score":0.95224935,"about_ca_system_score_codex":0.00027086693,"about_ca_system_score_gemma":0.0002463573,"threshold_uncertainty_score":0.8679962},"labels":[],"label_agreement":null},{"id":"W2128894842","doi":"10.1109/4233.966105","title":"Wavelet-based space-frequency compression of ultrasound images","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Set partitioning in hierarchical trees; Computer science; Artificial intelligence; Computer vision; Codec; Wavelet; Image compression; Ultrasound; Wavelet transform; Data compression; Grayscale; Pattern recognition (psychology); Mathematics; Image (mathematics); Image processing; Discrete wavelet transform; Radiology; Medicine","score_opus":0.008296223714880096,"score_gpt":0.2590608214615913,"score_spread":0.2507645977467112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128894842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035059056,0.00004651224,0.9907877,0.0036542106,0.00027953455,0.00033725143,0.000036194713,0.000813087,0.0005396396],"genre_scores_gemma":[0.8109948,0.00020398028,0.18840946,0.00025857243,0.0000065357385,0.00008094276,0.00001733342,0.0000069306925,0.000021451051],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983743,0.00003644261,0.00072578667,0.00022758126,0.00035857275,0.00027734874],"domain_scores_gemma":[0.9983473,0.00017289144,0.00031414352,0.0009376972,0.00016731626,0.000060690058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023366493,0.0002031264,0.00029521782,0.0027483767,0.000106498126,0.000018946997,0.00094346143,0.00025398124,0.000058393387],"category_scores_gemma":[0.000041529725,0.00017605987,0.00004834382,0.0024854045,0.00035040404,0.0014663701,0.000008871579,0.00041512024,0.00003338253],"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":[0.00013118265,0.0007837359,0.00052396156,0.00015858177,0.00003547883,0.000034188706,0.00040728017,0.0016613409,0.30538613,0.015383534,0.0029299515,0.6725646],"study_design_scores_gemma":[0.0018404225,0.0005601355,0.00052849436,0.0004098293,0.000008535385,0.00009666131,0.00014730138,0.008929879,0.9711959,0.012305938,0.003681142,0.000295771],"about_ca_topic_score_codex":0.000036349025,"about_ca_topic_score_gemma":0.0000027239496,"teacher_disagreement_score":0.8074889,"about_ca_system_score_codex":0.0001053151,"about_ca_system_score_gemma":0.00006142535,"threshold_uncertainty_score":0.7179513},"labels":[],"label_agreement":null},{"id":"W2129908023","doi":"10.1109/titb.2009.2017017","title":"Rough Sets and Near Sets in Medical Imaging: A Review","year":2009,"lang":"en","type":"review","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":160,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Rough set; Medical imaging; Computer science; Generalization; Artificial intelligence; Fuzzy set; Set (abstract data type); Pattern recognition (psychology); Particle swarm optimization; Computational intelligence; Image segmentation; Image (mathematics); Fuzzy logic; Machine learning; Mathematics","score_opus":0.017415438784651068,"score_gpt":0.3134696562771803,"score_spread":0.29605421749252925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129908023","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.108089e-7,0.87971836,0.10378063,0.01444736,0.00041195186,0.000949185,0.000017215663,0.0002974909,0.0003769934],"genre_scores_gemma":[0.00008557422,0.9918153,0.0051229,0.0027205318,0.000010301428,0.00019567012,0.000035033438,0.000010081439,0.000004590246],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970267,0.00009977757,0.0015383295,0.00039526983,0.0005123834,0.00042755148],"domain_scores_gemma":[0.99858344,0.00012220322,0.00037851383,0.0007347773,0.000054197266,0.00012689947],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008719554,0.0004325217,0.001395612,0.0025574875,0.00010065796,0.000058706213,0.0010544945,0.00071974285,0.00003662137],"category_scores_gemma":[0.000054189022,0.00033168387,0.00012048929,0.0038710379,0.00033384253,0.0007925723,0.000015491765,0.0013628436,0.00010975548],"study_design_candidate":"design_other","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":[0.0000013073285,0.000058698162,5.8163494e-7,0.0035889826,0.000011223044,0.000053800333,0.00007463032,0.0000018338966,8.47437e-9,0.0002158945,0.00071066833,0.99528235],"study_design_scores_gemma":[0.0008189086,0.00018087805,0.0000060592797,0.043331206,0.00007217401,0.0011675655,0.000019042485,0.007871607,5.881905e-7,0.0009145471,0.9452077,0.00040969905],"about_ca_topic_score_codex":0.000026199765,"about_ca_topic_score_gemma":0.00000981813,"teacher_disagreement_score":0.9948727,"about_ca_system_score_codex":0.00019711125,"about_ca_system_score_gemma":0.000321634,"threshold_uncertainty_score":0.9999135},"labels":[],"label_agreement":null},{"id":"W2132678630","doi":"10.1109/titb.2011.2163414","title":"Agent-Based Modeling of the Spread of Influenza-Like Illness in an Emergency Department: A Simulation Study","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":72,"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 Manitoba; Orthopaedic Innovation Centre","funders":"Public Health Agency","keywords":"Emergency department; Counterintuitive; Context (archaeology); Health care; Ordinary least squares; Computer science; Control (management); Medical emergency; Medicine; Operations research; Engineering; Nursing; Artificial intelligence; Machine learning","score_opus":0.1612729783600407,"score_gpt":0.39462643127347063,"score_spread":0.23335345291342993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132678630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68349934,0.000009869391,0.31536552,0.00013930677,0.00015529315,0.00072561513,0.000013999825,0.00006405657,0.000027003764],"genre_scores_gemma":[0.9988029,0.00001177407,0.00090979284,0.00009639414,0.000002835695,0.0001664397,0.0000020972036,0.0000063649477,0.0000013947877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980154,0.0000997755,0.001337637,0.00013700569,0.00023297417,0.0001772192],"domain_scores_gemma":[0.9987707,0.0002123894,0.00040219285,0.00044525648,0.00014631385,0.000023135155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006770288,0.00015621964,0.00038285865,0.0010851386,0.000074219664,0.0000011691754,0.0002831463,0.00019476908,0.000048374342],"category_scores_gemma":[0.00024164961,0.0001088008,0.000058343176,0.0015354486,0.00017366004,0.00025044152,0.0000065319114,0.00024681014,0.0000025897],"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":[0.0008821718,0.0063876295,0.13534167,0.0006570614,0.00018326423,0.0000029834737,0.023738042,0.79477453,0.0006558903,0.0018674821,0.00001800663,0.035491254],"study_design_scores_gemma":[0.00673172,0.0025641986,0.033255566,0.00042723434,0.00019920377,0.000001552808,0.014572525,0.89369893,0.009323932,0.038698066,0.000056462635,0.00047060318],"about_ca_topic_score_codex":0.00025792138,"about_ca_topic_score_gemma":0.00045012461,"teacher_disagreement_score":0.3153036,"about_ca_system_score_codex":0.00010797868,"about_ca_system_score_gemma":0.000028807392,"threshold_uncertainty_score":0.4436768},"labels":[],"label_agreement":null},{"id":"W2135033947","doi":"10.1109/titb.2002.1006305","title":"Extending ventilation duration estimations approach from adult to neonatal intensive care patients using artificial neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Children's Hospital of Eastern Ontario; Nortel (Canada); University of Ottawa","funders":"","keywords":"Mechanical ventilation; Overfitting; Neonatal intensive care unit; Medicine; Artificial ventilation; Intensive care unit; Intensive care; Artificial neural network; Ventilation (architecture); Duration (music); Intensive care medicine; Emergency medicine; Pediatrics; Computer science; Anesthesia; Machine learning; Internal medicine; Engineering; Respiratory disease","score_opus":0.021155928572647826,"score_gpt":0.2609623797273681,"score_spread":0.23980645115472024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135033947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3949386,0.000020330226,0.60296327,0.00055478327,0.00064570183,0.0006517131,0.000038884282,0.00014822539,0.000038520808],"genre_scores_gemma":[0.9915615,0.000008534236,0.0068074977,0.0009983254,0.000065407134,0.000108609136,0.00042440146,0.000016525566,0.00000924178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983627,0.000019051668,0.00079400913,0.0002287231,0.00031892964,0.00027658112],"domain_scores_gemma":[0.9986513,0.000025074976,0.00020532712,0.00028613,0.00071783806,0.00011430492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007064145,0.00021253931,0.00028906803,0.0019608731,0.00020374615,0.000029081784,0.00009938572,0.0003365357,0.00010967831],"category_scores_gemma":[0.00007667632,0.00020518672,0.00006569093,0.0013687607,0.0000947624,0.0006295759,0.0000034589111,0.00041930945,0.00005403],"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":[0.0008232496,0.00040376393,0.0029599264,0.00013413302,0.00012538234,0.000024576106,0.012987382,0.08032043,0.0061695897,0.00066112407,0.00029288212,0.89509755],"study_design_scores_gemma":[0.0029341292,0.0009924094,0.001243429,0.00025996944,0.00015407315,0.000045347704,0.015117835,0.9610808,0.0173928,0.00012631196,0.000334471,0.00031844733],"about_ca_topic_score_codex":0.000036372872,"about_ca_topic_score_gemma":0.000011251195,"teacher_disagreement_score":0.8947791,"about_ca_system_score_codex":0.00027910224,"about_ca_system_score_gemma":0.000021647507,"threshold_uncertainty_score":0.83672714},"labels":[],"label_agreement":null},{"id":"W2136265800","doi":"10.1109/titb.2012.2189439","title":"Semantic Image Retrieval in Magnetic Resonance Brain Volumes","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":38,"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":"Aligarh Muslim University","keywords":"Computer science; Artificial intelligence; Support vector machine; Robustness (evolution); Pattern recognition (psychology); Image retrieval; Modalities; Image (mathematics); Information retrieval","score_opus":0.007994592828460484,"score_gpt":0.24636078662035474,"score_spread":0.23836619379189425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136265800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009671077,0.00037842488,0.97295296,0.015223809,0.00041466262,0.000386753,0.0000069986595,0.0006127817,0.00035252766],"genre_scores_gemma":[0.9830859,0.00020459616,0.015655844,0.0007472787,0.00001768772,0.00007044853,0.000004176872,0.0000074129307,0.00020669185],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984006,0.000045153898,0.00065072,0.00017937679,0.0002985434,0.00042555277],"domain_scores_gemma":[0.9991085,0.00008815794,0.00012652815,0.0005202045,0.00009017031,0.000066448265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063945254,0.00017278265,0.00022392384,0.0025011178,0.000082231054,0.000035339923,0.00057306903,0.00027046626,0.000044628927],"category_scores_gemma":[0.000062889994,0.00016177335,0.00003827298,0.0038399666,0.00027079077,0.0019711857,0.000007396437,0.00047419954,0.00020640901],"study_design_candidate":"design_other","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":[0.000094134695,0.00053549965,0.0010107225,0.0001047208,0.000008060402,0.000011442461,0.002100247,0.00000619356,0.028777644,0.013552883,0.0010367582,0.9527617],"study_design_scores_gemma":[0.0043336796,0.0012869424,0.016449705,0.00049194903,0.000017180057,0.00024667013,0.0011703455,0.039263483,0.89341533,0.008041447,0.03433034,0.00095291255],"about_ca_topic_score_codex":0.000024227946,"about_ca_topic_score_gemma":0.000005065855,"teacher_disagreement_score":0.9734148,"about_ca_system_score_codex":0.00018334716,"about_ca_system_score_gemma":0.00005010993,"threshold_uncertainty_score":0.6596926},"labels":[],"label_agreement":null},{"id":"W2137313573","doi":"10.1109/titb.2005.847188","title":"A Knowledge Creation Info-Structure to Acquire and Crystallize the Tacit Knowledge of Health-Care Experts","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Izaak Walton Killam Health Centre; Dalhousie University","funders":"","keywords":"Tacit knowledge; Knowledge management; Experiential knowledge; Health care; Explicit knowledge; Knowledge value chain; Reuse; Computer science; Organizational learning; Engineering; Political science; Epistemology","score_opus":0.008062049210254349,"score_gpt":0.28837475035248994,"score_spread":0.2803127011422356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137313573","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6611216,0.0064652553,0.29983342,0.029763415,0.0005665903,0.001070091,0.00013393843,0.00017577794,0.000869888],"genre_scores_gemma":[0.9972076,0.0002903594,0.0016427222,0.00062941737,0.000040383,0.00006905354,0.00003809515,0.000006641825,0.000075760225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99903077,0.00003692952,0.0004920409,0.00015320374,0.00008906997,0.00019798751],"domain_scores_gemma":[0.99939644,0.000028337294,0.00012606746,0.00028157773,0.00011286776,0.000054704145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016948595,0.00014894827,0.00021074698,0.0005962896,0.00012575161,0.000008529212,0.00019277644,0.00034485542,0.000018999832],"category_scores_gemma":[0.000045386878,0.00010647046,0.000032278702,0.0006261787,0.00043075043,0.00001771736,0.000009102333,0.00015075454,0.000006655257],"study_design_candidate":"design_other","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":[0.00008990692,0.00007073037,0.00006401611,0.00009209566,0.000030778585,1.3190711e-7,0.00890277,0.00012839567,0.01795753,0.000097297845,0.0016309991,0.97093534],"study_design_scores_gemma":[0.003365063,0.002772386,0.0013796077,0.00037197152,0.00003198824,0.00008057429,0.02219249,0.0005747574,0.22007847,0.00014902855,0.7485874,0.00041622642],"about_ca_topic_score_codex":0.000017056384,"about_ca_topic_score_gemma":0.00015283906,"teacher_disagreement_score":0.9705191,"about_ca_system_score_codex":0.00006113233,"about_ca_system_score_gemma":0.00008829019,"threshold_uncertainty_score":0.43417394},"labels":[],"label_agreement":null},{"id":"W2138840790","doi":"10.1109/titb.2010.2055574","title":"Feature-Space-Based fMRI Analysis Using the Optimal Linear Transformation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Functional Brain Connectivity 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":"University of Toronto","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Feature vector; Cluster analysis; Computer science; Signature (topology); Transformation (genetics); Feature (linguistics); Feature extraction; Mathematics","score_opus":0.017635023893772334,"score_gpt":0.2727619143947141,"score_spread":0.2551268905009417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138840790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2203426,0.000004366877,0.7072842,0.07074364,0.0007215966,0.00041451174,0.00004784529,0.0002778683,0.00016334774],"genre_scores_gemma":[0.99489766,0.000013439796,0.0026645206,0.002249176,0.000030362055,0.00009861494,0.000008868552,0.00000865515,0.000028719372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865603,0.000049600872,0.00038649148,0.00022206666,0.00040298392,0.00028282884],"domain_scores_gemma":[0.99844617,0.0008120795,0.00017053448,0.00041990442,0.00010882264,0.000042500822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004138483,0.00020626967,0.00025630931,0.0032324346,0.00048732795,0.00002963052,0.00029816813,0.00030345988,0.000063329506],"category_scores_gemma":[0.0003380632,0.00015200928,0.000119488184,0.005576995,0.0006250725,0.00070122053,0.0000023327739,0.0010026499,0.00004399662],"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":[0.00063132157,0.0005291313,0.0003517878,0.000098825316,0.00033664008,0.00001054744,0.0036427851,0.511963,0.43678373,0.0070050033,0.000815911,0.037831265],"study_design_scores_gemma":[0.0015242896,0.00025302896,0.00023984924,0.000023383727,0.00019297333,0.000043357006,0.001074939,0.42191646,0.56660783,0.00019973109,0.0076811183,0.00024303253],"about_ca_topic_score_codex":0.000029612876,"about_ca_topic_score_gemma":0.00009490105,"teacher_disagreement_score":0.774555,"about_ca_system_score_codex":0.0001066594,"about_ca_system_score_gemma":0.0000715504,"threshold_uncertainty_score":0.61987585},"labels":[],"label_agreement":null},{"id":"W2140554766","doi":"10.1109/titb.2007.907986","title":"Region of Interest and Multiresolution for Volume Rendering","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Computer Graphics and Visualization Techniques","field":"Computer 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":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Rendering (computer graphics); Computer graphics (images); Computer science; Geography; Geology","score_opus":0.042574537892572366,"score_gpt":0.27855993928929,"score_spread":0.23598540139671764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140554766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022935526,0.000022336968,0.97475785,0.0015735766,0.00017139291,0.0002522312,0.0000037849838,0.00026984877,0.0000134410275],"genre_scores_gemma":[0.9826858,0.00022473275,0.016913388,0.000092519906,0.000005054504,0.000061304665,0.000003487772,0.000003118228,0.000010594512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992839,0.000009386461,0.00039787288,0.00011904691,0.0000745697,0.000115207156],"domain_scores_gemma":[0.9994644,0.000033230397,0.00013941596,0.0002275942,0.00011031874,0.000025067195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012719698,0.000086762295,0.0001425172,0.0018331959,0.00009454544,0.000008205599,0.0002144903,0.00013601943,9.1261313e-7],"category_scores_gemma":[0.000009853789,0.000084805615,0.000027081025,0.0009618535,0.00018111864,0.0005209554,0.0000055450996,0.000113730006,9.073981e-7],"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":[0.00010040504,0.00035371594,0.0011062786,0.0002963055,0.000051575724,0.000006659902,0.004474586,0.00025228807,0.0032901827,0.3255568,0.00122531,0.66328585],"study_design_scores_gemma":[0.0027226033,0.001708928,0.0015790067,0.0003205999,0.000010652173,0.00031551125,0.00025273196,0.86052155,0.111139655,0.016774634,0.0043222927,0.00033181053],"about_ca_topic_score_codex":0.000020391091,"about_ca_topic_score_gemma":0.0000058460864,"teacher_disagreement_score":0.9597503,"about_ca_system_score_codex":0.000032390675,"about_ca_system_score_gemma":0.000019824767,"threshold_uncertainty_score":0.34582725},"labels":[],"label_agreement":null},{"id":"W2140996450","doi":"10.1109/titb.2006.884364","title":"A Framework for Medical Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques With Relevance Feedback","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":210,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Relevance feedback; Similarity (geometry); Relevance (law); Computer science; Matching (statistics); Image retrieval; Artificial intelligence; Pattern recognition (psychology); Information retrieval; Image (mathematics); Similarity learning; Machine learning; Data mining; Mathematics; Statistics","score_opus":0.011303342718813232,"score_gpt":0.3019587482381651,"score_spread":0.2906554055193519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140996450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030029572,0.00003770858,0.99081784,0.004670749,0.00009763763,0.0004897504,0.00001838698,0.00083168095,0.00003325984],"genre_scores_gemma":[0.2840946,0.0001726914,0.715337,0.00033287838,0.000016587008,0.000018600203,0.000008232774,0.00001055803,0.000008852668],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982678,0.00003072761,0.00063749374,0.00026269822,0.00045242795,0.00034883947],"domain_scores_gemma":[0.99868983,0.0005331908,0.00021320353,0.0002625285,0.00019144737,0.000109819506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012719732,0.00019788816,0.0002685111,0.0011603954,0.00025355341,0.000059520455,0.00034952568,0.00044320503,0.000015568083],"category_scores_gemma":[0.00025262084,0.00016453063,0.000026290843,0.0015223424,0.00048469548,0.00081412186,0.00001022916,0.00095895084,0.000003364541],"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":[0.0013495744,0.00046198702,0.00037206424,0.00055912003,0.000088582034,0.00007383322,0.0015969854,0.000020413861,0.03829578,0.14980024,0.000025247002,0.8073562],"study_design_scores_gemma":[0.0020505795,0.0018557052,0.00018243516,0.0008454737,0.000037611153,0.0005095482,0.00071893475,0.07791798,0.8478066,0.06411435,0.0033452786,0.0006155253],"about_ca_topic_score_codex":0.00002501664,"about_ca_topic_score_gemma":0.0000082725255,"teacher_disagreement_score":0.8095108,"about_ca_system_score_codex":0.00014584299,"about_ca_system_score_gemma":0.00008865963,"threshold_uncertainty_score":0.67093647},"labels":[],"label_agreement":null},{"id":"W2141337564","doi":"10.1109/titb.2010.2047507","title":"An EMI-Aware Prioritized Wireless Access Scheme for e-Health Applications in Hospital Environments","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Handshaking; Computer science; Computer network; Wireless; EMI; Quality of service; Wireless distribution system; Fixed wireless; Radio resource management; Access control; Wireless network; Wi-Fi array; Electromagnetic interference; Telecommunications","score_opus":0.00761529037329856,"score_gpt":0.29992759063561364,"score_spread":0.2923123002623151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141337564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018688427,0.000004598557,0.967566,0.0067418762,0.000325428,0.0063909907,0.000032287106,0.00023338644,0.000017015886],"genre_scores_gemma":[0.9668985,0.000039180126,0.015648294,0.00039565656,0.000030413557,0.016925476,0.000042768228,0.000009503991,0.000010202858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985391,0.000018518569,0.0006353577,0.00026450786,0.00019431522,0.00034821208],"domain_scores_gemma":[0.99895304,0.000058425834,0.00021930343,0.0006357832,0.00004065908,0.00009281061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034514634,0.0001725246,0.00025842353,0.0012667635,0.0001496572,0.00006373893,0.0010025073,0.0003245619,0.000009074172],"category_scores_gemma":[0.00000388888,0.00016478103,0.00003496547,0.001422194,0.00016331168,0.001550139,0.0000074420577,0.0005770901,0.000011706452],"study_design_candidate":"design_other","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":[0.0000717455,0.0014267326,0.0017192586,0.00018796034,0.00002508064,0.0000018546602,0.0013288571,0.0010831846,0.00316764,0.014172067,0.0003989535,0.97641665],"study_design_scores_gemma":[0.02271761,0.005924621,0.011240793,0.00083508535,0.000026403457,0.000055326633,0.0020055317,0.7426781,0.08040119,0.02464366,0.10700707,0.002464595],"about_ca_topic_score_codex":0.000036976617,"about_ca_topic_score_gemma":0.000073876945,"teacher_disagreement_score":0.97395205,"about_ca_system_score_codex":0.00010996462,"about_ca_system_score_gemma":0.000095172145,"threshold_uncertainty_score":0.6719575},"labels":[],"label_agreement":null},{"id":"W2141362080","doi":"10.1109/titb.2010.2087343","title":"A Hybrid Clustering Method for ROI Delineation in Small-Animal Dynamic PET Images: Application to the Automatic Estimation of FDG Input Functions","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","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":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; Petroleum Technology Alliance Canada","keywords":"Artificial intelligence; Cluster analysis; Computer science; Computer vision; Region of interest; Pattern recognition (psychology)","score_opus":0.00878367171435464,"score_gpt":0.31153585783742016,"score_spread":0.30275218612306554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141362080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12444547,0.000003726512,0.8430052,0.029775852,0.00013658428,0.0022642098,0.00007083288,0.00027547136,0.000022645423],"genre_scores_gemma":[0.7652835,0.000012295168,0.2318846,0.0004927903,0.000017326129,0.0021216802,0.00014873159,0.000013547418,0.000025517742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831873,0.000022861354,0.0009972496,0.00022024606,0.00020105917,0.00023985088],"domain_scores_gemma":[0.99872303,0.0001845687,0.0002510978,0.0005444748,0.00021863519,0.00007819517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072950736,0.00018703396,0.00032907593,0.0019869409,0.00012077246,0.000013078875,0.00022862681,0.00014896454,0.000024141236],"category_scores_gemma":[0.00019306042,0.0001494546,0.00006937669,0.001587473,0.00016285473,0.00020583041,0.000006238052,0.00060683343,0.0000260838],"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":[0.00021714199,0.0004419956,0.0001152737,0.00038406803,0.00003883338,0.0000015633528,0.00044524056,0.012831401,0.06529492,0.00046414864,0.0007771694,0.9189882],"study_design_scores_gemma":[0.0013962373,0.00042242,0.0010773467,0.00017415837,0.00007672775,0.0001430814,0.00027533705,0.9741585,0.020342648,0.00046945974,0.0013372761,0.0001267683],"about_ca_topic_score_codex":0.00013125107,"about_ca_topic_score_gemma":0.00029375535,"teacher_disagreement_score":0.96132714,"about_ca_system_score_codex":0.00014961374,"about_ca_system_score_gemma":0.00009233041,"threshold_uncertainty_score":0.60945815},"labels":[],"label_agreement":null},{"id":"W2145677202","doi":"10.1109/titb.2008.921043","title":"A Robust 3-D IVUS Transducer Tracking Using Single-Plane Cineangiography","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Coronary Interventions and Diagnostics","field":"Medicine","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":"Montreal Heart Institute; Université de Montréal","funders":"","keywords":"Pullback; Transducer; Biplane; Computer vision; Computer science; Intravascular ultrasound; Cineangiography; Tracking (education); Artificial intelligence; Trajectory; Position (finance); Plane (geometry); Stereoscopy; Acoustics; Mathematics; Physics; Radiology; Geometry; Medicine; Engineering","score_opus":0.044196741598324776,"score_gpt":0.264444882020841,"score_spread":0.22024814042251623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145677202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5527836,0.00014266236,0.44240525,0.0027861525,0.0005267584,0.0004985911,0.000050642844,0.00034784287,0.00045851746],"genre_scores_gemma":[0.9944798,0.00022101427,0.004627002,0.0004553322,0.00003917699,0.00004563662,0.000055608463,0.00001733665,0.000059120943],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984066,0.0000160018,0.0007761929,0.00018785517,0.00028463878,0.00032870247],"domain_scores_gemma":[0.9991873,0.00006708233,0.00013866698,0.00034090457,0.00016489202,0.00010119297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012987698,0.00022855218,0.00036902897,0.0041478984,0.00018786013,0.000009298735,0.00011858025,0.0003298342,0.0003921045],"category_scores_gemma":[0.00002324306,0.00021232857,0.00017983138,0.0024113746,0.00037981413,0.00041580325,0.0000013682893,0.00048296957,0.000056271147],"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":[0.004124512,0.0149127785,0.032937136,0.0020608334,0.0017275923,0.0020574038,0.010580523,0.03516322,0.18911229,0.0011892528,0.0050958274,0.70103866],"study_design_scores_gemma":[0.07617152,0.026801027,0.06301451,0.0103042815,0.0027330164,0.042274427,0.017598849,0.07000501,0.64882606,0.0014300934,0.036645234,0.0041959914],"about_ca_topic_score_codex":0.000075553704,"about_ca_topic_score_gemma":0.000021982105,"teacher_disagreement_score":0.6968426,"about_ca_system_score_codex":0.00017073523,"about_ca_system_score_gemma":0.00006707337,"threshold_uncertainty_score":0.86585075},"labels":[],"label_agreement":null},{"id":"W2145803277","doi":"10.1109/titb.2011.2164258","title":"Electromagnetic Interference-Aware Transmission Scheduling and Power Control for Dynamic Wireless Access in Hospital Environments","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Greedy algorithm; Scheduling (production processes); Power control; Distributed computing; Computer network; Computational complexity theory; Mathematical optimization; Wireless; Power (physics); Algorithm; Telecommunications; Mathematics","score_opus":0.011550636330575615,"score_gpt":0.26128874729286755,"score_spread":0.24973811096229193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145803277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08618482,0.000054653974,0.9093833,0.0034008997,0.00011625548,0.00070790213,0.000005416563,0.00010631197,0.000040415718],"genre_scores_gemma":[0.9925684,0.0003832707,0.006644103,0.000106587795,0.0000018289151,0.00027161147,0.0000055228934,0.000007771318,0.000010908061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986885,0.000043741275,0.0005284745,0.00022251844,0.00018085344,0.00033586952],"domain_scores_gemma":[0.9992178,0.000098243465,0.000117976626,0.00045699894,0.00004468006,0.00006426837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028499373,0.00016276258,0.00022428944,0.0018241699,0.00008612569,0.00004075385,0.0010033447,0.00024707982,0.000016805545],"category_scores_gemma":[0.0000074211616,0.00015295552,0.000028164264,0.0010790067,0.00023861969,0.0011949692,0.000013341488,0.00049462117,0.000007618267],"study_design_candidate":"design_other","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":[0.0003626482,0.0010013275,0.0006498396,0.00012824044,0.000061011062,0.000007649577,0.0056265844,0.0014928586,0.0064099547,0.0031000453,0.000027001288,0.98113286],"study_design_scores_gemma":[0.005837326,0.0023722912,0.004052219,0.00035568618,0.000010593111,0.000021220543,0.00066751963,0.9692511,0.012644537,0.004293074,0.00013333016,0.000361116],"about_ca_topic_score_codex":0.000022292423,"about_ca_topic_score_gemma":0.000015321622,"teacher_disagreement_score":0.9807717,"about_ca_system_score_codex":0.0001425382,"about_ca_system_score_gemma":0.00004388305,"threshold_uncertainty_score":0.6237345},"labels":[],"label_agreement":null},{"id":"W2147769521","doi":"10.1109/titb.2005.847169","title":"A Support Vectors Classifier Approach to Predicting the Risk of Progression of Adolescent Idiopathic Scoliosis","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"","keywords":"Logistic regression; Scoliosis; Stepwise regression; Idiopathic scoliosis; Support vector machine; Medicine; Brace; Classifier (UML); Regression; Trunk; Artificial intelligence; Linear regression; Statistics; Computer science; Machine learning; Mathematics; Internal medicine; Surgery","score_opus":0.014809979055866663,"score_gpt":0.27901564054548056,"score_spread":0.2642056614896139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147769521","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9385318,0.0002925357,0.048900872,0.0088592265,0.00027288208,0.0023221895,0.000044502165,0.00016733899,0.0006086267],"genre_scores_gemma":[0.99769366,0.00028829632,0.0012238324,0.00032104232,0.000027188362,0.0004250665,0.000005596771,0.000009972109,0.0000053619115],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982516,0.000036443813,0.00091422314,0.00015529692,0.00041786657,0.00022457114],"domain_scores_gemma":[0.99894345,0.000030174837,0.0003671793,0.00041308193,0.00016773804,0.000078386875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050893717,0.00017230709,0.0004090122,0.001846586,0.000085419284,0.0000045997017,0.00015249224,0.00024455655,0.000015765043],"category_scores_gemma":[0.00005739504,0.00010845254,0.0000994292,0.0019068454,0.0002621884,0.0001654607,0.0000041742137,0.00040560693,0.00001672723],"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":[0.0017270009,0.009818123,0.27396327,0.0016375734,0.00075325475,0.0000056676076,0.009746644,0.0050209635,0.0074851913,0.00056827307,0.0009240668,0.68834996],"study_design_scores_gemma":[0.011086132,0.008152749,0.2567088,0.0076865517,0.0010452139,0.00018416668,0.007993713,0.009046494,0.69583654,0.00006831817,0.0017928658,0.00039844567],"about_ca_topic_score_codex":0.000034355988,"about_ca_topic_score_gemma":0.00000882753,"teacher_disagreement_score":0.68835133,"about_ca_system_score_codex":0.00018695265,"about_ca_system_score_gemma":0.00009698563,"threshold_uncertainty_score":0.44225666},"labels":[],"label_agreement":null},{"id":"W2148558227","doi":"10.1109/titb.2008.926496","title":"A Framework for the Design of a Novel Haptic-Based Medical Training Simulator","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Haptic technology; Kinesthetic learning; Computer science; Modular design; Medical simulation; Imaging phantom; Simulation; Software; Computer architecture simulator; Ultrasound; Medical physics; Radiology; Medicine","score_opus":0.0743624826659165,"score_gpt":0.3270926994348333,"score_spread":0.2527302167689168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148558227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010480536,0.000027221497,0.97735167,0.0108142495,0.00024569462,0.00084047084,0.000014813171,0.00017027301,0.00005507116],"genre_scores_gemma":[0.9659048,0.00003144653,0.03206032,0.0017774529,0.000028193244,0.00016942198,0.000008251086,0.000010994665,0.000009166893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846536,0.00001500873,0.0007089762,0.00012156846,0.00045290074,0.00023620119],"domain_scores_gemma":[0.99754435,0.001784247,0.00016018306,0.00026095053,0.00015461913,0.00009562901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053563406,0.00014724005,0.00035107066,0.0010587608,0.00015655883,0.0000032061853,0.0001502329,0.00043756625,0.00019438691],"category_scores_gemma":[0.0005037614,0.00010020906,0.00009490219,0.001296087,0.000579762,0.00010884615,0.0000010456712,0.0005021711,0.000008233102],"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":[0.005586257,0.0021551643,0.00055224996,0.00039576524,0.00055185216,0.000044895078,0.00761222,0.3799611,0.0013452423,0.01917298,0.00012056618,0.5825017],"study_design_scores_gemma":[0.015572281,0.0010629811,0.0003090268,0.0005110248,0.00008206161,0.000111834546,0.0014498348,0.97412103,0.003914284,0.00065913313,0.0020633652,0.00014316697],"about_ca_topic_score_codex":0.0000065251356,"about_ca_topic_score_gemma":0.0000011832146,"teacher_disagreement_score":0.9554242,"about_ca_system_score_codex":0.000059291444,"about_ca_system_score_gemma":0.00032254658,"threshold_uncertainty_score":0.40864068},"labels":[],"label_agreement":null},{"id":"W2149274912","doi":"10.1109/titb.2010.2050778","title":"Detection of Left Ventricular Motion Abnormality Via Information Measures and Bayesian Filtering","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":20,"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; St Joseph's Health Care; London Health Sciences Centre; CARE Canada","funders":"","keywords":"Abnormality; Entropy (arrow of time); Artificial intelligence; Segmentation; Pattern recognition (psychology); Kalman filter; Bayesian probability; Mathematics; Computer science; Physics; Medicine","score_opus":0.004820707205031846,"score_gpt":0.21283927291822702,"score_spread":0.20801856571319516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149274912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3502369,0.000013898475,0.6481098,0.00045014347,0.0006066218,0.00035299966,0.000010460176,0.00014320912,0.00007600315],"genre_scores_gemma":[0.999105,0.00011067193,0.00058295135,0.00010938382,0.000021310518,0.000027515862,0.000031254058,0.0000061690384,0.00000575698],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987153,0.000020679954,0.0006539515,0.000107324486,0.00033210075,0.00017067039],"domain_scores_gemma":[0.9992108,0.000021501202,0.00018724952,0.00030294847,0.00019939708,0.000078126366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043251185,0.00015579087,0.0002958818,0.0023524493,0.000101052814,0.000011760544,0.00005933729,0.00038917252,0.000051816016],"category_scores_gemma":[0.00003925285,0.00013559723,0.000098064294,0.00083850883,0.00021999194,0.0008313083,0.0000023569874,0.0005855896,0.00002223028],"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":[0.0003494283,0.00016632248,0.0062996843,0.00033610646,0.00017013126,0.0000074377463,0.0007916902,0.00047890135,0.07985691,0.00006911809,0.000015135564,0.91145915],"study_design_scores_gemma":[0.007835333,0.0011227727,0.099610336,0.00024232663,0.00032761978,0.0028129008,0.0016748651,0.014888552,0.85971075,0.00032143042,0.01100835,0.00044475312],"about_ca_topic_score_codex":0.00010583568,"about_ca_topic_score_gemma":0.000044151762,"teacher_disagreement_score":0.9110144,"about_ca_system_score_codex":0.00007880558,"about_ca_system_score_gemma":0.000030450767,"threshold_uncertainty_score":0.5529495},"labels":[],"label_agreement":null},{"id":"W2149343899","doi":"10.1109/titb.2008.2007649","title":"Mining, Modeling, and Evaluation of Subnetworks From Large Biomolecular Networks and Its Comparison Study","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","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 Saskatchewan","funders":"","keywords":"Computer science; Subnetwork; Cluster analysis; Biological network; Probabilistic logic; Data mining; Gene regulatory network; Context (archaeology); Theoretical computer science; Artificial intelligence; Computational biology","score_opus":0.01254205238942762,"score_gpt":0.28052636317488183,"score_spread":0.2679843107854542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149343899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7203367,0.0012999164,0.27774286,0.0001998069,0.000064329106,0.0003182046,0.000006830735,0.000019367068,0.000012007715],"genre_scores_gemma":[0.9992676,0.00028635026,0.00022658365,0.00008150692,0.000023457686,0.000031095522,0.00007427743,0.000006313946,0.000002865631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873924,0.00007131611,0.0005458746,0.00021638436,0.00024191491,0.00018524252],"domain_scores_gemma":[0.9993231,0.000009407481,0.00017138205,0.00027018593,0.00017851965,0.000047358884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005928692,0.00016082615,0.00026846922,0.0006361038,0.000075080665,0.000008866287,0.00010893955,0.00032033242,0.000009138155],"category_scores_gemma":[0.000013367596,0.00015795064,0.000031151765,0.0006091888,0.00006979027,0.000021021126,0.000004808699,0.0001514104,7.9508345e-7],"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":[0.00037873664,0.001188484,0.009460365,0.000025185636,0.0005337944,0.000002265615,0.0013639814,0.762998,0.03348478,0.000076291464,0.00017138757,0.19031677],"study_design_scores_gemma":[0.0026418564,0.0009820325,0.001721858,0.000039994185,0.00021427582,0.0000047855597,0.0014685888,0.97933996,0.01326656,0.00010097585,0.00006483737,0.00015430752],"about_ca_topic_score_codex":0.000017507382,"about_ca_topic_score_gemma":0.0000623429,"teacher_disagreement_score":0.27893087,"about_ca_system_score_codex":0.000023701312,"about_ca_system_score_gemma":0.000025732375,"threshold_uncertainty_score":0.644104},"labels":[],"label_agreement":null},{"id":"W2150216628","doi":"10.1109/titb.2010.2087385","title":"Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":256,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Montréal","funders":"","keywords":"Silhouette; Computer vision; Computer science; Artificial intelligence; Sitting; Position (finance); Sensitivity (control systems); Medicine; Engineering; Business","score_opus":0.00776068218227131,"score_gpt":0.24995854664394765,"score_spread":0.24219786446167635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150216628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17436221,0.0000012147128,0.82086295,0.002920573,0.00064886495,0.00048094545,0.0000436938,0.00063487445,0.00004465],"genre_scores_gemma":[0.9920746,0.0000019573322,0.007091732,0.0005633762,0.000024423549,0.00018504857,0.000043092383,0.000010805017,0.0000049693917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980844,0.00010504465,0.0006006356,0.0003631049,0.0005078319,0.0003389336],"domain_scores_gemma":[0.99835163,0.00032329498,0.00015986069,0.0007807631,0.0002525808,0.00013184683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060947455,0.00025792493,0.0002926837,0.0016947318,0.00028994938,0.00007360558,0.0004679523,0.00041468756,0.00001649966],"category_scores_gemma":[0.00008093442,0.00021652239,0.000053216612,0.0020807248,0.00021070866,0.0014275385,0.0000053759777,0.0009301181,0.000080294645],"study_design_candidate":"design_other","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":[0.00090212096,0.0010038681,0.00050721946,0.00006660612,0.000052495554,0.00003053816,0.00085944845,0.0031312557,0.11399204,0.00069375685,0.00006682702,0.8786938],"study_design_scores_gemma":[0.0038244366,0.002234897,0.002691588,0.00013886436,0.000025094061,0.00014383969,0.00031832146,0.7667801,0.2212373,0.0002610865,0.0019382053,0.00040626118],"about_ca_topic_score_codex":0.0002938583,"about_ca_topic_score_gemma":0.0015897102,"teacher_disagreement_score":0.87828755,"about_ca_system_score_codex":0.00022536717,"about_ca_system_score_gemma":0.000102920734,"threshold_uncertainty_score":0.8829527},"labels":[],"label_agreement":null},{"id":"W2150482351","doi":"10.1109/titb.2008.917905","title":"Characterization of Atherosclerotic Plaques and Mural Thrombi With Intravascular Ultrasound Elastography: A Potential Method Evaluated in an Aortic Rabbit Model and a Human Coronary Artery","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":34,"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 Montréal","funders":"Pfizer","keywords":"Intravascular ultrasound; Medicine; Elastography; Thrombus; Radiology; Vulnerable plaque; Ultrasound; Biomedical engineering; Coronary atherosclerosis; Coronary artery disease; Cardiology","score_opus":0.013063208530408961,"score_gpt":0.26927034239387987,"score_spread":0.2562071338634709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150482351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7880725,0.000019159885,0.2110435,0.00023311334,0.000030492969,0.0005071602,0.000028622653,0.000060721843,0.0000046881937],"genre_scores_gemma":[0.99698,0.00020010372,0.0025038188,0.00009289532,0.000004988064,0.00008250917,0.00011928429,0.000011032699,0.0000053800577],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99884415,0.000039140818,0.0005842151,0.00016730283,0.00019856096,0.00016660812],"domain_scores_gemma":[0.99943626,0.00003590699,0.00014146527,0.00020843995,0.00011554694,0.000062377796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018336205,0.00016616896,0.00034423574,0.0017269256,0.000091100555,0.0000065313043,0.000054486452,0.00019670428,0.000037708487],"category_scores_gemma":[0.000009948763,0.00014322592,0.000034916335,0.0006952555,0.00048159593,0.00037394805,0.0000021436285,0.00028046954,9.0346214e-7],"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":[0.0018916909,0.002835865,0.071815334,0.00058460585,0.00040711457,0.0001238103,0.0066417223,0.0073199417,0.8526481,0.00031031034,0.00000565274,0.055415854],"study_design_scores_gemma":[0.013089277,0.008424351,0.81440383,0.0010701691,0.000381902,0.0035030756,0.0020927466,0.12727492,0.02880667,0.00058333814,0.000003847511,0.0003658885],"about_ca_topic_score_codex":0.00006768871,"about_ca_topic_score_gemma":0.00007612112,"teacher_disagreement_score":0.82384145,"about_ca_system_score_codex":0.000051218278,"about_ca_system_score_gemma":0.00004557168,"threshold_uncertainty_score":0.5840583},"labels":[],"label_agreement":null},{"id":"W2152063387","doi":"10.1109/titb.2010.2104326","title":"Mobility Support for Health Monitoring at Home Using Wearable Sensors","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":107,"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 British Columbia","funders":"","keywords":"Wearable computer; Computer science; Computer network; Relay; Wireless sensor network; Real-time computing; Network packet; SIGNAL (programming language); Point (geometry); Embedded system","score_opus":0.020656302853121287,"score_gpt":0.25674840855664294,"score_spread":0.23609210570352165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152063387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44039515,0.000060828716,0.5560959,0.00023477068,0.0013932185,0.0006323346,0.000047119745,0.0009205835,0.00022007928],"genre_scores_gemma":[0.9931311,0.00017615415,0.0064180205,0.000041626885,0.000028230816,0.00014836382,0.000009189539,0.000021526279,0.000025797266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987031,0.000009838469,0.0006188744,0.00012845674,0.00013357373,0.0004061368],"domain_scores_gemma":[0.9994428,0.00003395338,0.00008946959,0.0003051628,0.00005441792,0.00007417982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029020954,0.0001798478,0.00027798157,0.0012331058,0.00014891007,0.000006080128,0.00014747781,0.00028892906,0.000058162754],"category_scores_gemma":[0.0000045071897,0.00018772586,0.0000449806,0.00087187206,0.00014059155,0.00042557367,0.0000019775407,0.00032012296,0.000035167024],"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":[0.00068598246,0.0006185301,0.0043352745,0.002177398,0.00038808264,0.000015809352,0.01226865,0.6697817,0.013672903,0.00036612214,0.0013524847,0.29433706],"study_design_scores_gemma":[0.0068457345,0.0017239539,0.004495248,0.0010147819,0.00008140517,0.00023734261,0.0036540255,0.58496755,0.39074737,0.0012020232,0.00379489,0.0012356631],"about_ca_topic_score_codex":0.00004141016,"about_ca_topic_score_gemma":0.000013084174,"teacher_disagreement_score":0.5527359,"about_ca_system_score_codex":0.0006095446,"about_ca_system_score_gemma":0.000034173412,"threshold_uncertainty_score":0.76552385},"labels":[],"label_agreement":null},{"id":"W2154306361","doi":"10.1109/titb.2011.2159806","title":"Nonrigid Image Registration Using an Entropic Similarity","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Image registration; Similarity (geometry); Artificial intelligence; Similarity measure; Computer science; Image (mathematics); Computer vision; Volume (thermodynamics); Measure (data warehouse); Pattern recognition (psychology); Mathematics; Data mining","score_opus":0.03244974579841401,"score_gpt":0.2899415480893299,"score_spread":0.2574918022909159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154306361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067634443,0.00000490234,0.9899335,0.0012521051,0.0003206044,0.00035259002,0.000006679663,0.0008269318,0.00053923414],"genre_scores_gemma":[0.6278703,0.00003309595,0.37113172,0.00086052535,0.000013250501,0.000060169194,0.000010609231,0.000006164918,0.000014168287],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984872,0.000049761504,0.00065842405,0.00022141973,0.0003264608,0.00025675137],"domain_scores_gemma":[0.9988986,0.000023199498,0.0002268677,0.00061264826,0.00014376693,0.00009488908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036729846,0.00016435777,0.00018537244,0.0017640238,0.0001258943,0.000040059134,0.00061314955,0.00034640467,0.0001298953],"category_scores_gemma":[0.000025975289,0.0001562804,0.000032639906,0.0014705698,0.00032305528,0.0032519428,0.0000051028824,0.00052853645,0.00005013673],"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":[0.00011941926,0.0018701574,0.00030030924,0.00016605757,0.00007225249,0.00008152811,0.008436694,0.0001725817,0.102775745,0.015538628,0.0009661847,0.86950046],"study_design_scores_gemma":[0.0017710254,0.0010361401,0.0005266332,0.000107805376,0.000021051932,0.0001223643,0.0007175539,0.08434164,0.9013804,0.009340375,0.0002593774,0.00037563642],"about_ca_topic_score_codex":0.00012487287,"about_ca_topic_score_gemma":0.000015626822,"teacher_disagreement_score":0.8691248,"about_ca_system_score_codex":0.00016014703,"about_ca_system_score_gemma":0.00007787548,"threshold_uncertainty_score":0.6372929},"labels":[],"label_agreement":null},{"id":"W2156325834","doi":"10.1109/titb.2006.872069","title":"Predicting High-Risk Preterm Birth Using Artificial Neural Networks","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Preterm Birth and Chorioamnionitis","field":"Medicine","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":"Children's Hospital of Eastern Ontario; University of Ottawa; Carleton University","funders":"","keywords":"Medicine; Obstetrics; Population; Gestation; Artificial neural network; Pregnancy; Machine learning; Computer science; Biology","score_opus":0.007777177023262165,"score_gpt":0.23038639024462454,"score_spread":0.22260921322136237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156325834","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6871475,0.000044430537,0.30980343,0.0009378638,0.0010241473,0.0004385855,0.000055993776,0.00042466147,0.00012335833],"genre_scores_gemma":[0.99802905,0.00007361548,0.0012607338,0.00026517682,0.00019884751,0.00006228856,0.00006715384,0.000018339428,0.00002477404],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815565,0.000030639392,0.00087090675,0.00020843347,0.00029292595,0.0004414368],"domain_scores_gemma":[0.9991253,0.000051765528,0.00027390043,0.0003534013,0.00010671444,0.000088938716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020587396,0.0002408477,0.00035584142,0.0019385949,0.00023039624,0.000024395842,0.00013224049,0.0004589423,0.00007971377],"category_scores_gemma":[0.000024041237,0.00022066965,0.00007814675,0.0014726982,0.00030737618,0.00046603158,0.000002892028,0.0008496776,0.000019330953],"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":[0.0024154105,0.00093884405,0.09311835,0.00043790013,0.0002753028,0.0001797661,0.0007709687,0.13253546,0.007237349,0.001985752,0.00041156786,0.7596933],"study_design_scores_gemma":[0.0068463,0.0020021272,0.015997041,0.0005202608,0.00026764814,0.00074711075,0.000808834,0.94195116,0.02750152,0.0022885124,0.00052569015,0.00054378377],"about_ca_topic_score_codex":0.0005201706,"about_ca_topic_score_gemma":0.000087013126,"teacher_disagreement_score":0.8094157,"about_ca_system_score_codex":0.00018569222,"about_ca_system_score_gemma":0.000052913158,"threshold_uncertainty_score":0.89986473},"labels":[],"label_agreement":null},{"id":"W2161045721","doi":"10.1109/titb.2002.806095","title":"Three-dimensional object-oriented modeling of the stomach for the purpose of microprocessor-controlled functional stimulation","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Gastrointestinal motility and disorders","field":"Medicine","cited_by":12,"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; University of Calgary; Nortel (Canada)","funders":"","keywords":"Stimulation; Biomedical engineering; Materials science; Parametric statistics; Microelectrode; Functional electrical stimulation; Electric field; Electrode; Computer science; Neuroscience; Chemistry; Physics; Engineering; Mathematics; Biology","score_opus":0.017731155974098876,"score_gpt":0.23794240032371244,"score_spread":0.22021124434961356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161045721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.266007,0.00010550793,0.7266021,0.005682699,0.00023346284,0.0012537842,0.000026522717,0.00004790235,0.00004103126],"genre_scores_gemma":[0.99807984,0.000011315667,0.001504469,0.00020336233,0.000011021674,0.00014525125,0.000011737445,0.000006051402,0.000026963915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870324,0.000011531744,0.00071841694,0.00010632535,0.00030785816,0.00015262321],"domain_scores_gemma":[0.99885595,0.00020297582,0.00023986559,0.00026689252,0.0004094958,0.00002481078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026255214,0.00012719161,0.00029943392,0.0007151832,0.00013515181,0.0000024692897,0.0001108172,0.00014762895,0.000073093404],"category_scores_gemma":[0.00014825127,0.00007570826,0.00012607947,0.001018932,0.00033596874,0.00014042706,0.0000023671964,0.0002894903,0.0000040030236],"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":[0.012703081,0.002586464,0.0057484307,0.0008949562,0.0005278141,0.0000011915492,0.0013587419,0.8496201,0.029283173,0.001612498,0.0006186862,0.09504485],"study_design_scores_gemma":[0.012662566,0.0007096931,0.0020659606,0.00023119977,0.00016840735,0.00004752786,0.0005531547,0.97730356,0.005448541,0.0006931444,0.000052926138,0.00006334049],"about_ca_topic_score_codex":0.0000458738,"about_ca_topic_score_gemma":0.00003369634,"teacher_disagreement_score":0.73207283,"about_ca_system_score_codex":0.000055286888,"about_ca_system_score_gemma":0.000052239462,"threshold_uncertainty_score":0.30872932},"labels":[],"label_agreement":null},{"id":"W2162688024","doi":"10.1109/titb.2006.889693","title":"Knowledge-Based Data Analysis: First Step Toward the Creation of Clinical Prediction Rules Using a New Typicality Measure","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Time Series Analysis and Forecasting","field":"Computer Science","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":"Simon Fraser University; University of British Columbia; Thompson Rivers University","funders":"","keywords":"Computer science; Data mining; Outlier; Preprocessor; Machine learning; Measure (data warehouse); Artificial intelligence; Medical diagnosis; Fuzzy logic; Data pre-processing; Process (computing)","score_opus":0.07308882996104556,"score_gpt":0.3408959320944781,"score_spread":0.26780710213343256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162688024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009726898,0.00006069679,0.9865556,0.002866885,0.00030335598,0.00019625385,0.00003731688,0.00014006939,0.00011293137],"genre_scores_gemma":[0.9665584,0.000025198126,0.033245612,0.000071744595,0.00003849803,0.0000045617226,0.00004558691,0.0000032534065,0.0000071466634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783313,0.000054688164,0.0013559047,0.00024009717,0.00031836928,0.0001977969],"domain_scores_gemma":[0.9980177,0.00021965888,0.00047412768,0.0010154917,0.0002104667,0.00006251911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024593056,0.00013173504,0.00036242808,0.0017730106,0.00015753265,0.00002892785,0.0008649675,0.00026797046,0.0000218894],"category_scores_gemma":[0.00011129897,0.000094919196,0.00011573866,0.004757897,0.00028282593,0.00077221077,0.000014440338,0.00035515134,0.000009228773],"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":[0.00020421446,0.0003748078,0.0052095996,0.00006533924,0.0006063813,0.0000014239006,0.0012972468,0.026951028,0.00008793847,0.0017717305,0.00019731962,0.963233],"study_design_scores_gemma":[0.00095507497,0.00019686497,0.0080957785,0.00008753719,0.0003283016,0.000004823546,0.0005494436,0.987033,0.00088085886,0.00020561105,0.0015639398,0.00009872707],"about_ca_topic_score_codex":0.00024560592,"about_ca_topic_score_gemma":0.00036542036,"teacher_disagreement_score":0.9631342,"about_ca_system_score_codex":0.00007628613,"about_ca_system_score_gemma":0.00015465313,"threshold_uncertainty_score":0.38706926},"labels":[],"label_agreement":null},{"id":"W2163117770","doi":"10.1109/titb.2003.811881","title":"Weight-elimination neural networks applied to coronary surgery mortality prediction","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":42,"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 Ottawa; Carleton University","funders":"","keywords":"Artificial neural network; Mortality rate; Medicine; Backpropagation; Feedforward neural network; Statistics; Machine learning; Internal medicine; Computer science; Mathematics","score_opus":0.012489105140206886,"score_gpt":0.24673999504237423,"score_spread":0.23425088990216736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163117770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4716572,0.00006771759,0.510046,0.009489038,0.0017816287,0.0016100145,0.000025420743,0.00075696624,0.004566025],"genre_scores_gemma":[0.9974375,0.000121573714,0.0003627378,0.0016194793,0.000039139923,0.0002416134,0.000074045754,0.0000136699355,0.00009027272],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982137,0.000045791407,0.0008073465,0.00021306452,0.0003874632,0.0003326278],"domain_scores_gemma":[0.9990143,0.00024086967,0.00011990593,0.0003659667,0.0001085456,0.00015042302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006013387,0.00022334965,0.0005999337,0.0020966427,0.00011189119,0.000011263946,0.00007332655,0.0004160802,0.00012198656],"category_scores_gemma":[0.000063953834,0.00019483644,0.00015585395,0.0019140698,0.00016286474,0.00026573447,0.0000012561856,0.0004482282,0.000061983155],"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":[0.0030898128,0.0019022116,0.11970176,0.00052344636,0.0007693595,0.00027935675,0.0014449765,0.03711431,0.0023909346,0.024241827,0.004268152,0.80427384],"study_design_scores_gemma":[0.0158383,0.0028890192,0.8016889,0.00064365123,0.0010199011,0.0027269165,0.0037063686,0.039307576,0.03338125,0.0006272769,0.09649766,0.0016731599],"about_ca_topic_score_codex":0.000011274363,"about_ca_topic_score_gemma":0.000004773742,"teacher_disagreement_score":0.8026007,"about_ca_system_score_codex":0.00021081993,"about_ca_system_score_gemma":0.00005671751,"threshold_uncertainty_score":0.79451996},"labels":[],"label_agreement":null},{"id":"W2164083354","doi":"10.1109/titb.2010.2042608","title":"A Novel Middleware Solution to Improve Ubiquitous Healthcare Systems Aided by Affective Information","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":43,"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 Guelph","funders":"","keywords":"Addiction; Middleware (distributed applications); Health care; Affective computing; Dissemination; Ubiquitous computing; Rehabilitation; Psychology; Arousal; Computer science; Internet privacy; Human–computer interaction; Psychiatry; Social psychology; Neuroscience","score_opus":0.011016957240760033,"score_gpt":0.2783969480170043,"score_spread":0.2673799907762443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164083354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083534434,0.000026555,0.8903511,0.011854795,0.009092795,0.002267487,0.00064993964,0.000808128,0.0014147395],"genre_scores_gemma":[0.9961153,0.00001650593,0.0007139712,0.001602808,0.00006556055,0.0010475843,0.0002676555,0.000015617075,0.00015498906],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980177,0.000044798828,0.0009277782,0.00023708667,0.0003106116,0.00046197118],"domain_scores_gemma":[0.9986422,0.00006834499,0.00032572314,0.00044103293,0.0003572868,0.00016542275],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043269133,0.0002738083,0.00031633762,0.0031531253,0.00021606093,0.00004137427,0.00023162672,0.000863598,0.00013702693],"category_scores_gemma":[0.000050555707,0.00026667374,0.000061576306,0.001689602,0.00017664085,0.0010926161,0.0000038547105,0.0010329796,0.0010221443],"study_design_candidate":"design_other","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":[0.00077999005,0.0010264849,0.00009287572,0.00040101312,0.00017450217,0.0000038131614,0.010798187,0.0002848103,0.12331252,0.007085791,0.0127943605,0.8432457],"study_design_scores_gemma":[0.07908865,0.02646499,0.008307677,0.0031524794,0.00057779095,0.0029731453,0.1344483,0.025210463,0.42239493,0.0036165172,0.28706333,0.0067017064],"about_ca_topic_score_codex":0.0009124626,"about_ca_topic_score_gemma":0.00026628986,"teacher_disagreement_score":0.91258085,"about_ca_system_score_codex":0.00024810544,"about_ca_system_score_gemma":0.00006507805,"threshold_uncertainty_score":0.99997854},"labels":[],"label_agreement":null},{"id":"W2165759407","doi":"10.1109/titb.2009.2018286","title":"Texture Analysis for Automatic Segmentation of Intervertebral Disks of Scoliotic Spines From MR Images","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Sagittal plane; Computer science; Computer vision; Intervertebral disk; Feature (linguistics); Anatomy; Medicine","score_opus":0.0057958037254250555,"score_gpt":0.24778663366996337,"score_spread":0.2419908299445383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165759407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19653587,0.000064514796,0.8020965,0.0008172065,0.00009585455,0.00014428578,0.00007985979,0.000145528,0.000020399522],"genre_scores_gemma":[0.9947796,0.00006141231,0.0049876864,0.000053612348,0.000007606272,0.000024326662,0.000075559714,0.0000039315682,0.0000062503145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989939,0.000008246359,0.00065965456,0.00007320863,0.00014753832,0.00011748819],"domain_scores_gemma":[0.9995559,0.000041631694,0.00012862742,0.00017866706,0.000066557644,0.000028650078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000109352615,0.00011190467,0.0003601493,0.0025471777,0.000021866317,0.000005287769,0.00012702998,0.00013274091,0.0000664868],"category_scores_gemma":[0.000020310383,0.000096060074,0.000109904366,0.0020868555,0.00011522413,0.00024769446,5.602413e-7,0.00013762659,0.0000032016283],"study_design_candidate":"design_other","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":[0.000028020973,0.00029099124,0.001530632,0.00060313434,0.0013908814,0.0000012797457,0.0015386356,0.10210034,0.067466885,0.00009158839,0.00046235978,0.82449526],"study_design_scores_gemma":[0.001508385,0.00025127476,0.0051249857,0.00032803952,0.00073454395,0.0000015596028,0.0011534442,0.6864204,0.30339646,0.0009117234,0.000018805038,0.00015042107],"about_ca_topic_score_codex":0.00004129579,"about_ca_topic_score_gemma":0.000008050186,"teacher_disagreement_score":0.8243448,"about_ca_system_score_codex":0.00004306431,"about_ca_system_score_gemma":0.000008948971,"threshold_uncertainty_score":0.3917216},"labels":[],"label_agreement":null},{"id":"W2165918841","doi":"10.1109/titb.2011.2163724","title":"Fisher–Tippett Region-Merging Approach to Transrectal Ultrasound Prostate Lesion Segmentation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Prostate; Ultrasound; Artificial intelligence; Computer science; Image segmentation; Contrast (vision); Medicine; Pattern recognition (psychology); Radiology","score_opus":0.02190050953933941,"score_gpt":0.2556427577277109,"score_spread":0.23374224818837147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165918841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058159227,0.000008069094,0.9879403,0.0020683417,0.00040130314,0.0009863181,0.000007476313,0.0010692772,0.0017029528],"genre_scores_gemma":[0.71315,0.00010534087,0.28455374,0.0015932067,0.000013702354,0.00048225085,0.000022556627,0.000013234361,0.000065966975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980156,0.000055643923,0.0007804425,0.0003370865,0.00043788928,0.00037337508],"domain_scores_gemma":[0.9989884,0.000059741724,0.00018541835,0.00050387107,0.00012366768,0.00013893549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038552837,0.00024148104,0.00024705176,0.0028046155,0.00016618485,0.00004532795,0.0006661527,0.00032317822,0.000036591417],"category_scores_gemma":[0.00002977113,0.0002249212,0.000052115833,0.0028609599,0.00021125416,0.001976783,0.000006171269,0.0005671808,0.00008805848],"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":[0.000119768585,0.00060170965,0.00012983293,0.00011693734,0.000042787935,0.000016434044,0.017210709,0.00023800482,0.02158549,0.0017333084,0.0018850457,0.95632],"study_design_scores_gemma":[0.0029453896,0.0014353733,0.00076003216,0.00030442784,0.000029444027,0.0003528547,0.0041182465,0.00873116,0.97707105,0.0029330621,0.00062827126,0.00069071853],"about_ca_topic_score_codex":0.00006119357,"about_ca_topic_score_gemma":0.0000048538914,"teacher_disagreement_score":0.9556292,"about_ca_system_score_codex":0.00020300632,"about_ca_system_score_gemma":0.00005608324,"threshold_uncertainty_score":0.91720206},"labels":[],"label_agreement":null},{"id":"W2169006449","doi":"10.1109/titb.2009.2034972","title":"Enhancing Battery Efficiency for Pervasive Health-Monitoring Systems Based on Electronic Textiles","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Green IT and Sustainability","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Battery (electricity); Computer science; Metric (unit); Usability; Computation; Scheduling (production processes); Performance metric; Reliability engineering; Mathematical optimization; Engineering; Algorithm; Power (physics); Mathematics; Human–computer interaction; Operations management","score_opus":0.004928201955096443,"score_gpt":0.23348646556194663,"score_spread":0.2285582636068502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169006449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08231794,0.0001409897,0.9120885,0.0030295008,0.0007751881,0.0008760619,0.000016117017,0.000669131,0.00008656234],"genre_scores_gemma":[0.9992291,0.00006327515,0.00025903748,0.00017655198,0.0000325982,0.00020908254,0.0000103876655,0.000009877155,0.000010106271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986357,0.000014542862,0.0005754291,0.00013162669,0.00017136478,0.0004713386],"domain_scores_gemma":[0.99942815,0.00010083584,0.000072740186,0.00026474992,0.000083280895,0.000050263145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003585468,0.00018310855,0.00026477585,0.0016805611,0.00014915373,0.000016956312,0.00015173672,0.0002198391,0.0000057650736],"category_scores_gemma":[0.000020642732,0.0001755058,0.00004968287,0.0009005032,0.00006223529,0.00024118698,3.8911742e-7,0.00041827487,0.000012399197],"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":[0.00016799115,0.0002984128,0.00016137221,0.0011937998,0.00004010482,0.0000027636463,0.0020931743,0.6917722,0.0046214946,0.0008874205,0.00024146371,0.2985198],"study_design_scores_gemma":[0.0064648557,0.007375386,0.0017137625,0.0017811693,0.000046046433,0.0000323963,0.012851728,0.829238,0.13362731,0.0013620366,0.0045262543,0.0009810895],"about_ca_topic_score_codex":0.000015045966,"about_ca_topic_score_gemma":0.0000074601767,"teacher_disagreement_score":0.9169111,"about_ca_system_score_codex":0.0007585246,"about_ca_system_score_gemma":0.000094090974,"threshold_uncertainty_score":0.7156919},"labels":[],"label_agreement":null},{"id":"W2169086223","doi":"10.1109/titb.2004.834386","title":"The Use of Computer Vision in an Intelligent Environment to Support Aging-in-Place, Safety, and Independence in the Home","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":205,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto","funders":"","keywords":"Independence (probability theory); Dementia; Aging in place; Independent living; Ubiquitous computing; Health care; Human–computer interaction; Computer science; Gerontology; Medicine","score_opus":0.01944149384030724,"score_gpt":0.2593543807422513,"score_spread":0.23991288690194407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169086223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23476131,0.000009559802,0.7543564,0.010055694,0.00018446849,0.00058370706,0.00000605604,0.00003624341,0.000006568014],"genre_scores_gemma":[0.99755377,0.00017055581,0.0016878024,0.00049941713,0.000004217359,0.000074748,0.0000029635942,0.0000032671082,0.0000032363046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984249,0.00008369101,0.00075126835,0.00019389096,0.0003320696,0.00021418148],"domain_scores_gemma":[0.9991286,0.00021117459,0.00013541282,0.00045379327,0.000034866982,0.00003617623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009517162,0.00013035431,0.00020605256,0.0019866836,0.00006573406,0.00005051487,0.000517046,0.00015268102,0.0000039797455],"category_scores_gemma":[0.000009837462,0.00009307624,0.000018237752,0.0014876478,0.00018077856,0.0012357455,0.000016837863,0.00042208223,0.000020787757],"study_design_candidate":"design_other","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":[0.00011936286,0.0003821682,0.0015725564,0.000028961595,0.00000795989,0.00002151398,0.009074795,0.029229917,0.00039127152,0.0010073065,0.00001748866,0.9581467],"study_design_scores_gemma":[0.04053425,0.0289172,0.3333229,0.005840789,0.000059847986,0.0025653348,0.026354222,0.41715315,0.060594715,0.016028369,0.06493261,0.0036965923],"about_ca_topic_score_codex":0.00023263328,"about_ca_topic_score_gemma":0.0013615034,"teacher_disagreement_score":0.95445013,"about_ca_system_score_codex":0.00023900773,"about_ca_system_score_gemma":0.00004647288,"threshold_uncertainty_score":0.37955388},"labels":[],"label_agreement":null},{"id":"W2170750180","doi":"10.1109/titb.2005.855526","title":"A Support Vector Machines Classifier to Assess the Severity of Idiopathic Scoliosis From Surface Topography","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"","keywords":"Support vector machine; Artificial intelligence; Scoliosis; Pattern recognition (psychology); Classifier (UML); Linear discriminant analysis; Trunk; Idiopathic scoliosis; Computer science; Curvature; Decision tree; Mathematics; Medicine; Surgery","score_opus":0.00853636648952531,"score_gpt":0.22998384469849634,"score_spread":0.22144747820897104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170750180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4931169,0.000073145406,0.5016781,0.00402867,0.00038491003,0.00015989272,0.000059294878,0.0002986619,0.00020046023],"genre_scores_gemma":[0.9990498,0.000041868672,0.00064453133,0.00017687475,0.00002120994,0.000035310066,0.000011496315,0.0000074526056,0.000011476701],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989456,0.000019332203,0.00053132966,0.00009196456,0.00023388595,0.00017791739],"domain_scores_gemma":[0.9995238,0.0000358404,0.00006255613,0.00027645627,0.000058992595,0.000042318035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023555616,0.0001346894,0.00025591717,0.0009882029,0.00005851253,0.000011863374,0.00021273621,0.00016975927,0.00005883482],"category_scores_gemma":[0.000012485273,0.00009964165,0.00006375072,0.002178348,0.00018498603,0.00018181844,0.0000017629332,0.00034949405,0.000029858598],"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":[0.00019172055,0.0012002711,0.07642093,0.0013770122,0.001213101,0.00003774064,0.004607315,0.23775198,0.18046212,0.0014649702,0.013378577,0.48189425],"study_design_scores_gemma":[0.0043181553,0.0005585859,0.069293685,0.0016577359,0.00051505945,0.000057703186,0.0031643766,0.32938793,0.57710284,0.0037382809,0.008973967,0.0012316845],"about_ca_topic_score_codex":0.00034008213,"about_ca_topic_score_gemma":0.00005571091,"teacher_disagreement_score":0.50593287,"about_ca_system_score_codex":0.000046575136,"about_ca_system_score_gemma":0.000018231754,"threshold_uncertainty_score":0.40632686},"labels":[],"label_agreement":null},{"id":"W2171348895","doi":"10.1109/titb.2011.2105497","title":"Energy Efficiency and Reliability in Wireless Biomedical Implant Systems","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":75,"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; Defence Research and Development Canada","funders":"","keywords":"Physical layer; Frequency-shift keying; Wireless; Efficient energy use; Reliability (semiconductor); Keying; Transceiver; Protocol (science); Energy (signal processing)","score_opus":0.005913028162938165,"score_gpt":0.19313807881470324,"score_spread":0.18722505065176506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171348895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3195627,0.00007832719,0.6780619,0.00018760565,0.0009697723,0.00021737714,0.000022207943,0.0005186598,0.0003814394],"genre_scores_gemma":[0.9992181,0.0003610793,0.0002070542,0.00003395672,0.000014042986,0.00013762814,0.000012429825,0.0000115751845,0.000004102124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986802,0.000019395775,0.00067168917,0.00014437141,0.00016737726,0.00031696504],"domain_scores_gemma":[0.9995276,0.000048033246,0.000054454646,0.00026698937,0.00003141003,0.00007151097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026838676,0.00017923512,0.00027676948,0.0021173093,0.000044656495,0.000008135643,0.00017531482,0.00043680347,0.000015834787],"category_scores_gemma":[0.0000048643005,0.00016431196,0.000018834515,0.0014958133,0.00035178283,0.00030625312,0.0000025400523,0.0004212863,0.0000126466875],"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":[0.000532006,0.0016778058,0.0032783463,0.0018066398,0.00017751643,0.00024887625,0.011753197,0.11465843,0.0053133448,0.012610193,0.0023432719,0.84560037],"study_design_scores_gemma":[0.0033511678,0.0006447537,0.0017084971,0.0006090746,0.00002382873,0.0003597238,0.0018176396,0.9774666,0.010921217,0.00089650246,0.0016249467,0.00057608605],"about_ca_topic_score_codex":0.00025980803,"about_ca_topic_score_gemma":0.000050098264,"teacher_disagreement_score":0.8628081,"about_ca_system_score_codex":0.0001638367,"about_ca_system_score_gemma":0.000019859921,"threshold_uncertainty_score":0.6700447},"labels":[],"label_agreement":null},{"id":"W2171735580","doi":"10.1109/titb.2007.904631","title":"Registration of 3-D CT and 2-D Flat Images of Mouse via Affine Transformation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Optical Imaging and Spectroscopy Techniques","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":"Robarts Clinical Trials; Western University","funders":"U.S. National Library of Medicine","keywords":"Affine transformation; Image registration; Artificial intelligence; Computer vision; Transformation (genetics); Visualization; Computer science; Simplex; Computed tomography; Image (mathematics); Nuclear medicine; Mathematics; Medicine; Radiology; Geometry; Biology","score_opus":0.008664930247546944,"score_gpt":0.26707216267357914,"score_spread":0.2584072324260322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171735580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55405635,0.000041782074,0.4401148,0.004088845,0.000049679013,0.00039108624,0.000021748458,0.00019561415,0.0010400976],"genre_scores_gemma":[0.99197274,0.0006819737,0.0071023297,0.00008819149,0.0000051453726,0.000028897044,0.00002288524,0.0000064953488,0.00009130889],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879634,0.0000108125605,0.0007433296,0.00009382135,0.00022041283,0.0001352771],"domain_scores_gemma":[0.99939185,0.000032770724,0.0001810743,0.00021538431,0.00013758506,0.00004131449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017758412,0.00012585754,0.00033360347,0.0015920468,0.00004851656,0.000002303491,0.00006000942,0.00011004269,0.000026416494],"category_scores_gemma":[0.000022046512,0.00010895572,0.00004164246,0.0007822631,0.0006225773,0.00041506742,8.795728e-7,0.00026968552,0.0000035528253],"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":[0.00072522747,0.00058452215,0.0011313981,0.00087316555,0.000095288044,0.000018723273,0.001461739,0.00008135137,0.8913138,0.0018581387,0.0004091638,0.10144747],"study_design_scores_gemma":[0.0017910772,0.0011047877,0.0006260825,0.00016805605,0.000040291852,0.0004110954,0.00027658427,0.0019214327,0.9930789,0.00032582285,0.00017030041,0.00008558857],"about_ca_topic_score_codex":0.0000730224,"about_ca_topic_score_gemma":0.0000035631506,"teacher_disagreement_score":0.43791643,"about_ca_system_score_codex":0.000052475698,"about_ca_system_score_gemma":0.000039292805,"threshold_uncertainty_score":0.44430852},"labels":[],"label_agreement":null}]}