{"meta":{"query_hash":"f85cb8fd066a","filters":{"topic":"Hand Gesture Recognition Systems"},"cohort_total":495,"direct_labels_cover":0,"predictions_cover":495,"exported":495,"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/f85cb8fd066a","api":"https://metacan.xera.ac/api/v1/cohort?topic=Hand+Gesture+Recognition+Systems"},"results":[{"id":"W110889992","doi":"10.1007/978-3-319-11782-9_11","title":"Motion Database Retrieval with Application to Gesture Recognition in a Virtual Reality Dance Training System","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dance; Computer science; Gesture recognition; Gesture; Virtual reality; Artificial intelligence; Hidden Markov model; Motion capture; Motion (physics); Computer vision; Coding (social sciences); Feature (linguistics); Human–computer interaction; Mathematics","score_opus":0.04215188215609003,"score_gpt":0.24825520689949576,"score_spread":0.20610332474340573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W110889992","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":[0.00009232088,0.000022676371,0.85739535,0.00034704595,0.00027162765,0.0013119758,0.00009586956,0.00040889898,0.14005423],"genre_scores_gemma":[0.8679016,0.000042143038,0.035270795,0.0013946926,0.0022983754,0.00056174723,0.0025191098,0.0002455908,0.08976592],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99666923,0.00015507323,0.00073956384,0.0013084955,0.000783346,0.0003442634],"domain_scores_gemma":[0.9975899,0.00016253388,0.00047948942,0.0011601349,0.0003680762,0.00023987872],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015236282,0.00046289773,0.0006399869,0.0004403962,0.00009852973,0.00017254465,0.0006282617,0.00045134,0.0000146587845],"category_scores_gemma":[0.00005296223,0.0004036262,0.000077703815,0.00030297265,0.000036437425,0.0003832745,0.00014383269,0.00053138856,0.000549589],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002758213,0.00006006545,0.000028144368,0.00081691786,0.00009014185,0.00009381775,0.0024296634,0.00036321612,0.0005279759,0.38191208,0.0014100489,0.6119921],"study_design_scores_gemma":[0.016745972,0.006327874,0.0025124,0.060188163,0.00073250814,0.004912072,0.0034381794,0.20128681,0.0044270996,0.022577576,0.65862197,0.018229404],"about_ca_topic_score_codex":0.00006399117,"about_ca_topic_score_gemma":0.00045691634,"teacher_disagreement_score":0.8678093,"about_ca_system_score_codex":0.00034855414,"about_ca_system_score_gemma":0.00013400229,"threshold_uncertainty_score":0.9998416},"labels":[],"label_agreement":null},{"id":"W140131361","doi":"10.1007/978-3-642-31298-4_14","title":"Heuristic and Voxel-Based Signature for Hand Posture Recognition Using a Range Camera","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Gesture; Computer science; Signature (topology); Artificial intelligence; Voxel; Computer vision; Heuristic; Range (aeronautics); Gesture recognition; Pattern recognition (psychology); Speech recognition; Mathematics","score_opus":0.03591211699898068,"score_gpt":0.2601487970287477,"score_spread":0.22423668002976704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W140131361","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":[0.00041598352,0.0047505503,0.9906996,0.0005070011,0.0020347862,0.0011794048,0.000082003935,0.00011376491,0.00021692259],"genre_scores_gemma":[0.35856226,0.00006556844,0.6352232,0.0034102898,0.0022997533,0.00007299731,0.00008545768,0.00011293846,0.00016754311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640995,0.00007397528,0.00053562963,0.001471195,0.00074615725,0.0007631048],"domain_scores_gemma":[0.996986,0.00093871023,0.00041016005,0.0007825391,0.0005892174,0.00029341716],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010849596,0.00064365263,0.00069612794,0.00084455847,0.00047018027,0.0008455407,0.0010565419,0.0006654237,0.00001699044],"category_scores_gemma":[0.00016718183,0.0005735075,0.00016186257,0.00048523152,0.0006104162,0.00062848895,0.00035282192,0.00078753533,0.000019661458],"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":[0.000031454416,0.000044224664,0.000072985735,0.00033646973,0.000025814054,0.000043746546,0.0008622924,0.0015272301,0.0011347847,0.00033564115,0.000044345812,0.99554104],"study_design_scores_gemma":[0.0040140855,0.0009055468,0.00038217442,0.005173716,0.00021419588,0.00092964264,0.0000015590178,0.8559612,0.007383827,0.10906306,0.011970644,0.0040003597],"about_ca_topic_score_codex":0.000020388332,"about_ca_topic_score_gemma":0.00006958242,"teacher_disagreement_score":0.9915407,"about_ca_system_score_codex":0.00022454157,"about_ca_system_score_gemma":0.0005152303,"threshold_uncertainty_score":0.99967164},"labels":[],"label_agreement":null},{"id":"W1456593600","doi":"10.63317/3iqewee2q6mv","title":"Detecting Reduplication in Videos of American Sign Language","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reduplication; Computer science; American Sign Language; Sign language; Artificial intelligence; Robustness (evolution); A priori and a posteriori; Natural language processing; Inflection; Speech recognition; Linguistics","score_opus":0.015430796880887304,"score_gpt":0.27492246333912335,"score_spread":0.25949166645823607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1456593600","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":[0.54050076,0.00010079272,0.4495996,0.00015841445,0.00008757401,0.00012398657,2.9983153e-7,0.000076409466,0.009352172],"genre_scores_gemma":[0.9851164,0.0000010257892,0.014695505,0.000060132876,0.000043622313,0.000012114151,3.1143705e-7,0.0000024590065,0.000068434354],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994825,0.000058237147,0.00014835822,0.00009154892,0.00009430679,0.0001250601],"domain_scores_gemma":[0.99956924,0.00008684219,0.00008707168,0.0001938878,0.000024949068,0.000037986196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003547308,0.00003904585,0.000085339896,0.00008441681,0.00001091446,0.000011003142,0.00015604976,0.00001528799,0.0000073077713],"category_scores_gemma":[0.000046002966,0.00003314793,0.000017071501,0.00041029204,0.000012272005,0.00021805373,0.00003409076,0.000038889226,0.00004235274],"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":[0.0000025312656,0.000121972924,0.05347868,0.000023671428,0.000007057139,0.0000010682375,0.012566592,0.000009868785,0.05433394,0.0035970795,0.0000906903,0.8757669],"study_design_scores_gemma":[0.0007078488,0.00022343668,0.36186945,0.00012297508,0.000006803501,0.000054210792,0.005199461,0.0096619865,0.6185198,0.00032177556,0.002774299,0.00053798256],"about_ca_topic_score_codex":0.00027816632,"about_ca_topic_score_gemma":0.000041123963,"teacher_disagreement_score":0.8752289,"about_ca_system_score_codex":0.000015846774,"about_ca_system_score_gemma":0.000009130741,"threshold_uncertainty_score":0.13517335},"labels":[],"label_agreement":null},{"id":"W1479896847","doi":"10.1007/978-3-540-24670-1_22","title":"Hand Gesture Recognition within a Linguistics-Based Framework","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Gesture; Gesture recognition; Speech recognition; Natural language processing; Artificial intelligence","score_opus":0.02539077613193447,"score_gpt":0.2575266673691594,"score_spread":0.23213589123722495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1479896847","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":[0.000028537235,0.000443933,0.9866517,0.0006591889,0.006928133,0.000667704,0.000022249147,0.00031926227,0.0042792615],"genre_scores_gemma":[0.2585492,0.000015339581,0.73562366,0.0031290676,0.0024304749,0.000029676048,0.000028975674,0.00006970407,0.00012387635],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9946892,0.00007525303,0.0008749071,0.0020670837,0.0015070346,0.0007865082],"domain_scores_gemma":[0.99548393,0.00090194907,0.00061628065,0.0016065347,0.0010566264,0.00033470042],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011457463,0.0008001771,0.00078412413,0.0011529115,0.00046414603,0.0013933999,0.0026811524,0.0009953863,0.00005026826],"category_scores_gemma":[0.0011846942,0.00073264743,0.00021703394,0.0009850813,0.00086168735,0.00027009167,0.00053459464,0.0018662028,0.00028033459],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003863804,0.0001874707,0.00008274302,0.00045137564,0.000062982035,0.00088157353,0.0038153103,0.081000425,0.0001704999,0.066202514,0.00007564447,0.8470308],"study_design_scores_gemma":[0.0007966492,0.00039186183,0.00004853744,0.0051445784,0.000030724594,0.00011766736,3.4666658e-7,0.098088786,0.005582639,0.8841951,0.003970008,0.0016330825],"about_ca_topic_score_codex":0.000030034676,"about_ca_topic_score_gemma":0.00009830393,"teacher_disagreement_score":0.8453978,"about_ca_system_score_codex":0.0005508175,"about_ca_system_score_gemma":0.00133181,"threshold_uncertainty_score":0.99964327},"labels":[],"label_agreement":null},{"id":"W1485592096","doi":"10.1109/acv.2002.1182174","title":"Arm gesture detection in a classroom environment","year":2003,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Gesture; Task (project management); Gesture recognition; Computer vision; Artificial intelligence; Segmentation; Feature (linguistics); Identification (biology); Feature extraction; Motion (physics); Image segmentation; Human–computer interaction; Engineering","score_opus":0.011665455235463815,"score_gpt":0.2041557257328556,"score_spread":0.19249027049739179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1485592096","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":[0.026263412,0.00009645176,0.92879325,0.00031365146,0.00035088623,0.00020058131,2.2280636e-7,0.000101992206,0.043879528],"genre_scores_gemma":[0.9912366,0.000012413837,0.0076246257,0.00018623892,0.000022429751,0.000027763783,2.284752e-7,0.0000037043544,0.0008859928],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992057,0.00010262489,0.0001440044,0.0002355734,0.00015667583,0.00015538983],"domain_scores_gemma":[0.9996417,0.000032294556,0.000030713138,0.00023534177,0.000008501114,0.000051460946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024708637,0.00007609191,0.00008405407,0.00008398125,0.000029751105,0.000046960573,0.00014096302,0.000065182205,0.00006627481],"category_scores_gemma":[0.000015929429,0.0000633672,0.000027772045,0.00018570627,0.0000075386197,0.00016081422,0.00002443922,0.00009966831,0.00042507134],"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":[0.000009469457,0.0012481071,0.02920535,0.000060867464,0.000053900236,0.0001617056,0.0035703443,0.0007189071,0.026875362,0.10170461,0.0013422262,0.83504915],"study_design_scores_gemma":[0.0028774536,0.00041374608,0.052555207,0.00007032881,0.000008846789,0.0004510492,0.0004889134,0.010429841,0.16052926,0.022523584,0.7485453,0.0011064794],"about_ca_topic_score_codex":0.000012203332,"about_ca_topic_score_gemma":0.0000577145,"teacher_disagreement_score":0.9649732,"about_ca_system_score_codex":0.000059880735,"about_ca_system_score_gemma":0.000013979236,"threshold_uncertainty_score":0.5463574},"labels":[],"label_agreement":null},{"id":"W1522114458","doi":"10.1109/bigmm.2015.31","title":"Evaluating 3D Hand Motion with a Softkinetic Camera","year":2015,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hidden Markov model; Gesture; Computer science; Artificial intelligence; Gesture recognition; Computer vision; Focus (optics); Motion (physics); Interface (matter); Human–computer interaction","score_opus":0.09195651445968901,"score_gpt":0.31926387979608856,"score_spread":0.22730736533639956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522114458","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":[0.039187696,0.000055690616,0.9479352,0.0004227375,0.00017720697,0.00015578502,2.4681586e-7,0.00012984953,0.011935562],"genre_scores_gemma":[0.885492,3.590416e-7,0.11317124,0.00020243968,0.00008447784,0.000019956406,0.0000012323436,0.000005016514,0.0010232327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990438,0.00008629507,0.00012560349,0.0002208721,0.00037979276,0.00014358485],"domain_scores_gemma":[0.9992967,0.000039380604,0.0000534472,0.00024068271,0.0002558764,0.00011386405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049495127,0.00007714773,0.00009504839,0.000056287965,0.00005428097,0.00020280138,0.00018454182,0.000028042665,0.000015845146],"category_scores_gemma":[0.00005636218,0.000051957482,0.000013497641,0.00024984352,0.000020824511,0.0002600455,0.00004359886,0.000052138184,0.00025393686],"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":[0.000020535024,0.00012015501,0.0034922913,0.000040237504,0.000044766788,0.00003342175,0.007313387,0.002279493,0.0019464204,0.0033440765,0.0028001384,0.9785651],"study_design_scores_gemma":[0.0057229004,0.0021540022,0.005475338,0.00026185383,0.00004173517,0.0008533911,0.0006596285,0.9566695,0.009307123,0.0035675413,0.014314991,0.00097197737],"about_ca_topic_score_codex":0.000051444793,"about_ca_topic_score_gemma":0.000031938358,"teacher_disagreement_score":0.9775931,"about_ca_system_score_codex":0.000025154857,"about_ca_system_score_gemma":0.00008366429,"threshold_uncertainty_score":0.32639292},"labels":[],"label_agreement":null},{"id":"W1525670818","doi":"10.1109/icassp.2015.7179043","title":"Meta-level tracking for gestural intent recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 British Columbia","funders":"","keywords":"Computer science; Hidden Markov model; Perplexity; Artificial intelligence; Gesture; Gesture recognition; Rule-based machine translation; Generative model; Context model; Machine learning; Speech recognition; Natural language processing; Generative grammar; Language model; Object (grammar)","score_opus":0.5982216110851789,"score_gpt":0.3374335192506463,"score_spread":0.2607880918345326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1525670818","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":[0.0020399014,0.00014890482,0.98871064,0.0020504931,0.0009191119,0.00039060932,0.000011337874,0.00024312611,0.0054858746],"genre_scores_gemma":[0.7935039,0.000003497657,0.20225877,0.0011308345,0.0002981985,0.00027759373,0.000023883787,0.000013740447,0.0024896099],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989336,0.000068310525,0.00025630652,0.00028862883,0.00024002712,0.00021310462],"domain_scores_gemma":[0.99886316,0.000133288,0.0000827181,0.00022657453,0.00054122356,0.0001530165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077045016,0.0001223339,0.00022596211,0.000085920416,0.000052742314,0.00018924839,0.00031494524,0.00005754002,0.000020526079],"category_scores_gemma":[0.00013949382,0.00008557462,0.00016459209,0.00017524287,0.000012270484,0.00061514403,0.00006114363,0.000064391665,0.00025344122],"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":[0.000032727687,0.00017770768,0.00013723345,0.00006172767,0.001146277,0.000013031789,0.0037936645,0.000022780478,0.0008697068,0.011581918,0.032654807,0.9495084],"study_design_scores_gemma":[0.014486238,0.0026572498,0.0043081613,0.00030887476,0.00296241,0.0016862436,0.005057615,0.087740764,0.14919633,0.24560533,0.48116237,0.0048283995],"about_ca_topic_score_codex":0.000026433023,"about_ca_topic_score_gemma":0.000032992008,"teacher_disagreement_score":0.94468004,"about_ca_system_score_codex":0.00003941074,"about_ca_system_score_gemma":0.00005127251,"threshold_uncertainty_score":0.34896317},"labels":[],"label_agreement":null},{"id":"W1528201305","doi":"10.1109/iswc.1999.806928","title":"The WristCam as input device","year":2003,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Computer science; Wearable computer; Artificial intelligence; Computer graphics (images); Rest (music); Movement (music); Position (finance); Set (abstract data type); Wrist; Base (topology); Movement control; Acoustics; Mathematics; Embedded system; Physical medicine and rehabilitation","score_opus":0.01719567399229465,"score_gpt":0.2560064446068901,"score_spread":0.23881077061459544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528201305","genre_codex":"other","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":[0.0021909939,0.00026913176,0.30064574,0.0021417593,0.00070483,0.00012999403,1.3627677e-7,0.00016516524,0.6937522],"genre_scores_gemma":[0.97466,0.000020372563,0.006422614,0.0018012583,0.000056993827,0.00002042999,1.9316568e-7,0.0000045927495,0.017013581],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927,0.00011764565,0.00012334941,0.00015371271,0.000176562,0.00015868577],"domain_scores_gemma":[0.99927986,0.00018742666,0.000032495846,0.00036547042,0.00007413104,0.00006064234],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044778863,0.00005947207,0.000056662502,0.000019200153,0.00017873617,0.00022257333,0.00040460727,0.0000290073,0.000036358586],"category_scores_gemma":[0.00010284857,0.000033487424,0.000029910965,0.000239331,0.00001636343,0.00014048425,0.00003969184,0.00005926048,0.0022751715],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.8743157e-7,0.000020491118,0.00030452237,0.000004050656,0.000014456705,0.000008414357,0.0003249446,0.000001985357,0.00012085791,0.8939573,0.014368062,0.09087422],"study_design_scores_gemma":[0.00010530846,0.00001883453,0.00022007304,0.0000051621587,0.0000010730882,0.00007898052,0.00007129044,0.00020894824,0.003220853,0.0071234005,0.9888626,0.00008351771],"about_ca_topic_score_codex":0.0000146056145,"about_ca_topic_score_gemma":0.00003018215,"teacher_disagreement_score":0.9744945,"about_ca_system_score_codex":0.0000113466285,"about_ca_system_score_gemma":0.00004693279,"threshold_uncertainty_score":0.99850166},"labels":[],"label_agreement":null},{"id":"W1548468817","doi":"","title":"Synthetic Handwritten Gesture Generation Using Sigma-Lognormal Model for Evolving Handwriting Classifiers","year":2011,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Gesture; Handwriting; Computer science; Gesture recognition; Artificial intelligence; Classifier (UML); Speech recognition; Handwriting recognition; Context (archaeology); Pattern recognition (psychology); Feature extraction","score_opus":0.050713990233209286,"score_gpt":0.2601478140051852,"score_spread":0.20943382377197595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548468817","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":[0.0069319056,0.0023543446,0.9840656,0.0010586285,0.000994958,0.0026246682,0.00013577286,0.00125982,0.00057430606],"genre_scores_gemma":[0.57515067,0.00008683697,0.42141733,0.000705986,0.00069626886,0.001384577,0.00008508539,0.00011328553,0.00035995286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99448323,0.00037443306,0.0012609438,0.001783706,0.0007195824,0.0013781023],"domain_scores_gemma":[0.9954359,0.00022457508,0.0010147904,0.002053606,0.00075444154,0.00051670056],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002177356,0.00094234804,0.0010474116,0.00094773155,0.0008253838,0.0014791051,0.002070254,0.0015549349,0.000009146658],"category_scores_gemma":[0.000451192,0.0009840982,0.00063077576,0.00045618147,0.00011375632,0.00101492,0.0015081201,0.0013768547,0.000003980481],"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":[0.00013348596,0.0006919123,0.003143811,0.0016265127,0.000680171,0.00015853322,0.0072309035,0.7886761,0.050708294,0.061957166,0.006903116,0.07809002],"study_design_scores_gemma":[0.0005022611,0.00006900254,0.00014553514,0.0006093908,0.00012513246,0.00018808305,0.00006914953,0.98107654,0.0075098933,0.0084819915,0.00015830906,0.0010647406],"about_ca_topic_score_codex":0.0017974951,"about_ca_topic_score_gemma":0.0010681549,"teacher_disagreement_score":0.56821877,"about_ca_system_score_codex":0.0009275023,"about_ca_system_score_gemma":0.0011494895,"threshold_uncertainty_score":0.99974126},"labels":[],"label_agreement":null},{"id":"W1564392261","doi":"10.1109/access.2015.2432679","title":"An Elicitation Study on Gesture Preferences and Memorability Toward a Practical Hand-Gesture Vocabulary for Smart Televisions","year":2015,"lang":"en","type":"article","venue":"IEEE Access","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Vocabulary; Gesture; Computer science; Gesture recognition; Session (web analytics); Human–computer interaction; Multimedia; Test (biology); Natural language processing; Artificial intelligence; World Wide Web; Linguistics","score_opus":0.21802118540415585,"score_gpt":0.42836567112045126,"score_spread":0.2103444857162954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564392261","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":[0.8245546,0.00006959249,0.17012988,0.002143746,0.00096105016,0.0016953634,0.00001577748,0.00015725404,0.00027272283],"genre_scores_gemma":[0.9952063,0.0000042110214,0.0038158423,0.00037124357,0.0002875765,0.0002727008,0.000010701285,0.000012773667,0.00001865036],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99764514,0.00046101198,0.0003325997,0.0007287334,0.0005699026,0.00026261102],"domain_scores_gemma":[0.99776083,0.0005839996,0.0001596695,0.00061654823,0.00056297984,0.00031598442],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012912191,0.00022252895,0.0003146506,0.00012714927,0.0002115367,0.0010651227,0.00069291395,0.00015427115,0.0000019847137],"category_scores_gemma":[0.0005509586,0.00016575851,0.00004517274,0.0003548994,0.00006355256,0.0018082751,0.00010551673,0.00024044278,0.000014797991],"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":[0.0015989769,0.014562414,0.36633363,0.0007080897,0.00058645,0.00027645615,0.13409781,0.0007250511,0.0033820574,0.0043217917,0.043785755,0.42962152],"study_design_scores_gemma":[0.008621685,0.011974182,0.9007435,0.00034817972,0.00025532773,0.00026392323,0.008650356,0.016919473,0.008037218,0.027310532,0.014879373,0.001996239],"about_ca_topic_score_codex":0.000074855634,"about_ca_topic_score_gemma":0.00016122595,"teacher_disagreement_score":0.5344099,"about_ca_system_score_codex":0.00004036488,"about_ca_system_score_gemma":0.00020048465,"threshold_uncertainty_score":0.99997187},"labels":[],"label_agreement":null},{"id":"W1567602382","doi":"10.1109/crv.2005.67","title":"Recognizing Hand-Raising Gestures using HMM","year":2005,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gesture; Hidden Markov model; Similarity (geometry); Computer science; Gesture recognition; Security token; Speech recognition; Artificial intelligence","score_opus":0.06586464395511359,"score_gpt":0.2952127063509651,"score_spread":0.22934806239585148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1567602382","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":[0.035821237,0.00033189202,0.9337234,0.001360121,0.00053162826,0.00013301149,6.0084915e-7,0.00031624807,0.027781844],"genre_scores_gemma":[0.7865763,0.000005687439,0.21122566,0.00088963384,0.00051971263,0.0000036567872,7.998804e-7,0.000010929485,0.0007676419],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987296,0.00007635479,0.00026945095,0.00035426614,0.00026080344,0.0003095113],"domain_scores_gemma":[0.9992621,0.00009616264,0.00008376464,0.00033449175,0.00011002076,0.00011344658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038204264,0.00014022742,0.00016735177,0.00014314422,0.00024510722,0.00049402146,0.00039521657,0.00007928899,0.000053485288],"category_scores_gemma":[0.00004701446,0.000117716874,0.000068493835,0.0003375823,0.000027158283,0.0007789823,0.00012872317,0.000120553304,0.00033339986],"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":[0.0000038315584,0.00007776819,0.00086275773,0.000027309326,0.000046114565,0.000023873286,0.0030740004,0.0005820075,0.034987856,0.0052961367,0.003854121,0.95116425],"study_design_scores_gemma":[0.0022180162,0.00012492064,0.0030667062,0.00060350116,0.000045546505,0.0014124938,0.00038757536,0.2129937,0.19983952,0.0056001404,0.57171136,0.0019965179],"about_ca_topic_score_codex":0.000042768457,"about_ca_topic_score_gemma":0.000044952038,"teacher_disagreement_score":0.9491677,"about_ca_system_score_codex":0.000051747502,"about_ca_system_score_gemma":0.00005309749,"threshold_uncertainty_score":0.48003548},"labels":[],"label_agreement":null},{"id":"W1604243478","doi":"10.1007/978-3-540-24837-8_5","title":"A Vision-Based Gestural Guidance Interface for Mobile Robotic Platforms","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Gesture","score_opus":0.018607334491236808,"score_gpt":0.27550978532548265,"score_spread":0.25690245083424584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604243478","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":[0.00008738783,0.0009826156,0.99230546,0.00059969636,0.003371389,0.0016605956,0.000013275905,0.00030001852,0.0006795687],"genre_scores_gemma":[0.48466286,0.000012751967,0.51250964,0.0014672184,0.00065997714,0.00017345644,0.000013406381,0.00007136513,0.0004292999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950202,0.000021988706,0.00086996023,0.0020177776,0.0011313261,0.0009387656],"domain_scores_gemma":[0.9961799,0.0009261002,0.00048868475,0.0015852236,0.0005456402,0.00027446888],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001103984,0.00076905783,0.0008556701,0.0008563732,0.00033275085,0.000811625,0.0037700073,0.00048422717,0.000022667935],"category_scores_gemma":[0.000128047,0.0006318986,0.00030138713,0.0006624744,0.0005643888,0.00081175845,0.00070646947,0.00074320094,0.000109466295],"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":[0.000020106238,0.00006929344,0.000021498301,0.00022819635,0.000020597883,0.000052121188,0.00074868905,0.5952761,0.0002201662,0.0077471095,0.00005762765,0.39553845],"study_design_scores_gemma":[0.0014588463,0.0011790348,0.0000675713,0.003083259,0.00001814888,0.0001282074,4.991116e-7,0.81206363,0.0049207592,0.17235024,0.0031833719,0.0015464476],"about_ca_topic_score_codex":0.00001591099,"about_ca_topic_score_gemma":0.000065713764,"teacher_disagreement_score":0.48457548,"about_ca_system_score_codex":0.00071496284,"about_ca_system_score_gemma":0.0012150423,"threshold_uncertainty_score":0.9996132},"labels":[],"label_agreement":null},{"id":"W1727782192","doi":"10.1109/icpr.2000.903744","title":"Visual tracking of hand posture with occlusion handling","year":2002,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Visibility; Gesture; Occlusion; Tracking (education); Kinematics; Pose; Inverse kinematics; Psychology; Robot","score_opus":0.019417583664141647,"score_gpt":0.23617641823751076,"score_spread":0.2167588345733691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1727782192","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":[0.19570668,0.00027199404,0.79265386,0.00042039645,0.0001636811,0.000141772,7.066219e-7,0.0001000789,0.010540807],"genre_scores_gemma":[0.9915814,0.000006507716,0.0076513942,0.00012063521,0.000054332028,0.0000015792366,5.71893e-7,0.000005029578,0.0005785962],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920034,0.00003468789,0.00016444214,0.00019912867,0.00026242476,0.00013898351],"domain_scores_gemma":[0.9995069,0.000057002875,0.000077170815,0.0001703546,0.00013406783,0.00005450814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013530222,0.00008598834,0.00014105916,0.0000803048,0.00010171415,0.00011010696,0.00020764903,0.000049628852,0.000062707935],"category_scores_gemma":[0.000012680473,0.00005666078,0.0000306557,0.00028701845,0.00002647419,0.00029579824,0.000059901624,0.00006987535,0.00004053932],"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":[0.00007197608,0.0011139634,0.029902957,0.00038108212,0.00022827803,0.00037003407,0.03198041,0.00055150734,0.17482539,0.007884823,0.0043663746,0.7483232],"study_design_scores_gemma":[0.0067317737,0.002602503,0.02744802,0.0017214513,0.000065247215,0.0019080758,0.0011545132,0.3504049,0.5856956,0.0006838108,0.019787036,0.0017970655],"about_ca_topic_score_codex":0.000013155413,"about_ca_topic_score_gemma":0.000024103076,"teacher_disagreement_score":0.79587466,"about_ca_system_score_codex":0.00000849945,"about_ca_system_score_gemma":0.000010338074,"threshold_uncertainty_score":0.23105596},"labels":[],"label_agreement":null},{"id":"W17515232","doi":"","title":"Sign Language MMS to Make Cell Phones Accessible to the Deaf and Hard-of-hearing Community.","year":2007,"lang":"en","type":"article","venue":"Acta Orthopaedica Belgica","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Phone; Sign language; Videotelephony; Multimedia; Deaf community; Context (archaeology); Camera phone; Animation; Human–computer interaction; Artificial intelligence; Computer graphics (images)","score_opus":0.026268880211918918,"score_gpt":0.27643633967708725,"score_spread":0.25016745946516833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W17515232","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":[0.9038201,0.00044957956,0.07916389,0.0035135674,0.00043197002,0.0005983001,0.000010859245,0.00016775643,0.011843969],"genre_scores_gemma":[0.98700905,0.000020287902,0.010473486,0.0016150024,0.00014872474,0.000019727948,0.0000031017,0.000016448656,0.0006941874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981501,0.0002603268,0.00039278757,0.00031064686,0.00046546513,0.00042065317],"domain_scores_gemma":[0.9978731,0.00064359343,0.00012935621,0.00089748285,0.000109745284,0.00034667138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022044415,0.00018213692,0.00026415125,0.00020848626,0.00038478387,0.0001801137,0.0012213185,0.00009279202,0.000027494001],"category_scores_gemma":[0.0001696604,0.00012982659,0.00007013834,0.0009814693,0.000048738428,0.00018881272,0.0008016355,0.00036334046,0.000097055854],"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":[0.00012571659,0.0006195021,0.030948564,0.00041184181,0.000121858335,0.00016111362,0.092357256,0.000035288107,0.10221441,0.0010413851,0.015276703,0.7566864],"study_design_scores_gemma":[0.0022560295,0.0011827011,0.35825822,0.0007893457,0.00008662559,0.0003056875,0.012136416,0.00048416978,0.10699592,0.0002894125,0.5155902,0.0016253128],"about_ca_topic_score_codex":0.00031110505,"about_ca_topic_score_gemma":0.00087474444,"teacher_disagreement_score":0.75506103,"about_ca_system_score_codex":0.000019877636,"about_ca_system_score_gemma":0.000057206908,"threshold_uncertainty_score":0.52941746},"labels":[],"label_agreement":null},{"id":"W1792293334","doi":"","title":"Evaluation of an on-line adaptive gesture interface with command prediction","year":2005,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Hand Gesture Recognition Systems","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":"University of Toronto","funders":"","keywords":"Gesture; Computer science; Interface (matter); Gesture recognition; Adaptation (eye); Exploit; Human–computer interaction; Line (geometry); Speech recognition; User interface; Artificial intelligence; Operating system","score_opus":0.06802720059281632,"score_gpt":0.32006932914932923,"score_spread":0.2520421285565129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1792293334","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":[0.30371687,0.00034877151,0.69138014,0.00076504,0.0003390099,0.00053691113,0.000027276576,0.00015423968,0.0027317645],"genre_scores_gemma":[0.9948678,0.000013520251,0.0047740564,0.000113764145,0.00011519954,0.000035735906,0.0000061486967,0.00001431005,0.00005948668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997551,0.0004220784,0.00034000562,0.00043211336,0.0010653677,0.00018943963],"domain_scores_gemma":[0.9979215,0.00009351887,0.00023314054,0.00062932103,0.0010244858,0.000098009084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017291933,0.00019810599,0.00022168802,0.00023605395,0.00008236999,0.00007152568,0.0005268752,0.00017103319,0.000016792266],"category_scores_gemma":[0.00004984975,0.00015575677,0.000049879785,0.0004911651,0.000087100714,0.00061616814,0.00007358904,0.00043307542,0.00002850864],"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":[0.0009355299,0.0025823922,0.001251257,0.00012287349,0.0010405107,0.000009491958,0.024519032,0.3589008,0.0056793178,0.03260405,0.0035224007,0.5688324],"study_design_scores_gemma":[0.0021253487,0.003456977,0.001728718,0.00048315056,0.000114705836,0.000082621635,0.00042566416,0.92360055,0.062783435,0.0019445281,0.0029125058,0.00034178878],"about_ca_topic_score_codex":0.00001912652,"about_ca_topic_score_gemma":0.0002982637,"teacher_disagreement_score":0.6911509,"about_ca_system_score_codex":0.00008576565,"about_ca_system_score_gemma":0.000086945474,"threshold_uncertainty_score":0.6351577},"labels":[],"label_agreement":null},{"id":"W180773419","doi":"","title":"Implementation of the Discrete Hidden Markov Model in Max/MSP Environment.","year":2005,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Gesture; Hidden Markov model; Computer science; Alphabet; Speech recognition; Gesture recognition; Place of articulation; Set (abstract data type); Dynamic time warping; Software; Artificial intelligence; Computer vision; Programming language; Consonant; Linguistics","score_opus":0.013477844791859924,"score_gpt":0.25669247846775106,"score_spread":0.24321463367589113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W180773419","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":[0.18052053,0.00006019203,0.79314953,0.0067712096,0.00012518192,0.00056506536,0.000010586783,0.000045774414,0.018751953],"genre_scores_gemma":[0.96946454,0.000008703478,0.029302083,0.00020433005,0.000022743425,0.000017550285,0.000001077261,0.0000031752515,0.00097577856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999237,0.000057098416,0.00022305032,0.00015279494,0.00021090679,0.00011914322],"domain_scores_gemma":[0.999593,0.000019212517,0.00007043037,0.00028460173,0.000008380639,0.000024351337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020300206,0.000060729766,0.00007750387,0.000042260457,0.000023966979,0.000022259639,0.00034645904,0.000024346162,0.00010386813],"category_scores_gemma":[0.0000019241643,0.00003955914,0.000037548125,0.00010917182,0.00001463954,0.00025620926,0.00013696762,0.0000444164,0.000043611464],"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":[0.0000050213293,0.00008425081,0.03526289,0.000025594441,0.000024061215,0.0000012991582,0.004243548,0.002046683,0.0064375647,0.014049182,0.0027552515,0.9350647],"study_design_scores_gemma":[0.0029118587,0.000082114464,0.16699989,0.00007553975,0.000018084278,0.000025387935,0.0013680883,0.7344287,0.07641648,0.0063971244,0.010641705,0.0006350459],"about_ca_topic_score_codex":0.0000675822,"about_ca_topic_score_gemma":0.00024776574,"teacher_disagreement_score":0.9344296,"about_ca_system_score_codex":0.00003940924,"about_ca_system_score_gemma":0.000022370845,"threshold_uncertainty_score":0.1613175},"labels":[],"label_agreement":null},{"id":"W1897317492","doi":"10.1109/robot.2000.844839","title":"Visual hand posture tracking in a gripper guiding application","year":2002,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Artificial intelligence; Teleoperation; Computer science; Kinematics; Inverse kinematics; Robotic arm; Robot; Gesture; Task (project management); Position (finance); Tracking (education); Engineering","score_opus":0.03766624419180814,"score_gpt":0.2755787195896155,"score_spread":0.23791247539780735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1897317492","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":[0.0745241,0.00031420507,0.9010732,0.0018336854,0.00021970921,0.0003065635,4.3008316e-7,0.00017855292,0.021549553],"genre_scores_gemma":[0.996084,0.0000070189735,0.0029222332,0.00037532297,0.00009328065,0.000027641623,9.762076e-7,0.000004815875,0.0004847536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991667,0.00004318626,0.00019296091,0.00026014264,0.00016906828,0.00016789911],"domain_scores_gemma":[0.9996364,0.00004279931,0.000042777843,0.00017846028,0.000052573017,0.00004700087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020939429,0.00007990555,0.00010079403,0.000116686504,0.0000674572,0.00019982761,0.00024039317,0.00006485977,0.000040100582],"category_scores_gemma":[0.000019391517,0.000067012494,0.000027627426,0.0004098118,0.00001038259,0.00038701916,0.00004719849,0.000060308037,0.00029490367],"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":[0.00000321699,0.00028152927,0.013436637,0.00004136286,0.000016989101,0.000030768566,0.0076053287,0.00005313459,0.066131055,0.013293595,0.004069471,0.89503694],"study_design_scores_gemma":[0.003139432,0.0002468391,0.06534472,0.0003322155,0.000012552256,0.00045796228,0.00075433904,0.7185216,0.055661216,0.0027929214,0.1512318,0.001504428],"about_ca_topic_score_codex":0.000023919347,"about_ca_topic_score_gemma":0.000060437174,"teacher_disagreement_score":0.9215599,"about_ca_system_score_codex":0.000028750028,"about_ca_system_score_gemma":0.000005771927,"threshold_uncertainty_score":0.37904885},"labels":[],"label_agreement":null},{"id":"W1906424745","doi":"10.1109/icsmc.1998.728080","title":"Intelligent multivariable fault tolerant control systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Alberta","funders":"","keywords":"Multivariable calculus; Actuator; Fault tolerance; Control theory (sociology); Control engineering; Control system; Computer science; Controller (irrigation); Fault (geology); Control (management); Stability (learning theory); Engineering; Artificial intelligence; Distributed computing","score_opus":0.029954721548803655,"score_gpt":0.23164623169126677,"score_spread":0.20169151014246312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1906424745","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":[0.00033695417,0.0005688555,0.93752265,0.00059172773,0.0013303627,0.00038651185,0.0000030467595,0.00033415144,0.05892572],"genre_scores_gemma":[0.9864131,0.000014752705,0.0034307102,0.00045350537,0.0001694575,0.000062979765,7.1125703e-7,0.000008043719,0.009446692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985667,0.00012542441,0.0003533392,0.0003431044,0.00030977896,0.00030169298],"domain_scores_gemma":[0.99899405,0.00015481876,0.0000805558,0.0004707452,0.00015062345,0.00014921895],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031351187,0.00014313335,0.00024105792,0.00008619443,0.00008692809,0.00028556297,0.00050920586,0.00007803863,0.00023666908],"category_scores_gemma":[0.000035424007,0.0001060538,0.00006655932,0.00025907607,0.000014980633,0.0002958705,0.000054824264,0.00010241235,0.0026558072],"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":[0.000027081676,0.0016701153,0.0021080466,0.00039850816,0.0006454923,0.00036936312,0.009046185,0.030052653,0.0070953397,0.4021194,0.18925992,0.35720792],"study_design_scores_gemma":[0.0005793627,0.00004348776,0.000026328762,0.000045058736,0.0000043490227,0.00008311364,0.00007454439,0.8466306,0.0006817914,0.00006896765,0.15156776,0.00019463956],"about_ca_topic_score_codex":0.00012439433,"about_ca_topic_score_gemma":0.0000038781977,"teacher_disagreement_score":0.9860762,"about_ca_system_score_codex":0.00004027217,"about_ca_system_score_gemma":0.000010605885,"threshold_uncertainty_score":0.9981207},"labels":[],"label_agreement":null},{"id":"W1906672595","doi":"10.1109/iembs.1993.979098","title":"Clustering of motor unit action potentials using shape and temporal information","year":2005,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Action (physics); Motor unit; Artificial intelligence; Pattern recognition (psychology); Data mining; Physics; Psychology; Neuroscience","score_opus":0.056869100637666266,"score_gpt":0.2884101825804008,"score_spread":0.23154108194273454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1906672595","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":[0.26014352,0.000011974625,0.73867774,0.0001286473,0.00011253633,0.00009353804,0.0000010800491,0.000046219473,0.00078475603],"genre_scores_gemma":[0.94563335,0.0000034337518,0.054161374,0.00007846935,0.00005317231,0.0000015422303,0.0000016677759,0.0000016248227,0.00006536135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999488,0.000026438973,0.00021919714,0.00006740922,0.00012484344,0.00007413166],"domain_scores_gemma":[0.99966466,0.000014714836,0.000110286484,0.00009645381,0.00007894522,0.00003492453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018562793,0.000050204355,0.00008070694,0.00011483935,0.00004183629,0.000084745916,0.00008706937,0.00003655127,0.000021206033],"category_scores_gemma":[0.000009256995,0.000043823868,0.000017412174,0.00012096491,0.000009059691,0.0017765749,0.00006267279,0.000029566067,0.000016486418],"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":[0.000014760938,0.000033516226,0.0040434273,0.00016875532,0.000029239969,7.6458025e-7,0.0017311812,0.00094066054,0.040933166,0.0008304743,0.000101781756,0.9511723],"study_design_scores_gemma":[0.00026833286,0.000028749331,0.004358069,0.000036354824,0.0000037409666,0.00004727156,0.000116340794,0.9841168,0.0072964104,0.000034549652,0.0036039623,0.00008939592],"about_ca_topic_score_codex":0.000058547175,"about_ca_topic_score_gemma":0.000019277839,"teacher_disagreement_score":0.9831762,"about_ca_system_score_codex":0.000012673197,"about_ca_system_score_gemma":0.00001741333,"threshold_uncertainty_score":0.17870854},"labels":[],"label_agreement":null},{"id":"W1963819810","doi":"10.1109/icca.2014.6871127","title":"Combining features for Chinese sign language recognition with Kinect","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Sign language; Support vector machine; Gesture; Feature (linguistics); Computer vision; Gesture recognition; Pattern recognition (psychology); Feature extraction; Representation (politics); Wired glove; Kinematics; Speech recognition","score_opus":0.010887781160225917,"score_gpt":0.24507077208065842,"score_spread":0.2341829909204325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963819810","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":[0.03565535,0.000030590752,0.93634915,0.0005065231,0.00023077303,0.00033585457,0.0000037507236,0.00033090363,0.026557097],"genre_scores_gemma":[0.94349676,6.804124e-7,0.054771274,0.0005933566,0.00018960689,0.0000753883,0.000022656048,0.000011070328,0.000839202],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991956,0.00007255451,0.00012384579,0.0002677074,0.00015867614,0.00018163359],"domain_scores_gemma":[0.9991904,0.00031538145,0.000066693116,0.00024839825,0.000111110814,0.00006805323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035535783,0.00012147652,0.0001579017,0.000080797196,0.0000853199,0.00014732443,0.0002421561,0.00004974326,0.00001391132],"category_scores_gemma":[0.000096279065,0.00007646907,0.000045275672,0.00022591541,0.000012498601,0.00025324785,0.000030583873,0.00006798527,0.000065121494],"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":[0.00009659523,0.00018449055,0.002420107,0.00019583784,0.00010633933,0.000015376741,0.0058349473,0.00005103913,0.006983968,0.01345963,0.008116665,0.962535],"study_design_scores_gemma":[0.060797114,0.018139953,0.19695702,0.003264019,0.00036194175,0.0038135767,0.0052402834,0.22113173,0.22716227,0.12857117,0.12214066,0.012420279],"about_ca_topic_score_codex":0.000027651196,"about_ca_topic_score_gemma":0.000064829626,"teacher_disagreement_score":0.9501147,"about_ca_system_score_codex":0.000009177171,"about_ca_system_score_gemma":0.000015619256,"threshold_uncertainty_score":0.3118318},"labels":[],"label_agreement":null},{"id":"W1964293000","doi":"10.1504/ijcat.2007.014061","title":"Towards understanding expression for tele-operation","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Applications in Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Ambiguity; Gesture; Expression (computer science); Human–computer interaction; Facial expression; Flexibility (engineering); Domain (mathematical analysis); Artificial intelligence; Programming language","score_opus":0.030399873534107284,"score_gpt":0.3245996025565653,"score_spread":0.294199729022458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964293000","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":[0.0036341895,0.00007976423,0.9921431,0.0026135468,0.0008161201,0.00027172235,0.000002124673,0.000063390806,0.0003760755],"genre_scores_gemma":[0.7186466,0.000013414804,0.2808854,0.00009193244,0.00032018562,0.00002761458,0.000003076891,0.000005426273,0.000006392523],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987506,0.00001881304,0.00061205117,0.00017768127,0.0002892075,0.00015162297],"domain_scores_gemma":[0.9987526,0.00017258732,0.00034185586,0.0001923941,0.00049999193,0.000040580024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007287852,0.00009226777,0.00016246486,0.0011904566,0.000053061016,0.000084794745,0.001183851,0.00013167434,0.0000026190728],"category_scores_gemma":[0.000028741946,0.00008186097,0.00006947362,0.00040136863,0.00003953943,0.0003357209,0.00013016023,0.00017828782,0.000004407498],"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":[0.000030090137,0.00021599856,0.0018835255,0.000010480778,0.000057497888,0.000021197424,0.00043148448,0.00046789765,0.0060341824,0.61618346,0.0006218079,0.3740424],"study_design_scores_gemma":[0.004866173,0.00059318694,0.0034888212,0.00041533256,0.000018480256,0.0016541368,0.0009184838,0.029956963,0.14771698,0.66787374,0.1418399,0.00065782183],"about_ca_topic_score_codex":0.0000011462356,"about_ca_topic_score_gemma":0.0000059916015,"teacher_disagreement_score":0.7150124,"about_ca_system_score_codex":0.0002834291,"about_ca_system_score_gemma":0.00007742679,"threshold_uncertainty_score":0.33381936},"labels":[],"label_agreement":null},{"id":"W1964708881","doi":"10.1145/1957656.1957809","title":"Selecting and commanding groups in a multi-robot vision based system","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":14,"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":"Gesture; Robot; Computer science; Task (project management); Human–computer interaction; Artificial intelligence; Computer vision; Population; Mobile robot; Engineering","score_opus":0.06836443820562008,"score_gpt":0.2688403118521813,"score_spread":0.20047587364656125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964708881","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":[0.047211178,0.00004660619,0.94709516,0.000039685372,0.00017658497,0.00018480657,2.0748868e-7,0.00021725887,0.0050285053],"genre_scores_gemma":[0.9256309,6.9642044e-7,0.074239,0.00006235221,0.0000134106485,0.000012640125,3.535247e-7,0.0000050510375,0.000035541372],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909735,0.00013221827,0.0002144445,0.00026057643,0.000115707524,0.00017972599],"domain_scores_gemma":[0.99955785,0.0000982992,0.000059703834,0.00017774594,0.00003903168,0.0000673663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061513024,0.00009037717,0.00014575868,0.00016943789,0.00008067643,0.00008947804,0.00018760948,0.00005251332,0.0000046524056],"category_scores_gemma":[0.000018075816,0.0000735653,0.000020348214,0.00032693322,0.000010227078,0.00028765277,0.00008090118,0.00008845705,0.000026774442],"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":[0.000086565,0.0011353884,0.62782526,0.0020613095,0.0000901179,0.0005565768,0.042501472,0.0001778456,0.019450666,0.080274895,0.00040873067,0.22543116],"study_design_scores_gemma":[0.002120174,0.00014728651,0.07441923,0.0007735025,0.0000047600615,0.0001508311,0.0008965969,0.914811,0.0060805986,0.000071623406,0.00013868582,0.0003856866],"about_ca_topic_score_codex":0.00019050065,"about_ca_topic_score_gemma":0.0002033211,"teacher_disagreement_score":0.91463315,"about_ca_system_score_codex":0.000040741328,"about_ca_system_score_gemma":0.000014820167,"threshold_uncertainty_score":0.2999906},"labels":[],"label_agreement":null},{"id":"W1965759840","doi":"10.1109/tim.2014.2351331","title":"Dynamic Sign Language Recognition for Smart Home Interactive Application Using Stochastic Linear Formal Grammar","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":84,"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; University of Ottawa","funders":"","keywords":"Computer science; Gesture; Gesture recognition; Sign language; Artificial intelligence; Modality (human–computer interaction); Context (archaeology); Support vector machine; Hidden Markov model; Grammar; Speech recognition; Computer vision; Natural language processing; Pattern recognition (psychology)","score_opus":0.034739943132476664,"score_gpt":0.276420089194616,"score_spread":0.24168014606213936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965759840","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":[0.0439728,0.000008857404,0.9541472,0.00012353777,0.00066308264,0.0009027956,0.000024644743,0.000105926585,0.00005115154],"genre_scores_gemma":[0.97760576,0.000004632657,0.021767054,0.00017339384,0.00003519583,0.0003628817,0.000020268806,0.00001400187,0.00001683513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986934,0.000101621205,0.000301526,0.00033450229,0.00036325344,0.00020567731],"domain_scores_gemma":[0.9992651,0.00007470454,0.0001502258,0.00017559966,0.00023685073,0.000097566095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057828194,0.00016026742,0.00015146131,0.00021426586,0.00027803594,0.00009210121,0.00010566816,0.00006422017,0.000005514173],"category_scores_gemma":[0.000009958219,0.0001602201,0.000069686685,0.00017548488,0.000023147499,0.00060409884,0.0000018158547,0.000109790184,0.000026329491],"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":[0.00009272343,0.00015627264,0.0000030279023,0.000062088424,0.00005906113,1.6940945e-7,0.0014443793,0.002300101,0.023961501,0.00010640071,0.000004294644,0.97181],"study_design_scores_gemma":[0.0034366858,0.0006161719,0.00020810502,0.00020605154,0.00010843259,0.000038984697,0.0010454815,0.94530934,0.04740394,0.0009257139,0.00024156018,0.00045955006],"about_ca_topic_score_codex":0.000038290655,"about_ca_topic_score_gemma":0.00009713872,"teacher_disagreement_score":0.97135043,"about_ca_system_score_codex":0.00017932706,"about_ca_system_score_gemma":0.000041282186,"threshold_uncertainty_score":0.6533586},"labels":[],"label_agreement":null},{"id":"W1974321641","doi":"10.1109/icsens.2011.6127321","title":"Touchless capacitive sensor for hand gesture detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Capacitive sensing; Computer science; Electronics; Mobile device; Tracking (education); Accelerometer; Interface (matter); Gesture; Gesture recognition; Computer hardware; Embedded system; Real-time computing; Artificial intelligence; Electrical engineering; Engineering","score_opus":0.05130527225954943,"score_gpt":0.2336912301373029,"score_spread":0.18238595787775347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974321641","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":[0.01693359,0.000018169198,0.9652916,0.000100305246,0.00063508184,0.0003467466,0.000005069081,0.00015571671,0.016513683],"genre_scores_gemma":[0.96926975,0.0000014792574,0.028458955,0.00020807561,0.00014456338,0.00008474745,0.0000010982417,0.00000707877,0.001824259],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926716,0.000043208594,0.00013504639,0.00026405015,0.00010970493,0.00018081085],"domain_scores_gemma":[0.999378,0.000073228875,0.000053686254,0.00022341854,0.00019905312,0.000072568866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016390072,0.00009730077,0.000115981544,0.00006869471,0.00013938386,0.00008589757,0.00020146798,0.00008491556,0.000022208727],"category_scores_gemma":[0.00003225033,0.00007592186,0.000060684182,0.00014635068,0.000028692868,0.00026450685,0.000030523304,0.000064330554,0.00009328952],"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":[0.00015384656,0.00042054828,0.001205401,0.00024059857,0.0002855809,0.00005596382,0.051798027,0.0000064605233,0.106716834,0.06620443,0.004988311,0.767924],"study_design_scores_gemma":[0.0011380394,0.00037377773,0.005484392,0.000044182732,0.00001830716,0.00017953575,0.0009228065,0.0034122125,0.9587998,0.006455076,0.022687795,0.0004841048],"about_ca_topic_score_codex":0.00005607834,"about_ca_topic_score_gemma":0.00013084224,"teacher_disagreement_score":0.95233613,"about_ca_system_score_codex":0.000019269566,"about_ca_system_score_gemma":0.000018475283,"threshold_uncertainty_score":0.30960035},"labels":[],"label_agreement":null},{"id":"W1976293493","doi":"10.1109/icfhr.2014.45","title":"Neuromuscular Representation and Synthetic Generation of Handwritten Whiteboard Notes","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Whiteboard; Computer science; Representation (politics); Speech recognition; Artificial intelligence; Natural language processing; Multimedia","score_opus":0.03676898234378707,"score_gpt":0.2510036472407998,"score_spread":0.21423466489701276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976293493","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":[0.14988977,0.00003397517,0.8466387,0.00084187154,0.00016700839,0.000096527554,3.0756794e-7,0.00004815046,0.0022836768],"genre_scores_gemma":[0.98739773,0.0000075802022,0.012298376,0.00011744116,0.0000691054,0.000007039837,0.0000018236868,0.0000032372748,0.000097676195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992771,0.00013102687,0.00015935797,0.00021079773,0.0001526294,0.00006907373],"domain_scores_gemma":[0.99944943,0.00011134856,0.000059408234,0.0002600678,0.00008257503,0.00003719655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024907378,0.00005446711,0.000103465114,0.00006389409,0.000036142035,0.00006962141,0.00010848186,0.000029985567,0.0000060804027],"category_scores_gemma":[0.0001244619,0.00004442492,0.000026125655,0.00012508237,0.000018765826,0.00019501608,0.000043495358,0.000025408985,0.000014818351],"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":[0.0000057202956,0.00008919214,0.00904807,0.0001172658,0.00004470061,0.000004621386,0.0019831886,0.00036437198,0.31600568,0.056038726,0.00091413636,0.61538434],"study_design_scores_gemma":[0.0010573763,0.0002839798,0.025070427,0.00007771725,0.000031175394,0.000119975506,0.000058755337,0.59069204,0.3727189,0.0037919169,0.005691562,0.00040616773],"about_ca_topic_score_codex":0.000027857543,"about_ca_topic_score_gemma":0.000012314194,"teacher_disagreement_score":0.83750796,"about_ca_system_score_codex":0.0000026469156,"about_ca_system_score_gemma":0.000006054419,"threshold_uncertainty_score":0.18115957},"labels":[],"label_agreement":null},{"id":"W1977029873","doi":"10.4108/icst.mobihealth.2014.257526","title":"Automated Diagnosis of Knee Pathology Using Sensory Data","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"McGill University","funders":"","keywords":"Goniometer; Wearable computer; Computer science; Artificial intelligence; Data set; Set (abstract data type); Domain (mathematical analysis); Medical diagnosis; Computer vision; Pattern recognition (psychology); Medicine; Pathology; Embedded system; Mathematics","score_opus":0.08194083523465476,"score_gpt":0.31041091893187667,"score_spread":0.2284700836972219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977029873","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":[0.146417,0.000054349443,0.84650034,0.0002922817,0.00058768486,0.00013317859,0.000015591071,0.00065857393,0.0053409724],"genre_scores_gemma":[0.93556136,0.0000036639522,0.064092204,0.00020466957,0.000055808032,0.0000036907638,0.000006627878,0.000004841827,0.000067135734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990386,0.00019592662,0.00021592199,0.00028105927,0.00013274791,0.00013574363],"domain_scores_gemma":[0.998603,0.00022076201,0.0000924415,0.0009596971,0.00007830904,0.000045792127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004919132,0.00006920347,0.00016549963,0.000080661666,0.000031344345,0.00002853485,0.0005959844,0.000059034992,0.000022064776],"category_scores_gemma":[0.000124133,0.000056504916,0.000020513005,0.00019194723,0.000028220933,0.00024712537,0.00037177326,0.000040988994,0.00007560117],"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":[0.000014166801,0.0008218656,0.11899537,0.0005174183,0.0002759791,0.0002148155,0.0030977002,0.0009607128,0.057727266,0.053484935,0.040247247,0.7236425],"study_design_scores_gemma":[0.0003369569,0.00005935751,0.0046222163,0.000043814172,0.000010797269,0.00015242294,0.000025442168,0.9779487,0.00834929,0.0003295687,0.007949584,0.00017184019],"about_ca_topic_score_codex":0.00004137316,"about_ca_topic_score_gemma":0.000010932676,"teacher_disagreement_score":0.976988,"about_ca_system_score_codex":0.00000661136,"about_ca_system_score_gemma":0.000024111298,"threshold_uncertainty_score":0.23042035},"labels":[],"label_agreement":null},{"id":"W1977420546","doi":"10.1145/2468356.2479629","title":"Posture training with real-time visual feedback","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":13,"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 Saskatchewan","funders":"University of Saskatchewan","keywords":"Computer science; Training (meteorology); Visual feedback; Fidelity; Computer vision; Artificial intelligence; Visualization; High fidelity; Human–computer interaction; Engineering","score_opus":0.012666016784659844,"score_gpt":0.22643906832680316,"score_spread":0.21377305154214332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977420546","genre_codex":"other","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":[0.31791776,0.000026669964,0.2057487,0.0045087268,0.0003258491,0.0007813459,0.0000014786667,0.0011789155,0.46951053],"genre_scores_gemma":[0.9526095,0.000002532262,0.036734417,0.00056368834,0.00014989641,0.000035829835,0.0000032944488,0.000012343571,0.009888506],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990204,0.000051208688,0.00014662366,0.00028228544,0.00024630738,0.00025319817],"domain_scores_gemma":[0.99936765,0.00006540681,0.000051715648,0.00024409546,0.00013651924,0.00013460283],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013094055,0.00012147658,0.00015584852,0.000061021627,0.00006735828,0.00026066112,0.00031633012,0.000060255352,0.00040540958],"category_scores_gemma":[0.000007722459,0.000077862365,0.000030457357,0.0002799733,0.000021592987,0.00054736953,0.000056978413,0.000080317004,0.0037374278],"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":[0.000029031899,0.00037845338,0.0036708482,0.00009160099,0.00030222235,0.00013553223,0.02518634,0.000045427278,0.09462786,0.022954913,0.09691801,0.75565976],"study_design_scores_gemma":[0.017805988,0.008614239,0.4822255,0.0017872468,0.00014267433,0.0064514615,0.015960382,0.24389811,0.04339222,0.013497424,0.15563421,0.010590547],"about_ca_topic_score_codex":0.000103107675,"about_ca_topic_score_gemma":0.000011451063,"teacher_disagreement_score":0.7450692,"about_ca_system_score_codex":0.000013510598,"about_ca_system_score_gemma":0.00005444323,"threshold_uncertainty_score":0.9970383},"labels":[],"label_agreement":null},{"id":"W1978015757","doi":"10.1145/2522848.2522882","title":"Aiding human discovery of handwriting recognition errors","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Handwriting; Computer science; Handwriting recognition; Task (project management); Intelligent character recognition; Speech recognition; Artificial intelligence; Character (mathematics); Character recognition; Pattern recognition (psychology); Feature extraction; Image (mathematics)","score_opus":0.03541413455302867,"score_gpt":0.25594453336927375,"score_spread":0.2205303988162451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978015757","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":[0.5484862,0.000026006885,0.40964976,0.00027003523,0.00021821653,0.00022990501,0.0000017702822,0.00014894357,0.040969186],"genre_scores_gemma":[0.9891473,0.0000020518637,0.009857668,0.00009957239,0.00007501918,0.000027291615,0.000004864675,0.000005611541,0.00078063254],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990775,0.00006222855,0.00029646457,0.00020975007,0.00018756976,0.0001664622],"domain_scores_gemma":[0.9993968,0.00007765672,0.00012218604,0.00021450828,0.00014244716,0.00004642751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024095736,0.00008380288,0.0001426129,0.00011031158,0.00009853563,0.00019207214,0.0002528554,0.000045314566,0.00008175915],"category_scores_gemma":[0.000035051835,0.000069858645,0.00006001291,0.00022310158,0.000023546907,0.0015299505,0.00008646574,0.0000636097,0.00026690066],"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":[0.0000031850902,0.00028600506,0.009956806,0.00034816324,0.00010624558,0.000012032588,0.003065046,0.0000126966925,0.4643091,0.0495115,0.0059911218,0.4663981],"study_design_scores_gemma":[0.0024682083,0.0004972341,0.02787977,0.0015726531,0.000034390272,0.00016950225,0.0037485436,0.011335411,0.8621829,0.08697483,0.0015072399,0.0016293245],"about_ca_topic_score_codex":0.00018783791,"about_ca_topic_score_gemma":0.00001678046,"teacher_disagreement_score":0.46476877,"about_ca_system_score_codex":0.000014038411,"about_ca_system_score_gemma":0.000014187107,"threshold_uncertainty_score":0.34305573},"labels":[],"label_agreement":null},{"id":"W1978939920","doi":"10.1016/j.compedu.2009.12.009","title":"SignMT: An alternative language learning tool","year":2009,"lang":"en","type":"article","venue":"Computers & Education","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Natural language processing; Sentence; Sign language; Syntax; Interpreter; Machine translation; Meaning (existential); Artificial intelligence; Grammar; Linguistics; Rule-based machine translation; Language interpretation; Word order; Psychology; Programming language","score_opus":0.012651813792506886,"score_gpt":0.2855755231406615,"score_spread":0.2729237093481546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978939920","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":[0.29743242,0.00017748645,0.69625014,0.0009928221,0.0018537281,0.0002098926,4.2765052e-7,0.00029305022,0.0027900296],"genre_scores_gemma":[0.97105366,0.000006199047,0.026725916,0.0008878806,0.00073605875,0.000010127714,0.000024815166,0.0000065096556,0.0005488448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988851,0.00017569681,0.00019033256,0.0003527284,0.00020396606,0.00019218998],"domain_scores_gemma":[0.9992585,0.00005451367,0.000118642696,0.0003472261,0.000117760625,0.00010332395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002549316,0.00012471835,0.00012516703,0.00014704399,0.00013207403,0.00027740077,0.0005114263,0.00004509317,0.000008899246],"category_scores_gemma":[0.000022233611,0.00012374131,0.000042968433,0.00028209068,0.000011494072,0.0008293467,0.000029396802,0.00014452072,0.00014881963],"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":[0.0000020426073,0.0001682227,0.00011251218,0.000005861656,0.000007419599,0.000004110172,0.021162689,0.0003491973,0.0020790333,0.005764188,0.0014503085,0.9688944],"study_design_scores_gemma":[0.0032393686,0.0034421112,0.07475475,0.0010359031,0.00006791904,0.000650778,0.012735926,0.697949,0.037476975,0.021216089,0.1435664,0.0038647568],"about_ca_topic_score_codex":0.000029464669,"about_ca_topic_score_gemma":0.0000025855247,"teacher_disagreement_score":0.96502966,"about_ca_system_score_codex":0.000067527195,"about_ca_system_score_gemma":0.00016986406,"threshold_uncertainty_score":0.50460243},"labels":[],"label_agreement":null},{"id":"W1982838549","doi":"10.1504/ijamc.2009.026854","title":"Human Computer interaction for smart environment applications using hand gestures and facial expressions","year":2009,"lang":"en","type":"article","venue":"International Journal of Advanced Media and Communication","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Communications Research Centre Canada; Innovation, Science and Economic Development Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Computer science; Modalities; Facial expression; Human–computer interaction; Gesture recognition; Body language; Multimedia; Artificial intelligence; Communication","score_opus":0.02776568103470938,"score_gpt":0.32073851453301616,"score_spread":0.2929728334983068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982838549","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":[0.07913647,0.0008129439,0.9171662,0.002259026,0.00030837674,0.00022841862,0.00000598821,0.00001283732,0.00006975142],"genre_scores_gemma":[0.88024175,0.0004295997,0.118912324,0.00016513663,0.00021168598,0.000015916085,0.000011306807,0.0000036460053,0.000008659104],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992173,0.00005568976,0.00033117607,0.000118071745,0.00020471474,0.00007305989],"domain_scores_gemma":[0.9990137,0.00016344778,0.00035260877,0.00017370342,0.0002235501,0.00007296325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019383818,0.00008270184,0.00012797338,0.00013823659,0.0001848353,0.00014474196,0.00038789716,0.00004236739,0.0000020488187],"category_scores_gemma":[0.000021972144,0.000073444346,0.00004178145,0.000036594054,0.000048288002,0.0005631712,0.000083888444,0.00012597762,4.982391e-7],"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":[0.000053669984,0.0001979268,0.00021720346,0.000008694092,0.000069871836,0.000002315705,0.0033405912,0.0009094163,0.08336583,0.0073019397,0.0001324792,0.90440005],"study_design_scores_gemma":[0.016962597,0.0020325694,0.09732314,0.0024769136,0.00026566366,0.0027913623,0.003170129,0.060539298,0.041399933,0.2492217,0.52203107,0.0017856369],"about_ca_topic_score_codex":0.0000031061375,"about_ca_topic_score_gemma":0.000003834779,"teacher_disagreement_score":0.9026144,"about_ca_system_score_codex":0.00004525357,"about_ca_system_score_gemma":0.000017441655,"threshold_uncertainty_score":0.29949737},"labels":[],"label_agreement":null},{"id":"W1982993750","doi":"10.1145/1330572.1330573","title":"Bringing context into play","year":2007,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Computer science; Human–computer interaction; Popularity; Context (archaeology); Relation (database); Game design; Simple (philosophy); Action (physics); Multimedia; Game mechanics; Interaction technique; Control (management); Video game; Metagaming; Sequential game; Artificial intelligence; Game theory; Simultaneous game; Gesture; Psychology","score_opus":0.013587076246718477,"score_gpt":0.2546341390631772,"score_spread":0.24104706281645874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982993750","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":[0.010668334,0.000062525396,0.8873421,0.0003119387,0.00038150654,0.00005403819,5.4363085e-8,0.00019317384,0.10098636],"genre_scores_gemma":[0.9795438,7.351937e-7,0.017807297,0.0007046675,0.00009432503,0.000001491156,2.4308915e-7,0.0000029145556,0.0018445489],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938047,0.000013688349,0.0001400104,0.00015912279,0.00014037828,0.00016634505],"domain_scores_gemma":[0.9995597,0.0000960546,0.00002776248,0.0001914184,0.00004698896,0.0000780875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050582684,0.0000542252,0.00007101299,0.00007534255,0.0000543892,0.000080068,0.00026450923,0.000030157906,0.000036617348],"category_scores_gemma":[0.000019591744,0.00004457694,0.000027284541,0.00018700492,0.000009386393,0.00020558249,0.00006894917,0.000050197414,0.00059840875],"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":[0.0000016919888,0.000020705587,0.001754939,0.000009884823,0.000012467919,0.00004344213,0.0030918808,0.0000019337945,0.0020763772,0.08150574,0.0032854925,0.90819544],"study_design_scores_gemma":[0.0014735798,0.0001291066,0.019268107,0.00015629092,0.0000057991315,0.00039320454,0.0013803135,0.008670929,0.14431976,0.0071492246,0.8161327,0.00092096475],"about_ca_topic_score_codex":0.00007187889,"about_ca_topic_score_gemma":0.00014707171,"teacher_disagreement_score":0.96887547,"about_ca_system_score_codex":0.000019514066,"about_ca_system_score_gemma":0.000013813514,"threshold_uncertainty_score":0.76915336},"labels":[],"label_agreement":null},{"id":"W1988786795","doi":"10.1109/wcica.2014.7052933","title":"Chinese sign language recognition with 3D hand motion trajectories and depth images","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Sign language; Feature (linguistics); Representation (politics); Pattern recognition (psychology); Motion (physics); Computer vision; Feature extraction; Support vector machine; Sign (mathematics); Kinematics; Expression (computer science); Mathematics","score_opus":0.008906277194384199,"score_gpt":0.23077486429989283,"score_spread":0.22186858710550864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988786795","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":[0.21999453,0.000067507106,0.7688897,0.00015837724,0.00011552526,0.00014113974,0.000002593939,0.00016984655,0.010460762],"genre_scores_gemma":[0.9766635,0.0000056907684,0.02268638,0.00011628724,0.00014369495,0.00001873879,0.000010580218,0.0000079758265,0.0003472021],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991668,0.000109557965,0.00012535472,0.00028079306,0.00017225376,0.00014522953],"domain_scores_gemma":[0.99946904,0.0001132559,0.00005718607,0.00019171383,0.0000926433,0.00007617187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002687816,0.00012721245,0.00014196719,0.00008103305,0.000111220295,0.0002977594,0.000117106865,0.000046498404,0.000018462952],"category_scores_gemma":[0.000052391373,0.00008167873,0.000018093515,0.00021702307,0.00004796299,0.0005522328,0.000030346251,0.00006470496,0.00005081749],"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":[0.000018925966,0.000060605136,0.011041489,0.00007670339,0.000027965429,0.000012143076,0.0038654327,0.000005900072,0.007497867,0.00037585347,0.0002481311,0.976769],"study_design_scores_gemma":[0.011066207,0.0030462402,0.73129165,0.0007253744,0.00013553281,0.0018814472,0.0025321522,0.036402304,0.19182383,0.012281099,0.0052030436,0.0036111204],"about_ca_topic_score_codex":0.00007455524,"about_ca_topic_score_gemma":0.00024662793,"teacher_disagreement_score":0.9731579,"about_ca_system_score_codex":0.0000084408175,"about_ca_system_score_gemma":0.000011478029,"threshold_uncertainty_score":0.3330762},"labels":[],"label_agreement":null},{"id":"W1991250841","doi":"10.5194/isprsarchives-xxxix-b5-217-2012","title":"ROBUST REAL-TIME AND ROTATION-INVARIANT AMERICAN SIGN LANGUAGE ALPHABET RECOGNITION USING RANGE CAMERA","year":2012,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Computer science; Invariant (physics); Artificial intelligence; Computer vision; Gesture recognition; Rotation (mathematics); Pattern recognition (psychology); Alphabet; Sign language; Speech recognition; Mathematics","score_opus":0.02575862140829814,"score_gpt":0.25721739860442605,"score_spread":0.2314587771961279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991250841","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":[0.03840865,0.00004240011,0.9524953,0.002386801,0.0013590998,0.000693184,0.00011176679,0.00006828795,0.0044345297],"genre_scores_gemma":[0.98172015,0.0001694415,0.01692065,0.00087430933,0.00021812458,5.907576e-7,0.00004213683,0.0000120173645,0.000042573734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954325,0.0005370806,0.0013308582,0.0004188851,0.0016876836,0.000592998],"domain_scores_gemma":[0.9957933,0.0012685128,0.0018999428,0.000475072,0.00033210302,0.00023109531],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020568583,0.00041997395,0.0004740071,0.000961807,0.0012638298,0.0009996549,0.0014181585,0.00008357855,0.0000067634364],"category_scores_gemma":[0.00061986496,0.00027559698,0.0002754325,0.0011856174,0.002378706,0.0011272018,0.0008806421,0.0003625707,0.00000658509],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066606386,0.000021479731,0.00032600467,0.000031520038,0.00007959597,3.467579e-7,0.0075398223,0.0011090626,0.0039997967,0.000006931746,0.000023221108,0.9867956],"study_design_scores_gemma":[0.00065598945,0.00011613797,0.004400816,0.00032550786,0.000046876095,0.0002741195,0.0026160679,0.9844792,0.0048079533,0.0012710377,0.0006643884,0.00034190016],"about_ca_topic_score_codex":0.76631075,"about_ca_topic_score_gemma":0.070720404,"teacher_disagreement_score":0.9864537,"about_ca_system_score_codex":0.000055907665,"about_ca_system_score_gemma":0.00020422423,"threshold_uncertainty_score":0.9999696},"labels":[],"label_agreement":null},{"id":"W1992880878","doi":"10.1145/1866029.1866063","title":"Sensing foot gestures from the pocket","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":89,"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":"Gesture; Accelerometer; Computer science; Foot (prosody); Mobile phone; Human–computer interaction; Task (project management); Mobile device; Phone; Heel; Gesture recognition; Computer vision; Rotation (mathematics); Artificial intelligence; Engineering; Telecommunications; World Wide Web","score_opus":0.014556645730243538,"score_gpt":0.23158360084944762,"score_spread":0.21702695511920408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992880878","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":[0.24683438,0.0001486131,0.6485925,0.022002427,0.004856246,0.00029735948,0.0000056171225,0.0006165306,0.07664634],"genre_scores_gemma":[0.9592908,0.0000012103635,0.036563873,0.003076818,0.00052803865,0.0000015708268,0.0000015333286,0.0000048629454,0.0005312948],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992948,0.000060733433,0.000113270995,0.0002053167,0.00018490695,0.00014093383],"domain_scores_gemma":[0.99898857,0.00034075603,0.000037314472,0.0005192733,0.000059935384,0.00005412881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023245042,0.000076647986,0.00007828441,0.000018627537,0.0001224944,0.00025104536,0.00047699476,0.00005795857,0.00007536366],"category_scores_gemma":[0.00006187989,0.000041934047,0.000040251536,0.00013012369,0.00002480714,0.00013509192,0.00011545853,0.00019773023,0.00032971628],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004105582,0.00004981589,0.0037563285,0.0000048713114,0.000076016244,0.000042513155,0.0053002574,0.000003462746,0.06512299,0.050938677,0.12817362,0.7465274],"study_design_scores_gemma":[0.00064174423,0.000042831733,0.058317285,0.00004003188,0.000015435882,0.00023061194,0.00037369228,0.0087225605,0.0437777,0.03520935,0.8520418,0.00058697205],"about_ca_topic_score_codex":0.00026401892,"about_ca_topic_score_gemma":0.00051771087,"teacher_disagreement_score":0.7459404,"about_ca_system_score_codex":0.0000031493153,"about_ca_system_score_gemma":0.00002459214,"threshold_uncertainty_score":0.42379457},"labels":[],"label_agreement":null},{"id":"W1996147736","doi":"10.1109/istas.2013.6613098","title":"Augmediated reality system based on 3D camera selfgesture sensing","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Computer science; Computer vision; Gesture recognition; Wearable computer; Artificial intelligence; Augmented reality; Context (archaeology); Computer graphics (images); Embedded system","score_opus":0.01610329819367095,"score_gpt":0.22554897703353305,"score_spread":0.2094456788398621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996147736","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":[0.010707285,0.000024520077,0.8430373,0.0048275017,0.0016496503,0.0009802197,0.0000069727635,0.0021091036,0.13665742],"genre_scores_gemma":[0.98280567,5.432045e-7,0.015328599,0.0012565511,0.00018403312,0.000017249897,0.000009001606,0.000012252329,0.00038612678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811906,0.00031173055,0.00031867382,0.00046651674,0.00045724568,0.0003267588],"domain_scores_gemma":[0.9984243,0.00023103111,0.00011769839,0.0007305879,0.00029093542,0.00020544308],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045028492,0.00019451187,0.00025299203,0.00012769521,0.00012825541,0.0002934615,0.00035123082,0.00013994158,0.000047174686],"category_scores_gemma":[0.000047625716,0.00014406878,0.00006746472,0.0004724931,0.000020964213,0.00028579446,0.000056843157,0.00018107842,0.001608165],"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":[0.000053852924,0.00085250195,0.0046719736,0.0017915186,0.00035527517,0.0007915579,0.0052251806,0.0055590635,0.014448485,0.030183278,0.23758313,0.6984842],"study_design_scores_gemma":[0.00081520405,0.000111105415,0.0058798217,0.00030453154,0.00001111538,0.00012859133,0.00021326629,0.98197836,0.0029279848,0.0000736025,0.007036901,0.0005195397],"about_ca_topic_score_codex":0.00062367227,"about_ca_topic_score_gemma":0.000022999586,"teacher_disagreement_score":0.97641927,"about_ca_system_score_codex":0.00010632723,"about_ca_system_score_gemma":0.000080758,"threshold_uncertainty_score":0.9991692},"labels":[],"label_agreement":null},{"id":"W1996962087","doi":"10.1504/ijamc.2007.014812","title":"Navigating a 3D virtual environment of learning objects by hand gestures","year":2007,"lang":"en","type":"article","venue":"International Journal of Advanced Media and Communication","topic":"Hand Gesture Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Gesture; Computer science; Interface (matter); Avatar; Human–computer interaction; Gesture recognition; Virtual machine; AdaBoost; User interface; Object (grammar); Artificial intelligence; Support vector machine","score_opus":0.008006914228734877,"score_gpt":0.27234080515790415,"score_spread":0.26433389092916926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996962087","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":[0.565809,0.0038303959,0.42856002,0.00096953637,0.00042465748,0.00007247619,0.0000020351738,0.000011826222,0.00032004883],"genre_scores_gemma":[0.9686195,0.0013860153,0.029849974,0.000054052998,0.00006339031,0.0000015416488,0.0000068649947,0.000004372074,0.000014255678],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884415,0.000089528156,0.00044730515,0.00008368967,0.0004498046,0.00008551014],"domain_scores_gemma":[0.998407,0.0005478435,0.0006273155,0.00014590597,0.00020386498,0.00006806389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007230029,0.000072466035,0.00013754639,0.00006170954,0.00006308295,0.00004968117,0.00056060444,0.000041320785,0.0000029555354],"category_scores_gemma":[0.00020293245,0.0000642556,0.000038404454,0.000062206185,0.00007075683,0.00039645957,0.00012727728,0.0003107309,0.0000014750397],"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":[0.000059164657,0.00008986389,0.0013133738,0.000005006502,0.00006751452,0.000009097598,0.011213387,0.00052129925,0.077755295,0.00063310773,0.00003130201,0.9083016],"study_design_scores_gemma":[0.01704366,0.0030691016,0.04562328,0.0062736445,0.00014145489,0.00247728,0.033568326,0.010829493,0.7331844,0.01783228,0.12827839,0.0016786716],"about_ca_topic_score_codex":0.000006769248,"about_ca_topic_score_gemma":0.0000061283135,"teacher_disagreement_score":0.90662295,"about_ca_system_score_codex":0.00004271636,"about_ca_system_score_gemma":0.000022284523,"threshold_uncertainty_score":0.26202673},"labels":[],"label_agreement":null},{"id":"W1997769987","doi":"10.1109/civemsa.2014.6841431","title":"Dynamic hand gesture recognition for human-robot and inter-robot communication","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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 Ottawa","funders":"","keywords":"Gesture; Computer science; Artificial intelligence; Support vector machine; Histogram; Gesture recognition; Robot; Computer vision; Context (archaeology); Pattern recognition (psychology); Orientation (vector space); Speech recognition; Image (mathematics); Mathematics","score_opus":0.030050911326645606,"score_gpt":0.28450897460943325,"score_spread":0.25445806328278764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997769987","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":[0.010663228,0.00011355223,0.9831777,0.0018706649,0.00018370604,0.0004162889,0.000004229021,0.0001629858,0.003407601],"genre_scores_gemma":[0.92229044,0.0000201343,0.07623544,0.000438158,0.000042950775,0.00010007669,0.000060115624,0.000011513434,0.0008011778],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99897575,0.000152094,0.00025663315,0.00032341533,0.000113417176,0.00017868265],"domain_scores_gemma":[0.998837,0.00026894046,0.00011746472,0.0005057249,0.00019178622,0.00007907163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005709539,0.0001358903,0.00018210088,0.00010537848,0.00030540305,0.000345973,0.00039455786,0.00010809311,0.000012554321],"category_scores_gemma":[0.00006780503,0.000117343014,0.00004937782,0.00012596462,0.00006234056,0.00038365705,0.00013971403,0.0001129948,0.000051853396],"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":[0.000015966376,0.00014511806,0.000468791,0.00019402987,0.000060821007,7.7907447e-7,0.0020156135,0.000015184609,0.017793894,0.017628701,0.002707032,0.9589541],"study_design_scores_gemma":[0.012752657,0.0024257924,0.04683433,0.0019565192,0.000200035,0.00054384884,0.0010149315,0.35286096,0.030286264,0.39010844,0.15735072,0.0036655073],"about_ca_topic_score_codex":0.000033358865,"about_ca_topic_score_gemma":0.00037092745,"teacher_disagreement_score":0.9552886,"about_ca_system_score_codex":0.00001997278,"about_ca_system_score_gemma":0.00001116708,"threshold_uncertainty_score":0.47851092},"labels":[],"label_agreement":null},{"id":"W2004513049","doi":"10.1145/1639601.1639619","title":"Device agnostic 3D gesture recognition using hidden Markov models","year":2009,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"Carleton University","funders":"","keywords":"Hidden Markov model; Gesture; Gesture recognition; Computer science; Accelerometer; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Motion (physics); Computer vision; Signature recognition; Feature extraction","score_opus":0.05703407982085332,"score_gpt":0.2706525375026988,"score_spread":0.21361845768184548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004513049","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":[0.024693048,0.0002460271,0.9245106,0.0012027925,0.00046196175,0.0003685308,0.000004572503,0.0004689091,0.048043553],"genre_scores_gemma":[0.7370021,0.000023505061,0.25989345,0.0024088253,0.0002508993,0.0000088389115,0.0000108603745,0.000010395655,0.0003911592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845064,0.00012795531,0.00030731718,0.0004394643,0.0003489025,0.00032574128],"domain_scores_gemma":[0.99898195,0.00013823084,0.000107196895,0.00039730215,0.00022920044,0.00014613658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030574237,0.0001889604,0.00020832714,0.00015302139,0.00013437582,0.00023893834,0.00044079422,0.00013969529,0.000055966007],"category_scores_gemma":[0.00005305099,0.00016429911,0.00007082365,0.00050297554,0.000012996959,0.0010682447,0.00006321103,0.00013677627,0.00028949013],"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":[0.000007946911,0.00011582,0.000049773702,0.000026832948,0.000024088913,0.00008469253,0.0006090895,0.00031847943,0.0012364879,0.0013054644,0.0026709824,0.99355036],"study_design_scores_gemma":[0.0016553306,0.0002858386,0.0019343178,0.00063074543,0.00008515684,0.0014126454,0.00014212453,0.913372,0.0031820724,0.07158912,0.0041558207,0.0015548678],"about_ca_topic_score_codex":0.000035387366,"about_ca_topic_score_gemma":0.000018460714,"teacher_disagreement_score":0.99199545,"about_ca_system_score_codex":0.00005881962,"about_ca_system_score_gemma":0.00006861528,"threshold_uncertainty_score":0.6699923},"labels":[],"label_agreement":null},{"id":"W2007201420","doi":"10.1007/s12369-012-0169-4","title":"Perception and Generation of Affective Hand Movements","year":2012,"lang":"en","type":"article","venue":"International Journal of Social Robotics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Council for the Arts","keywords":"Sadness; Movement (music); Artificial intelligence; Perception; Computer science; Feature (linguistics); Happiness; Cognitive psychology; Psychology; Anger; Affective computing; Emotion classification; Affect (linguistics); Disgust; Pattern recognition (psychology); Communication; Social psychology","score_opus":0.03741370635878763,"score_gpt":0.30726154020890384,"score_spread":0.26984783385011624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007201420","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":[0.3194937,0.0000864662,0.67706287,0.0008145582,0.0021319615,0.000050596453,0.0000030446133,0.0000041524663,0.00035267143],"genre_scores_gemma":[0.9901838,0.00002219114,0.0084804585,0.00008509801,0.001185525,5.347043e-7,0.0000015957678,0.0000028737313,0.00003793786],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991885,0.000058842055,0.00024467922,0.000046186487,0.00039229434,0.000069502596],"domain_scores_gemma":[0.9989692,0.000036195026,0.0003182908,0.000028691189,0.0006043442,0.000043288102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033267503,0.000048948783,0.00010179407,0.00010309375,0.000045184563,0.00006642682,0.00016591392,0.000041626,0.000004853504],"category_scores_gemma":[0.00004919212,0.000043624335,0.000043654392,0.00006198244,0.000031576852,0.0005544753,0.000044277946,0.00006767041,0.0000029678677],"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":[0.000099164856,0.0012369032,0.0929881,0.00006299982,0.0013698622,0.000026856715,0.064258695,0.0021211412,0.2812784,0.12459494,0.003979703,0.42798322],"study_design_scores_gemma":[0.008759088,0.0011076458,0.88094926,0.00045544977,0.0001976373,0.0011349339,0.00452462,0.03490627,0.045746017,0.012671751,0.008458282,0.0010890506],"about_ca_topic_score_codex":0.0000035172936,"about_ca_topic_score_gemma":0.0000015970948,"teacher_disagreement_score":0.7879611,"about_ca_system_score_codex":0.000064888045,"about_ca_system_score_gemma":0.000030509704,"threshold_uncertainty_score":0.17789488},"labels":[],"label_agreement":null},{"id":"W2009416217","doi":"10.1145/2069000.2069013","title":"Motion-path based in car gesture control of the multimedia devices","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Ottawa","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Multimedia; Motion (physics); Human–computer interaction; Path (computing); Touchscreen; Interaction technique; Interface (matter); Set (abstract data type); Modality (human–computer interaction); Computer vision","score_opus":0.020198887916265982,"score_gpt":0.2116866387763182,"score_spread":0.1914877508600522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009416217","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":[0.06843502,0.00011313099,0.91230804,0.0007256757,0.00084216864,0.0006675571,0.000008660189,0.000107436055,0.016792316],"genre_scores_gemma":[0.9897307,4.3800748e-7,0.009783021,0.0003755053,0.000023669265,0.000016969083,3.673804e-7,0.0000031610298,0.00006613166],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991811,0.00014349908,0.0002018224,0.00016556824,0.00018611623,0.000121889134],"domain_scores_gemma":[0.99932766,0.00010501672,0.00009172186,0.0003447459,0.00009183909,0.000039037186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028974525,0.000076007265,0.00013338467,0.000059358616,0.000023898787,0.000014460357,0.0005121763,0.00005923563,0.000042691197],"category_scores_gemma":[0.00004215486,0.000043752745,0.00005420063,0.00027131583,0.00002573185,0.00013429647,0.00003587279,0.000080348516,0.000030082221],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001836871,0.0004630032,0.94280475,0.00011248667,0.000047490477,0.000014947471,0.009521828,0.00013838102,0.0029638126,0.009769793,0.00037704737,0.03376806],"study_design_scores_gemma":[0.0018357696,0.000048496848,0.882807,0.00011383934,0.000009669281,0.0000071298186,0.00019244157,0.10046638,0.013274826,0.00036929193,0.0006841964,0.00019093888],"about_ca_topic_score_codex":0.00018829732,"about_ca_topic_score_gemma":0.00040401882,"teacher_disagreement_score":0.9212957,"about_ca_system_score_codex":0.000010861615,"about_ca_system_score_gemma":0.000046264016,"threshold_uncertainty_score":0.17841852},"labels":[],"label_agreement":null},{"id":"W2010007259","doi":"10.1145/2556288.2557219","title":"Making big gestures","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"University of Saskatchewan","funders":"","keywords":"Gesture; Observability; Computer science; Identifiability; Human–computer interaction; Artificial intelligence; Machine learning; Mathematics","score_opus":0.05149231583524431,"score_gpt":0.27794375097778623,"score_spread":0.22645143514254193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010007259","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":[0.00081871974,0.000020658726,0.8031798,0.0008029654,0.00051724026,0.000037539678,7.219491e-8,0.00021774584,0.19440527],"genre_scores_gemma":[0.96024793,8.550944e-7,0.03678828,0.0012650233,0.00026565616,0.0000054899983,2.1091348e-7,0.0000033569481,0.0014231873],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993901,0.00005620849,0.00011088899,0.0001712909,0.00014242424,0.0001290628],"domain_scores_gemma":[0.9995409,0.00007147016,0.000033278953,0.00027914488,0.00003702047,0.000038186663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026899556,0.00005745721,0.00007415047,0.00005363499,0.00004917056,0.00012463,0.00034047835,0.000032521053,0.000015568563],"category_scores_gemma":[0.000038633978,0.000042235315,0.00002849599,0.00014512386,0.000008052912,0.00009792055,0.00007801713,0.000045347886,0.00048609378],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.42344e-7,0.00001615667,0.0010490622,0.000010798678,0.000007898868,0.000003147911,0.0003762361,0.0000064176306,0.000444795,0.20923163,0.007353452,0.78149986],"study_design_scores_gemma":[0.00033800217,0.00006235548,0.00817562,0.00005090792,0.0000030815536,0.00008579611,0.000033307228,0.01849285,0.0031071103,0.015192532,0.9541347,0.0003237501],"about_ca_topic_score_codex":0.0000055165765,"about_ca_topic_score_gemma":0.00001799001,"teacher_disagreement_score":0.9594292,"about_ca_system_score_codex":0.000005673884,"about_ca_system_score_gemma":0.000009444507,"threshold_uncertainty_score":0.6247914},"labels":[],"label_agreement":null},{"id":"W2011350558","doi":"10.1145/2702123.2702133","title":"Myopoint","year":2015,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":101,"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":"Inertial measurement unit; Computer science; Computer vision; Pointer (user interface); Accelerometer; Artificial intelligence; Inertial frame of reference; Acceleration; Physics","score_opus":0.062213010316895065,"score_gpt":0.2575772565151777,"score_spread":0.19536424619828263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011350558","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":[0.0007261858,0.000023686896,0.7335468,0.0014587285,0.00036476314,0.000031789823,7.908973e-8,0.00016999533,0.26367798],"genre_scores_gemma":[0.95049417,5.293394e-7,0.043315835,0.00076091214,0.00009537965,0.000005676673,3.2600846e-7,0.000002111802,0.0053250315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962676,0.000021477177,0.00006216896,0.00009337193,0.0001241665,0.00007203682],"domain_scores_gemma":[0.9996325,0.000011843021,0.000012232428,0.00018039435,0.000064650725,0.000098385564],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018857815,0.000029424764,0.000040881387,0.000025215551,0.000010580985,0.000055763714,0.00020335945,0.000015053601,0.000014167902],"category_scores_gemma":[0.000019110577,0.000021195494,0.000013474872,0.00011257862,0.0000044652593,0.00016226123,0.000054986,0.000021263622,0.0018727392],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001970902,0.00006811168,0.0013334162,0.0000067618716,0.000014988871,0.000036294972,0.0022840458,0.000015913023,0.00023113028,0.57852,0.2350183,0.18246904],"study_design_scores_gemma":[0.0006833072,0.000104001294,0.0007358754,0.000011471593,0.0000014242763,0.00015206347,0.00018717689,0.009644586,0.0056831287,0.02808321,0.95446783,0.00024593726],"about_ca_topic_score_codex":0.000010054508,"about_ca_topic_score_gemma":0.0000034085667,"teacher_disagreement_score":0.949768,"about_ca_system_score_codex":0.000010017728,"about_ca_system_score_gemma":0.00003234582,"threshold_uncertainty_score":0.9989044},"labels":[],"label_agreement":null},{"id":"W2015143081","doi":"10.1016/j.patrec.2007.11.011","title":"On-line handwritten digit recognition based on trajectory and velocity modeling","year":2007,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computer science; Handwriting; Handwriting recognition; Arabic numerals; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Classifier (UML); Intelligent character recognition; Fuzzy logic; Artificial neural network; Feature extraction; Character recognition; Image (mathematics)","score_opus":0.049382401834348125,"score_gpt":0.25534269034991847,"score_spread":0.20596028851557036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015143081","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":[0.3553033,0.000009606993,0.6406092,0.0021276255,0.00046274488,0.00032162885,0.000047429927,0.00025077313,0.0008677132],"genre_scores_gemma":[0.97226554,0.000009660098,0.003942831,0.023088817,0.00039785932,0.000060924165,0.00018594359,0.000037739333,0.000010701786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972721,0.00018883054,0.0005635587,0.00082822476,0.00060935697,0.00053797197],"domain_scores_gemma":[0.99839526,0.00058090943,0.00019117295,0.00039551963,0.00017559985,0.00026156218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00092979515,0.00036335693,0.00031405018,0.0005541851,0.0002343338,0.00027746236,0.00028952793,0.00016410284,0.000059943886],"category_scores_gemma":[0.000108139226,0.0003572987,0.0001345349,0.00035905003,0.00005398588,0.0004866524,0.00004669559,0.00040769222,0.00056204817],"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":[0.0000841442,0.00018780511,0.00029602478,0.000073171504,0.000033111257,0.00008637997,0.00028011386,0.00095615507,0.0010302379,0.000004677167,0.00047858007,0.9964896],"study_design_scores_gemma":[0.008089917,0.0013044602,0.0035868222,0.0023045018,0.000097135344,0.00020652593,0.00017706188,0.94459486,0.032372348,0.0036445044,0.00084420614,0.002777644],"about_ca_topic_score_codex":0.000036054233,"about_ca_topic_score_gemma":0.000029676137,"teacher_disagreement_score":0.99371195,"about_ca_system_score_codex":0.00011366463,"about_ca_system_score_gemma":0.000028814306,"threshold_uncertainty_score":0.9998879},"labels":[],"label_agreement":null},{"id":"W201536414","doi":"10.1007/978-3-642-23147-6_18","title":"Learning of Facial Gestures Using SVMs","year":2011,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gesture; Support vector machine; Computer science; Artificial intelligence; Computer vision; Gesture recognition; Pattern recognition (psychology); Speech recognition","score_opus":0.07610475952834107,"score_gpt":0.2987381642267626,"score_spread":0.22263340469842152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W201536414","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":[0.0002199167,0.00037280953,0.6678031,0.000107580745,0.00039465755,0.00032260217,0.0000066628954,0.00007917806,0.3306935],"genre_scores_gemma":[0.58842754,0.003637713,0.40460047,0.00056156126,0.00013267409,0.000028572462,0.000051993866,0.000025490428,0.002533979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984393,0.0000482292,0.000719764,0.00021079186,0.00040685877,0.00017504624],"domain_scores_gemma":[0.9975261,0.00013126775,0.0005532447,0.0012152763,0.0004965938,0.00007749986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010172111,0.00017681588,0.00027974104,0.0010100418,0.00032092113,0.00025086963,0.0024188606,0.0001394109,0.000007204549],"category_scores_gemma":[0.0000438031,0.00017200477,0.00005011178,0.00036596233,0.0006594159,0.004309176,0.0017255136,0.00041262986,0.000030190007],"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":[0.0000018524809,0.000014191851,0.00014755192,0.000051080784,0.000009019198,3.503718e-7,0.008512917,0.00029322586,0.000019323452,0.5253747,0.000039636252,0.46553618],"study_design_scores_gemma":[0.000766198,0.00024600216,0.0027413704,0.0012496639,0.000021976219,0.00015490968,0.00012743968,0.55222666,0.00017477538,0.02628261,0.4149623,0.0010461049],"about_ca_topic_score_codex":0.000025890033,"about_ca_topic_score_gemma":0.0000037820937,"teacher_disagreement_score":0.5882076,"about_ca_system_score_codex":0.00006474556,"about_ca_system_score_gemma":0.00026223395,"threshold_uncertainty_score":0.7014151},"labels":[],"label_agreement":null},{"id":"W2016071976","doi":"10.1109/tbme.2012.2190734","title":"A Sign-Component-Based Framework for Chinese Sign Language Recognition Using Accelerometer and sEMG Data","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":128,"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; Sign language; Preprocessor; Vocabulary; Component (thermodynamics); Artificial intelligence; Speech recognition; Accelerometer; Gesture recognition; Orientation (vector space); Sign (mathematics); Gesture; Segmentation; Pattern recognition (psychology); Hidden Markov model; Feature extraction; Sentence; Data pre-processing; Computer vision","score_opus":0.05542881120831031,"score_gpt":0.299276931395551,"score_spread":0.2438481201872407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016071976","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":[0.03928747,0.00014473009,0.958397,0.00015112369,0.0013128574,0.0003066995,0.00014752737,0.0002485496,0.000004078601],"genre_scores_gemma":[0.78916544,0.0000063589773,0.21039109,0.00011128139,0.00021519291,0.000050892028,0.000034494686,0.000022268414,0.0000029971477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866277,0.000043085827,0.00026665023,0.00035811952,0.00028765705,0.0003817239],"domain_scores_gemma":[0.998589,0.0006051521,0.00005315342,0.00046447333,0.000033159075,0.0002550653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045646715,0.00020516725,0.00022382903,0.00032487314,0.00010918671,0.000095220996,0.00037393285,0.0001820605,0.000024782059],"category_scores_gemma":[0.00005138312,0.00018009434,0.00006699988,0.0005127728,0.000031225718,0.0005845402,0.000007646266,0.00025962136,0.000019319099],"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":[0.00006049368,0.0008794146,0.000021315716,0.0005475191,0.0002521206,0.000019499148,0.0017540692,0.0032387534,0.10636659,0.000055020773,0.00007202725,0.8867332],"study_design_scores_gemma":[0.0011157257,0.00013402292,0.00015122011,0.00036834617,0.000059938742,0.00009153449,0.00004404923,0.9845835,0.011470035,0.000071013084,0.0013802935,0.0005303138],"about_ca_topic_score_codex":0.000009951479,"about_ca_topic_score_gemma":5.663565e-7,"teacher_disagreement_score":0.98134476,"about_ca_system_score_codex":0.000043227683,"about_ca_system_score_gemma":0.000027246557,"threshold_uncertainty_score":0.73440343},"labels":[],"label_agreement":null},{"id":"W2018093209","doi":"10.1016/j.eswa.2013.09.009","title":"Automatic detection of musicians’ ancillary gestures based on video analysis","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Gesture; Computer science; Computer vision; Artificial intelligence; Point (geometry); Speech recognition; Mathematics","score_opus":0.009497945365193616,"score_gpt":0.22637336110067688,"score_spread":0.21687541573548327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018093209","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":[0.005440341,0.0003205666,0.98984927,0.0003622118,0.00011907421,0.0015452303,0.00000589216,0.00030913425,0.002048272],"genre_scores_gemma":[0.9871268,0.0000035723183,0.0078773135,0.00026456616,0.00012330007,0.004507267,0.0000105648805,0.000014342787,0.00007222905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982596,0.00014669713,0.00045780296,0.0004387469,0.00048783954,0.00020930266],"domain_scores_gemma":[0.99789965,0.00021648075,0.00032488015,0.0010925467,0.00034431036,0.00012210828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023007003,0.00018572321,0.00036849958,0.000472683,0.00015928611,0.00014070148,0.0004537964,0.00009578227,0.000018856705],"category_scores_gemma":[0.000015742218,0.00013805038,0.00011218743,0.0019903304,0.000040731975,0.00023484301,0.00002582128,0.0000863062,0.00017353539],"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":[0.00006596796,0.0039661173,0.015763476,0.0022013201,0.0055832514,0.00002649221,0.018301265,0.08954439,0.15463942,0.030526511,0.024727456,0.6546543],"study_design_scores_gemma":[0.0006309955,0.00024261125,0.015786117,0.00021585163,0.00011873887,0.000025632542,0.0005675393,0.9504913,0.005998256,0.000098333876,0.02528701,0.00053760735],"about_ca_topic_score_codex":0.00052721397,"about_ca_topic_score_gemma":0.000050739545,"teacher_disagreement_score":0.981972,"about_ca_system_score_codex":0.00006782557,"about_ca_system_score_gemma":0.000060669925,"threshold_uncertainty_score":0.5629531},"labels":[],"label_agreement":null},{"id":"W2018388187","doi":"10.1145/2674396.2674405","title":"Gesture recognition in smart home using passive RFID technology","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":18,"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 Chicoutimi","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Home automation; Speech recognition; Artificial intelligence; Telecommunications","score_opus":0.01975205656390573,"score_gpt":0.23686585961841114,"score_spread":0.2171138030545054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018388187","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":[0.28040504,0.00005449452,0.7029919,0.002833422,0.0008240532,0.00029609844,0.0000019952317,0.00047280412,0.012120209],"genre_scores_gemma":[0.9669869,0.0000054827633,0.032346297,0.00041129332,0.00009838239,0.000025246021,0.0000034098468,0.0000086790415,0.00011432056],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998826,0.000116737356,0.00025187916,0.00037052316,0.00016519522,0.00026964684],"domain_scores_gemma":[0.9992933,0.00008109601,0.000090367415,0.0003472506,0.0001297128,0.00005824876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924452,0.00012725852,0.00020156302,0.0004674842,0.000058993504,0.00007772017,0.00036425813,0.0002049067,0.000021390704],"category_scores_gemma":[0.00008246553,0.000109953646,0.000035935704,0.0009320674,0.000032834203,0.00032452375,0.00011777103,0.00019578927,0.00035096038],"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":[0.000008675271,0.00017156497,0.01961553,0.00006413633,0.000031605916,0.00006307173,0.0007451515,0.000051324507,0.009946085,0.037978638,0.000727139,0.93059707],"study_design_scores_gemma":[0.009260507,0.0011731975,0.07138254,0.0017093122,0.00006234494,0.0026353793,0.00144723,0.19745688,0.08821877,0.52365977,0.09843038,0.004563713],"about_ca_topic_score_codex":0.00003500775,"about_ca_topic_score_gemma":0.0001066033,"teacher_disagreement_score":0.9260334,"about_ca_system_score_codex":0.00005187011,"about_ca_system_score_gemma":0.000035766607,"threshold_uncertainty_score":0.4511003},"labels":[],"label_agreement":null},{"id":"W2024952194","doi":"10.1109/smc.2013.638","title":"Use of Foot for Direct Interactions with Entities of a Virtual Environment Displayed on a Mobile Device","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Computer science; Interface (matter); Mobile device; Gesture; Human–computer interaction; Accelerometer; Wearable technology; Virtual machine; Mobile interaction; User interface; Embedded system; Artificial intelligence; World Wide Web; Operating system","score_opus":0.030775895334557775,"score_gpt":0.24115485358516778,"score_spread":0.21037895825061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024952194","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":[0.46549553,0.00001447787,0.53021395,0.00012848724,0.00016900919,0.0011293794,0.000030403115,0.000047111833,0.002771633],"genre_scores_gemma":[0.9866222,0.000003913926,0.010381848,0.000048014415,0.000014406753,0.00040966287,0.00000308193,0.0000053726408,0.002511457],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993334,0.00003694265,0.00020312416,0.00016662056,0.00016126978,0.000098669516],"domain_scores_gemma":[0.99912304,0.00039878272,0.00011617939,0.00025294413,0.00006947627,0.00003958968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006361967,0.0000810339,0.00015824482,0.00007604279,0.000028484334,0.000043223383,0.000144866,0.000019534406,0.000079965765],"category_scores_gemma":[0.000012957641,0.00005487531,0.000051712264,0.000077001176,0.000031488988,0.00039228352,0.00004209601,0.000030933275,0.00003821629],"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":[0.0009540075,0.009826908,0.017393619,0.0014312326,0.002998472,0.000012029983,0.03771176,0.019163247,0.23908813,0.1300645,0.037132353,0.50422376],"study_design_scores_gemma":[0.004017641,0.0134197725,0.01606797,0.0012510417,0.00015381309,0.000060401024,0.0041846456,0.07092615,0.6656263,0.00062767655,0.22226195,0.00140265],"about_ca_topic_score_codex":0.0001694106,"about_ca_topic_score_gemma":0.00006314477,"teacher_disagreement_score":0.52112675,"about_ca_system_score_codex":0.000017683697,"about_ca_system_score_gemma":0.000015637397,"threshold_uncertainty_score":0.22377501},"labels":[],"label_agreement":null},{"id":"W2025381671","doi":"10.1117/12.889379","title":"Performance analysis of different classification methods for hand gesture recognition using range cameras","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Classifier (UML); Histogram; Gesture recognition; Gesture; Computer vision; Orientation (vector space); Transformation (genetics); Image processing; Translation (biology); Image (mathematics); Mathematics","score_opus":0.051653559742620626,"score_gpt":0.2842713018340134,"score_spread":0.23261774209139277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025381671","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":[0.9692883,0.000068997455,0.028816985,0.0002588024,0.00026261093,0.00066785834,0.000044343164,0.000052191186,0.00053992524],"genre_scores_gemma":[0.5276496,0.000053241114,0.47191635,0.000028607421,0.00012814187,0.00016513058,0.000011815329,0.000023163266,0.000023929917],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798113,8.439245e-8,0.0008206381,0.00042896948,0.0004715403,0.00029765113],"domain_scores_gemma":[0.9961693,0.00022468565,0.0007472177,0.000090932925,0.0026698604,0.00009797848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010528382,0.00027261442,0.0005842883,0.0002978374,0.00010454705,0.00009753939,0.0009429205,0.0002039871,0.000006560434],"category_scores_gemma":[0.00034986995,0.00021717104,0.00079042907,0.00080764707,0.00015643191,0.00070138846,0.00011657559,0.00018422607,4.3415054e-7],"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":[0.0001474187,0.00029090614,0.0034617037,0.0010975007,0.002657153,2.986439e-8,0.00199328,0.00005939803,0.8742985,0.10169522,0.00018716945,0.014111727],"study_design_scores_gemma":[0.0011004655,0.00039831162,0.017252421,0.00039906,0.0012703742,0.000010162389,0.0007923061,0.6582277,0.3184946,0.0012784678,0.00034465632,0.00043148713],"about_ca_topic_score_codex":0.000011211367,"about_ca_topic_score_gemma":2.8713077e-7,"teacher_disagreement_score":0.6581683,"about_ca_system_score_codex":0.00012042162,"about_ca_system_score_gemma":0.000029646742,"threshold_uncertainty_score":0.8855978},"labels":[],"label_agreement":null},{"id":"W2026604969","doi":"10.5539/cis.v4n3p157","title":"Multimedia Enhancing Computer Based Training Modules for The Deaf, Supported by Case Study","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; CLIPS; Training (meteorology); Multimedia; Software; Authoring system; Sign language; Spelling; Human–computer interaction; Artificial intelligence","score_opus":0.05066765324277075,"score_gpt":0.2687038888901652,"score_spread":0.21803623564739447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026604969","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":[0.0701329,0.000014030026,0.92797726,0.00007970726,0.0007916208,0.0007273633,0.000006997665,0.00011293076,0.00015718129],"genre_scores_gemma":[0.860295,0.0000013906052,0.13870615,0.00085066335,0.00007509546,0.00006152167,0.000003571056,0.0000031709994,0.0000034514394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984661,0.000050847415,0.00046871597,0.00029307092,0.00039520924,0.00032605883],"domain_scores_gemma":[0.99854743,0.00032193316,0.00018789638,0.00039115202,0.0003904425,0.00016114989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018043821,0.00015517756,0.00016667937,0.00023759171,0.00070639764,0.00066711946,0.00074284506,0.000036472265,0.0000051924108],"category_scores_gemma":[0.0000354253,0.00010628142,0.000041674954,0.0006041759,0.00017514969,0.005241538,0.00020521325,0.00009213728,0.000015621534],"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":[0.0000065544973,0.00008178807,0.0005676832,0.000028743565,0.000018045768,0.000018692655,0.08258461,0.00018244087,0.000093800154,0.0005650082,0.00086439593,0.9149882],"study_design_scores_gemma":[0.0007557224,0.00022779845,0.0029623695,0.000017420642,0.000006940768,0.00034327077,0.0016225829,0.99097544,0.00088366336,0.000024502095,0.002002003,0.00017828852],"about_ca_topic_score_codex":0.00004620489,"about_ca_topic_score_gemma":0.000012325434,"teacher_disagreement_score":0.990793,"about_ca_system_score_codex":0.000022206728,"about_ca_system_score_gemma":0.0001291209,"threshold_uncertainty_score":0.6433048},"labels":[],"label_agreement":null},{"id":"W2027626353","doi":"10.1109/have.2012.6374443","title":"Dynamic hand gesture recognition from Bag-of-Features and local part model","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Computer science; Histogram; Support vector machine; Artificial intelligence; Gesture; Gesture recognition; Pattern recognition (psychology); Context (archaeology); Orientation (vector space); Computer vision; Histogram of oriented gradients; Image (mathematics); Mathematics","score_opus":0.023770037752312463,"score_gpt":0.24309567740728436,"score_spread":0.2193256396549719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027626353","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":[0.10242123,0.0013872357,0.89196754,0.00034286018,0.0004555073,0.00015994406,0.00003609619,0.0000934725,0.0031361033],"genre_scores_gemma":[0.9595968,0.00005131187,0.039549373,0.000235468,0.00008824733,0.0000143670595,0.000029162347,0.000008086198,0.00042717444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901795,0.00006875935,0.000210127,0.00025672416,0.0002148467,0.00023158676],"domain_scores_gemma":[0.9993033,0.00010808709,0.00008158699,0.00026789994,0.00008713888,0.00015198361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022352238,0.00013572925,0.00019222578,0.0000697238,0.000071812494,0.00008549864,0.00018807861,0.00014217668,0.000029350442],"category_scores_gemma":[0.000020371643,0.00010600582,0.000043253734,0.00013256981,0.000070919945,0.0005730165,0.00009813245,0.00012450834,0.00005765568],"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":[0.00004823093,0.00040497913,0.0035510422,0.00013806578,0.0001803759,0.000008520784,0.007929692,0.0004480979,0.0102396775,0.0058088535,0.01267721,0.95856524],"study_design_scores_gemma":[0.0036363353,0.00029590976,0.047743797,0.0006801342,0.00016607328,0.00039936384,0.00095620635,0.8297752,0.05362598,0.047774374,0.0129593825,0.0019872452],"about_ca_topic_score_codex":0.00006078391,"about_ca_topic_score_gemma":0.00007563475,"teacher_disagreement_score":0.956578,"about_ca_system_score_codex":0.00001636848,"about_ca_system_score_gemma":0.000025978414,"threshold_uncertainty_score":0.4322792},"labels":[],"label_agreement":null},{"id":"W2028959579","doi":"10.7490/f1000research.1512.1","title":"Left hemisphere advantage in the visual processing of graspable objects","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Open peer review; Plant biology; Neuroscience; Right hemisphere; Lateralization of brain function; Cognitive psychology; Psychology; Biology","score_opus":0.025807812952979063,"score_gpt":0.25822926590360834,"score_spread":0.23242145295062927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028959579","genre_codex":"other","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":[0.14983632,0.00039194213,0.29746786,0.00014722058,0.0002223729,0.00043855672,6.8443285e-7,0.00015503408,0.55134004],"genre_scores_gemma":[0.99306357,0.000003104198,0.006348436,0.00014767582,0.000018010405,0.0000063747434,3.3497523e-7,0.0000033936726,0.00040912122],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992401,0.00006632576,0.00018531777,0.00016366593,0.00019196658,0.00015263761],"domain_scores_gemma":[0.99961144,0.000044306802,0.000069298454,0.00019470771,0.00005432589,0.000025913047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036239126,0.00007082405,0.00011006283,0.000040416253,0.000032983782,0.000038443653,0.00046352606,0.000035794143,0.00005816687],"category_scores_gemma":[0.00001672258,0.000042812462,0.000026562504,0.00026723594,0.000021088574,0.00033042938,0.000053412776,0.00007161345,0.000031398566],"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":[0.00006918274,0.002510843,0.14996268,0.0014535721,0.000067256224,0.0003069116,0.23570544,0.000025412115,0.032068096,0.03574328,0.0030290508,0.53905827],"study_design_scores_gemma":[0.0048334687,0.001333211,0.092912324,0.0017133018,0.00004130515,0.0011338919,0.06130216,0.036572497,0.7637622,0.02262255,0.011550947,0.0022221596],"about_ca_topic_score_codex":0.000064806554,"about_ca_topic_score_gemma":0.00015095384,"teacher_disagreement_score":0.8432272,"about_ca_system_score_codex":0.000006843707,"about_ca_system_score_gemma":0.00005115763,"threshold_uncertainty_score":0.17458417},"labels":[],"label_agreement":null},{"id":"W2030176975","doi":"10.1117/12.686229","title":"A robust vision-based technique for human arm kinematics identification","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kinematics; Motion capture; Artificial intelligence; Computer vision; Computer science; Motion (physics); Robotics; Exoskeleton; Motion estimation; Robot kinematics; Robotic arm; Motion analysis; Pose; Robot; Simulation; Mobile robot; Physics","score_opus":0.01647352131444682,"score_gpt":0.24622625079403737,"score_spread":0.22975272947959055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030176975","genre_codex":"empirical","genre_gemma":"methods","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":[0.8704791,0.00004180817,0.12265275,0.002940551,0.00030067476,0.001798689,0.000051475497,0.00021014058,0.0015247883],"genre_scores_gemma":[0.4485209,0.000005108173,0.5495983,0.00008166854,0.00048947905,0.0010000616,0.000024879142,0.000052485262,0.00022714153],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974339,2.4797004e-8,0.0009903116,0.00047652415,0.0007443801,0.00035484543],"domain_scores_gemma":[0.99629897,0.0002289944,0.0006589831,0.000107560765,0.0026175287,0.00008797403],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001187144,0.00029530228,0.00039343594,0.00017929672,0.00014999983,0.0003109611,0.0014624711,0.00021628315,0.000003908013],"category_scores_gemma":[0.00035077435,0.00025201796,0.00062596827,0.00047115888,0.00013099327,0.00063700695,0.00012503813,0.0002047661,0.0000017723228],"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":[0.000010906246,0.00015910504,0.000044376742,0.00053184043,0.00006668546,3.2408884e-8,0.00004226712,0.0003666755,0.525084,0.47090742,0.0026666694,0.000119989134],"study_design_scores_gemma":[0.0016163046,0.0005022134,0.00064970023,0.00079331954,0.00012572928,0.000020791167,0.0002564055,0.18612103,0.783857,0.022018889,0.0034318734,0.00060676696],"about_ca_topic_score_codex":0.0000107723545,"about_ca_topic_score_gemma":3.1160937e-7,"teacher_disagreement_score":0.44888854,"about_ca_system_score_codex":0.00015081848,"about_ca_system_score_gemma":0.00004202621,"threshold_uncertainty_score":0.9999932},"labels":[],"label_agreement":null},{"id":"W2030528862","doi":"10.1109/icpr.2010.460","title":"Prototype-Based Methodology for the Statistical Analysis of Local Features in Stereotypical Handwriting Tasks","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Handwriting; Trajectory; Computer science; Kinematics; Task (project management); Statistical analysis; Handwriting recognition; Artificial intelligence; Feature extraction; Mathematics; Statistics; Engineering","score_opus":0.05025785512933614,"score_gpt":0.3458194763213914,"score_spread":0.2955616211920552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030528862","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":[0.0045245984,0.000018509556,0.99364066,0.0007440187,0.00014619312,0.0005867538,0.000016991033,0.000027644654,0.00029461834],"genre_scores_gemma":[0.7471785,1.9204398e-7,0.2524029,0.00019329727,0.000030153118,0.00015669061,0.000006315555,0.0000032243083,0.000028739098],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987504,0.0003021367,0.00031695777,0.00025903055,0.00017269616,0.00019877905],"domain_scores_gemma":[0.9945505,0.004824832,0.00007898254,0.0003279641,0.00016634465,0.00005136458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016256958,0.00008792715,0.0003131076,0.00022019331,0.000048723545,0.000047722082,0.00040864316,0.00011047965,0.000050129103],"category_scores_gemma":[0.00049334415,0.000052595795,0.00009024318,0.0007806585,0.000118570235,0.00005363262,0.00004957548,0.00022234971,0.000002921271],"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":[0.00029925263,0.00024000276,0.014669076,0.00012492032,0.0005654811,0.000009944557,0.00062147866,0.0010136808,0.0047899163,0.53296375,0.00038964517,0.44431284],"study_design_scores_gemma":[0.0024046104,0.00050660584,0.22045662,0.000038601553,0.0004284716,0.000019878216,0.00046411416,0.7375374,0.02928117,0.00425683,0.0041721566,0.000433575],"about_ca_topic_score_codex":0.00014398854,"about_ca_topic_score_gemma":0.0027938187,"teacher_disagreement_score":0.7426539,"about_ca_system_score_codex":0.000008027793,"about_ca_system_score_gemma":0.00008091357,"threshold_uncertainty_score":0.21447943},"labels":[],"label_agreement":null},{"id":"W2031784641","doi":"10.1145/2670473.2670487","title":"Gesture interface for an interactive kiosk","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Computer Research Institute of Montréal","funders":"","keywords":"Gesture; Interactive kiosk; Computer science; Gesture recognition; Human–computer interaction; User interface; Natural user interface; Multimedia; Interface (matter); User experience design; Artificial intelligence; User interface design; World Wide Web; Programming language","score_opus":0.022123486849689317,"score_gpt":0.2997494782497633,"score_spread":0.27762599140007393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031784641","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":[0.003120402,0.000010860482,0.9688042,0.0008811889,0.0006757149,0.00022824157,0.0000015405202,0.00022878159,0.026049072],"genre_scores_gemma":[0.9544065,5.4798915e-7,0.043139122,0.00069898245,0.00019716646,0.00004357558,0.0000023320977,0.000007449041,0.0015043429],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992091,0.00008449957,0.00013827777,0.00030146926,0.00010432476,0.00016233623],"domain_scores_gemma":[0.99915713,0.0001942634,0.00005523226,0.00035760997,0.00014184239,0.000093917144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000293366,0.0000970006,0.00012658244,0.000056158333,0.00005443524,0.00016339734,0.0004887377,0.000055332242,0.000015543286],"category_scores_gemma":[0.00007171359,0.00007342906,0.000047027654,0.000100520265,0.000011215109,0.0006046227,0.00007821642,0.000076494005,0.000158824],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005151599,0.0003027998,0.00035323738,0.00007719534,0.00008927873,0.0000016262962,0.008396618,0.00009547767,0.010512021,0.1969486,0.040281378,0.74289024],"study_design_scores_gemma":[0.0017990639,0.0016087072,0.0014470513,0.00011608816,0.0000148224035,0.000097789125,0.000659219,0.183532,0.057985436,0.026852466,0.72508323,0.0008041399],"about_ca_topic_score_codex":0.000012576177,"about_ca_topic_score_gemma":0.000046553825,"teacher_disagreement_score":0.9512861,"about_ca_system_score_codex":0.000016973787,"about_ca_system_score_gemma":0.000014354477,"threshold_uncertainty_score":0.29943502},"labels":[],"label_agreement":null},{"id":"W2035392457","doi":"10.1145/1631272.1631407","title":"Motion-path based gesture interaction with smart home services","year":2009,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Ottawa","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Motion (physics); Computer vision; Path (computing); Artificial intelligence","score_opus":0.008151959943838945,"score_gpt":0.21928750238112799,"score_spread":0.21113554243728905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035392457","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":[0.037674803,0.00002811349,0.9335472,0.004459825,0.00046522054,0.00023416066,0.0000015122425,0.0005400631,0.023049109],"genre_scores_gemma":[0.98194337,0.0000013736828,0.015736096,0.0018253829,0.00009201386,0.000008533685,0.000008277095,0.000004596881,0.00038034687],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990481,0.000055578726,0.00017272972,0.0002997095,0.00025938184,0.00016450998],"domain_scores_gemma":[0.9993059,0.00003656866,0.000089871595,0.000339915,0.00014178039,0.00008593772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015653193,0.0001266028,0.000135965,0.00010850646,0.00007624155,0.00022402575,0.00030863212,0.000058123485,0.00003778262],"category_scores_gemma":[0.0000027597878,0.000085748754,0.000036969144,0.0003480149,0.0000066763637,0.0006973686,0.000017076421,0.00010831288,0.00023539997],"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":[0.0002931683,0.0024551048,0.07984432,0.0006228332,0.00024643957,0.00032503542,0.008580342,0.0034106188,0.0093044685,0.053534444,0.008825758,0.8325575],"study_design_scores_gemma":[0.0050612004,0.0023778032,0.6060025,0.001156126,0.00005846291,0.0007328498,0.0009228208,0.28980166,0.015992105,0.0043146345,0.07141981,0.0021600188],"about_ca_topic_score_codex":0.000034280605,"about_ca_topic_score_gemma":0.00008544658,"teacher_disagreement_score":0.9442686,"about_ca_system_score_codex":0.000026489191,"about_ca_system_score_gemma":0.000027920489,"threshold_uncertainty_score":0.34967327},"labels":[],"label_agreement":null},{"id":"W2037855341","doi":"10.1109/embc.2012.6347551","title":"Multi-constrained inverse kinematics for the human hand","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inverse kinematics; Kinematics; Constraint (computer-aided design); Trajectory; Computer science; Motion (physics); Motion capture; Set (abstract data type); Classification of discontinuities; Inverse dynamics; Artificial intelligence; Task (project management); Computer vision; Mathematics; Robot; Engineering","score_opus":0.07807407804832459,"score_gpt":0.3074768310139965,"score_spread":0.2294027529656719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037855341","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":[0.0032261186,0.000041409923,0.99261737,0.0006400062,0.0004730373,0.00038418965,0.0000022032807,0.00007658469,0.002539065],"genre_scores_gemma":[0.8348316,9.476183e-7,0.1618528,0.0005796534,0.00023830358,0.00007211246,0.000001799173,0.0000056171943,0.0024171495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940765,0.000030182302,0.00015575915,0.00009768769,0.000105873965,0.00020284025],"domain_scores_gemma":[0.99928826,0.0002306392,0.00005320747,0.00028323135,0.00006893644,0.000075729666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045925117,0.00007321118,0.000088522276,0.0000310742,0.00021838097,0.00012829521,0.0003225333,0.000036145266,0.000031497653],"category_scores_gemma":[0.000053150292,0.000041434494,0.000054123302,0.00009989548,0.000049376078,0.00023387808,0.000054896453,0.00004209321,0.0001034524],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009803502,0.0014545869,0.010465029,0.00061012857,0.00056782376,0.000006381714,0.0491833,0.00018018714,0.0945276,0.5679894,0.12833507,0.14667067],"study_design_scores_gemma":[0.010403943,0.00027984552,0.015259081,0.00020566165,0.00015888538,0.0002635924,0.0041339565,0.56044817,0.0634559,0.0043789092,0.3392453,0.001766776],"about_ca_topic_score_codex":0.000010710892,"about_ca_topic_score_gemma":0.00003314251,"teacher_disagreement_score":0.8316055,"about_ca_system_score_codex":0.000008173989,"about_ca_system_score_gemma":0.000014679515,"threshold_uncertainty_score":0.16896497},"labels":[],"label_agreement":null},{"id":"W2040766211","doi":"10.1117/12.515704","title":"Real-time estimation of 3D human arm motion from markerless images for human-machine interaction","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Initialization; Match moving; Robotic arm; Pose; Motion capture; Motion estimation; Tracking (education); Thumb; Orientation (vector space); Motion (physics); Mathematics","score_opus":0.013533943914053168,"score_gpt":0.2551770427022294,"score_spread":0.24164309878817625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040766211","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":[0.9835705,0.00003501576,0.010899023,0.00045657862,0.00030269768,0.00076230086,0.00008484808,0.0001161008,0.003772909],"genre_scores_gemma":[0.73161894,0.00002238404,0.2677164,0.000019814806,0.00020186664,0.0001829559,0.000041258034,0.00004111296,0.00015523455],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977033,9.172955e-8,0.000917964,0.00046227095,0.00062291103,0.000293468],"domain_scores_gemma":[0.99694026,0.00027403704,0.0007783651,0.00009546761,0.0018182056,0.00009366143],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00097428507,0.0002990209,0.00047584483,0.0001589134,0.00012881134,0.00016480031,0.0008682363,0.0001851649,0.000017673461],"category_scores_gemma":[0.00049564947,0.0002654408,0.0004846169,0.0003032576,0.00011274763,0.0010547397,0.00010628309,0.00020706486,0.0000021734334],"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":[0.000030918025,0.00018795219,0.0001260773,0.00038182494,0.00025013767,4.101249e-8,0.00023711228,0.00025509717,0.64724475,0.3492555,0.00081284525,0.0012177611],"study_design_scores_gemma":[0.003165419,0.00074514985,0.0019478791,0.0011588597,0.00028968067,0.000027081427,0.0009347428,0.22064327,0.74719584,0.021756466,0.0013271694,0.0008084332],"about_ca_topic_score_codex":0.000040104547,"about_ca_topic_score_gemma":2.6590436e-7,"teacher_disagreement_score":0.32749903,"about_ca_system_score_codex":0.00016145938,"about_ca_system_score_gemma":0.000024400222,"threshold_uncertainty_score":0.9999798},"labels":[],"label_agreement":null},{"id":"W2042318956","doi":"10.1142/s0219467805001902","title":"PALMPRINTS: A COOPERATIVE CO-EVOLUTIONARY ALGORITHM FOR CLUSTERING HAND IMAGES","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University; Royal Society of Canada","keywords":"Cluster analysis; Fitness function; Genetic algorithm; Computer science; Curse of dimensionality; Artificial intelligence; Function (biology); Image (mathematics); Evolutionary algorithm; Correlation clustering; Space (punctuation); Pattern recognition (psychology); Data mining; Algorithm; Machine learning; Biology","score_opus":0.015548977745141402,"score_gpt":0.2969492022614951,"score_spread":0.2814002245163537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042318956","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":[0.0023999983,0.00062014663,0.99272674,0.003234461,0.000532797,0.00009761843,0.000045631296,0.000012452125,0.0003301458],"genre_scores_gemma":[0.50388443,0.0009487746,0.49125347,0.0015732922,0.0019760013,0.000018624381,0.000013945906,0.000017665978,0.00031380972],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989636,0.00004661248,0.00037575574,0.000144125,0.0003494991,0.00012043933],"domain_scores_gemma":[0.99805415,0.00016857359,0.00024369267,0.000080976984,0.0013655245,0.00008707745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004015384,0.000104251194,0.00015812497,0.00027300234,0.00010770827,0.0003388666,0.00041959673,0.00004607034,0.000009614598],"category_scores_gemma":[0.0000764728,0.000086762026,0.000105263905,0.000098441196,0.00009312173,0.0010453841,0.000074257056,0.00014423601,0.0000057145257],"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":[0.000080474034,0.000277796,0.00078798487,0.000028133696,0.0006018104,0.00019387942,0.0019174482,0.0000864662,0.009031789,0.0041460646,0.014182358,0.9686658],"study_design_scores_gemma":[0.01572146,0.001511952,0.016987247,0.0011639429,0.00012675615,0.01452349,0.0009379329,0.38235068,0.063685454,0.015629733,0.4858357,0.0015256217],"about_ca_topic_score_codex":0.0000034034924,"about_ca_topic_score_gemma":0.0000044579847,"teacher_disagreement_score":0.9671402,"about_ca_system_score_codex":0.000032522687,"about_ca_system_score_gemma":0.00007021142,"threshold_uncertainty_score":0.35380527},"labels":[],"label_agreement":null},{"id":"W2042967086","doi":"10.5555/2447556.2447679","title":"Gestures for industry: intuitive human-robot communication from human observation","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gesture; Computer science; Human–robot interaction; Task (project management); Human–computer interaction; Context (archaeology); Robot; Set (abstract data type); Lexicon; Artificial intelligence; Engineering; Systems engineering","score_opus":0.07553752340890948,"score_gpt":0.3144638627444046,"score_spread":0.2389263393354951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042967086","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":[0.36137116,0.00010441057,0.61364305,0.0063237865,0.00030308153,0.0018243205,0.0000150669275,0.00056907773,0.015846048],"genre_scores_gemma":[0.9546655,0.0000021142096,0.04217017,0.0008522972,0.00014910512,0.00041622334,0.0001474929,0.000010623793,0.0015865042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9989027,0.00012902843,0.00031580307,0.00029338174,0.00018105496,0.00017807531],"domain_scores_gemma":[0.99851626,0.00022845535,0.00016892656,0.00065373804,0.00035797522,0.00007463512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002400882,0.00013015824,0.00016517525,0.00007316235,0.00031034183,0.00035191057,0.00075110653,0.00023183599,0.00012834574],"category_scores_gemma":[0.000047196616,0.00011307619,0.000052242423,0.00018352142,0.00003486143,0.0008677779,0.00015863442,0.0002273984,0.00014796157],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006340111,0.0004983435,0.023287898,0.0000646222,0.00025744791,0.0000014541329,0.01076704,0.00007502383,0.10488966,0.5796018,0.16072078,0.11982958],"study_design_scores_gemma":[0.0023855187,0.0003417,0.61373085,0.00027832543,0.00003458227,0.0000050487974,0.0015001888,0.006799085,0.03261117,0.30683598,0.034425627,0.0010519568],"about_ca_topic_score_codex":0.0017377887,"about_ca_topic_score_gemma":0.00025855258,"teacher_disagreement_score":0.5932943,"about_ca_system_score_codex":0.000044689634,"about_ca_system_score_gemma":0.00002139531,"threshold_uncertainty_score":0.4611113},"labels":[],"label_agreement":null},{"id":"W2050735977","doi":"10.4108/icst.immerscom2007.2114","title":"Fast Stroke Matching by Angle Quantization","year":2007,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Quantization (signal processing); Computer science; Similarity (geometry); Matching (statistics); Feature (linguistics); Feature matching; Dimension (graph theory); Artificial intelligence; Computer vision; Point (geometry); Space (punctuation); Task (project management); Pattern recognition (psychology); Feature extraction; Mathematics; Geometry; Image (mathematics); Combinatorics; Engineering","score_opus":0.012180969060380221,"score_gpt":0.24876238357003203,"score_spread":0.2365814145096518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050735977","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":[0.01508522,0.000040339113,0.944392,0.0002308807,0.0003028594,0.00006858984,0.0000019535078,0.00019703583,0.039681096],"genre_scores_gemma":[0.9721922,0.0000023958721,0.023242127,0.00034027582,0.00007325399,0.0000017590332,0.000005626057,0.000004832486,0.0041374792],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992927,0.000023391163,0.00015633261,0.00016892867,0.00019363863,0.00016502809],"domain_scores_gemma":[0.9996179,0.000052231666,0.00004470429,0.00016972679,0.000051651998,0.000063819425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004077144,0.000060723516,0.000068516565,0.00006326867,0.00006280428,0.00011639239,0.00021925512,0.00003958683,0.000026434429],"category_scores_gemma":[0.000008024576,0.000051357296,0.00002417007,0.00020145778,0.000006433436,0.00032641724,0.000043109176,0.00004763158,0.000244054],"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":[0.00000849843,0.00024218149,0.008498199,0.000048239617,0.000048192138,0.000034472876,0.0042575905,0.00008600135,0.116011456,0.16773425,0.038426343,0.66460454],"study_design_scores_gemma":[0.0034719103,0.00044968422,0.020598361,0.00021981807,0.000022951297,0.00036333533,0.0037710553,0.03537406,0.49499327,0.010154524,0.4281778,0.0024032171],"about_ca_topic_score_codex":0.00004186635,"about_ca_topic_score_gemma":0.000043971082,"teacher_disagreement_score":0.957107,"about_ca_system_score_codex":0.000015704822,"about_ca_system_score_gemma":0.000010204153,"threshold_uncertainty_score":0.3136902},"labels":[],"label_agreement":null},{"id":"W2051601817","doi":"10.1016/j.humov.2006.07.004","title":"A multi-level representation paradigm for handwriting stroke generation","year":2006,"lang":"en","type":"article","venue":"Human Movement Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Handwriting; Kinematics; Context (archaeology); Representation (politics); Computer science; Perspective (graphical); Field (mathematics); Trajectory; Human–computer interaction; Artificial intelligence; Data science; Mathematics","score_opus":0.13062754399797388,"score_gpt":0.3374698586956167,"score_spread":0.2068423146976428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051601817","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":[0.077128485,0.000028216247,0.9207902,0.000262201,0.00031774052,0.0005190475,0.000009731126,0.00009451547,0.00084987923],"genre_scores_gemma":[0.9110505,9.3542803e-7,0.0866702,0.00027375115,0.00031440932,0.0001550731,0.000017404755,0.0000056307513,0.001512067],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981832,0.000034366305,0.00033274214,0.0005704549,0.0005465768,0.00033268167],"domain_scores_gemma":[0.9991899,0.00003756287,0.000163455,0.00036187642,0.00018042515,0.0000667942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009299447,0.000109751556,0.00011091379,0.00019747464,0.00078921544,0.00061172206,0.0005563895,0.000028414632,0.000006086269],"category_scores_gemma":[0.00004079516,0.00010556601,0.000050128594,0.00041725513,0.00009789946,0.0009917192,0.00009461154,0.000044250817,0.000017211236],"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":[0.0000012914662,0.00012669046,0.0027802805,0.000014867184,0.0000046018163,0.0000022454597,0.00040264867,0.00057479885,0.8627633,0.11582221,0.00095826184,0.016548837],"study_design_scores_gemma":[0.0027745895,0.00020565376,0.08426163,0.00007397706,0.000011687218,0.000009127462,0.00017498089,0.47193763,0.42524555,0.012101607,0.0025411206,0.0006624268],"about_ca_topic_score_codex":0.000093077855,"about_ca_topic_score_gemma":0.00015344334,"teacher_disagreement_score":0.83412,"about_ca_system_score_codex":0.000091881506,"about_ca_system_score_gemma":0.0000646576,"threshold_uncertainty_score":0.6070087},"labels":[],"label_agreement":null},{"id":"W2051802081","doi":"10.1109/ghtc.2012.65","title":"Sahaaya: Gesture Recognising System to Provide Effective Communication for Specially-Abled People","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ryerson University","keywords":"Gesture; Computer science; Gesture recognition; Cursor (databases); Hindi; Human–computer interaction; Head (geology); ALARM; Computer vision; Multimedia; Artificial intelligence; Engineering","score_opus":0.014681138980477902,"score_gpt":0.2611738291537495,"score_spread":0.24649269017327158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051802081","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":[0.015857562,0.00027593764,0.9514244,0.0016211816,0.0012799072,0.0038030697,0.000007317235,0.0005713757,0.025159203],"genre_scores_gemma":[0.8725094,0.0000029071648,0.12527396,0.00033264296,0.0004445717,0.000778454,0.000010569438,0.000019963367,0.0006275236],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983182,0.0003038705,0.00033599432,0.00033792222,0.00025216,0.0004518553],"domain_scores_gemma":[0.997839,0.0006939592,0.00014503977,0.00075198937,0.0003295013,0.00024055036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013943819,0.00018854212,0.0003206466,0.00014164041,0.0002871222,0.00025237512,0.0006572827,0.00012148696,0.000007599935],"category_scores_gemma":[0.00019387668,0.00015746908,0.00009833638,0.0005033964,0.000009879525,0.0008134328,0.00021697594,0.000121179706,0.00037251133],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002022425,0.0007594417,0.017571019,0.0018357302,0.00037970065,0.0000041411936,0.04457787,0.00015273746,0.00748701,0.20586058,0.042290553,0.67887896],"study_design_scores_gemma":[0.008113083,0.0014622108,0.115407005,0.0035046155,0.00028435062,0.00069289515,0.008985534,0.025410775,0.053579394,0.002602813,0.7756828,0.0042745094],"about_ca_topic_score_codex":0.00008384221,"about_ca_topic_score_gemma":0.00037075533,"teacher_disagreement_score":0.85665184,"about_ca_system_score_codex":0.00021217755,"about_ca_system_score_gemma":0.000054088923,"threshold_uncertainty_score":0.64214027},"labels":[],"label_agreement":null},{"id":"W2055267925","doi":"10.1109/memea.2013.6549723","title":"Dynamic sign language and voice recognition for smart home interactive application","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":18,"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":"Computer science; Sign language; Home automation; Speech recognition; Human–computer interaction; Multimedia; Linguistics; Telecommunications","score_opus":0.009906795775987251,"score_gpt":0.2498724787590844,"score_spread":0.23996568298309714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055267925","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":[0.06587334,0.000038588565,0.92954177,0.00067493867,0.00013781297,0.0008566172,0.0000070402916,0.00015163868,0.0027182752],"genre_scores_gemma":[0.966706,0.000004853374,0.031608865,0.00030738988,0.000035275312,0.00060645165,0.00003266772,0.000007218634,0.0006913018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999341,0.000039203744,0.00014163392,0.00026948834,0.00008170052,0.00012694474],"domain_scores_gemma":[0.9993131,0.00022826117,0.00007045816,0.0001743804,0.00015383733,0.000059934722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014850074,0.000083431005,0.000098475095,0.00007646729,0.000054350712,0.00015278363,0.00014128146,0.000048165846,0.00001943481],"category_scores_gemma":[0.000027532253,0.00007048693,0.000026993297,0.00012268512,0.000012826086,0.00065430935,0.00004268298,0.000050575207,0.0006965716],"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":[0.0000047932053,0.000037195394,0.00009247431,0.000038476846,0.000018048762,4.3045813e-7,0.0014684844,4.557378e-7,0.011130719,0.0004573682,0.0008003636,0.9859512],"study_design_scores_gemma":[0.0048697162,0.0008767291,0.051740024,0.0003047898,0.000070607486,0.00028958518,0.007826067,0.7870165,0.030473044,0.09709619,0.017282099,0.0021546301],"about_ca_topic_score_codex":0.00010852195,"about_ca_topic_score_gemma":0.000054346143,"teacher_disagreement_score":0.98379654,"about_ca_system_score_codex":0.000020116755,"about_ca_system_score_gemma":0.000010505102,"threshold_uncertainty_score":0.8953251},"labels":[],"label_agreement":null},{"id":"W2060823974","doi":"10.1145/2733373.2806385","title":"An Elicitation Study on Gesture Attitudes and Preferences Towards an Interactive Hand-Gesture Vocabulary","year":2015,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Gesture; Vocabulary; Computer science; Gesture recognition; Set (abstract data type); Human–computer interaction; Control (management); Multimedia; Natural language processing; Artificial intelligence; Linguistics","score_opus":0.06017043405313506,"score_gpt":0.3414963871016495,"score_spread":0.28132595304851443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060823974","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":[0.96569234,0.0000958705,0.028319146,0.0004200582,0.0005057869,0.0006480976,0.000004266638,0.00025770924,0.004056698],"genre_scores_gemma":[0.9962576,0.0000041023163,0.0029764364,0.0003692886,0.00018607792,0.00005581529,0.000013653466,0.000010696538,0.00012634325],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979582,0.00046582174,0.00023216718,0.0006302937,0.000514501,0.00019903088],"domain_scores_gemma":[0.99866503,0.000086962405,0.0001028381,0.00048886484,0.00035743523,0.00029887495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060350157,0.00022643576,0.00024571482,0.00016349962,0.00013662723,0.00071321294,0.0005045739,0.000110986526,0.0000061792393],"category_scores_gemma":[0.00006494919,0.00016016129,0.000024347104,0.0002521746,0.000043581374,0.001693722,0.00009647611,0.00022044804,0.00004117713],"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":[0.00035866036,0.006785836,0.19498709,0.000089543835,0.00040967748,0.0001535732,0.29353023,0.00025857927,0.0030876081,0.005575287,0.0029063716,0.49185753],"study_design_scores_gemma":[0.0019549795,0.008252864,0.9619958,0.00012148564,0.000029991363,0.000073259405,0.017819934,0.0033171314,0.0020809676,0.002506451,0.0011893238,0.00065783295],"about_ca_topic_score_codex":0.00022450581,"about_ca_topic_score_gemma":0.0006329674,"teacher_disagreement_score":0.76700866,"about_ca_system_score_codex":0.000040275736,"about_ca_system_score_gemma":0.00009972537,"threshold_uncertainty_score":0.6877529},"labels":[],"label_agreement":null},{"id":"W2062833884","doi":"10.1109/icces.2012.6408505","title":"Statistical vs. visual data generation in hand gesture recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Computer science; Gesture; Artificial intelligence; Gesture recognition; Synthetic data; Process (computing); Generalization; Machine learning; Pattern recognition (psychology); Training set","score_opus":0.10537804790105906,"score_gpt":0.33052996591816525,"score_spread":0.22515191801710618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062833884","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":[0.03187401,0.00011008511,0.9643535,0.00051884085,0.000678058,0.0001997496,0.000026295731,0.00007455157,0.002164883],"genre_scores_gemma":[0.9601892,0.000011242666,0.037990097,0.00043391692,0.00063138036,0.000017145894,0.0005906191,0.0000066144835,0.00012976107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874234,0.00017346878,0.00024467334,0.0003258752,0.00024413112,0.00026951093],"domain_scores_gemma":[0.9992593,0.000099097684,0.000046973066,0.00041176158,0.000059712747,0.00012318078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072531035,0.00010106114,0.00012864593,0.000100586665,0.00006608249,0.00021008043,0.0003433797,0.0000890009,0.000117424424],"category_scores_gemma":[0.000114654365,0.00008503129,0.000011108871,0.0002384367,0.000021963331,0.0014182845,0.00018599357,0.000118839795,0.0005810076],"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":[0.000025046555,0.0008676861,0.02257971,0.00006745981,0.00003893886,0.000034678622,0.002384204,0.000014260832,0.0060568033,0.0152559765,0.085446,0.8672292],"study_design_scores_gemma":[0.0044254754,0.0004995364,0.22007445,0.00016793855,0.000055830165,0.0005460755,0.0003207427,0.5983222,0.024348043,0.003241322,0.1458664,0.0021319701],"about_ca_topic_score_codex":0.000054199798,"about_ca_topic_score_gemma":0.00019367458,"teacher_disagreement_score":0.9283152,"about_ca_system_score_codex":0.000028648863,"about_ca_system_score_gemma":0.00003781311,"threshold_uncertainty_score":0.7467871},"labels":[],"label_agreement":null},{"id":"W2065157330","doi":"10.1016/j.jvlc.2014.12.001","title":"Hand pose recognition from monocular images by geometrical and texture analysis","year":2014,"lang":"en","type":"article","venue":"Journal of Visual Languages & Computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Texture (cosmology); Monocular; Monocular vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.00782187932541159,"score_gpt":0.27805490438240016,"score_spread":0.27023302505698854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065157330","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":[0.41485032,0.0024166515,0.5822172,0.00017187907,0.00015233184,0.00004041355,0.0000076210017,0.000024486644,0.00011904725],"genre_scores_gemma":[0.97547644,0.000028011338,0.023577686,0.00023370878,0.000640346,3.7835494e-7,0.00001542296,0.000009755711,0.00001822242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981323,0.00033895177,0.00056145375,0.00028503613,0.00045288968,0.00022935176],"domain_scores_gemma":[0.9980844,0.00066647225,0.0005878309,0.0001674489,0.00029745433,0.00019638808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010390297,0.00017280497,0.0005017984,0.0005907265,0.00015008167,0.0005175989,0.0003354257,0.000108003565,0.00001744973],"category_scores_gemma":[0.00034705698,0.00013831869,0.00019472509,0.00105197,0.000039026505,0.00037131883,0.00013937983,0.00030523769,0.000012134248],"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":[0.00001655666,0.00015439301,0.007217817,0.000022351656,0.00066072476,0.000089320005,0.0013188078,0.00007899168,0.013504515,0.000014204677,0.0014160031,0.9755063],"study_design_scores_gemma":[0.009194469,0.0037267578,0.18761234,0.001337928,0.0034154775,0.0018637415,0.002932091,0.67715,0.09018532,0.0028867745,0.01661515,0.0030799417],"about_ca_topic_score_codex":0.000069450805,"about_ca_topic_score_gemma":0.0000040664463,"teacher_disagreement_score":0.97242635,"about_ca_system_score_codex":0.00002999809,"about_ca_system_score_gemma":0.000017820359,"threshold_uncertainty_score":0.5640472},"labels":[],"label_agreement":null},{"id":"W2070584460","doi":"10.1109/icassp.2010.5495895","title":"Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, &amp;#x00026; compressive sensing","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":114,"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":"Gesture; Accelerometer; Dynamic time warping; Gesture recognition; Computer science; Speech recognition; Artificial intelligence; Compressed sensing; Acceleration; Pattern recognition (psychology); Computer vision","score_opus":0.020549028874336628,"score_gpt":0.24976698017454496,"score_spread":0.22921795130020833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070584460","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":[0.2016804,0.000011602993,0.79052246,0.0011892578,0.0010537092,0.00063632766,0.00001558145,0.00082421984,0.004066456],"genre_scores_gemma":[0.7688407,0.000001580447,0.22955889,0.00061804935,0.00020025531,0.000025187084,0.00014565834,0.000030732845,0.0005789753],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974361,0.00027624515,0.00052559027,0.0007545759,0.00054472435,0.0004627797],"domain_scores_gemma":[0.9974622,0.0003245031,0.0003522445,0.00086032937,0.0007573384,0.00024336959],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006135147,0.0003439202,0.00036813036,0.00031673387,0.00034416548,0.0004986152,0.0006441044,0.0003054771,0.00033637058],"category_scores_gemma":[0.00019898493,0.00029865227,0.00014161931,0.0006949421,0.000087761306,0.0008315716,0.00015610654,0.000609778,0.0020528713],"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":[0.00003362096,0.00042374007,0.00033713435,0.00014497402,0.00010266132,0.000039071776,0.0010204025,0.00008774396,0.58708864,0.00015533586,0.0032297955,0.4073369],"study_design_scores_gemma":[0.0053635314,0.0005179763,0.013595909,0.00077747065,0.00014856935,0.0011967544,0.00009846531,0.6696478,0.2488252,0.0111343,0.04456621,0.0041277986],"about_ca_topic_score_codex":0.000054042892,"about_ca_topic_score_gemma":0.00031026013,"teacher_disagreement_score":0.6695601,"about_ca_system_score_codex":0.000056295845,"about_ca_system_score_gemma":0.00013382429,"threshold_uncertainty_score":0.99994653},"labels":[],"label_agreement":null},{"id":"W2070619709","doi":"10.1142/s0218001404003721","title":"THE GENERATION OF VELOCITY PROFILES WITH AN ARTIFICIAL SIMULATOR","year":2004,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Hand Gesture Recognition Systems","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; MATLAB; Context (archaeology); Simulation; Handwriting; Artificial intelligence; Kinematics; Segmentation; Computer architecture simulator; Similarity (geometry); Pattern recognition (psychology); Image (mathematics)","score_opus":0.11593757893859569,"score_gpt":0.31843226925943746,"score_spread":0.20249469032084177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070619709","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":[0.46402445,0.000032840064,0.53402317,0.0011424411,0.00064822036,0.000083189,0.000011746833,0.000010858703,0.000023047647],"genre_scores_gemma":[0.99518704,0.000060220835,0.0039195907,0.00017397464,0.00063773757,0.000004142594,0.000007927054,0.000007099857,0.0000022435754],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983319,0.00010448225,0.00070088997,0.00017509071,0.0005627145,0.00012489317],"domain_scores_gemma":[0.99780333,0.00011676577,0.0005766845,0.00012330528,0.0012856992,0.00009421597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005981418,0.00011556908,0.0001514135,0.00014814647,0.00013674182,0.00032674416,0.00048255362,0.000053037245,0.000017664135],"category_scores_gemma":[0.00009629037,0.0000772142,0.000062871426,0.00014013404,0.00012285257,0.0006774417,0.000043595872,0.00015601153,0.000020359217],"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":[0.000092983384,0.00017624478,0.00008942889,0.0000060188395,0.000069349124,0.000030363777,0.0012816443,0.0007557242,0.008001428,0.0046084714,0.0000054525563,0.9848829],"study_design_scores_gemma":[0.0005358103,0.0020874508,0.0009449091,0.0006283485,0.000056396628,0.0011223395,0.0027023663,0.056975923,0.7652174,0.16855954,0.00056787167,0.000601616],"about_ca_topic_score_codex":0.000033140896,"about_ca_topic_score_gemma":0.00014151388,"teacher_disagreement_score":0.9842813,"about_ca_system_score_codex":0.00003985894,"about_ca_system_score_gemma":0.00010588102,"threshold_uncertainty_score":0.31508017},"labels":[],"label_agreement":null},{"id":"W2071747204","doi":"10.1109/saci.2012.6249973","title":"Finger-based gesture control of a collaborative online workspace","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Workspace; Gesture; Computer science; Set (abstract data type); Human–computer interaction; Interface (matter); Gesture recognition; Control (management); Range (aeronautics); Multimedia; Artificial intelligence; Computer vision; Robot; Engineering; Operating system","score_opus":0.014225453477900761,"score_gpt":0.25442670820917135,"score_spread":0.24020125473127057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071747204","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":[0.006713162,0.0009589205,0.9854583,0.0030134614,0.0004019196,0.000289527,0.000023372995,0.00012135369,0.0030199585],"genre_scores_gemma":[0.9410457,0.000002842103,0.05762957,0.0008001127,0.00014576413,0.00001615433,0.0000029526066,0.0000054491643,0.00035147797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99903107,0.00013972534,0.00020292666,0.00015428603,0.00023804771,0.00023396361],"domain_scores_gemma":[0.9988532,0.00028911137,0.00013611338,0.00030641627,0.00029145312,0.00012371514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003088752,0.00011100054,0.00022419893,0.0000815801,0.000035250196,0.000033926364,0.00028083188,0.00008345001,0.00003663049],"category_scores_gemma":[0.000080293044,0.0000825727,0.0000502744,0.0006131409,0.00002847922,0.000280544,0.000030487936,0.000094625335,0.000047119855],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043018349,0.009874692,0.2390187,0.00061163545,0.0011218492,0.00006225055,0.03266417,0.002644637,0.041135743,0.28441486,0.1101456,0.2778757],"study_design_scores_gemma":[0.026914906,0.0021180112,0.17498454,0.0014583173,0.00025680417,0.000120035904,0.005259348,0.14741701,0.09764525,0.0016809873,0.5381151,0.0040296735],"about_ca_topic_score_codex":0.0000087714,"about_ca_topic_score_gemma":0.000023034085,"teacher_disagreement_score":0.9343325,"about_ca_system_score_codex":0.000018288183,"about_ca_system_score_gemma":0.00009726583,"threshold_uncertainty_score":0.3367217},"labels":[],"label_agreement":null},{"id":"W2072404679","doi":"10.1016/j.procs.2013.06.061","title":"Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Accelerometer; Gesture; Tracking (education); Mobile phone; Computer vision; Artificial intelligence; Movement (music); Gesture recognition; Foot (prosody); Position (finance); Phone; Human–computer interaction; Acoustics; Telecommunications","score_opus":0.06685342729545717,"score_gpt":0.26673380139855246,"score_spread":0.19988037410309528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072404679","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":[0.5571603,0.000094029594,0.44103256,0.00017522357,0.0003679374,0.0010717007,0.0000038097369,0.00006561186,0.000028825085],"genre_scores_gemma":[0.7963743,0.000016958253,0.20297825,0.0002032926,0.00008156317,0.00032817473,0.0000015296514,0.000007062078,0.000008856608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981679,0.000037423353,0.0003873295,0.00065423857,0.00036226903,0.00039089457],"domain_scores_gemma":[0.9984555,0.00032888108,0.00017589417,0.00029967393,0.00060141884,0.0001386143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063898385,0.00015992981,0.00025244764,0.00045004254,0.00010148396,0.00061168935,0.000674966,0.000073271265,0.000003866147],"category_scores_gemma":[0.00012369244,0.00013799922,0.000044679142,0.0011619981,0.0001663338,0.0032170697,0.00027621817,0.00011440464,0.000013548705],"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":[0.0000043684454,0.00009684226,0.0045646722,0.00015711188,0.000005758744,0.0000016237536,0.0027342187,0.000045727444,0.009052954,0.00018771052,0.00019315451,0.9829559],"study_design_scores_gemma":[0.0028757572,0.0017732423,0.25240642,0.001241495,0.000023095628,0.00033448002,0.00016381805,0.6411785,0.07822993,0.016354613,0.0037291949,0.0016894898],"about_ca_topic_score_codex":0.000026295993,"about_ca_topic_score_gemma":0.000011593992,"teacher_disagreement_score":0.9812664,"about_ca_system_score_codex":0.00003338277,"about_ca_system_score_gemma":0.00011374297,"threshold_uncertainty_score":0.58985347},"labels":[],"label_agreement":null},{"id":"W2072421451","doi":"10.3390/s140203293","title":"Vertical Dynamic Deflection Measurement in Concrete Beams with the Microsoft Kinect","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deflection (physics); Frame rate; Computer science; RGB color model; Displacement (psychology); Computer vision; Segmentation; Artificial intelligence; Vertical displacement; Acoustics; Computer graphics (images); Structural engineering; Geology; Engineering; Optics; Physics","score_opus":0.013291916840289502,"score_gpt":0.22255376206999078,"score_spread":0.2092618452297013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072421451","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":[0.78270274,0.00005413371,0.21097806,0.0031705245,0.00031049058,0.00028835726,4.2949384e-7,0.0001198962,0.0023753445],"genre_scores_gemma":[0.99844223,0.000001766732,0.0011517322,0.00029273308,0.000040400642,0.000013551227,5.124333e-7,0.0000073148194,0.000049747505],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998805,0.00023400197,0.000141482,0.00024851243,0.00035734437,0.00021369358],"domain_scores_gemma":[0.99942404,0.00010291039,0.000031089006,0.00028763252,0.00010437311,0.000049963415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066409516,0.00010183227,0.0001235184,0.000058406797,0.000072218565,0.000081893726,0.00022199425,0.000044810513,0.0000022155782],"category_scores_gemma":[0.00006529301,0.000061860635,0.000031194766,0.0003037436,0.000043002823,0.00007011826,0.0000295873,0.0001340077,0.0000908542],"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":[0.00074416667,0.00031918744,0.032226127,0.00049598777,0.00070071756,0.0002606114,0.029716419,0.023555905,0.53528255,0.028809821,0.0055487123,0.3423398],"study_design_scores_gemma":[0.0071536163,0.0017521011,0.16282906,0.00076916703,0.00009084356,0.0009642845,0.000727501,0.6451524,0.08749567,0.0014189011,0.08964362,0.0020028746],"about_ca_topic_score_codex":0.000060407616,"about_ca_topic_score_gemma":0.00030209697,"teacher_disagreement_score":0.62159646,"about_ca_system_score_codex":0.00008466273,"about_ca_system_score_gemma":0.00003100046,"threshold_uncertainty_score":0.25226036},"labels":[],"label_agreement":null},{"id":"W2075022376","doi":"10.5555/1734454.1734575","title":"Selecting and commanding individual robots in a vision-based multi-robot system","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Gesture; Robot; Computer science; Computer vision; Task (project management); Artificial intelligence; Face (sociological concept); Motion (physics); Eye contact; Human–computer interaction; Mobile robot; Communication; Engineering; Psychology","score_opus":0.021846564145499587,"score_gpt":0.27454076861052235,"score_spread":0.2526942044650228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075022376","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":[0.20995916,0.00003300145,0.78668994,0.00033506815,0.0006547697,0.00033125354,0.0000013652623,0.00035070052,0.0016447234],"genre_scores_gemma":[0.9152285,2.3613067e-7,0.08455095,0.00010796316,0.000047196612,0.000018874696,0.0000014563494,0.0000071852437,0.000037677775],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987947,0.000114246664,0.00027451938,0.00033958702,0.00022468535,0.00025229857],"domain_scores_gemma":[0.9991908,0.00031084535,0.000083173974,0.00024599454,0.00005971375,0.000109442524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000955687,0.00012581264,0.00019148951,0.0002461772,0.00013742977,0.00032833984,0.00033541807,0.00010364919,0.0000047074896],"category_scores_gemma":[0.00006307943,0.00010496853,0.000026578158,0.00043893696,0.000021251524,0.0002863109,0.0001263901,0.00028995905,0.00002694371],"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":[0.000028859184,0.00080383813,0.5821191,0.0010761555,0.00011606143,0.00031140744,0.011858504,0.0029710063,0.06753074,0.03192345,0.00071473833,0.30054614],"study_design_scores_gemma":[0.0029426727,0.0001022053,0.094552554,0.00048690595,0.00000848287,0.00024575338,0.00049244077,0.89393324,0.006286921,0.000043578846,0.00035933475,0.0005459395],"about_ca_topic_score_codex":0.00011872337,"about_ca_topic_score_gemma":0.0007952612,"teacher_disagreement_score":0.8909622,"about_ca_system_score_codex":0.000024282403,"about_ca_system_score_gemma":0.000050001818,"threshold_uncertainty_score":0.42804924},"labels":[],"label_agreement":null},{"id":"W2075688392","doi":"10.1007/s11042-012-1137-6","title":"Design and implementation of a system for body posture recognition","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Sitting; Context (archaeology); Identity (music); Human–computer interaction; Body posture; Physical medicine and rehabilitation; Medicine","score_opus":0.04516898883011084,"score_gpt":0.30203860060345167,"score_spread":0.25686961177334083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075688392","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":[0.007919286,0.00032400986,0.98965824,0.00013874762,0.00006684045,0.0016646563,0.00009897656,0.000054982494,0.0000742655],"genre_scores_gemma":[0.8406534,0.000032594435,0.1572696,0.0000308031,0.00015430634,0.0017521511,0.00009191747,0.000006303049,0.000008887504],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939036,0.000039578288,0.00019936298,0.00015679824,0.00007896765,0.00013495426],"domain_scores_gemma":[0.9992917,0.0002672861,0.000116179726,0.0001298675,0.000108736625,0.000086235355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031741525,0.00007455879,0.00011459762,0.00004998806,0.00009792355,0.000054465727,0.000077151424,0.000048042228,0.000001789865],"category_scores_gemma":[0.000007912819,0.00006606839,0.000019477971,0.00011375132,0.000019209036,0.0003332093,0.000027492784,0.000029892508,0.000007657053],"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":[0.0000056302106,0.000055275545,0.0010588791,0.00024147272,0.000027409045,6.516233e-8,0.0016020257,9.5334724e-7,0.017721193,0.0047298325,0.00017715187,0.97438014],"study_design_scores_gemma":[0.016671007,0.0012571384,0.21886544,0.0009063306,0.0007379418,0.0005464627,0.016523315,0.095142625,0.49732605,0.0074721696,0.14140305,0.003148483],"about_ca_topic_score_codex":0.0000100231355,"about_ca_topic_score_gemma":0.0000013443897,"teacher_disagreement_score":0.97123164,"about_ca_system_score_codex":0.0000119044,"about_ca_system_score_gemma":0.00001633478,"threshold_uncertainty_score":0.26941907},"labels":[],"label_agreement":null},{"id":"W2076810595","doi":"10.1016/j.compbiomed.2008.08.009","title":"Screening of knee-joint vibroarthrographic signals using the strict 2-surface proximal classifier and genetic algorithm","year":2008,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Classifier (UML); Pattern recognition (psychology); Computer science; Artificial intelligence; Gaussian; Nonlinear system; Feature vector; Algorithm; Robustness (evolution); Mathematics; Physics","score_opus":0.057379967241183445,"score_gpt":0.29640786070160463,"score_spread":0.2390278934604212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076810595","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":[0.33610418,0.0038581719,0.6591508,0.00040888847,0.00024532955,0.00018431315,0.0000010010302,0.000016772598,0.000030582087],"genre_scores_gemma":[0.84577405,0.00042102323,0.15341935,0.00025453418,0.00011777082,0.0000035480773,0.0000015408132,0.000004002182,0.000004159491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988167,0.00027465713,0.00032354912,0.00029050832,0.00009733837,0.00019719334],"domain_scores_gemma":[0.999274,0.00029024057,0.0001311223,0.00018540866,0.000050519582,0.000068693116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046051337,0.00012559653,0.00031698667,0.00014724775,0.0001236095,0.0000069923917,0.00016855978,0.000096893156,0.0000018795696],"category_scores_gemma":[0.000023383065,0.000078126235,0.000024210238,0.00037602757,0.00065388146,0.000059602582,0.00013661469,0.00015594336,2.122331e-7],"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":[0.00004018675,0.000099613346,0.1323577,0.00010909322,0.00017729863,0.0001793103,0.0060762055,0.00045927975,0.017479805,0.0015340734,0.00030595693,0.84118146],"study_design_scores_gemma":[0.0054821935,0.0022301408,0.3432224,0.0011817575,0.00005618053,0.003663102,0.00056748535,0.6361877,0.0014122988,0.0036597208,0.0017382494,0.0005987661],"about_ca_topic_score_codex":0.00004077828,"about_ca_topic_score_gemma":0.0000019206338,"teacher_disagreement_score":0.8405827,"about_ca_system_score_codex":0.0000056797276,"about_ca_system_score_gemma":0.000026871701,"threshold_uncertainty_score":0.31858954},"labels":[],"label_agreement":null},{"id":"W2077948728","doi":"10.1145/2393091.2393096","title":"On the performance evaluation of a vision-based human-robot interaction framework","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; Usability; Robot; Variety (cybernetics); Interface (matter); Human–robot interaction; Dialog box; Software deployment; Task (project management); Set (abstract data type); Mobile robot; Artificial intelligence; Software engineering; Systems engineering; Engineering; Programming language; Operating system","score_opus":0.0753996606130986,"score_gpt":0.360919472927398,"score_spread":0.2855198123142994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077948728","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":[0.609781,0.00002902674,0.37583864,0.0007929286,0.0007992133,0.00032448515,3.3035187e-7,0.000060711278,0.012373636],"genre_scores_gemma":[0.9970678,5.775831e-7,0.0023681119,0.00037815308,0.00009327685,0.000040248877,0.0000011748112,0.000003492823,0.0000471383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875724,0.00024479796,0.00018317459,0.000116183015,0.00057489425,0.00012368865],"domain_scores_gemma":[0.99875635,0.0004909377,0.00012062427,0.00037654338,0.00022020149,0.00003531927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001934511,0.00006963575,0.000077730314,0.00007437631,0.00010046066,0.000043327007,0.0002456075,0.0000490592,0.0002658941],"category_scores_gemma":[0.00013598257,0.0000409297,0.00004084085,0.00023620095,0.000016522907,0.00036414518,0.000027584292,0.00011820795,0.00023109058],"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":[0.000046178935,0.0014396688,0.011809697,0.000113694085,0.00010154197,3.5139558e-7,0.0070468416,0.014643425,0.013618336,0.3620459,0.006994218,0.58214015],"study_design_scores_gemma":[0.000752884,0.00048536988,0.07914479,0.0007602305,0.000037147016,0.0000076213055,0.00021722127,0.82844514,0.08320828,0.004590961,0.0019996604,0.00035068448],"about_ca_topic_score_codex":0.0000061856686,"about_ca_topic_score_gemma":0.0000017022006,"teacher_disagreement_score":0.8138017,"about_ca_system_score_codex":0.000043538665,"about_ca_system_score_gemma":0.000030650237,"threshold_uncertainty_score":0.29702792},"labels":[],"label_agreement":null},{"id":"W2079987883","doi":"10.7603/s40601-013-0042-9","title":"Gesture Recognition with Accelerometers for Game Controllers, Phones and Wearables","year":2014,"lang":"en","type":"article","venue":"GSTF international journal on computing/GSTF unternational journal on computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Institute of Standards and Technology","keywords":"Hidden Markov model; Gesture; Gesture recognition; Accelerometer; Computer science; Wearable computer; Speech recognition; Motion (physics); Artificial intelligence; Discretization; Series (stratigraphy); Pattern recognition (psychology); Embedded system; Mathematics","score_opus":0.020532516425041763,"score_gpt":0.26609984608775455,"score_spread":0.2455673296627128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079987883","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":[0.24505281,0.00013710988,0.739718,0.007038488,0.006342718,0.0003854218,0.000024721752,0.00013301273,0.0011677002],"genre_scores_gemma":[0.9549298,0.00010315521,0.03527377,0.0036815985,0.0057593677,0.0000074196896,0.000030278172,0.00006860151,0.00014599542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941204,0.0005145046,0.0014016399,0.0008370463,0.0023816875,0.0007447129],"domain_scores_gemma":[0.99228567,0.0023538321,0.0019316527,0.00028738927,0.0026051172,0.000536333],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0029058442,0.00064414425,0.00072354986,0.001325989,0.0008818206,0.0029257915,0.0018768277,0.0002031811,0.00003273119],"category_scores_gemma":[0.0006138354,0.00051461573,0.00038846058,0.0003578992,0.000107368425,0.00097144675,0.00024594602,0.0013788755,0.000062188854],"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":[0.0021670214,0.0010859774,0.003654129,0.00007604202,0.0027308606,0.00061239867,0.0017930355,0.08499171,0.0009812454,0.03297565,0.0048494623,0.86408246],"study_design_scores_gemma":[0.03315294,0.0061412337,0.019383995,0.0067782844,0.000182106,0.03760655,0.0003621075,0.77816254,0.0021686286,0.021994973,0.091196254,0.0028704],"about_ca_topic_score_codex":0.000005197495,"about_ca_topic_score_gemma":0.0000038361245,"teacher_disagreement_score":0.8612121,"about_ca_system_score_codex":0.00043731031,"about_ca_system_score_gemma":0.00022029254,"threshold_uncertainty_score":0.9997305},"labels":[],"label_agreement":null},{"id":"W2081570653","doi":"10.1145/1179622.1179805","title":"UbiHand","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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 Alberta","funders":"","keywords":"Computer science; Gesture; Gesture recognition; Computer vision; Wearable computer; Augmented reality; Artificial intelligence; Virtual reality; Interface (matter); Representation (politics); Wired glove; Human–computer interaction; Embedded system","score_opus":0.006014770231459029,"score_gpt":0.19255650299870694,"score_spread":0.18654173276724792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081570653","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":[0.0027363838,0.00003311534,0.70891744,0.00044783266,0.0001574597,0.000029607852,1.3691657e-7,0.00014102296,0.287537],"genre_scores_gemma":[0.9755336,4.3413792e-7,0.015118753,0.00016772006,0.00011179652,0.0000038411154,5.283273e-7,0.0000014547916,0.00906187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996714,0.0000119682545,0.00006416461,0.00009486268,0.000082218656,0.00007541521],"domain_scores_gemma":[0.9997932,0.000016593565,0.00001212498,0.00013611045,0.00002344973,0.000018549083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006401989,0.000029897312,0.00003704725,0.000026142556,0.000024113722,0.000069372996,0.00014833773,0.00001571579,0.000033807264],"category_scores_gemma":[0.0000019903503,0.000022297127,0.000015632175,0.00011254841,0.0000045724796,0.0001236546,0.000025489362,0.000016593745,0.00061543245],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.0586407e-7,0.00006761823,0.003252003,0.00000883176,0.000006177798,0.000024901401,0.00009451687,0.000016585313,0.003017894,0.8195815,0.088543534,0.085385725],"study_design_scores_gemma":[0.00062236324,0.000045624834,0.01879504,0.000015790965,0.0000022231748,0.00012221903,0.000015990432,0.005467645,0.031516906,0.0430696,0.8999824,0.00034421365],"about_ca_topic_score_codex":0.00003953765,"about_ca_topic_score_gemma":0.000019237414,"teacher_disagreement_score":0.9727972,"about_ca_system_score_codex":0.0000041141097,"about_ca_system_score_gemma":0.000007759384,"threshold_uncertainty_score":0.79103446},"labels":[],"label_agreement":null},{"id":"W2083548770","doi":"10.1145/2786784.2786794","title":"Hands on","year":2015,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canada Research Chairs","keywords":"Haptic technology; Computer science; Animation; Rendering (computer graphics); Contact force; Human–computer interaction; Motion (physics); Trajectory; Virtual reality; Interface (matter); Object (grammar); Artificial intelligence; Computer vision; Computer graphics (images)","score_opus":0.07674068944013136,"score_gpt":0.2697836413002494,"score_spread":0.19304295186011805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083548770","genre_codex":"other","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":[0.004738199,0.000009459317,0.395779,0.0013124032,0.00043341418,0.000044013515,1.1372246e-7,0.00019913004,0.59748423],"genre_scores_gemma":[0.9884785,3.175766e-7,0.0037592745,0.0008484063,0.00008174209,0.0000040754207,2.8226754e-7,0.0000015732371,0.0068258345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961984,0.000020062691,0.000051177383,0.00009885656,0.00014141249,0.000068670524],"domain_scores_gemma":[0.99965906,0.000018443867,0.00001100357,0.00017943834,0.00004614335,0.00008592188],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015195005,0.000031983203,0.000040516356,0.00003181525,0.000013086746,0.000057737343,0.000182501,0.00001736761,0.000010817248],"category_scores_gemma":[0.000017765562,0.000022133117,0.000012619799,0.000107228785,0.0000037835919,0.00010086537,0.000029780045,0.000024485209,0.002036307],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006244379,0.00010133029,0.0013141156,0.000004867143,0.000013278884,0.000031535747,0.001510582,0.000030481546,0.00008483722,0.37474394,0.33465877,0.28750002],"study_design_scores_gemma":[0.002692613,0.0012681793,0.0016927653,0.000044885423,0.0000025299387,0.00011371571,0.00013663835,0.013903062,0.018378021,0.020772751,0.9405134,0.00048144377],"about_ca_topic_score_codex":0.0000045480874,"about_ca_topic_score_gemma":0.000002382526,"teacher_disagreement_score":0.98374027,"about_ca_system_score_codex":0.000009338101,"about_ca_system_score_gemma":0.000022407094,"threshold_uncertainty_score":0.99874073},"labels":[],"label_agreement":null},{"id":"W2085489682","doi":"10.1109/iccvw.2013.69","title":"A Multi-scale Approach to Gesture Detection and Recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Gesture; Gesture recognition; Discriminative model; Artificial intelligence; Modality (human–computer interaction); Speech recognition; Pattern recognition (psychology); Noise (video); Motion (physics); Computer vision; Dimension (graph theory); Scale (ratio); Modalities; Set (abstract data type); Data set","score_opus":0.027879329899677816,"score_gpt":0.2241780785033164,"score_spread":0.19629874860363858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085489682","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":[0.054781374,0.00002322212,0.9348656,0.00034107067,0.00017148482,0.00053773157,8.6526404e-7,0.00023065944,0.009047996],"genre_scores_gemma":[0.7019591,0.000003317214,0.29629505,0.000571349,0.0000735485,0.00021535746,0.000001885146,0.0000063630036,0.0008740362],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991648,0.000059171096,0.00013897406,0.00034125743,0.00013203993,0.00016371699],"domain_scores_gemma":[0.9994552,0.000026852553,0.00003179517,0.00020184364,0.00012886083,0.00015544439],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001504417,0.0000989824,0.00010430624,0.000107184766,0.0000770878,0.00023331419,0.00014912314,0.00008002689,0.000015580978],"category_scores_gemma":[0.000019914878,0.00007859161,0.00002433151,0.00028315664,0.000010586215,0.00048536033,0.000078130084,0.0000825585,0.0008293736],"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":[0.0000023948132,0.00014319892,0.00037229777,0.00003727589,0.000013798202,7.1248775e-7,0.0022209508,0.0000052918517,0.042495478,0.000047622332,0.0012137559,0.9534472],"study_design_scores_gemma":[0.0049880487,0.0008937223,0.3196451,0.00023371715,0.000047318095,0.0013841568,0.0029487659,0.5356022,0.0974763,0.006598858,0.02716171,0.0030200786],"about_ca_topic_score_codex":0.00014567889,"about_ca_topic_score_gemma":0.000047551992,"teacher_disagreement_score":0.9504272,"about_ca_system_score_codex":0.000015817728,"about_ca_system_score_gemma":0.0000071851427,"threshold_uncertainty_score":0.9999486},"labels":[],"label_agreement":null},{"id":"W2085709207","doi":"10.5555/2331067.2331073","title":"Concepture: a regular language based framework for recognizing gestures with varying and repetitive patterns","year":2012,"lang":"en","type":"article","venue":"Sketch Based Interfaces and Modeling","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gesture; Computer science; Grammar; Gesture recognition; Workflow; Alphabet; Natural language processing; Rule-based machine translation; Annotation; Construct (python library); Artificial intelligence; Human–computer interaction; Programming language; Database; Linguistics","score_opus":0.03365099422506337,"score_gpt":0.283673997431029,"score_spread":0.25002300320596565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085709207","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":[0.27244297,0.0018253081,0.7246827,0.0004500478,0.00015184257,0.0002886286,0.000012931676,0.00008660603,0.000058962054],"genre_scores_gemma":[0.8359847,0.000017164879,0.16304252,0.00069772307,0.00014919294,0.00007028759,0.000006983777,0.000020502517,0.00001090304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986718,0.00008915331,0.00022960827,0.00044542522,0.00017781348,0.0003861511],"domain_scores_gemma":[0.9990424,0.00028567665,0.00011483934,0.0002708876,0.0001239392,0.00016227174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005018683,0.00022214043,0.00025566804,0.000095837204,0.00017656793,0.00022154163,0.0001702565,0.00012375593,0.000006187002],"category_scores_gemma":[0.00008347992,0.000171707,0.000042764426,0.000106908716,0.000035495545,0.00044707692,0.00006526477,0.00020093517,0.0000012405254],"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":[0.0019308055,0.0010276067,0.04402761,0.006761379,0.0011989727,0.00012178571,0.18094715,0.041357722,0.05274791,0.02344933,0.00044036825,0.64598936],"study_design_scores_gemma":[0.0012318157,0.00023427165,0.00006882402,0.0024495632,0.00005179674,0.000033826145,0.0023166866,0.95669407,0.035520278,0.00059648696,0.00028110118,0.00052125845],"about_ca_topic_score_codex":0.0000604373,"about_ca_topic_score_gemma":0.000014781273,"teacher_disagreement_score":0.9153364,"about_ca_system_score_codex":0.000020616606,"about_ca_system_score_gemma":0.000035896657,"threshold_uncertainty_score":0.70020086},"labels":[],"label_agreement":null},{"id":"W2087194693","doi":"10.1109/isspa.2012.6310483","title":"Online handwritten gesture recognition based on Takagi-Sugeno fuzzy models","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"École de Technologie Supérieure","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Cluster analysis; Scratch; Set (abstract data type); Artificial intelligence; Fuzzy logic; Fuzzy set; Fuzzy clustering; Function (biology); Pattern recognition (psychology); Data mining; Machine learning","score_opus":0.05462892820952618,"score_gpt":0.2584157908501266,"score_spread":0.20378686264060042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087194693","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":[0.012731762,0.00024053696,0.86553705,0.00383956,0.0014183434,0.00057406415,0.000056411416,0.0007874581,0.11481481],"genre_scores_gemma":[0.94428724,0.000012978251,0.049400482,0.0044244914,0.00072846236,0.00004992396,0.00010558181,0.000023186185,0.00096767413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810255,0.00017959873,0.00030996613,0.00040769833,0.0004953228,0.00050485315],"domain_scores_gemma":[0.9986356,0.0001637337,0.00010877311,0.00059598236,0.00019552547,0.00030038113],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005282568,0.00024108449,0.0002391763,0.00020888643,0.00012951795,0.00014153752,0.0004597253,0.00017996061,0.00009008316],"category_scores_gemma":[0.000040909064,0.00019039716,0.00011062698,0.0004383184,0.000022745422,0.0009656753,0.00007213489,0.00023662274,0.0009961859],"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":[0.000117742886,0.006011465,0.0030905656,0.0002475344,0.00017251927,0.00006903344,0.0033987851,0.0033743994,0.002661654,0.049032606,0.05305255,0.8787711],"study_design_scores_gemma":[0.008243347,0.0014943575,0.018161098,0.001056887,0.0001479958,0.00042532635,0.00059490465,0.77307963,0.01647023,0.060285006,0.115639485,0.004401758],"about_ca_topic_score_codex":0.000026330206,"about_ca_topic_score_gemma":0.000016357822,"teacher_disagreement_score":0.93155545,"about_ca_system_score_codex":0.000055667595,"about_ca_system_score_gemma":0.00005716137,"threshold_uncertainty_score":0.99978167},"labels":[],"label_agreement":null},{"id":"W2089252532","doi":"10.1117/12.580349","title":"Neural-network approach to determine operator hand orientation for teleoperated control of a robot manipulator","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Orientation (vector space); Artificial neural network; Computer science; Computer vision; Teleoperation; Preprocessor; Robot; Operator (biology); Radial basis function; Pattern recognition (psychology); Mathematics","score_opus":0.016905064668863986,"score_gpt":0.2313539558111489,"score_spread":0.2144488911422849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089252532","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":[0.94378656,0.00006355402,0.05266001,0.0012215755,0.00034696521,0.0015285835,0.000042893917,0.000072556424,0.00027727737],"genre_scores_gemma":[0.71347463,0.0000057772095,0.2855629,0.00015663137,0.00034089148,0.00038249476,0.0000072196212,0.00003183033,0.000037616188],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976428,3.7313107e-8,0.0008445663,0.0004926546,0.0006087971,0.00041112347],"domain_scores_gemma":[0.9964945,0.00012500004,0.00038770793,0.000083151615,0.0027401405,0.00016955301],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006899446,0.000303044,0.0005286217,0.00011271403,0.000113780916,0.00022416633,0.0011140723,0.00016510229,0.00000143411],"category_scores_gemma":[0.00041042943,0.0002465624,0.00044710742,0.00059701136,0.0001044776,0.0006846508,0.00012671527,0.00016393198,0.0000010892671],"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":[0.00014771947,0.00022572975,0.00024456804,0.0006173297,0.00039089523,8.731678e-8,0.0006367979,0.0147707835,0.55971485,0.42195302,0.0006523165,0.0006458945],"study_design_scores_gemma":[0.009332383,0.0018797788,0.0019930038,0.00094497507,0.00029713166,0.000083580686,0.0010136085,0.49514925,0.4836711,0.002429592,0.0021352556,0.0010703251],"about_ca_topic_score_codex":0.000008428937,"about_ca_topic_score_gemma":2.2522553e-7,"teacher_disagreement_score":0.48037848,"about_ca_system_score_codex":0.0001309079,"about_ca_system_score_gemma":0.000058787948,"threshold_uncertainty_score":0.9999987},"labels":[],"label_agreement":null},{"id":"W2089283333","doi":"10.1109/syscon.2013.6549932","title":"Systems approach for the development of a Silent Wireless Communicator","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Eyewear; Wired glove; Wireless; Computer science; Visibility; Orientation (vector space); Human–computer interaction; Engineering; Computer security; Telecommunications; Virtual reality","score_opus":0.045807557025855254,"score_gpt":0.2505833940114913,"score_spread":0.20477583698563606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089283333","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":[0.004543188,0.0002278624,0.9880724,0.0002508457,0.00017278762,0.0011453703,9.4715114e-7,0.000059304475,0.0055273133],"genre_scores_gemma":[0.87580806,0.0000030119477,0.12259203,0.000060516984,0.000024841094,0.0009568267,0.0000021549392,0.0000048749766,0.00054770685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990989,0.00007134415,0.00033983446,0.00014548555,0.0001963812,0.0001480766],"domain_scores_gemma":[0.99871975,0.00031255692,0.00011439151,0.000616288,0.00018884557,0.000048185677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054249656,0.000081440914,0.00015776165,0.000035212113,0.0001274077,0.0001110685,0.0010562942,0.000039067803,0.0000061300047],"category_scores_gemma":[0.000009348492,0.000044380264,0.000045097142,0.00013955284,0.000025712572,0.00012800045,0.00018675275,0.000048288268,0.000041243482],"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":[0.000017091068,0.0012576331,0.0013725241,0.0018319379,0.0008661392,3.9780446e-7,0.04324507,0.00062078977,0.007928048,0.2368449,0.054699626,0.65131587],"study_design_scores_gemma":[0.0011449729,0.00008086288,0.0015698816,0.00014332803,0.000016186128,0.000024100133,0.010876849,0.9074194,0.013550947,0.00018102246,0.06449065,0.00050180656],"about_ca_topic_score_codex":0.00009066862,"about_ca_topic_score_gemma":0.000005668164,"teacher_disagreement_score":0.9067986,"about_ca_system_score_codex":0.000020323292,"about_ca_system_score_gemma":0.000069204005,"threshold_uncertainty_score":0.1962875},"labels":[],"label_agreement":null},{"id":"W2091656310","doi":"10.1007/978-3-642-28305-5_26","title":"Gesture Control: A New and Intelligent Man-Machine Interface","year":2012,"lang":"en","type":"book-chapter","venue":"Topics in intelligent engineering and informatics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gesture; Gesture recognition; Computer science; Interface (matter); Human–computer interaction; Set (abstract data type); Multimedia; Input device; Artificial intelligence; Programming language","score_opus":0.016193788590435052,"score_gpt":0.22245168201311433,"score_spread":0.20625789342267928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091656310","genre_codex":"methods","genre_gemma":"other","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":[0.000104133316,0.016249513,0.94685477,0.00031734997,0.0011100798,0.00049965904,0.000014518259,0.00018335436,0.034666628],"genre_scores_gemma":[0.21425013,0.099566415,0.18431951,0.003939394,0.0053836764,0.00016611218,0.0001737812,0.0005677814,0.49163318],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983645,0.000009693131,0.00084261224,0.00019852647,0.00025473,0.00032990478],"domain_scores_gemma":[0.99893475,0.00013524236,0.00020356596,0.0004088182,0.000059188482,0.00025843686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032482482,0.00043144668,0.00052499445,0.00034387864,0.000034523553,0.00019401718,0.0003830072,0.00036915188,0.000029121342],"category_scores_gemma":[0.00004989702,0.0003900093,0.00007070948,0.00005957823,0.000030662497,0.00033746677,0.00026809773,0.00071835046,0.00006511071],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012638346,0.000032300562,0.00016579733,0.0015190433,0.00025618437,0.000017160519,0.014461604,0.003763379,0.000005168828,0.38470653,0.0013713441,0.59368885],"study_design_scores_gemma":[0.000332745,0.000104515086,0.000038060094,0.0011363575,0.000038493778,0.00016329443,0.00006436316,0.08708144,0.00014962771,0.0026564144,0.9075558,0.0006788797],"about_ca_topic_score_codex":0.000013676884,"about_ca_topic_score_gemma":0.000009956333,"teacher_disagreement_score":0.9061845,"about_ca_system_score_codex":0.000086356835,"about_ca_system_score_gemma":0.000037521135,"threshold_uncertainty_score":0.99985516},"labels":[],"label_agreement":null},{"id":"W2091857174","doi":"10.1007/s11042-012-1236-4","title":"Target-shooting exergame with a hand gesture control","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada; University of Ottawa","funders":"","keywords":"Computer science; Gesture; Human–computer interaction; Interface (matter); Natural user interface; Input device; Control (management); Gesture recognition; Multimedia; User interface; Artificial intelligence; User experience design; User interface design; Programming language","score_opus":0.015317035951776749,"score_gpt":0.22986059585966864,"score_spread":0.2145435599078919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091857174","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":[0.0036460506,0.00092752726,0.99171376,0.0010800813,0.000079820304,0.0007562871,0.00003266965,0.00013689537,0.0016268792],"genre_scores_gemma":[0.93957615,0.000013665622,0.058628295,0.0002859725,0.0004748095,0.0007918537,0.000016362588,0.000010846645,0.00020202617],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991031,0.00003456283,0.00016581726,0.00024721897,0.00016007586,0.0002892049],"domain_scores_gemma":[0.9990948,0.00021517985,0.00008744765,0.000295819,0.0000842023,0.00022257277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020187456,0.0001308352,0.00016799645,0.000045365396,0.00023598883,0.00023436261,0.000207354,0.000066705,0.000012078185],"category_scores_gemma":[0.000021688242,0.00009631663,0.000026270687,0.0002235072,0.000063707674,0.00047454005,0.00004366446,0.00011837602,0.00010374186],"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":[0.000017269906,0.0005175387,0.06749051,0.00010304883,0.00014093274,0.0000046206133,0.0063802046,0.000062613464,0.009872959,0.019240398,0.0019112536,0.8942586],"study_design_scores_gemma":[0.0039133467,0.00011180924,0.11303216,0.000117914315,0.00007756523,0.00021792602,0.00042303602,0.023872713,0.0049810577,0.0007083019,0.85152406,0.0010201284],"about_ca_topic_score_codex":0.00000659653,"about_ca_topic_score_gemma":0.0000041090443,"teacher_disagreement_score":0.93593013,"about_ca_system_score_codex":0.000011854167,"about_ca_system_score_gemma":0.000025289963,"threshold_uncertainty_score":0.39276782},"labels":[],"label_agreement":null},{"id":"W2093841401","doi":"10.4028/www.scientific.net/amm.479-480.938","title":"A Gesture Recognition Based on Accelerometer and Hidden Markov Model for Human Computer Communication","year":2013,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gesture; Gesture recognition; Accelerometer; Hidden Markov model; Computer science; Human–computer interaction; Remote control; Interface (matter); Artificial intelligence; Computer vision; Computer hardware","score_opus":0.031759417113633656,"score_gpt":0.23968287104803193,"score_spread":0.20792345393439826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093841401","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":[0.053834207,0.000013995731,0.94379115,0.0005256175,0.0001372896,0.0012983439,0.000038492333,0.00009940923,0.00026147853],"genre_scores_gemma":[0.7552882,0.000020320693,0.24290113,0.0010051092,0.000060505416,0.0006029457,0.000082577055,0.000015666721,0.000023562256],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99903756,0.00005520404,0.00025734378,0.00034689237,0.00012014259,0.00018285164],"domain_scores_gemma":[0.9992249,0.00009844552,0.00013708389,0.00036544751,0.00009310131,0.00008102287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043156988,0.00016546529,0.00024118034,0.000093437586,0.00017941752,0.00045161648,0.00022541096,0.00013311907,0.000020292255],"category_scores_gemma":[0.000005079667,0.0001427487,0.000022865064,0.00005780641,0.000009625779,0.00017646985,0.00013750668,0.000055211145,0.000020877093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006254915,0.00017977016,0.0000013744882,0.00038866297,0.000059620863,7.820358e-7,0.0009207057,0.000010549423,0.25410488,0.36102656,0.0031181104,0.38012645],"study_design_scores_gemma":[0.002796742,0.00032889325,0.00014876954,0.00020274318,0.00004347523,0.000011990891,0.000047963076,0.33716106,0.035040252,0.6231086,0.0003957121,0.0007137956],"about_ca_topic_score_codex":0.000012293576,"about_ca_topic_score_gemma":0.0000031451689,"teacher_disagreement_score":0.701454,"about_ca_system_score_codex":0.000013138635,"about_ca_system_score_gemma":0.000011370042,"threshold_uncertainty_score":0.5821123},"labels":[],"label_agreement":null},{"id":"W2096042855","doi":"10.1109/crv.2006.26","title":"Detection and tracking of pianist hands and fingers","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"University of Ottawa","keywords":"MIDI; Piano; Computer science; Key (lock); Tracking (education); Track (disk drive); Multimedia; Computer graphics (images); Computer vision; Human–computer interaction; Speech recognition; Art; Psychology","score_opus":0.00949080929861673,"score_gpt":0.2050947537043168,"score_spread":0.19560394440570006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096042855","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":[0.43144065,0.0001457619,0.556275,0.000112511916,0.00008856915,0.00006420521,3.5391e-7,0.000057964546,0.011814963],"genre_scores_gemma":[0.99770534,0.000004220245,0.0020237921,0.000019432176,0.000027909025,0.0000015656412,1.4590289e-7,0.0000015745704,0.0002160471],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999663,0.000014902686,0.000092997514,0.00010893232,0.00006375882,0.000056392142],"domain_scores_gemma":[0.9998233,0.00003239686,0.000033910812,0.00006314999,0.000028337881,0.000018911072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010884954,0.000037018697,0.00006188696,0.000043058422,0.00004210182,0.0000674149,0.000039618342,0.000024413923,0.0000020957182],"category_scores_gemma":[0.00000554172,0.000030945183,0.000009304784,0.000087221386,0.00001977596,0.00014836743,0.000019252562,0.000022132654,0.0000014012386],"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":[0.000006694201,0.00004453349,0.017271383,0.00011410691,0.000015460959,0.0000066844873,0.0008205573,0.000010865414,0.082305774,0.011389154,0.0002340556,0.8877807],"study_design_scores_gemma":[0.0027703287,0.0005115599,0.3382814,0.00018572877,0.00002362118,0.00046629237,0.00024785832,0.03749034,0.59823734,0.00932099,0.011824889,0.00063968584],"about_ca_topic_score_codex":0.00010335925,"about_ca_topic_score_gemma":0.00012925013,"teacher_disagreement_score":0.88714105,"about_ca_system_score_codex":0.0000035117373,"about_ca_system_score_gemma":0.000004191215,"threshold_uncertainty_score":0.1261908},"labels":[],"label_agreement":null},{"id":"W2096619491","doi":"10.1109/ccece.2008.4564687","title":"Computerized recognition of human gestures","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Gesture recognition; Computer science; Artificial intelligence; Computer vision; Speech recognition; Human–computer interaction","score_opus":0.0340291135050909,"score_gpt":0.2113567566753023,"score_spread":0.17732764317021138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096619491","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":[0.55663365,0.0001475558,0.43396625,0.0011723846,0.00053652865,0.00073468883,0.000018445506,0.0005767147,0.006213799],"genre_scores_gemma":[0.991156,0.00006803354,0.008268839,0.00023454151,0.00016504734,0.000034717064,0.000008959663,0.000017171164,0.000046683945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980396,0.000020164594,0.0004302517,0.0006036237,0.00031841776,0.00058795576],"domain_scores_gemma":[0.9985,0.00006158278,0.00013866901,0.00017954447,0.0005254417,0.0005947558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016020419,0.0003347399,0.00048181028,0.000604019,0.00021242817,0.00022729686,0.0006198933,0.00018269388,0.000018554641],"category_scores_gemma":[0.000028883742,0.00032910184,0.000074495496,0.0006018168,0.00007392558,0.00038081963,0.00008839139,0.00038116015,0.000020153577],"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":[0.000047659603,0.0003288947,0.005521113,0.0005205578,0.00028505467,0.00025952255,0.0077622877,0.00008151459,0.029116794,0.59045947,0.0040105763,0.3616066],"study_design_scores_gemma":[0.0018539014,0.0018145927,0.03038115,0.0008264023,0.000029578698,0.00089184154,0.00003919553,0.94635254,0.0075191045,0.005771517,0.002799437,0.0017207629],"about_ca_topic_score_codex":0.0010682801,"about_ca_topic_score_gemma":0.00015206238,"teacher_disagreement_score":0.946271,"about_ca_system_score_codex":0.000078030695,"about_ca_system_score_gemma":0.00029593878,"threshold_uncertainty_score":0.9999161},"labels":[],"label_agreement":null},{"id":"W2097672823","doi":"10.1007/978-3-540-77457-0_52","title":"Grasp Recognition and Manipulation with the Tango","year":2008,"lang":"en","type":"book-chapter","venue":"Springer tracts in advanced robotics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"GRASP; Computer science; Avatar; Human–computer interaction; Computer vision; 3D interaction; Haptic technology; Artificial intelligence; Interface (matter); Invariant (physics); Virtual reality; Mathematics","score_opus":0.03957955666140511,"score_gpt":0.22809047669587637,"score_spread":0.18851092003447126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097672823","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":[0.0014514849,0.0036103576,0.7702789,0.0022950305,0.0015552221,0.0023729291,0.000021501752,0.0004904988,0.21792409],"genre_scores_gemma":[0.41372764,0.012021438,0.4388022,0.0019598864,0.0016694707,0.00021741488,0.00024191305,0.00052356557,0.13083647],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984429,0.000032616128,0.00035168199,0.00052878173,0.0003883286,0.00025570128],"domain_scores_gemma":[0.9987828,0.00017866364,0.00032513,0.00048957265,0.0001432233,0.00008059506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018139658,0.00033119798,0.00033099085,0.00019911527,0.0001271587,0.00009680903,0.0003114503,0.00023777227,0.000004263904],"category_scores_gemma":[0.00002014412,0.00024305782,0.000050028884,0.000106811305,0.00009090226,0.00040651122,0.00008611639,0.0005717999,0.00007253845],"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":[0.000093016584,0.00013722005,0.00031609024,0.00037440512,0.00018326523,0.00076889567,0.0027659792,0.036343094,0.00017874577,0.057289623,0.00039669932,0.90115297],"study_design_scores_gemma":[0.011658645,0.002063671,0.044123605,0.014072918,0.0005366916,0.005837239,0.00032422633,0.021126488,0.0012710122,0.16170853,0.7264767,0.010800215],"about_ca_topic_score_codex":0.0000040245245,"about_ca_topic_score_gemma":0.000115545765,"teacher_disagreement_score":0.8903527,"about_ca_system_score_codex":0.00008529641,"about_ca_system_score_gemma":0.000053016945,"threshold_uncertainty_score":0.99116105},"labels":[],"label_agreement":null},{"id":"W2098449075","doi":"10.1109/vecims.2011.6053846","title":"Multimodal control of virtual game environments through gestures and physical controllers","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gesture; Computer science; Controller (irrigation); Accelerometer; Video game; Gesture recognition; Virtual reality; Interface (matter); Gyroscope; Human–computer interaction; Object (grammar); Artificial intelligence; Computer vision; Multimedia; Engineering","score_opus":0.018598010468040084,"score_gpt":0.22386519135368546,"score_spread":0.20526718088564538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098449075","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":[0.20407985,0.000078867444,0.7875224,0.00011546435,0.00017157853,0.00032676847,0.0000069012585,0.000058946716,0.0076392293],"genre_scores_gemma":[0.9955635,0.000008465581,0.0038678078,0.00025125244,0.00005226623,0.000017843888,5.0971806e-7,0.00000593187,0.00023240557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990315,0.000091313865,0.00019816267,0.00027575932,0.00022590517,0.00017739194],"domain_scores_gemma":[0.9994317,0.00012358718,0.00009653027,0.0002515547,0.000019730725,0.000076880766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010899713,0.0001300576,0.00028900587,0.00003249369,0.000029405264,0.000020666504,0.00025898466,0.00005644998,0.000021949238],"category_scores_gemma":[0.000017108903,0.00009569399,0.000065973094,0.000057548707,0.00011156281,0.00030357187,0.000071563314,0.000074151525,0.000048202743],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085260434,0.004422123,0.02769658,0.00011390957,0.0017796158,0.00013419028,0.14340581,0.00024844194,0.14283253,0.47538784,0.0015212758,0.20160508],"study_design_scores_gemma":[0.07889567,0.0072949724,0.3196226,0.0002789646,0.00040140224,0.00030798707,0.004648299,0.29135725,0.21675172,0.040174667,0.03627698,0.003989466],"about_ca_topic_score_codex":0.000049362687,"about_ca_topic_score_gemma":0.0000022330578,"teacher_disagreement_score":0.79148364,"about_ca_system_score_codex":0.000008109634,"about_ca_system_score_gemma":0.0000119774895,"threshold_uncertainty_score":0.39022878},"labels":[],"label_agreement":null},{"id":"W2101518335","doi":"10.1109/pacrim.2007.4313313","title":"A Comparative Study on Wearable Sensors for Signal Processing on the North Indian Tabla","year":2007,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wearable computer; Computer science; Accelerometer; Gesture; Inertial measurement unit; Wireless; Signal processing; Wearable technology; Artificial intelligence; Computer vision; SIGNAL (programming language); Computer hardware; Embedded system; Digital signal processing; Telecommunications","score_opus":0.06273667402372075,"score_gpt":0.3103600708097238,"score_spread":0.24762339678600304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101518335","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":[0.7617246,0.000010360975,0.21551734,0.00051423203,0.00011920892,0.0015724035,0.0000028745621,0.00012479091,0.02041419],"genre_scores_gemma":[0.9974616,1.3583092e-7,0.0010489326,0.0004782961,0.000106487154,0.00006140879,7.654126e-7,0.0000060040707,0.0008363473],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99874705,0.00010110375,0.0002138237,0.00031491235,0.00034011903,0.00028300754],"domain_scores_gemma":[0.99890494,0.00055182254,0.00009620776,0.0002288061,0.00014034273,0.000077879784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086868735,0.00013417553,0.00017674839,0.0001072109,0.00031599842,0.0002075308,0.00034689382,0.000031983604,0.000010957388],"category_scores_gemma":[0.000014848677,0.00007685459,0.000041615822,0.00044605628,0.000022598562,0.00014871694,0.000031851298,0.00013780186,0.00015200999],"study_design_candidate":"qualitative","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":[0.0010534152,0.010065288,0.057202097,0.00025482182,0.00077553967,0.0003655068,0.4748743,0.0051356396,0.0013982208,0.029606797,0.02206626,0.39720213],"study_design_scores_gemma":[0.015397197,0.024805836,0.4128401,0.0017267553,0.00013197838,0.0002283037,0.21088202,0.15836671,0.104644105,0.003917239,0.06196582,0.005093934],"about_ca_topic_score_codex":0.000017123954,"about_ca_topic_score_gemma":0.00030483017,"teacher_disagreement_score":0.3921082,"about_ca_system_score_codex":0.000031899304,"about_ca_system_score_gemma":0.000055055796,"threshold_uncertainty_score":0.31340393},"labels":[],"label_agreement":null},{"id":"W2102255393","doi":"10.1109/ccece.1995.526413","title":"Alternative manipulation strategies in a virtual reality training system","year":2002,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Polytechnique Montréal; McGill University","funders":"","keywords":"Virtual reality; Immersion (mathematics); Computer science; Human–computer interaction; Interface (matter); Training system; Training (meteorology); Test (biology); Field (mathematics); Simulation","score_opus":0.11729869698543299,"score_gpt":0.2897718317874269,"score_spread":0.1724731348019939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102255393","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":[0.017282125,0.000018135574,0.8644678,0.0001863881,0.00028254342,0.00014908866,0.0000010586992,0.0002114059,0.117401436],"genre_scores_gemma":[0.9982988,0.0000016236542,0.0012609513,0.00003587584,0.00008146484,0.000018394245,0.0000012063349,0.0000036074618,0.00029805783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989602,0.00015029764,0.00025962683,0.00024950682,0.00021814727,0.00016222693],"domain_scores_gemma":[0.99956983,0.00007193613,0.00007226621,0.00019371964,0.00004689445,0.000045344357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034628614,0.00008660473,0.0001395065,0.00011566188,0.00003937898,0.00018191253,0.00024093734,0.00004555776,0.000028071874],"category_scores_gemma":[0.000011601262,0.000074250136,0.000028084973,0.00028126437,0.000011450778,0.00071753626,0.00003594792,0.00007702132,0.00014026463],"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":[0.000002210296,0.00004198692,0.00027772927,0.000032748416,0.000014214877,0.00005896532,0.017677343,0.0041446975,0.000119307806,0.91662765,0.00017140702,0.060831774],"study_design_scores_gemma":[0.00047118426,0.0000620185,0.002162385,0.00011705959,0.0000015962007,0.00006788549,0.007495049,0.98649424,0.00015219027,0.002423991,0.0003648283,0.0001875455],"about_ca_topic_score_codex":0.00019502798,"about_ca_topic_score_gemma":0.00017772416,"teacher_disagreement_score":0.9823496,"about_ca_system_score_codex":0.00007150138,"about_ca_system_score_gemma":0.000014739168,"threshold_uncertainty_score":0.30278325},"labels":[],"label_agreement":null},{"id":"W2103665153","doi":"10.1109/saci.2011.5872992","title":"An intelligent gesture interface for controlling TV sets and set-top boxes","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Ottawa","funders":"","keywords":"Gesture; Computer science; Usability; Set (abstract data type); Interface (matter); Gesture recognition; Human–computer interaction; Control (management); Channel (broadcasting); Remote control; Multimedia; Artificial intelligence; Computer hardware; Telecommunications","score_opus":0.055704854370090595,"score_gpt":0.2990162948686543,"score_spread":0.24331144049856368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103665153","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":[0.040848114,0.00032439688,0.955466,0.00027478058,0.00042293023,0.00043391326,0.0000059919967,0.00018976786,0.002034163],"genre_scores_gemma":[0.9545375,0.00001880587,0.044715006,0.00033592377,0.00006896509,0.00004156089,0.0000021642213,0.000009343525,0.0002707096],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990046,0.00006247398,0.00022619445,0.0003690711,0.000113741706,0.00022390194],"domain_scores_gemma":[0.99923044,0.00010734974,0.00006955664,0.00031696638,0.00012773245,0.00014795059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038417807,0.00013944533,0.00019620401,0.00007200892,0.0000839301,0.00015872538,0.00037732007,0.00008549148,0.000030447407],"category_scores_gemma":[0.000026321122,0.00010473777,0.000044117023,0.00009098024,0.000026211223,0.0003963828,0.00007155469,0.00008160594,0.000033476037],"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":[0.00026498619,0.0005709416,0.0132370535,0.0003620104,0.00038410857,0.000041262112,0.08078522,0.00008946351,0.011101891,0.109696046,0.0076066833,0.7758603],"study_design_scores_gemma":[0.007427071,0.0042138416,0.009410788,0.0005912457,0.00014350274,0.000537158,0.008731682,0.3742306,0.40620914,0.055079427,0.13022923,0.0031963054],"about_ca_topic_score_codex":0.000051173705,"about_ca_topic_score_gemma":0.000041493502,"teacher_disagreement_score":0.91368943,"about_ca_system_score_codex":0.000012365244,"about_ca_system_score_gemma":0.000021093021,"threshold_uncertainty_score":0.4271082},"labels":[],"label_agreement":null},{"id":"W2103798879","doi":"10.5555/2666527.2666534","title":"Learning gestures for interacting with low-fidelity prototypes","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Calgary","funders":"","keywords":"Gesture; Fidelity; Computer science; Unification; Human–computer interaction; Gesture recognition; Artificial intelligence; Programming language","score_opus":0.0178019111401049,"score_gpt":0.27620140927743875,"score_spread":0.25839949813733387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103798879","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":[0.030286478,0.000060684928,0.95824057,0.00034219545,0.00036141538,0.00080176175,3.4791123e-7,0.0002852597,0.00962131],"genre_scores_gemma":[0.92740715,6.0032596e-7,0.07128203,0.00013493285,0.00022730858,0.00020132301,0.0000013640934,0.000007511589,0.00073775975],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99919826,0.00006369872,0.00014983225,0.00017794839,0.00013872418,0.00027154668],"domain_scores_gemma":[0.9992342,0.00029654935,0.00008657083,0.00016441033,0.0001302402,0.000088003944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053049426,0.000093336894,0.00011436513,0.00004442256,0.00011907806,0.00012594419,0.00019831408,0.000037187285,0.0000125531105],"category_scores_gemma":[0.0001539583,0.00006096118,0.00003523043,0.00011986398,0.000010167459,0.0007164158,0.00005499582,0.00012136092,0.000052716518],"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":[0.00020481934,0.00047361237,0.16377611,0.00085836445,0.0002784725,0.000008217981,0.028926495,0.0004549647,0.0060845707,0.061491426,0.006182665,0.7312603],"study_design_scores_gemma":[0.0050688325,0.0026703312,0.06541564,0.0017073808,0.00007711934,0.00079469447,0.0062143616,0.04228735,0.15810938,0.0024495744,0.7121654,0.003039916],"about_ca_topic_score_codex":0.00001637966,"about_ca_topic_score_gemma":0.000009842765,"teacher_disagreement_score":0.8971207,"about_ca_system_score_codex":0.000016482545,"about_ca_system_score_gemma":0.000023095246,"threshold_uncertainty_score":0.24859248},"labels":[],"label_agreement":null},{"id":"W2104056286","doi":"10.3390/s121114416","title":"Towards Real-Time and Rotation-Invariant American Sign Language Alphabet Recognition Using a Range Camera","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Invariant (physics); Gesture; Gesture recognition; Segmentation; Pattern recognition (psychology); Rotation (mathematics); Sign language; Biometrics; Speech recognition; Mathematics","score_opus":0.024726690478161374,"score_gpt":0.26882625950924444,"score_spread":0.24409956903108307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104056286","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":[0.98235774,0.00010879014,0.014067348,0.00026657773,0.00022427784,0.00027188295,0.000026733116,0.00018645689,0.0024901729],"genre_scores_gemma":[0.96128786,0.00003420166,0.037970837,0.00020262654,0.0003421742,0.000013218972,0.000017839226,0.00001894368,0.000112286485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986335,0.00028586606,0.0002226097,0.00027033218,0.00024432765,0.00034338044],"domain_scores_gemma":[0.999188,0.00012717574,0.00015965183,0.00024217051,0.00008997373,0.00019300055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047394336,0.00015441567,0.00023215632,0.00014231104,0.00010609741,0.0001088118,0.0001271759,0.000052428433,0.000032337215],"category_scores_gemma":[0.00007345176,0.00014189871,0.000047144174,0.00039974545,0.00006181824,0.00040496065,0.000050804214,0.00008845548,0.00022825542],"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":[0.000050170343,0.00023965664,0.00437297,0.00010509001,0.00019073616,0.0001263884,0.05885166,0.00009850974,0.07014902,0.00045217687,0.00071873894,0.8646449],"study_design_scores_gemma":[0.011767976,0.0018226184,0.22919443,0.0017059294,0.00078693876,0.0053799413,0.027695462,0.59794295,0.10217767,0.001728062,0.010666015,0.009132024],"about_ca_topic_score_codex":0.0012468479,"about_ca_topic_score_gemma":0.000019309553,"teacher_disagreement_score":0.85551286,"about_ca_system_score_codex":0.00004741491,"about_ca_system_score_gemma":0.000037618687,"threshold_uncertainty_score":0.5786461},"labels":[],"label_agreement":null},{"id":"W2105331304","doi":"10.1145/1055674.1055676","title":"American Sign Language natural language generation and machine translation","year":2005,"lang":"en","type":"article","venue":"ACM SIGACCESS Accessibility and Computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Closed captioning; American Sign Language; Interpreter; Computer science; Reading (process); Machine translation; Sign language; Linguistics; American English; Literacy; Sign (mathematics); Natural language processing; Artificial intelligence; Psychology; Pedagogy; Programming language","score_opus":0.030631645779298954,"score_gpt":0.30766708280602495,"score_spread":0.277035437026726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105331304","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":[0.82941663,0.0042090598,0.16471396,0.00095692155,0.00013639513,0.00020027178,0.000003336302,0.00017424044,0.00018916464],"genre_scores_gemma":[0.97658914,0.000020767186,0.022471165,0.00045586962,0.000406441,0.0000056867552,0.00001849985,0.0000091200955,0.000023317594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835986,0.00021263513,0.00036250247,0.0005842459,0.00023434973,0.00024640094],"domain_scores_gemma":[0.998902,0.00029908124,0.00019524667,0.00043690117,0.000062226376,0.000104517036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006496784,0.00018627306,0.00025271636,0.00010726712,0.00027422773,0.00063062634,0.00055878074,0.00005455726,0.0000065929203],"category_scores_gemma":[0.000111598165,0.00016102297,0.00003953473,0.00038492293,0.000077693636,0.0011913625,0.00031769238,0.0002079466,0.0000040360487],"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":[0.0000046328696,0.000025279369,0.0058140117,0.00003004504,0.0000103058555,0.000003634491,0.0051300856,0.00003163034,0.0029988321,0.000063005646,0.000012809785,0.9858757],"study_design_scores_gemma":[0.001099151,0.00008567435,0.09419649,0.0000738539,0.000024501518,0.00006665998,0.0009192076,0.8902171,0.01223716,0.0001925623,0.0002737558,0.00061384734],"about_ca_topic_score_codex":0.00025721357,"about_ca_topic_score_gemma":0.00041992392,"teacher_disagreement_score":0.98526186,"about_ca_system_score_codex":0.00002490653,"about_ca_system_score_gemma":0.000021545724,"threshold_uncertainty_score":0.6566326},"labels":[],"label_agreement":null},{"id":"W2107492274","doi":"10.1109/icsmc.2007.4414093","title":"Enabling gestural interaction by means of tracking dynamical systems models and assistive feedback","year":2007,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Gesture; Human–computer interaction; Affordance; Process (computing); Motion (physics); Artificial intelligence; Inference; Dynamical systems theory; Salient; Domain (mathematical analysis)","score_opus":0.03271162578955004,"score_gpt":0.2758045071366697,"score_spread":0.24309288134711968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107492274","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":[0.0955307,0.00020377261,0.8980625,0.000092366405,0.00047119946,0.00016044527,0.0000032584676,0.00008852996,0.0053872154],"genre_scores_gemma":[0.9962126,0.000010667813,0.0034473364,0.00003155478,0.000061240135,0.000004118411,0.0000038087508,0.000006985372,0.000221687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987424,0.00007355157,0.0003964179,0.00029906197,0.0002733802,0.00021520368],"domain_scores_gemma":[0.999098,0.00028298053,0.00015932182,0.00016809598,0.00019191638,0.00009969694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066389405,0.00012224782,0.00022140644,0.000123074,0.00006178614,0.00015405004,0.00018856006,0.00008960927,0.0000026079576],"category_scores_gemma":[0.000028847184,0.000099055076,0.00004316593,0.00021863969,0.000031193715,0.0008483318,0.00006499173,0.00013772442,0.0000060647803],"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":[0.0002611791,0.0008990269,0.011991069,0.0011208756,0.0007053036,0.00010747279,0.018234968,0.010251164,0.13835311,0.14029354,0.0033854553,0.6743968],"study_design_scores_gemma":[0.00084147777,0.00014287191,0.003999123,0.00039827832,0.000022181437,0.00030130462,0.0030778635,0.9795071,0.009811712,0.0004685458,0.0009875667,0.0004419983],"about_ca_topic_score_codex":0.0001475742,"about_ca_topic_score_gemma":0.000037194906,"teacher_disagreement_score":0.9692559,"about_ca_system_score_codex":0.00006266936,"about_ca_system_score_gemma":0.000015471645,"threshold_uncertainty_score":0.4039349},"labels":[],"label_agreement":null},{"id":"W2109648960","doi":"10.1145/2598784.2602795","title":"The consumed endurance workbench","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Manitoba","funders":"","keywords":"Workbench; Metric (unit); Computer science; Tracking (education); Human–computer interaction; Simulation; Engineering drawing; Artificial intelligence; Engineering; Visualization","score_opus":0.00989121410915561,"score_gpt":0.2164671241892032,"score_spread":0.20657591008004758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109648960","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":[0.001302455,0.00013585911,0.87644887,0.0060336585,0.00058382045,0.00014261149,8.6018815e-8,0.00019478517,0.11515786],"genre_scores_gemma":[0.98973554,0.0000073307833,0.004582927,0.00080097135,0.00009888408,0.00004145453,1.7852129e-7,0.0000025753611,0.004730158],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999351,0.000093951734,0.00011267059,0.00014151487,0.00015054793,0.00015027937],"domain_scores_gemma":[0.99909616,0.00038570596,0.00003391717,0.00037783518,0.000057526413,0.000048874597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004278987,0.000051388237,0.00005934089,0.00001415396,0.00015818977,0.00018224213,0.00050540833,0.000024198254,0.000013388821],"category_scores_gemma":[0.00006276411,0.000027963899,0.000025184796,0.00015671077,0.000029587365,0.000109744935,0.00004385061,0.00005396483,0.0006460957],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001144107,0.000010312255,0.00041880205,0.0000037830325,0.000010307875,0.0000019030758,0.0001606444,0.0000025274749,0.00041570314,0.3755341,0.019169854,0.60427094],"study_design_scores_gemma":[0.00017870925,0.000010895096,0.003716147,0.000011793263,8.9655174e-7,0.000037816797,0.000015800912,0.006102673,0.0011196546,0.0053192126,0.9833872,0.00009921662],"about_ca_topic_score_codex":0.000008816924,"about_ca_topic_score_gemma":0.000021086407,"teacher_disagreement_score":0.98843306,"about_ca_system_score_codex":0.0000051845464,"about_ca_system_score_gemma":0.000014516132,"threshold_uncertainty_score":0.83044684},"labels":[],"label_agreement":null},{"id":"W21116508","doi":"10.1007/s10517-010-0853-z","title":"3D Camera-based Generic Gesture Analysis for Video Game Interface.","year":2010,"lang":"en","type":"article","venue":"IPCV","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Gesture; Computer science; Video game; Computer vision; Artificial intelligence; Gesture recognition; Interface (matter); Grayscale; Video tracking; Video game development; Segmentation; Feature extraction; Object (grammar); Game design; Multimedia; Image (mathematics)","score_opus":0.019195986676833872,"score_gpt":0.26840012749830616,"score_spread":0.2492041408214723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W21116508","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":[0.057435855,0.00012138266,0.9387244,0.0011122533,0.0010968435,0.00027520928,0.000018314578,0.00019130159,0.001024424],"genre_scores_gemma":[0.95755106,0.000001018132,0.04091358,0.0008862407,0.00025743883,0.00009638677,0.00001005838,0.000011874292,0.0002723503],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876255,0.00005575589,0.00024561878,0.00043755458,0.00022404968,0.00027447188],"domain_scores_gemma":[0.9987037,0.00016966014,0.00012679647,0.00067515805,0.00019350265,0.00013114917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003203219,0.00015752138,0.00027601505,0.00026004392,0.000073559146,0.00018999976,0.00063634187,0.000106814376,0.00006878004],"category_scores_gemma":[0.00007673552,0.00013176638,0.0002132943,0.0009122238,0.000031962394,0.00017488857,0.00006130199,0.0001895684,0.00014334651],"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":[0.000101792786,0.00080545415,0.035247207,0.0003334176,0.0026962913,0.000120981815,0.004946416,0.01124363,0.2461858,0.004158095,0.03206243,0.66209847],"study_design_scores_gemma":[0.0019582622,0.00026677217,0.015576532,0.000046733567,0.00043540527,0.00006969189,0.00005068131,0.30470556,0.052030876,0.0011039559,0.6226353,0.0011202361],"about_ca_topic_score_codex":0.00003722496,"about_ca_topic_score_gemma":0.00022557899,"teacher_disagreement_score":0.9001152,"about_ca_system_score_codex":0.000019637248,"about_ca_system_score_gemma":0.000075137774,"threshold_uncertainty_score":0.53732777},"labels":[],"label_agreement":null},{"id":"W2115881480","doi":"10.3390/s150923303","title":"A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":53,"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":"National Natural Science Foundation of China","keywords":"Hidden Markov model; Gesture; Gesture recognition; Speech recognition; Computer science; Sign language; Vocabulary; Dynamic time warping; Orientation (vector space); Accelerometer; Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.03901278355635525,"score_gpt":0.2781663472348584,"score_spread":0.23915356367850316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115881480","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":[0.94919854,0.0006020495,0.04885402,0.00033682724,0.0002870273,0.0002838455,0.000014200272,0.00020697548,0.0002164811],"genre_scores_gemma":[0.9005466,0.000038056616,0.09899803,0.0002411421,0.00010286178,0.000004810983,0.0000070559213,0.000022990329,0.000038416987],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817866,0.00023805804,0.00029309915,0.0005908693,0.0002821172,0.0004171751],"domain_scores_gemma":[0.99881095,0.00030844499,0.00014670406,0.0003465765,0.00013270747,0.00025461343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050325587,0.00026955307,0.0003619382,0.00025530675,0.00011603682,0.00020989789,0.00019839038,0.00025919956,0.000007569523],"category_scores_gemma":[0.00024870486,0.00023050219,0.00006609203,0.0008217543,0.00013816981,0.00031574394,0.00011328469,0.0003340765,0.000027005592],"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":[0.0004751578,0.00090995076,0.028384052,0.0004347516,0.0011038473,0.00061451615,0.079723865,0.0010814669,0.6957976,0.0007993709,0.0007854907,0.1898899],"study_design_scores_gemma":[0.035276435,0.0045088544,0.10266123,0.0029235447,0.0008707147,0.024682982,0.015325328,0.5505254,0.16960083,0.070692085,0.008981969,0.013950599],"about_ca_topic_score_codex":0.00010636852,"about_ca_topic_score_gemma":0.000010193178,"teacher_disagreement_score":0.54944396,"about_ca_system_score_codex":0.00003294186,"about_ca_system_score_gemma":0.000041882322,"threshold_uncertainty_score":0.93996066},"labels":[],"label_agreement":null},{"id":"W2116316499","doi":"10.14236/ewic/hci2010.29","title":"Gesturing in the Wild: Understanding the Effects and Implications of Gesture-Based Interaction for Dynamic Presentations","year":2010,"lang":"en","type":"article","venue":"Electronic workshops in computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Waterloo","funders":"","keywords":"Gesture; Computer science; Context (archaeology); Human–computer interaction; Presentation (obstetrics); Modality (human–computer interaction); Multimedia; Field (mathematics); Variety (cybernetics); Natural (archaeology); Gesture recognition; Artificial intelligence","score_opus":0.017420284575184544,"score_gpt":0.303626278721369,"score_spread":0.2862059941461845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116316499","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":[0.25175497,0.00017670634,0.7434069,0.0035128088,0.00026186652,0.00072959735,4.0132846e-7,0.000029734367,0.0001270196],"genre_scores_gemma":[0.9973666,0.0000070790575,0.0023271525,0.00013895457,0.000056273075,0.000089342495,0.0000032842484,0.000008134234,0.0000031532277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988254,0.00018778582,0.00028309546,0.00025517622,0.00012686991,0.00032168833],"domain_scores_gemma":[0.99514335,0.0043229414,0.00015696799,0.00032223263,0.000035515422,0.000018990977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011776746,0.00010825449,0.00013424057,0.00016733528,0.00022586211,0.00013023116,0.00047975525,0.00006560003,3.378482e-7],"category_scores_gemma":[0.00020402968,0.000075725526,0.000043495176,0.0006398841,0.000048536436,0.0001488103,0.000060718674,0.0005857359,3.2080064e-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":[0.00006505847,0.0005652993,0.07452561,0.0006159284,0.00013043128,0.000005993843,0.030374596,0.059938956,0.009874977,0.47432736,0.00038198588,0.3491938],"study_design_scores_gemma":[0.001642671,0.00013908131,0.13469619,0.0004254352,0.00002433853,0.0000862487,0.0015922572,0.81621045,0.00046154606,0.04398891,0.00042619993,0.0003066794],"about_ca_topic_score_codex":0.000015304131,"about_ca_topic_score_gemma":0.0011400492,"teacher_disagreement_score":0.7562715,"about_ca_system_score_codex":0.00010342458,"about_ca_system_score_gemma":0.000086285625,"threshold_uncertainty_score":0.30879974},"labels":[],"label_agreement":null},{"id":"W2117933988","doi":"10.1109/icccn.2011.6006085","title":"A Multimodal Interaction Method that Combines Gestures and Physical Game Controllers","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Ottawa","funders":"","keywords":"Gesture; Computer science; Controller (irrigation); Accelerometer; Gyroscope; Object (grammar); Artificial intelligence; Computer vision; Virtual reality; Human–computer interaction; Engineering","score_opus":0.0514744814582501,"score_gpt":0.29804190867414676,"score_spread":0.24656742721589667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117933988","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":[0.084936135,0.000044403143,0.8885974,0.00050380616,0.00059474655,0.0002862033,0.0000011119333,0.00021586264,0.024820277],"genre_scores_gemma":[0.95954406,0.000004106942,0.0396759,0.00025974694,0.0000727984,0.000018805102,5.3018556e-7,0.0000055147916,0.00041853072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991559,0.00014847054,0.00011653488,0.00028153852,0.00014670032,0.00015088037],"domain_scores_gemma":[0.99937344,0.00020929839,0.00006759287,0.00019955465,0.000059659764,0.000090461406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018335933,0.000121099714,0.00020804541,0.00007645276,0.00003920769,0.000093152776,0.00019529175,0.000046628807,0.000029912193],"category_scores_gemma":[0.000019451618,0.00008568443,0.00005883252,0.000078663776,0.000027126172,0.0004440605,0.00008502419,0.00010127538,0.00010924722],"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":[0.0003734173,0.0015477436,0.013213999,0.00013224532,0.0006242557,0.00010770218,0.105122186,0.000038067505,0.016755233,0.10677373,0.005935248,0.7493762],"study_design_scores_gemma":[0.0125701055,0.0012366892,0.11755699,0.00019220337,0.00012765633,0.0009665641,0.005564757,0.7117538,0.088260785,0.037256148,0.02254545,0.0019688427],"about_ca_topic_score_codex":0.0001350671,"about_ca_topic_score_gemma":0.000018499719,"teacher_disagreement_score":0.8746079,"about_ca_system_score_codex":0.000009655752,"about_ca_system_score_gemma":0.000010527421,"threshold_uncertainty_score":0.34941098},"labels":[],"label_agreement":null},{"id":"W2119103116","doi":"10.1111/j.1365-2842.2007.01735.x","title":"Establishing a reliable protocol to measure tongue sensation","year":2007,"lang":"en","type":"article","venue":"Journal of Oral Rehabilitation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Misericordia Community Hospital; University of Alberta","funders":"","keywords":"Measure (data warehouse); Sensation; Protocol (science); Tongue; Audiology; Psychology; Computer science; Medicine; Cognitive psychology; Data mining; Pathology; Alternative medicine","score_opus":0.023431426633629543,"score_gpt":0.3168177840237562,"score_spread":0.29338635739012664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119103116","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":[0.038371243,0.000007939988,0.9394305,0.005883573,0.0007470678,0.014058461,5.7548135e-7,0.000051807507,0.0014488546],"genre_scores_gemma":[0.50386894,1.5856814e-7,0.49444085,0.0002573704,0.00032442014,0.0009986777,5.052036e-7,0.000011886409,0.00009718628],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980738,0.0001417809,0.0007156869,0.00016199579,0.00071555603,0.00019116892],"domain_scores_gemma":[0.99732995,0.00047903106,0.0004316928,0.00021820569,0.0013678514,0.00017326034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058309133,0.00009487311,0.00018947615,0.00038221362,0.000069488946,0.00020136517,0.00029234646,0.00007390332,0.0000062621107],"category_scores_gemma":[0.0026406262,0.00007700175,0.00009423546,0.0007557866,0.000012541547,0.0015885538,0.000032996522,0.00017616687,0.00002830178],"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":[0.0006188483,0.00051932206,0.016028242,0.00034642388,0.00006282489,0.00007361799,0.021663131,0.0025988321,0.04962013,0.007488799,0.03541521,0.86556464],"study_design_scores_gemma":[0.006603982,0.008732718,0.1709711,0.0023326403,0.000047222613,0.00078690785,0.003699495,0.0037545501,0.024118053,0.02202037,0.7558303,0.0011026756],"about_ca_topic_score_codex":0.000019932602,"about_ca_topic_score_gemma":0.000030136804,"teacher_disagreement_score":0.86446196,"about_ca_system_score_codex":0.00019945102,"about_ca_system_score_gemma":0.00013353623,"threshold_uncertainty_score":0.3161267},"labels":[],"label_agreement":null},{"id":"W2119656522","doi":"10.1109/tim.2011.2161140","title":"Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":482,"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":"Artificial intelligence; Support vector machine; Computer science; Gesture; Gesture recognition; Computer vision; Pattern recognition (psychology); Bag-of-words model in computer vision; Histogram; Feature vector; Cluster analysis; Classifier (UML); Scale-invariant feature transform; Bag-of-words model; Feature extraction; Codebook; Speech recognition; Image (mathematics); Visual Word; Image retrieval","score_opus":0.059453344237366805,"score_gpt":0.2558615736962119,"score_spread":0.19640822945884506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119656522","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":[0.35947135,0.00007638113,0.6384442,0.000072205665,0.00041520066,0.00071200513,0.00003191279,0.0001514286,0.0006253519],"genre_scores_gemma":[0.9811847,0.00025627192,0.018407408,0.00004649107,0.000018938852,0.00004673954,0.0000025175282,0.000010808109,0.000026131893],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988498,0.00010980942,0.0002793686,0.00031119434,0.00031983363,0.00013000096],"domain_scores_gemma":[0.9994376,0.000019633655,0.00013795846,0.00013448556,0.00016797477,0.00010234884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039899242,0.00016371968,0.00017475795,0.00023938162,0.00022219497,0.000082083454,0.000056918743,0.00009805538,0.000025522591],"category_scores_gemma":[0.000004248633,0.00015159344,0.0000333284,0.0001609417,0.000073787094,0.00040188874,0.0000028061586,0.000116169555,0.0000025254785],"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":[0.00007951571,0.000105953404,0.00005821444,0.00008181813,0.000060676295,0.0000017460258,0.0024061569,0.00000190963,0.3935004,0.000017475142,0.00000569023,0.6036805],"study_design_scores_gemma":[0.0008781351,0.0005814967,0.004616842,0.00016274134,0.00008291937,0.00019761374,0.00015236255,0.0010228234,0.99179226,0.00022165576,0.000059506878,0.00023161832],"about_ca_topic_score_codex":0.00024689137,"about_ca_topic_score_gemma":0.000116496114,"teacher_disagreement_score":0.62171334,"about_ca_system_score_codex":0.00006088754,"about_ca_system_score_gemma":0.00003517685,"threshold_uncertainty_score":0.6181801},"labels":[],"label_agreement":null},{"id":"W2120342577","doi":"10.1109/cimsa.2008.4595842","title":"Artificial neural networks for real-time optical hand posture recognition using a color-coded glove","year":2008,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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 Ottawa","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Workspace; Virtual reality; Artificial neural network; Wired glove","score_opus":0.0573306074079333,"score_gpt":0.27157453707377316,"score_spread":0.21424392966583986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120342577","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":[0.2929209,0.000020067477,0.7033991,0.00048263362,0.00059639337,0.00063798524,0.000011417786,0.00020791402,0.0017235845],"genre_scores_gemma":[0.89016706,0.000008275025,0.10779386,0.0005235958,0.0010197518,0.000056474444,0.00005490719,0.000025980371,0.0003501267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982647,0.000103440245,0.0004205723,0.0004923252,0.00026902292,0.00044995287],"domain_scores_gemma":[0.998795,0.00029454014,0.00012097735,0.00028168716,0.00032870428,0.0001791087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003150807,0.00020687666,0.0003092543,0.00009863804,0.00039317613,0.000233466,0.00028496142,0.0002195402,0.000035935034],"category_scores_gemma":[0.00007907373,0.00017795492,0.00014137197,0.0003776524,0.00008749582,0.00043484275,0.00008263438,0.00014372407,0.000100632904],"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":[0.002101094,0.0019342067,0.0008488465,0.00024350175,0.00061622006,0.0009187677,0.007371695,0.01725267,0.41322434,0.009741913,0.03679201,0.50895476],"study_design_scores_gemma":[0.0005121689,0.00023471648,0.00025598524,0.000029323994,0.000021531545,0.0005548342,0.00002556104,0.98859185,0.008467429,0.0007162605,0.0002662886,0.000324061],"about_ca_topic_score_codex":0.00004053881,"about_ca_topic_score_gemma":0.00002744991,"teacher_disagreement_score":0.97133917,"about_ca_system_score_codex":0.000054644173,"about_ca_system_score_gemma":0.000078037665,"threshold_uncertainty_score":0.7256791},"labels":[],"label_agreement":null},{"id":"W2123203295","doi":"","title":"The impact of technical parameters such as video sensor technology, system configuration, marker size and speed on the accuracy of motion analysis systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 New Brunswick","funders":"","keywords":"Motion capture; Computer science; Computer vision; Artificial intelligence; Kinematics; Motion analysis; Motion (physics); Standard deviation; Simulation; Mathematics; Statistics","score_opus":0.013228444319671635,"score_gpt":0.26624345971672325,"score_spread":0.2530150153970516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123203295","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":[0.7403109,0.00008403418,0.24814446,0.0022565105,0.00019532198,0.0012087311,0.000010698138,0.0002372712,0.0075520584],"genre_scores_gemma":[0.999458,0.000010412331,0.000345403,0.000018767101,0.000018692946,0.00002587202,0.0000013163245,0.000006118611,0.00011541867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786055,0.0005916337,0.00064816803,0.00032186563,0.00037988636,0.00019791801],"domain_scores_gemma":[0.99308354,0.0049103494,0.0005593941,0.00096342835,0.00042377607,0.000059543894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020371205,0.00017006414,0.00043977032,0.00023727567,0.00014960066,0.00017183062,0.000561618,0.00014358597,0.0000051135958],"category_scores_gemma":[0.0019620901,0.00007912546,0.00019663936,0.0013609594,0.0001939556,0.00013775424,0.000075966425,0.00014153715,0.000017514263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049750006,0.0006743115,0.069176495,0.00095226534,0.009381023,0.000018707326,0.0015491914,0.015098557,0.1594349,0.6656186,0.0042885477,0.073309906],"study_design_scores_gemma":[0.0026173447,0.0022992247,0.24927726,0.0011188657,0.0010630698,0.0007152239,0.006450207,0.6491997,0.08294236,0.0022525298,0.0007710532,0.0012931381],"about_ca_topic_score_codex":0.0004301855,"about_ca_topic_score_gemma":0.000017450682,"teacher_disagreement_score":0.6633661,"about_ca_system_score_codex":0.00006161506,"about_ca_system_score_gemma":0.000040507355,"threshold_uncertainty_score":0.32266426},"labels":[],"label_agreement":null},{"id":"W2124466726","doi":"10.1109/crv.2009.16","title":"Non-Accidental Features for Gesture Spotting","year":2009,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Waterloo","funders":"","keywords":"Accidental; Gesture; Spotting; Computer science; Artificial intelligence; Motion (physics); Computer vision; Mixture model; Component (thermodynamics); Pattern recognition (psychology); Feature extraction; Acoustics","score_opus":0.010227273349343658,"score_gpt":0.2614731159971898,"score_spread":0.25124584264784616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124466726","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":[0.028591458,0.0001860093,0.9169353,0.006556358,0.0010527284,0.00072002085,0.0000026916885,0.0005108298,0.045444556],"genre_scores_gemma":[0.9431328,0.0000020711723,0.051818356,0.0020083196,0.00034598177,0.000015353899,0.0000035516346,0.000004593132,0.0026689288],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992109,0.000011984181,0.00014272884,0.0002607333,0.00015626683,0.00021739448],"domain_scores_gemma":[0.9995168,0.000052239116,0.000050312796,0.00023574622,0.00007076912,0.00007415503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018586218,0.00010332656,0.00012579467,0.000060476566,0.000103684,0.00019782133,0.00039865263,0.00007055579,0.000008814942],"category_scores_gemma":[0.000022966224,0.00008001808,0.00008028123,0.00016612833,0.0000054074726,0.00030569918,0.000034478126,0.00007528734,0.00005930847],"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":[0.000024537043,0.00020759081,0.0020029023,0.00004833606,0.000046685975,0.000048259044,0.0029894083,0.000034620803,0.019582218,0.067390375,0.14444304,0.76318204],"study_design_scores_gemma":[0.0075380187,0.0018667381,0.5643624,0.00045033215,0.000051285533,0.0017940493,0.0013680243,0.018201435,0.18477064,0.047746275,0.16889878,0.0029519983],"about_ca_topic_score_codex":0.0000057838965,"about_ca_topic_score_gemma":0.0000093287645,"teacher_disagreement_score":0.91454136,"about_ca_system_score_codex":0.000016153843,"about_ca_system_score_gemma":0.000021341913,"threshold_uncertainty_score":0.32630426},"labels":[],"label_agreement":null},{"id":"W2126136748","doi":"10.1007/s11042-015-2667-5","title":"Gesture based human motion and game principles to aid understanding of science and cultural practices","year":2015,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Gesture; Computer science; Event (particle physics); Human–computer interaction; Natural (archaeology); Set (abstract data type); Visitor pattern; Process (computing); Heuristic; Representation (politics); Motion (physics); Multimedia; Artificial intelligence","score_opus":0.20951947345831862,"score_gpt":0.35306692601488787,"score_spread":0.14354745255656925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126136748","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":[0.2729483,0.00041804707,0.7178833,0.004477962,0.000105337735,0.0015597648,0.000038140854,0.00012642494,0.002442767],"genre_scores_gemma":[0.97297615,0.00001261948,0.026731543,0.000084473635,0.000053797023,0.000108461674,0.000005359199,0.000003378335,0.00002419255],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990982,0.000029139748,0.0001617735,0.00031669252,0.00026715273,0.00012707182],"domain_scores_gemma":[0.99902445,0.00013505234,0.00017638967,0.00019171704,0.00024596034,0.00022643009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056346634,0.0000837456,0.00011411437,0.00010370503,0.00021505883,0.00029268087,0.00017835794,0.00003885135,7.142195e-7],"category_scores_gemma":[0.0002543494,0.000067508554,0.000008617142,0.00039298023,0.00027815037,0.00064174255,0.00011718989,0.00006204481,0.0000026696573],"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":[0.00003499971,0.0005289782,0.04715878,0.0004909198,0.000061533035,0.000005083174,0.035964906,0.00029074593,0.17339249,0.2931992,0.0007658919,0.44810644],"study_design_scores_gemma":[0.008527058,0.00104654,0.3448768,0.0006426453,0.00017948622,0.00025131548,0.022775026,0.3410842,0.038905006,0.016719822,0.22255452,0.0024375743],"about_ca_topic_score_codex":0.000024799429,"about_ca_topic_score_gemma":0.000021757545,"teacher_disagreement_score":0.7000279,"about_ca_system_score_codex":0.00004151147,"about_ca_system_score_gemma":0.00006013671,"threshold_uncertainty_score":0.28223282},"labels":[],"label_agreement":null},{"id":"W2129114382","doi":"10.1109/tsp.2011.2165707","title":"A Novel Accelerometer-Based Gesture Recognition System","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":168,"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":"Gesture; Gesture recognition; Accelerometer; Computer science; Dynamic time warping; Speech recognition; Artificial intelligence; Subspace topology; Notation; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.08925510916687908,"score_gpt":0.24929883237730496,"score_spread":0.16004372321042587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129114382","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":[0.0040421374,0.00004198042,0.99132097,0.00006474099,0.0004754964,0.00030847813,0.000019623907,0.0007554912,0.0029710762],"genre_scores_gemma":[0.95041984,0.0000011219862,0.049038284,0.0002345235,0.000078000245,0.00013584555,0.0000025198485,0.000029026069,0.000060847444],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99807835,0.0000989371,0.00042320645,0.00057932426,0.00045758096,0.00036257753],"domain_scores_gemma":[0.99892694,0.00008440146,0.00020252677,0.00031657369,0.00029324603,0.00017631466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003512502,0.00027683433,0.00026807564,0.0004162716,0.00040082406,0.00025651068,0.00049668306,0.00018780281,0.000063446845],"category_scores_gemma":[0.0000021608032,0.00025199773,0.00015038402,0.00087388774,0.000044902106,0.00090420106,0.0000019944057,0.00036498514,0.00026362605],"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":[0.00008155518,0.00068016537,0.000007681536,0.0004416187,0.0000540643,0.00003568875,0.0017039211,0.00056991936,0.020753916,0.000031452753,0.0000271055,0.97561294],"study_design_scores_gemma":[0.0034191883,0.0009141916,0.0003037203,0.0030838614,0.0001746277,0.0006908013,0.0007959925,0.15281525,0.83535475,0.00031536078,0.000502088,0.00163017],"about_ca_topic_score_codex":0.000025982345,"about_ca_topic_score_gemma":0.000009495233,"teacher_disagreement_score":0.97398275,"about_ca_system_score_codex":0.00011370155,"about_ca_system_score_gemma":0.00016766106,"threshold_uncertainty_score":0.9999932},"labels":[],"label_agreement":null},{"id":"W2129603242","doi":"10.1186/1743-0003-10-114","title":"Hand contour detection in wearable camera video using an adaptive histogram region of interest","year":2013,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Toronto Rehabilitation Institute","keywords":"Computer vision; Artificial intelligence; Wearable computer; Histogram; Computer science; Frame (networking); A priori and a posteriori; Point (geometry); Wearable technology; Image (mathematics); Mathematics","score_opus":0.03209661868524967,"score_gpt":0.23949818365003103,"score_spread":0.20740156496478135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129603242","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":[0.7029553,0.000113852286,0.29646495,0.0001223788,0.00024348848,0.00008784042,9.028314e-8,0.0000074598406,0.0000046310142],"genre_scores_gemma":[0.99085265,0.000013268766,0.009068864,0.0000075456824,0.00004505452,0.0000028996037,4.8723376e-8,0.0000056591534,0.000003986151],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992068,0.00011449355,0.00038301424,0.00010806081,0.000099023964,0.00008861477],"domain_scores_gemma":[0.9991755,0.0001860706,0.00022873691,0.00009353137,0.00025303,0.00006312706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002372796,0.00007257381,0.00017511309,0.00032945635,0.00002756702,0.00005829999,0.00008447121,0.00003580063,4.5723579e-7],"category_scores_gemma":[0.0001561662,0.000062723964,0.000042526444,0.00020516053,0.000031217714,0.0008394958,0.000012857557,0.00014083284,3.6577566e-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":[0.00016201359,0.00040003198,0.0035956723,0.0004532905,0.00005117089,0.00005835079,0.0128240585,0.065903455,0.6092056,0.00033334637,0.00005223653,0.30696076],"study_design_scores_gemma":[0.0017501824,0.0060638767,0.08695788,0.0013680628,0.000021975377,0.0011824075,0.0014952231,0.89201504,0.007284858,0.0013117379,0.0002512306,0.00029753282],"about_ca_topic_score_codex":0.000117994074,"about_ca_topic_score_gemma":0.000022133729,"teacher_disagreement_score":0.82611156,"about_ca_system_score_codex":0.000050408857,"about_ca_system_score_gemma":0.00001690196,"threshold_uncertainty_score":0.2557809},"labels":[],"label_agreement":null},{"id":"W2129792427","doi":"10.1109/nbis.2011.60","title":"A Web-Based Sign Language Translator Using 3D Video Processing","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Gesture; Computer science; Sign language; American Sign Language; Gesture recognition; Vocabulary; Process (computing); Web application; Sign (mathematics); Interface (matter); Speech synthesis; Speech recognition; Human–computer interaction; Artificial intelligence; World Wide Web; Linguistics; Programming language","score_opus":0.04758568727372849,"score_gpt":0.2641325635112843,"score_spread":0.21654687623755578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129792427","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":[0.028092068,0.00018040091,0.95162946,0.00006499447,0.00014768455,0.0001652166,0.0000012658224,0.00038649625,0.019332405],"genre_scores_gemma":[0.82285166,2.3410946e-7,0.17661119,0.00035168222,0.00005122763,0.000009267723,4.885237e-7,0.000009064788,0.000115227216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901694,0.00007047768,0.00019696089,0.0002817841,0.00020916543,0.00022469638],"domain_scores_gemma":[0.99947894,0.000029850258,0.00006319397,0.00026016,0.00007033359,0.00009752503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026891124,0.00011751503,0.00013987566,0.000110381305,0.00008203041,0.00010711013,0.00034469055,0.00006217925,0.00012706786],"category_scores_gemma":[0.000010057326,0.00009394667,0.000052588413,0.00034932315,0.0000211635,0.00039814613,0.000021059852,0.00007478521,0.000102878184],"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":[0.000033400338,0.00038705184,0.0021560376,0.00037279268,0.00004887348,0.00024965612,0.030636175,0.00006552915,0.09809974,0.0016182861,0.0004608259,0.8658716],"study_design_scores_gemma":[0.0028037156,0.00017357784,0.000923871,0.00045247772,0.000040307375,0.00014991108,0.00067101524,0.8391012,0.14801005,0.00039621405,0.006212342,0.0010653139],"about_ca_topic_score_codex":0.00006476246,"about_ca_topic_score_gemma":0.000026463018,"teacher_disagreement_score":0.8648063,"about_ca_system_score_codex":0.00001781139,"about_ca_system_score_gemma":0.0001750101,"threshold_uncertainty_score":0.3831034},"labels":[],"label_agreement":null},{"id":"W2130909540","doi":"10.1109/aipr.2001.991206","title":"A basic hand gesture control system for PC applications","year":2002,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Control (management); Computer vision; Artificial intelligence; Speech recognition; Human–computer interaction","score_opus":0.019632587918975933,"score_gpt":0.21876244320319282,"score_spread":0.1991298552842169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130909540","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":[0.0000682111,0.00031617275,0.9801073,0.001513816,0.00021482242,0.0011213904,0.000014660481,0.00031083717,0.016332788],"genre_scores_gemma":[0.983224,0.0000025938125,0.0113200685,0.0004494972,0.00028359937,0.0013253111,0.0000024584415,0.000008973556,0.003383544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909264,0.000039575112,0.00020632957,0.00030517843,0.0001529638,0.00020331643],"domain_scores_gemma":[0.99907064,0.00017549402,0.00007020182,0.0004230974,0.00015976383,0.00010079495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015691543,0.0001057806,0.00017158684,0.0000663286,0.00019262258,0.00022524576,0.00039421613,0.000073762014,0.000023987428],"category_scores_gemma":[0.000011520238,0.00008237098,0.0000790133,0.00022859276,0.00001896465,0.00016763862,0.00002358892,0.000053679058,0.00041865578],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014593752,0.00043765875,0.0010223122,0.0008679894,0.00026498086,0.000015880596,0.002032296,0.0001405618,0.0038079326,0.54045355,0.12276311,0.32817912],"study_design_scores_gemma":[0.0032411378,0.00012087689,0.00029911756,0.0000842794,0.0000367593,0.00019039099,0.00016782754,0.13701524,0.0024512617,0.0006938708,0.8552438,0.00045541028],"about_ca_topic_score_codex":0.0000044895532,"about_ca_topic_score_gemma":0.0000070877036,"teacher_disagreement_score":0.9831557,"about_ca_system_score_codex":0.000031325817,"about_ca_system_score_gemma":0.000012053505,"threshold_uncertainty_score":0.53811127},"labels":[],"label_agreement":null},{"id":"W2132023035","doi":"10.1109/tim.2008.922070","title":"Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":177,"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":"Computer science; Gesture; Gesture recognition; AdaBoost; Artificial intelligence; String (physics); Hidden Markov model; Context (archaeology); Pattern recognition (psychology); Grammar; Speech recognition; Natural language processing; Context-free grammar; Rule-based machine translation; Mathematics; Classifier (UML)","score_opus":0.07649357193513841,"score_gpt":0.24937954847998958,"score_spread":0.17288597654485116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132023035","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":[0.123951375,0.0002198751,0.87378657,0.00044875496,0.00088214694,0.00051760144,0.0000226258,0.00009545225,0.000075597636],"genre_scores_gemma":[0.9935644,0.00009003546,0.005639718,0.0005066329,0.000043111002,0.000075749704,0.0000036463823,0.000013389561,0.0000633178],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983291,0.00012605828,0.00029081383,0.0004184145,0.0006188822,0.00021675677],"domain_scores_gemma":[0.9991778,0.000040747254,0.00011462469,0.00025753945,0.00023327804,0.00017602243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028730943,0.00021880718,0.00020059283,0.000228329,0.00068825873,0.00018474362,0.00013812496,0.00010104326,0.000011721239],"category_scores_gemma":[0.000011309622,0.0002060856,0.000056100027,0.0002223548,0.00011500264,0.00047654417,0.0000040176637,0.00019619669,0.000009960256],"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":[0.00020922434,0.0004425804,0.00009148049,0.00013473486,0.00028236854,0.000025471252,0.00958801,0.0007504429,0.023754949,0.0001643501,0.000602214,0.96395415],"study_design_scores_gemma":[0.10073821,0.008316985,0.046867304,0.0061145327,0.0021727285,0.02506742,0.018420156,0.10551692,0.65291953,0.014961311,0.00802851,0.010876392],"about_ca_topic_score_codex":0.000106808424,"about_ca_topic_score_gemma":0.000281386,"teacher_disagreement_score":0.9530778,"about_ca_system_score_codex":0.00012565505,"about_ca_system_score_gemma":0.00007859029,"threshold_uncertainty_score":0.8403927},"labels":[],"label_agreement":null},{"id":"W2132320335","doi":"10.1109/ccece.1998.682771","title":"The VideoWriter: towards active paper for a natural user interface","year":2002,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Desk; Clutter; User interface; Interface (matter); Natural user interface; Natural (archaeology); Computer vision; Artificial intelligence; Computer graphics (images); Human–computer interaction; User interface design","score_opus":0.02411498759482666,"score_gpt":0.26599312186397833,"score_spread":0.24187813426915167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132320335","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":[0.004860728,0.0015499956,0.8878104,0.029833412,0.003014063,0.0010936569,0.0000074801465,0.00044775035,0.07138251],"genre_scores_gemma":[0.96107626,0.000018746734,0.006180459,0.0011801821,0.00014801594,0.00009802823,3.8542456e-7,0.000007543541,0.03129036],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99917865,0.00005375872,0.00015379432,0.00023112925,0.00015572112,0.00022694295],"domain_scores_gemma":[0.9991793,0.00030699064,0.00004552416,0.00032318418,0.000093731665,0.00005128502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014265848,0.000100459176,0.000099765784,0.000028166953,0.00016880885,0.00026258128,0.00056727155,0.000041656283,0.000069900096],"category_scores_gemma":[0.000057319503,0.0000551239,0.000086986714,0.00014208656,0.000027985103,0.0003277927,0.00010955303,0.0000971987,0.00019248766],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013734367,0.00003448386,0.000012912694,0.000009689835,0.000055096873,0.0000018403358,0.002283523,0.0000021617604,0.0011044275,0.010727068,0.04149879,0.94425625],"study_design_scores_gemma":[0.00049986824,0.00007295214,0.0002803528,0.000023318114,0.00000458086,0.00003763928,0.00021468718,0.029435208,0.011324796,0.0010426034,0.9568719,0.00019207997],"about_ca_topic_score_codex":0.000007855388,"about_ca_topic_score_gemma":0.000035186946,"teacher_disagreement_score":0.95621556,"about_ca_system_score_codex":0.000021116735,"about_ca_system_score_gemma":0.000010217489,"threshold_uncertainty_score":0.2532077},"labels":[],"label_agreement":null},{"id":"W2133386674","doi":"10.5281/zenodo.1177508","title":"Fortouch : A Wearable Digital Ventriloquized Actor","year":2009,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Council for the Arts","keywords":"Gesture; Computer science; Wearable computer; Human–computer interaction; Relation (database); Speech synthesis; Formant; Speech recognition; Artificial intelligence; Database","score_opus":0.028575043571914212,"score_gpt":0.2370146079864001,"score_spread":0.20843956441448588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133386674","genre_codex":"other","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":[0.021969547,0.0003206295,0.43950433,0.005612022,0.00052746973,0.0012870278,0.00016647056,0.0048840684,0.5257284],"genre_scores_gemma":[0.99706864,0.000026494115,0.0007480486,0.0002444588,0.0001675547,2.9752163e-8,0.00019531857,0.0003098599,0.001239583],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99837023,0.00015183573,0.0002443798,0.00041880104,0.00042453152,0.00039020184],"domain_scores_gemma":[0.9987249,0.000023277113,0.00009633222,0.0005390014,0.00040149066,0.00021503097],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00041767856,0.00013188292,0.0001550524,0.00020068676,0.0011071439,0.0024109324,0.0014315231,0.000059582802,0.0014368357],"category_scores_gemma":[0.00026897574,0.00012997213,0.00006899303,0.00071359257,0.00004227467,0.0009469825,0.000526724,0.00018337472,0.011370446],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004025336,0.00023489569,0.000003645514,0.000024069486,0.000032890526,0.000043167292,0.0012137205,0.000012315353,0.0028743828,0.010139258,0.105423346,0.87995803],"study_design_scores_gemma":[0.0006127761,0.00027124974,0.00040921147,0.000038948638,0.0000035507992,0.0002215992,0.000058049816,0.00070347614,0.0005360057,0.001207985,0.9957538,0.00018335078],"about_ca_topic_score_codex":0.0000014265494,"about_ca_topic_score_gemma":2.8160054e-8,"teacher_disagreement_score":0.9750991,"about_ca_system_score_codex":0.0000895917,"about_ca_system_score_gemma":0.000005365903,"threshold_uncertainty_score":0.999476},"labels":[],"label_agreement":null},{"id":"W2134088111","doi":"10.1109/icma.2005.1626670","title":"Virtual programming with bare-hand-based interaction","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Virtual reality; Wired glove; Human–computer interaction; Application programming interface; Computer vision; Artificial intelligence; Operating system","score_opus":0.01094140654334647,"score_gpt":0.22816984033028784,"score_spread":0.21722843378694137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134088111","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":[0.025739038,0.000008016732,0.9653886,0.00036056997,0.00020699366,0.00015600688,3.3339975e-7,0.0002572915,0.007883175],"genre_scores_gemma":[0.9707907,7.396677e-8,0.028411373,0.00013423144,0.00011076158,0.0000329526,0.0000053416406,0.0000054640227,0.0005091084],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924064,0.00003910567,0.0001368172,0.00022167315,0.00020587927,0.00015587221],"domain_scores_gemma":[0.999585,0.000053263284,0.000034906367,0.0001914631,0.000093151866,0.00004226145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012240281,0.0000873101,0.00008557656,0.00008445818,0.000083535415,0.00031287462,0.00017003184,0.000032242064,0.000022698447],"category_scores_gemma":[0.000004899747,0.00006055518,0.0000282594,0.0002653376,0.000020862279,0.00035165055,0.000020347217,0.00006493702,0.00012453884],"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":[0.00008817129,0.00072342454,0.007588568,0.000066213055,0.00005923564,0.0001285564,0.0007434229,0.010430948,0.0055450792,0.05335858,0.00636087,0.9149069],"study_design_scores_gemma":[0.005243907,0.0022409945,0.00840067,0.00050573156,0.000033823457,0.00043205274,0.0007340662,0.19261894,0.2048363,0.0005866419,0.5827163,0.0016506001],"about_ca_topic_score_codex":0.0001234837,"about_ca_topic_score_gemma":0.000206507,"teacher_disagreement_score":0.94505167,"about_ca_system_score_codex":0.000026485635,"about_ca_system_score_gemma":0.000043028704,"threshold_uncertainty_score":0.30170572},"labels":[],"label_agreement":null},{"id":"W2139921509","doi":"10.1109/imtc.2005.1604461","title":"Dynamic Gesture Recognition","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":40,"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":"Gesture; Gesture recognition; Computer science; Hidden Markov model; Context (archaeology); Artificial intelligence; Computer vision; Projection (relational algebra); Speech recognition; Algorithm","score_opus":0.027801372897755836,"score_gpt":0.23279225419649605,"score_spread":0.2049908812987402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139921509","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":[0.72820544,0.00042391536,0.23607999,0.013969832,0.0016272226,0.0016299863,0.00002270156,0.0025387567,0.015502149],"genre_scores_gemma":[0.9783307,0.000055085864,0.020784281,0.00017465287,0.000072423776,0.00030970335,0.00001945412,0.000016789427,0.00023694578],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99741787,0.000016883787,0.0005265635,0.0006708079,0.0008444872,0.0005233671],"domain_scores_gemma":[0.99818385,0.0000114442755,0.00031549222,0.00021353309,0.0011897233,0.00008595284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005439999,0.0003002689,0.00026766714,0.0006334712,0.00025785726,0.00028649016,0.0006806756,0.0003091151,0.00004362841],"category_scores_gemma":[0.000039800416,0.00029138746,0.00006500531,0.0007728385,0.00012401563,0.00096894754,0.00006765094,0.0002946263,0.00025123832],"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":[0.000043929947,0.00074004167,0.020029545,0.00023271021,0.00019911533,0.000022800934,0.0010056996,0.0000078488065,0.39211702,0.08111102,0.016985474,0.4875048],"study_design_scores_gemma":[0.009508669,0.0007806882,0.027351744,0.0015077305,0.00020081845,0.0010824132,0.0036475246,0.012037559,0.5023244,0.38272107,0.055277143,0.0035602674],"about_ca_topic_score_codex":0.000025997113,"about_ca_topic_score_gemma":0.00009318615,"teacher_disagreement_score":0.48394454,"about_ca_system_score_codex":0.00030923312,"about_ca_system_score_gemma":0.00020744161,"threshold_uncertainty_score":0.9999538},"labels":[],"label_agreement":null},{"id":"W2140180878","doi":"10.1109/imtc.2007.379090","title":"Calibration and Error Model Analysis of 3D Monocular Vision Model Based Hand Posture Estimation","year":2007,"lang":"en","type":"article","venue":"Conference proceedings - IEEE Instrumentation/Measurement Technology Conference","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer vision; Silhouette; Computer science; Ground truth; Monocular; Monocular vision; Calibration; Pose; Process (computing); Measure (data warehouse); Image plane; Image (mathematics); Mathematics; Statistics","score_opus":0.04765787447099969,"score_gpt":0.28734151903053073,"score_spread":0.23968364455953106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140180878","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":[0.31910416,0.000036025343,0.67894775,0.0008505958,0.00007562057,0.0005106275,0.0000115486155,0.0002161321,0.00024754967],"genre_scores_gemma":[0.88065636,0.000016759714,0.11905524,0.00011789776,0.000009118052,0.000088003,0.000024784518,0.00001466607,0.000017174752],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99653184,0.000028399645,0.0009811736,0.0009113993,0.0010921399,0.00045505608],"domain_scores_gemma":[0.99612546,0.000035958685,0.0007782086,0.0003943192,0.002504757,0.00016127137],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013245647,0.00040474805,0.0006032301,0.0019038481,0.00031294886,0.0004238667,0.0007433828,0.00044326004,0.000010437495],"category_scores_gemma":[0.00016781551,0.00040228895,0.00009275148,0.0021741,0.00035767924,0.0015144285,0.0001261776,0.00032068015,0.0000029477108],"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":[0.0001424746,0.0004530871,0.021776233,0.00039484937,0.00078720483,0.000005022756,0.0053954595,0.027331743,0.7063286,0.06756856,0.00010001775,0.1697168],"study_design_scores_gemma":[0.0007899339,0.0001603013,0.0012416936,0.00018682046,0.00026725596,0.0000056803233,0.00038949747,0.8884847,0.10193989,0.0061852615,0.0000037594275,0.0003451897],"about_ca_topic_score_codex":0.000016927017,"about_ca_topic_score_gemma":0.00008798621,"teacher_disagreement_score":0.86115295,"about_ca_system_score_codex":0.00018159278,"about_ca_system_score_gemma":0.00047208552,"threshold_uncertainty_score":0.9998429},"labels":[],"label_agreement":null},{"id":"W2142891034","doi":"10.1109/have.2002.1106914","title":"Hand-based interaction in augmented reality","year":2003,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"National Research Council Canada; University of Toronto; Carleton University","funders":"","keywords":"Augmented reality; Computer science; Gesture; Gesture recognition; Simple (philosophy); Artificial intelligence; Computer vision; Interaction technique; Human–computer interaction; Virtual reality","score_opus":0.04243760878777573,"score_gpt":0.2953887633025693,"score_spread":0.25295115451479355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142891034","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":[0.0131822815,0.0000104944065,0.9352664,0.0007110613,0.000438575,0.00012693038,4.1797279e-7,0.00008981221,0.050174043],"genre_scores_gemma":[0.99493384,5.101229e-7,0.0044316393,0.00033610774,0.000010879434,0.000015366824,0.0000016740598,0.0000021360418,0.00026781703],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992423,0.0001736649,0.00016688192,0.00018304595,0.00011898309,0.0001151303],"domain_scores_gemma":[0.999599,0.00006458765,0.000038654332,0.00021230527,0.000044069377,0.00004139175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039502216,0.000057203186,0.00007976156,0.00009858024,0.000030696217,0.000089169924,0.0001206603,0.000033484757,0.000068625195],"category_scores_gemma":[0.000051606115,0.000047968475,0.000024928933,0.00030839114,0.0000090627345,0.00021985968,0.000011062013,0.00006469352,0.00012869328],"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":[0.0001269101,0.003134868,0.06573471,0.00031615995,0.00011628035,0.0002648678,0.005111304,0.006880448,0.029960407,0.40426108,0.032856293,0.45123667],"study_design_scores_gemma":[0.0060860445,0.00027107375,0.025028076,0.0002911559,0.000007959566,0.00011072668,0.00041712183,0.2560047,0.3438474,0.007830503,0.3590901,0.0010151481],"about_ca_topic_score_codex":0.00010604661,"about_ca_topic_score_gemma":0.00024764187,"teacher_disagreement_score":0.98175156,"about_ca_system_score_codex":0.00005020199,"about_ca_system_score_gemma":0.00003589961,"threshold_uncertainty_score":0.19560976},"labels":[],"label_agreement":null},{"id":"W2143183396","doi":"10.1109/tcsvt.2003.821976","title":"Quantifying and Recognizing Human Movement Patterns From Monocular Video Images—Part I: A New Framework for Modeling Human Motion","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":85,"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":"Artificial intelligence; Computer vision; Computer science; Biometrics; Hidden Markov model; Gait; Silhouette; Particle filter; Motion (physics); Motion capture; Human body; Movement (music); Gesture recognition; Trajectory; Pattern recognition (psychology); Filter (signal processing); Gesture","score_opus":0.08290589045058422,"score_gpt":0.30282875029661915,"score_spread":0.21992285984603493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143183396","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":[0.11797127,0.00070999644,0.8777528,0.0005738005,0.0009331655,0.0015147489,0.0001023454,0.0004336165,0.00000830024],"genre_scores_gemma":[0.98925805,0.00007525527,0.0095564425,0.00012789275,0.00018557002,0.00071052305,0.000011480109,0.00004658456,0.000028182647],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99753153,0.000055105967,0.0007412276,0.0009668623,0.0002264714,0.0004788051],"domain_scores_gemma":[0.99870175,0.00016475268,0.00024641491,0.0005738956,0.00014948922,0.00016366808],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046194484,0.0003389911,0.00055279763,0.0005744734,0.0008617355,0.0003591543,0.00034981297,0.0004374485,0.000002060927],"category_scores_gemma":[0.000023393748,0.00034440044,0.00014800642,0.0002764658,0.000047207966,0.0004203948,0.000008508835,0.0003441045,0.0000042051793],"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":[0.000051999614,0.0007769823,0.00084782625,0.0017702767,0.0012911014,0.000042777414,0.0046094647,0.029876433,0.11517528,0.10960262,0.00013030671,0.73582494],"study_design_scores_gemma":[0.015807113,0.003146871,0.00015460687,0.010055374,0.00067765557,0.00032861892,0.004988077,0.25296608,0.15520374,0.54982483,0.0031276145,0.0037194348],"about_ca_topic_score_codex":0.0009844233,"about_ca_topic_score_gemma":0.000254056,"teacher_disagreement_score":0.8712868,"about_ca_system_score_codex":0.00012854408,"about_ca_system_score_gemma":0.000045511348,"threshold_uncertainty_score":0.9999008},"labels":[],"label_agreement":null},{"id":"W2145937347","doi":"10.1109/ccece.2003.1226250","title":"Efficient face and gesture recognition techniques for robot control","year":2004,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Alberta","funders":"","keywords":"Computer science; Gesture; Artificial intelligence; Gesture recognition; Computer vision; Face (sociological concept); Facial recognition system; Feature (linguistics); Three-dimensional face recognition; Segmentation; Robot; Feature extraction; Pattern recognition (psychology); Matching (statistics); Speech recognition; Face detection; Mathematics","score_opus":0.017323406140367658,"score_gpt":0.24572335443826054,"score_spread":0.2283999482978929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145937347","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":[0.0040355767,0.00011163957,0.9901919,0.0027729666,0.00014558416,0.000817761,0.000009722919,0.00038495945,0.0015299001],"genre_scores_gemma":[0.8698379,0.00000639567,0.12904382,0.000745999,0.00008569015,0.00016539716,0.0000038261846,0.0000070836945,0.00010388231],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999191,0.000028633323,0.00016766369,0.00029919573,0.00013228558,0.00018121421],"domain_scores_gemma":[0.9994601,0.000090279435,0.000058200865,0.00017477782,0.00013811637,0.00007853155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028487528,0.000111032365,0.00014597556,0.00007541181,0.0000941526,0.000120905264,0.00015925016,0.00008862255,0.0000030170995],"category_scores_gemma":[0.000038627215,0.00008710552,0.000044792265,0.00013001678,0.000021622258,0.00009747633,0.00003157819,0.00006419017,0.000029156112],"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":[0.000050484552,0.00036467507,0.00011327371,0.00017847087,0.000083758045,0.00001558216,0.0020420703,0.0016065298,0.019661624,0.02766073,0.0013492812,0.94687355],"study_design_scores_gemma":[0.031284984,0.0044059567,0.0081313085,0.0017144413,0.00022896258,0.0017698448,0.0013404391,0.12888703,0.5810808,0.14702976,0.08907657,0.005049928],"about_ca_topic_score_codex":0.000013179283,"about_ca_topic_score_gemma":0.000009572429,"teacher_disagreement_score":0.9418236,"about_ca_system_score_codex":0.000029588058,"about_ca_system_score_gemma":0.000030208652,"threshold_uncertainty_score":0.355206},"labels":[],"label_agreement":null},{"id":"W2146458724","doi":"10.5281/zenodo.1177658","title":"A Wii-Based Gestural Interface For Computer Conducting Systems","year":2009,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Regina","funders":"","keywords":"Gesture; Interface (matter); Computer science; Human–computer interaction; Computer graphics (images); Computer vision; Operating system","score_opus":0.07816694768632809,"score_gpt":0.27709633050476973,"score_spread":0.19892938281844164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146458724","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":[0.0052059344,0.00011739216,0.98124963,0.0018775943,0.0005355905,0.000889333,0.000048695303,0.0014370941,0.008638708],"genre_scores_gemma":[0.9894933,0.0000034347568,0.008809309,0.00037328296,0.0004020298,1.4872244e-7,0.00021850146,0.0003702384,0.0003297667],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998052,0.00035164462,0.00033344424,0.00051766617,0.00033683825,0.00040839417],"domain_scores_gemma":[0.9982946,0.000071512426,0.00016102259,0.0005041348,0.00079279486,0.00017595163],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00089897774,0.0001682002,0.00020786702,0.0002126284,0.0012877831,0.0018756163,0.001393841,0.00007135826,0.00016339128],"category_scores_gemma":[0.00021163965,0.00016247596,0.000073563046,0.00048460544,0.00005235063,0.00046642855,0.000357772,0.00019354517,0.0016096864],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011558228,0.0004324761,0.000005124614,0.00035490797,0.00011611806,0.000035856676,0.004421618,0.0050664935,0.018073672,0.028783783,0.30843475,0.6341596],"study_design_scores_gemma":[0.0010922283,0.0007008199,0.00010651317,0.00013722353,0.000008911207,0.0002478207,0.00016435397,0.090302095,0.001520989,0.00013628247,0.90526617,0.00031658256],"about_ca_topic_score_codex":0.000005443047,"about_ca_topic_score_gemma":7.634515e-8,"teacher_disagreement_score":0.9842874,"about_ca_system_score_codex":0.00011654633,"about_ca_system_score_gemma":0.000006629677,"threshold_uncertainty_score":0.9991677},"labels":[],"label_agreement":null},{"id":"W2147373084","doi":"10.1109/icip.2007.4378967","title":"Segmentation and Recognition of Continuous Gestures","year":2007,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":18,"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":"Gesture; Dynamic time warping; Computer science; Gesture recognition; Computer vision; Artificial intelligence; Set (abstract data type); Segmentation; Motion (physics); Speech recognition; SIGNAL (programming language); Dynamic programming; Pattern recognition (psychology); Algorithm","score_opus":0.039290476528006525,"score_gpt":0.3094255041901049,"score_spread":0.27013502766209835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147373084","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":[0.3394628,0.00015938391,0.5248164,0.0016672522,0.000544782,0.00047470108,0.000012169682,0.00028432492,0.13257818],"genre_scores_gemma":[0.9484638,0.000033282635,0.0510511,0.00017319893,0.00011523425,0.000017388844,0.000008805309,0.000009333419,0.0001278702],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859256,0.000008888992,0.00038969915,0.00035984477,0.00046163655,0.00018737814],"domain_scores_gemma":[0.9980965,0.00004790598,0.00038779178,0.000055244494,0.0013349941,0.00007756021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062520045,0.00015036126,0.0001675045,0.00029497265,0.000086720196,0.0004520239,0.0003317119,0.000069671056,0.000022900736],"category_scores_gemma":[0.00015113215,0.00014059416,0.000030920066,0.0002191722,0.000076983815,0.0012983298,0.000080837555,0.00014732413,0.00001591967],"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":[0.00007378716,0.00011487713,0.001989318,0.00019029427,0.000033538327,0.0000070018496,0.004271833,1.7019251e-7,0.21656589,0.016737921,0.00015743863,0.75985795],"study_design_scores_gemma":[0.00337165,0.00064871146,0.018302806,0.0028183397,0.0000582255,0.00048624355,0.008940179,0.024233906,0.8568806,0.08178691,0.0011534527,0.0013189892],"about_ca_topic_score_codex":0.000009560106,"about_ca_topic_score_gemma":0.0000019570243,"teacher_disagreement_score":0.75853896,"about_ca_system_score_codex":0.000040110073,"about_ca_system_score_gemma":0.000045019686,"threshold_uncertainty_score":0.57332635},"labels":[],"label_agreement":null},{"id":"W2147453376","doi":"10.1109/iccv.2005.156","title":"Multi-scale gesture recognition from time-varying contours","year":2005,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":56,"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":"Computer science; Gesture; Artificial intelligence; Dynamic time warping; Pattern recognition (psychology); Gesture recognition; Parameterized complexity; Kernel (algebra); Mutual information; Computer vision; Similarity (geometry); Nonparametric statistics; Scale (ratio); Process (computing); Matching (statistics); Mathematics; Algorithm; Image (mathematics); Statistics","score_opus":0.02695280091519049,"score_gpt":0.247210695217242,"score_spread":0.22025789430205153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147453376","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":[0.032717984,0.00034183112,0.9222679,0.004120405,0.00064606685,0.00041145142,0.000033711327,0.000981874,0.038478754],"genre_scores_gemma":[0.35536024,0.00002088,0.633623,0.0027253355,0.0010374945,0.000047691592,0.000078257486,0.000023753411,0.0070833466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986647,0.00010516154,0.00027146554,0.00044120062,0.00025522936,0.00026225625],"domain_scores_gemma":[0.99911565,0.00013489286,0.000091965354,0.00037640982,0.00013312695,0.00014796537],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022007865,0.00016126623,0.00019992911,0.00008359804,0.00010681456,0.0001971515,0.000420157,0.00013434993,0.00048099543],"category_scores_gemma":[0.000027450385,0.0001384166,0.00008161824,0.00020750733,0.000019229943,0.0007372366,0.00008994346,0.00015406533,0.00702715],"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":[0.000011063406,0.00027787412,0.0009794562,0.00001033614,0.00006712599,0.0000196315,0.0023736802,0.00004965969,0.04336371,0.000045374687,0.021571398,0.93123066],"study_design_scores_gemma":[0.010115616,0.00026066697,0.023729935,0.0006267932,0.000103414946,0.000265354,0.000424733,0.5510889,0.16318615,0.003526211,0.24345686,0.003215316],"about_ca_topic_score_codex":0.000079835445,"about_ca_topic_score_gemma":0.00010768904,"teacher_disagreement_score":0.92801535,"about_ca_system_score_codex":0.000045259443,"about_ca_system_score_gemma":0.00003100836,"threshold_uncertainty_score":0.993746},"labels":[],"label_agreement":null},{"id":"W2148134892","doi":"10.1109/tim.2008.925725","title":"A Prototype for 3-D Hand Tracking and Posture Estimation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":48,"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":"Gesture; Computer science; Tracking (education); Gesture recognition; Computer vision; Artificial intelligence; Human–computer interaction; Simulation; Real-time computing","score_opus":0.07009000333292234,"score_gpt":0.27149076070954764,"score_spread":0.2014007573766253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148134892","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":[0.051243864,0.000046461748,0.94662607,0.0005106082,0.00032169034,0.0010876737,0.0000064666615,0.00006211721,0.00009507387],"genre_scores_gemma":[0.9842533,0.000042511554,0.015067417,0.0001897792,0.00001825644,0.00038217683,0.0000018362215,0.0000069130924,0.00003783436],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990772,0.000041509058,0.00019539725,0.00024921616,0.0003158146,0.00012088635],"domain_scores_gemma":[0.99955064,0.000026480993,0.00006487466,0.00010562243,0.00017189358,0.00008049642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024543077,0.000113000286,0.00010835447,0.000117595984,0.00047184527,0.00011845372,0.000056469085,0.000049447528,0.000003652802],"category_scores_gemma":[0.0000061350725,0.00010367592,0.00003174085,0.00011761875,0.000040472285,0.00038299029,7.162397e-7,0.00006837126,0.0000041180633],"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":[0.00014086362,0.00020834545,0.00011201541,0.00015298967,0.00008246311,0.0000027974486,0.0060155997,0.00044124835,0.021152271,0.0005850056,0.00008121251,0.97102517],"study_design_scores_gemma":[0.017697992,0.004231958,0.018433042,0.0008796073,0.00020245108,0.0014217512,0.0012084995,0.12142213,0.82245225,0.0022869296,0.008157136,0.0016062481],"about_ca_topic_score_codex":0.000010103509,"about_ca_topic_score_gemma":0.000021495349,"teacher_disagreement_score":0.96941894,"about_ca_system_score_codex":0.000054852575,"about_ca_system_score_gemma":0.00005266905,"threshold_uncertainty_score":0.42277813},"labels":[],"label_agreement":null},{"id":"W2148483252","doi":"10.1109/iembs.2009.5333085","title":"Towards a mobility diagnostic tool: Tracking rollator users' leg pose with a monocular vision system","year":2009,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research; Toronto Rehabilitation Institute","keywords":"Coronal plane; Sagittal plane; Computer vision; Computer science; Artificial intelligence; Monocular; Focus (optics); Tracking (education); Trajectory; Motion (physics); Psychology; Medicine","score_opus":0.011198971730180331,"score_gpt":0.24192515525805913,"score_spread":0.2307261835278788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148483252","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":[0.3444138,0.00021107198,0.6473586,0.0007096594,0.00033826692,0.0008512555,0.000003253187,0.0008137661,0.005300309],"genre_scores_gemma":[0.9834418,0.000004253519,0.016014494,0.0002827887,0.00011694481,0.00003490533,0.0000018656541,0.000010650786,0.00009223769],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99783313,0.00017835187,0.0004066935,0.000644145,0.00056846254,0.00036923276],"domain_scores_gemma":[0.99841243,0.00022737328,0.00012041601,0.0008118978,0.0002449163,0.00018296133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006880515,0.0002456677,0.00036114987,0.000119776916,0.00014141717,0.00046773668,0.0005429458,0.00011370846,0.000010729325],"category_scores_gemma":[0.00012244435,0.00017099187,0.00010901059,0.0005636333,0.000021971911,0.0009068409,0.00006873956,0.00014286289,0.000118941214],"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":[0.00018120866,0.0014777345,0.013286596,0.0006149608,0.0002337184,0.0011982507,0.0066551277,0.001341364,0.002815075,0.034954343,0.0017319514,0.9355097],"study_design_scores_gemma":[0.012712324,0.0082633,0.5526085,0.0059375525,0.0002999547,0.0036204983,0.0032466806,0.34447506,0.039571017,0.0034556712,0.020204546,0.0056048883],"about_ca_topic_score_codex":0.0000664293,"about_ca_topic_score_gemma":0.000020443305,"teacher_disagreement_score":0.92990476,"about_ca_system_score_codex":0.00012171373,"about_ca_system_score_gemma":0.00011758115,"threshold_uncertainty_score":0.6972846},"labels":[],"label_agreement":null},{"id":"W2149449227","doi":"10.1109/34.990134","title":"Visual input for pen-based computers","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"dPoint Technologies (Canada)","funders":"","keywords":"Computer science; Handwriting; Computer vision; Artificial intelligence; Trajectory; Handwriting recognition; Interface (matter); Set (abstract data type); Character (mathematics); Image resolution; Pattern recognition (psychology); Computer graphics (images); Feature extraction; Mathematics","score_opus":0.031516097130795735,"score_gpt":0.2798198410198337,"score_spread":0.24830374388903798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149449227","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":[0.00089059665,0.000078191166,0.9974058,0.0008215425,0.0003493095,0.00022835613,0.00003992179,0.000118570424,0.000067670895],"genre_scores_gemma":[0.99447215,0.00005470773,0.0041071344,0.0010624655,0.000035230747,0.00006272591,0.0000056426684,0.000011186171,0.00018878633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847376,0.000090627,0.0003832709,0.0005440087,0.00025766768,0.00025065985],"domain_scores_gemma":[0.9989304,0.00033618193,0.00010733015,0.00036169915,0.0001090102,0.00015539596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002225901,0.00021995019,0.0003390629,0.0005851864,0.00022273637,0.00019762148,0.0003693197,0.00007149939,0.00014918862],"category_scores_gemma":[0.000003896399,0.00019230705,0.00032970516,0.0009355097,0.000045507568,0.00017349744,0.0000029038124,0.00016080085,0.000072163886],"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":[0.000005815351,0.00023567214,0.0001925012,0.000026131524,0.00031704825,0.000005015524,0.0002482898,0.01690936,0.000082142724,0.00002724299,0.000052322546,0.9818985],"study_design_scores_gemma":[0.00019438412,0.00020907505,0.00013847217,0.000026068674,0.00021063942,0.000008297656,0.000014890088,0.9631217,0.035203323,0.00004591619,0.00057132257,0.000255888],"about_ca_topic_score_codex":0.00017216164,"about_ca_topic_score_gemma":0.00034728533,"teacher_disagreement_score":0.99358153,"about_ca_system_score_codex":0.000027304774,"about_ca_system_score_gemma":0.000010074899,"threshold_uncertainty_score":0.7842054},"labels":[],"label_agreement":null},{"id":"W2149473298","doi":"10.1504/ijcvr.2012.046416","title":"A novel approach for gesture control video games based on perceptual features: modelling, tracking and recognition","year":2012,"lang":"en","type":"article","venue":"International Journal of Computational Vision and Robotics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Video game; Artificial intelligence; Computer vision; Video tracking; Perception; Representation (politics); Object (grammar); Grayscale; Interface (matter); Human–computer interaction; Multimedia; Image (mathematics)","score_opus":0.03928474298264812,"score_gpt":0.29530241546378694,"score_spread":0.2560176724811388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149473298","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":[0.0032517505,0.0003144838,0.9934638,0.0019521343,0.0007030656,0.00016991791,0.000027074297,0.000017343815,0.00010040294],"genre_scores_gemma":[0.6580588,0.0000243811,0.34048158,0.00094936066,0.0004373865,0.000003451375,0.000022087257,0.000008103719,0.000014809513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985402,0.00007066301,0.00041905462,0.00015792409,0.0006598189,0.00015232961],"domain_scores_gemma":[0.99755216,0.00087375415,0.00035031498,0.00006193283,0.0010021665,0.00015967162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006335479,0.00015458002,0.00022153412,0.00029256323,0.00010071765,0.0002576076,0.00023500211,0.00009883658,0.0000025163756],"category_scores_gemma":[0.00012573671,0.000118693766,0.00010844905,0.00007662028,0.00004310556,0.00058798736,0.000029373643,0.00020001583,0.0000017951268],"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":[0.00015781664,0.0003677119,0.00018590788,0.000026392912,0.000091927766,0.0000023462358,0.000884037,0.95918375,0.00009623303,0.003700311,0.0006574909,0.034646083],"study_design_scores_gemma":[0.0027177178,0.0002511005,0.0038863893,0.00018249796,0.000028563643,0.00053732045,0.00013113377,0.9892113,0.000020090076,0.0023146519,0.00055816316,0.0001610372],"about_ca_topic_score_codex":0.0000012255914,"about_ca_topic_score_gemma":2.61029e-7,"teacher_disagreement_score":0.6548071,"about_ca_system_score_codex":0.000040958086,"about_ca_system_score_gemma":0.00007348285,"threshold_uncertainty_score":0.48401913},"labels":[],"label_agreement":null},{"id":"W2149584813","doi":"10.1109/cimsa.2011.6059935","title":"Hand gesture detection and recognition using principal component analysis","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":55,"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":"Artificial intelligence; Gesture; Principal component analysis; Gesture recognition; Computer vision; Computer science; Euclidean distance; Frame (networking); Rotation (mathematics); Pattern recognition (psychology); Background subtraction; Set (abstract data type); Face (sociological concept); Pixel","score_opus":0.07418554516723454,"score_gpt":0.24887202685536522,"score_spread":0.17468648168813067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149584813","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":[0.42666516,0.000031135416,0.5715984,0.00001235876,0.00012228549,0.00008957269,0.0000010972551,0.00007125589,0.0014087028],"genre_scores_gemma":[0.9686977,0.000005329122,0.031133188,0.00006177344,0.00004633382,0.000007871198,0.0000026377134,0.00000433703,0.000040793668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908024,0.000094100884,0.00019672648,0.00031421217,0.00016302566,0.0001516653],"domain_scores_gemma":[0.99946135,0.000027030497,0.000088734865,0.00021161261,0.00011376778,0.00009751765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002603695,0.00010693487,0.00016779828,0.00027793198,0.00015341955,0.00013797995,0.00012372446,0.000078226294,0.000040622257],"category_scores_gemma":[0.000011077842,0.00008938439,0.000065569206,0.00062543334,0.000031819116,0.00035316678,0.00007611179,0.000080229496,0.000031652376],"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":[0.00008119797,0.00048317635,0.040254477,0.00013350001,0.0019198674,0.00009001638,0.017551567,0.00009238345,0.07473086,0.00094763865,0.000028396136,0.8636869],"study_design_scores_gemma":[0.0017420368,0.00034804494,0.3611592,0.00008056471,0.000993735,0.0005739591,0.00049116486,0.47948205,0.14817086,0.004396334,0.0012731617,0.0012888587],"about_ca_topic_score_codex":0.0003045326,"about_ca_topic_score_gemma":0.00029101584,"teacher_disagreement_score":0.8623981,"about_ca_system_score_codex":0.0000251599,"about_ca_system_score_gemma":0.000010542018,"threshold_uncertainty_score":0.36449894},"labels":[],"label_agreement":null},{"id":"W2153420957","doi":"10.1109/icdsp.2013.6622679","title":"Hand posture recognition using K-NN and Support Vector Machine classifiers evaluated on our proposed HandReader dataset","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Support vector machine; Computer science; Pattern recognition (psychology); Preprocessor; Feature extraction; Classifier (UML); Computer vision; Kernel (algebra); Mathematics","score_opus":0.07007996090448448,"score_gpt":0.29771499185016165,"score_spread":0.22763503094567716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153420957","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":[0.6236876,0.00037904197,0.32198548,0.02630589,0.0049511255,0.009220349,0.003564574,0.0013812996,0.008524632],"genre_scores_gemma":[0.98847693,0.0000059144227,0.0077943397,0.001552837,0.00015524666,0.000034171404,0.0014468152,0.000022615235,0.00051109854],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815315,0.00021463654,0.00032334725,0.00058753707,0.00037782147,0.00034351938],"domain_scores_gemma":[0.99889576,0.000053943913,0.00013454366,0.00043951595,0.000243299,0.0002329527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040825756,0.00024893566,0.00025675073,0.0001850124,0.00022961988,0.00062982715,0.00027547893,0.00015625848,0.0002247324],"category_scores_gemma":[0.000066195644,0.00018353119,0.000039415903,0.0002997624,0.00004221904,0.0007941512,0.00010715372,0.00021207167,0.00074685534],"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":[0.00023334583,0.0010428232,0.002821899,0.00045414464,0.0005894811,0.0001788395,0.0038783036,0.000054468168,0.1866341,0.0004238527,0.29060024,0.5130885],"study_design_scores_gemma":[0.019793404,0.004789811,0.045623206,0.0011234309,0.0004398255,0.003247348,0.0024699573,0.74806577,0.112606846,0.003943566,0.052427694,0.005469126],"about_ca_topic_score_codex":0.00019894638,"about_ca_topic_score_gemma":0.000045369907,"teacher_disagreement_score":0.7480113,"about_ca_system_score_codex":0.000048306047,"about_ca_system_score_gemma":0.000088108965,"threshold_uncertainty_score":0.95995635},"labels":[],"label_agreement":null},{"id":"W2154147885","doi":"10.1109/crv.2007.3","title":"A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming","year":2007,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gesture recognition; Computer science; Computer vision; Gesture; Artificial intelligence; Tracking (education); Ambiguity; Process (computing); Kinematics; Hidden Markov model; Probabilistic logic","score_opus":0.0648838128204466,"score_gpt":0.3230261496756078,"score_spread":0.2581423368551612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154147885","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":[0.16165325,0.00018433484,0.8373789,0.00003165417,0.0001662895,0.00046288682,0.0000021007688,0.000040169492,0.00008038991],"genre_scores_gemma":[0.3953979,0.0000037961813,0.60444754,0.00004813793,0.00007912879,0.000009780242,0.0000015248797,0.000005953956,0.0000062272834],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99889994,0.000027317546,0.00037073018,0.00026613954,0.00017988721,0.0002559793],"domain_scores_gemma":[0.99919,0.0002099919,0.0001788909,0.00014988672,0.00019713232,0.00007410993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063118286,0.00010587981,0.00015932828,0.0001195939,0.000119405006,0.00014710412,0.00012868881,0.00011074207,0.0000044067256],"category_scores_gemma":[0.00009210193,0.00009405668,0.000040632905,0.00024951104,0.00003159107,0.00020562958,0.000043007603,0.00007754944,0.0000015601618],"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":[0.00000817186,0.000035527137,0.00407471,0.00009579589,0.000020361067,0.0000028811037,0.0007801544,0.0000020109069,0.0016902986,0.00033024626,0.0000044199533,0.99295545],"study_design_scores_gemma":[0.016258592,0.00434577,0.17590867,0.010119055,0.0007344514,0.0019363749,0.0061730947,0.22976398,0.3261485,0.17137073,0.051295646,0.0059451084],"about_ca_topic_score_codex":0.00001236499,"about_ca_topic_score_gemma":0.000023357361,"teacher_disagreement_score":0.9870103,"about_ca_system_score_codex":0.000017348853,"about_ca_system_score_gemma":0.000022736482,"threshold_uncertainty_score":0.38355204},"labels":[],"label_agreement":null},{"id":"W2154960361","doi":"10.1109/vr.2006.69","title":"Haptic-Based Biometrics: A Feasibility Study","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Haptic technology; Biometrics; Password; Computer science; Session (web analytics); Authentication (law); Task (project management); Human–computer interaction; Artificial intelligence; Computer security; Engineering; World Wide Web","score_opus":0.03847942573476557,"score_gpt":0.28094922435812636,"score_spread":0.2424697986233608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154960361","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":[0.15241235,0.000028364262,0.8293977,0.0003103481,0.00027341064,0.00052785105,0.0000016215366,0.00037095218,0.016677443],"genre_scores_gemma":[0.98514885,6.743729e-8,0.014054743,0.00013117926,0.00006284959,0.00002295985,0.0000013194282,0.0000040229106,0.00057401333],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998702,0.00012250248,0.000250127,0.00037615056,0.00036264697,0.0001865582],"domain_scores_gemma":[0.9990115,0.00014414093,0.000055094875,0.0005806639,0.00014515064,0.00006342841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063568633,0.00009960784,0.0001424806,0.0003492358,0.000068877984,0.00019396139,0.0004427886,0.000036277637,0.000040459177],"category_scores_gemma":[0.00005119899,0.00007662869,0.000054732838,0.0017760698,0.000016432217,0.00017974635,0.00006658909,0.000052749343,0.00036581047],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027385802,0.012804289,0.86253923,0.00008926447,0.00008123778,0.00020176727,0.0006658736,0.00018967155,0.004105884,0.02405436,0.014019732,0.081221275],"study_design_scores_gemma":[0.0063522854,0.0015289565,0.8844006,0.000030232426,0.000029717103,0.000032672328,0.00035899557,0.07673614,0.0060720877,0.006992526,0.016298663,0.001167149],"about_ca_topic_score_codex":0.00028806063,"about_ca_topic_score_gemma":0.0001328982,"teacher_disagreement_score":0.8327365,"about_ca_system_score_codex":0.00004616779,"about_ca_system_score_gemma":0.000052891963,"threshold_uncertainty_score":0.47018757},"labels":[],"label_agreement":null},{"id":"W2155464705","doi":"10.1109/icra.2011.5979633","title":"Towards quantitative modeling of task confirmations in human-robot dialog","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Dialog box; Computer science; Task (project management); Robot; Human–robot interaction; Human–computer interaction; Artificial intelligence; Engineering; Systems engineering; World Wide Web","score_opus":0.1566388182554187,"score_gpt":0.3192327484541283,"score_spread":0.1625939301987096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155464705","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":[0.013111685,0.000027491094,0.87362516,0.00006724937,0.00010949493,0.00012244313,0.0000022940192,0.000048138085,0.11288604],"genre_scores_gemma":[0.94437027,0.0000026852626,0.05548879,0.000046373065,0.000008239032,0.000015460198,0.0000018425154,0.000002953646,0.00006341056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991884,0.00007952334,0.00032001224,0.00015573048,0.00013811306,0.000118207834],"domain_scores_gemma":[0.9995028,0.000036780588,0.000071657494,0.00020489043,0.00014652972,0.000037378122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029831965,0.00006815755,0.00015444467,0.00016299616,0.000032838616,0.000019400786,0.00029803254,0.000040841685,0.00004395587],"category_scores_gemma":[0.000030163235,0.000058228332,0.000036837817,0.0002750831,0.000023312068,0.0003145087,0.000061056446,0.000060899394,0.000046680765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034349075,0.00015182224,0.0004927536,0.00003179671,0.00002200473,0.000005577241,0.023994686,0.0014929634,0.003338488,0.96714103,0.000073752795,0.003251697],"study_design_scores_gemma":[0.0011358602,0.0002593461,0.0048000524,0.00017730727,0.000010358367,0.000014507203,0.0016317888,0.88434047,0.01461912,0.09241781,0.0001299217,0.00046349497],"about_ca_topic_score_codex":0.00082851766,"about_ca_topic_score_gemma":0.00023881413,"teacher_disagreement_score":0.93125856,"about_ca_system_score_codex":0.000011952818,"about_ca_system_score_gemma":0.000043649165,"threshold_uncertainty_score":0.23744825},"labels":[],"label_agreement":null},{"id":"W2155996262","doi":"10.1109/icarcv.2004.1468914","title":"Continuous human activity recognition","year":2005,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Clutter; Computer science; Particle filter; Computer vision; Artificial intelligence; Robustness (evolution); Hidden Markov model; Smoothing; Heuristics; Motion capture; Kalman filter; Pattern recognition (psychology); Motion (physics); Radar","score_opus":0.03259160640068947,"score_gpt":0.2693188088693882,"score_spread":0.23672720246869872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155996262","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":[0.20383821,0.000021683161,0.5860674,0.0020416288,0.00027588787,0.00021281622,0.0000021542642,0.00063386327,0.20690638],"genre_scores_gemma":[0.98650575,0.000001631041,0.010235552,0.00039242112,0.00024801868,0.000016268436,0.0000022615993,0.0000043589353,0.0025937222],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926215,0.00006578623,0.00012474439,0.00022902143,0.00015768764,0.00016060242],"domain_scores_gemma":[0.9995103,0.00003736748,0.00005434111,0.00024670176,0.00008058688,0.00007070499],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022309233,0.00007899215,0.000108795524,0.0000651153,0.00009256588,0.00011989156,0.00023442064,0.00005111197,0.00015329686],"category_scores_gemma":[0.000012785797,0.000068297515,0.000044746637,0.00015210547,0.000012456542,0.0005604411,0.00005313556,0.000077006625,0.0017078167],"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":[0.000001077561,0.000073402625,0.0001269714,0.0000038854423,0.000007627341,0.0000031107013,0.00015354884,7.6351466e-7,0.0077635786,0.002016195,0.0032352665,0.9866146],"study_design_scores_gemma":[0.0034215625,0.00047965933,0.022968864,0.00013871225,0.00002541954,0.0004083223,0.000121007535,0.01057025,0.43633458,0.013212783,0.5105748,0.0017439959],"about_ca_topic_score_codex":0.0000311241,"about_ca_topic_score_gemma":0.000080775164,"teacher_disagreement_score":0.98487055,"about_ca_system_score_codex":0.00002650539,"about_ca_system_score_gemma":0.000013761399,"threshold_uncertainty_score":0.99906945},"labels":[],"label_agreement":null},{"id":"W2156577008","doi":"10.1109/percom.2006.2","title":"A Keystroke and Pointer Control Input Interface for Wearable Computers","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":22,"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":"Keystroke logging; Computer science; Wearable computer; Pointer (user interface); Smartwatch; Hidden Markov model; Wearable technology; Computer vision; Mobile device; Interface (matter); Artificial intelligence; Speech recognition; Human–computer interaction; Computer hardware; Embedded system; Computer security; Operating system","score_opus":0.007636840617424873,"score_gpt":0.22243041328822588,"score_spread":0.21479357267080101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156577008","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":[0.0063881846,0.0001686085,0.98483354,0.0018885696,0.00039615014,0.0003668303,0.000005227078,0.00015850678,0.0057944087],"genre_scores_gemma":[0.963442,0.0000013208376,0.033221334,0.0006274759,0.00012573927,0.000036222234,9.540644e-7,0.000007070694,0.002537849],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991214,0.00004044038,0.00021282192,0.0002950268,0.00010598315,0.00022432762],"domain_scores_gemma":[0.9994251,0.000172942,0.000056412286,0.00019655007,0.00008692806,0.00006208128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021632435,0.00011390412,0.00018191696,0.00007430422,0.00006240245,0.00024455137,0.0002465854,0.000047088506,0.00000733847],"category_scores_gemma":[0.000009153525,0.00009146738,0.00005422371,0.00008416371,0.000025502024,0.00026832256,0.00006610302,0.000052771054,0.000051964107],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037085128,0.0009517909,0.025879355,0.0007597286,0.0006146233,0.00006882599,0.0024625333,0.0018301724,0.030816937,0.23026949,0.4408103,0.2651654],"study_design_scores_gemma":[0.018223563,0.0009716829,0.007175748,0.0004381501,0.000041485288,0.0004839186,0.00013639721,0.625537,0.022344869,0.011446871,0.31189597,0.0013043944],"about_ca_topic_score_codex":0.00007636722,"about_ca_topic_score_gemma":0.00005146634,"teacher_disagreement_score":0.95705384,"about_ca_system_score_codex":0.000017240953,"about_ca_system_score_gemma":0.00001620268,"threshold_uncertainty_score":0.37299317},"labels":[],"label_agreement":null},{"id":"W2162269192","doi":"10.5539/cis.v5n3p110","title":"Survey on Gesture Recognition for Hand Image Postures","year":2012,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Gesture; Gesture recognition; Representation (politics); Human–computer interaction; Artificial intelligence; Context (archaeology); Computer vision; Selection (genetic algorithm); Object (grammar); Cognitive neuroscience of visual object recognition; Pattern recognition (psychology)","score_opus":0.03477949829373037,"score_gpt":0.27625717307466197,"score_spread":0.2414776747809316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162269192","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":[0.036543217,0.0000530428,0.95894116,0.000349469,0.001539495,0.000411427,0.000042638752,0.00009354217,0.0020260066],"genre_scores_gemma":[0.9621314,0.000016840526,0.03510477,0.0023827173,0.00025702314,0.000029641906,0.000057568122,0.0000035674727,0.000016501237],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99878925,0.000051495128,0.0002676633,0.00018537532,0.00037248182,0.0003337503],"domain_scores_gemma":[0.99865127,0.00023083348,0.0001318658,0.00024309737,0.00054735393,0.00019557717],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018729967,0.00012489205,0.00012609923,0.0003075737,0.0004655592,0.0010679846,0.00041986056,0.00005326975,0.000002896947],"category_scores_gemma":[0.00016364828,0.00009853953,0.000030416511,0.00058615056,0.00015671393,0.01169093,0.00013772544,0.000082053615,0.00014421633],"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":[0.00003114417,0.00008751249,0.0040231785,0.0000808382,0.000012182813,2.9472605e-7,0.008335,0.000017975506,0.00058005576,0.009851587,0.01741516,0.95956504],"study_design_scores_gemma":[0.0011461976,0.0003776502,0.8945692,0.00009314665,0.0000055887062,0.00006471116,0.00006180707,0.03817439,0.0069058817,0.00060750445,0.05744093,0.00055297633],"about_ca_topic_score_codex":0.000008050179,"about_ca_topic_score_gemma":0.0000014320285,"teacher_disagreement_score":0.9590121,"about_ca_system_score_codex":0.000022017952,"about_ca_system_score_gemma":0.000061330604,"threshold_uncertainty_score":0.999969},"labels":[],"label_agreement":null},{"id":"W2162899276","doi":"10.1109/tsmca.2005.843378","title":"Understanding Hand Gestures Using Approximate Graph Matching","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans","topic":"Hand Gesture Recognition Systems","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":"University of Waterloo","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Ambiguity; Human–computer interaction; Meaning (existential); Graph; Scalability; Artificial intelligence; Theoretical computer science; Psychology","score_opus":0.08808128840917358,"score_gpt":0.26362032193116886,"score_spread":0.1755390335219953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162899276","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":[0.048012868,0.0018583725,0.9458377,0.00010151137,0.001789679,0.00074509584,0.00003052756,0.00020618837,0.0014180648],"genre_scores_gemma":[0.9971603,0.00023008589,0.0006647815,0.00006110071,0.0003695194,0.00008499823,0.0000019405302,0.000042216892,0.0013850208],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972879,0.0002539649,0.00073160505,0.00072322116,0.00049486913,0.00050843763],"domain_scores_gemma":[0.99874264,0.000119836426,0.00026165266,0.00049361784,0.000086363565,0.0002958943],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006345269,0.0004223783,0.0005809155,0.0004025681,0.0010811707,0.0017474499,0.00027393256,0.00022065209,0.0000050281074],"category_scores_gemma":[0.0000013890653,0.0003786508,0.000111446825,0.00025817356,0.00015649541,0.00047475233,0.000009407923,0.00031737678,0.000016039878],"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":[0.00020078491,0.0017559067,0.00066695205,0.0075830375,0.002773094,0.00028329293,0.1313419,0.36752692,0.018017914,0.44940683,0.0050845332,0.015358825],"study_design_scores_gemma":[0.010781233,0.0015751442,0.00031819477,0.011212069,0.0009657034,0.0057436763,0.03723755,0.8117613,0.0031857747,0.006250339,0.10390837,0.0070606186],"about_ca_topic_score_codex":0.00041278487,"about_ca_topic_score_gemma":0.00026181695,"teacher_disagreement_score":0.94914746,"about_ca_system_score_codex":0.00012855796,"about_ca_system_score_gemma":0.000036443038,"threshold_uncertainty_score":0.99986655},"labels":[],"label_agreement":null},{"id":"W2163078993","doi":"10.1109/icmla.2005.67","title":"TSTMT: Step towards an Accurate Thai Sign Translation","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Sign language; Machine translation; Sign (mathematics); Natural language processing; Artificial intelligence; Translation (biology); Sentence; Speech recognition; Linguistics; Mathematics","score_opus":0.037036193090600494,"score_gpt":0.2726048703659759,"score_spread":0.23556867727537542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163078993","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":[0.005692717,0.000058698744,0.8946691,0.00068565406,0.00028061887,0.00017167574,0.0000025429729,0.00033345903,0.0981055],"genre_scores_gemma":[0.9623708,0.0000020352074,0.03613955,0.00019809717,0.00020451049,0.000014343818,0.000012194457,0.0000067864926,0.0010516816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989337,0.00010796772,0.00022256639,0.0002858073,0.00026183046,0.00018812707],"domain_scores_gemma":[0.99946004,0.000037148042,0.000053559088,0.00030835668,0.000073049254,0.000067837806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028536495,0.00010749933,0.00011657216,0.00007645873,0.00006920086,0.0002273643,0.00034900935,0.00006750207,0.000066457666],"category_scores_gemma":[0.000003313978,0.00008567837,0.000046939665,0.00028328775,0.00001215591,0.000897725,0.000019355395,0.000060677336,0.00021751731],"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":[0.0000062928075,0.00014353867,0.00029183817,0.000019662251,0.000013126077,0.000016439266,0.0010762032,0.0002653444,0.0031140775,0.038324494,0.0028578183,0.9538712],"study_design_scores_gemma":[0.004487688,0.00084902777,0.0417586,0.00011541779,0.000043277512,0.0002469494,0.0005949425,0.5662393,0.050485287,0.037167683,0.29557854,0.002433279],"about_ca_topic_score_codex":0.00021528092,"about_ca_topic_score_gemma":0.00020363663,"teacher_disagreement_score":0.9566781,"about_ca_system_score_codex":0.000015033325,"about_ca_system_score_gemma":0.00004486602,"threshold_uncertainty_score":0.34938627},"labels":[],"label_agreement":null},{"id":"W2163924061","doi":"10.1109/wfopc.2011.6089679","title":"Dataglove for consumer applications","year":2011,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Polytechnique Montréal","keywords":"Computer science; Sensory system; Human–computer interaction; Interface (matter); Degrees of freedom (physics and chemistry); Human–machine interface; Computer hardware; Embedded system; Human–machine system; Operating system","score_opus":0.08539540114504245,"score_gpt":0.27246295775085777,"score_spread":0.1870675566058153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163924061","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":[0.00001754867,0.00003050157,0.9140931,0.00009945489,0.00009226871,0.00033484792,0.000008313616,0.00010856947,0.08521541],"genre_scores_gemma":[0.2992092,0.0000080572145,0.6929968,0.0010535828,0.0001268021,0.0010067783,0.00001779948,0.000009291809,0.0055716746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959576,0.00000903158,0.00009269575,0.00015949059,0.000051555744,0.00009146618],"domain_scores_gemma":[0.99946904,0.000047401136,0.000026655383,0.00034019968,0.00007082319,0.000045873912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001025182,0.000040374973,0.00005241661,0.000028181901,0.00003971167,0.000028497408,0.000328357,0.000022297727,0.00005769401],"category_scores_gemma":[0.0000067620076,0.000032099422,0.000023328112,0.000096166725,0.0000120110835,0.00016679095,0.000044234483,0.000017867285,0.00064085337],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018595786,0.00007437578,0.00043458858,0.000018385519,0.00002351747,5.2176625e-7,0.00050578156,6.9277526e-8,0.000250679,0.8212086,0.021904564,0.15557706],"study_design_scores_gemma":[0.00024830413,0.000022638196,0.00039154667,0.0000037325435,0.0000046508526,0.000013599322,0.000037305734,0.00075157447,0.005117883,0.014219716,0.9790623,0.00012674094],"about_ca_topic_score_codex":0.00001033034,"about_ca_topic_score_gemma":0.0000070572733,"teacher_disagreement_score":0.95715773,"about_ca_system_score_codex":0.000003885024,"about_ca_system_score_gemma":0.000018353607,"threshold_uncertainty_score":0.8237088},"labels":[],"label_agreement":null},{"id":"W2164416728","doi":"10.1109/imtc.2007.379068","title":"Real-time Vision-based Hand Gesture Recognition Using Haar-like Features","year":2007,"lang":"en","type":"article","venue":"Conference proceedings - IEEE Instrumentation/Measurement Technology Conference","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":302,"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":"Computer science; Gesture recognition; Gesture; Artificial intelligence; Parsing; Haar-like features; Computer vision; AdaBoost; Set (abstract data type); Speech recognition; Context (archaeology); Pattern recognition (psychology); Face detection; Facial recognition system; Classifier (UML)","score_opus":0.05106941278014394,"score_gpt":0.2818192476971611,"score_spread":0.23074983491701717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164416728","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":[0.6288651,0.000081866594,0.35902026,0.0023939027,0.0013429722,0.0016528014,0.000022420301,0.0016871515,0.004933523],"genre_scores_gemma":[0.957065,0.00004617631,0.042092025,0.0003024619,0.0001381938,0.00015208569,0.000029985102,0.000045663972,0.00012842726],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9944597,0.000069033806,0.0011515855,0.0015140999,0.0016998312,0.0011057389],"domain_scores_gemma":[0.9934785,0.00009297816,0.0009382058,0.00060174894,0.0045533543,0.0003352261],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0020211223,0.0007435524,0.0006930435,0.0014714367,0.0008401495,0.0012645607,0.0016731799,0.00082183926,0.000119189186],"category_scores_gemma":[0.00027289326,0.00074477936,0.00014863645,0.0019833231,0.00060151285,0.0016849524,0.00020197236,0.00079711626,0.00023983404],"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":[0.000100441044,0.000307451,0.0050962125,0.00018660194,0.00013340423,0.000030528587,0.0013001964,0.000010152455,0.83363044,0.0035360618,0.0009202765,0.15474823],"study_design_scores_gemma":[0.0054774424,0.0012340106,0.009795824,0.0029213543,0.00022981195,0.00046418462,0.002803876,0.036288828,0.9169238,0.019957434,0.0010825509,0.0028208562],"about_ca_topic_score_codex":0.000061775994,"about_ca_topic_score_gemma":0.0000903076,"teacher_disagreement_score":0.32819986,"about_ca_system_score_codex":0.00061015395,"about_ca_system_score_gemma":0.0009871635,"threshold_uncertainty_score":0.99977225},"labels":[],"label_agreement":null},{"id":"W2164574606","doi":"10.1109/isspa.2012.6310603","title":"Signal processing for low cost optical dataglove","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Translation (biology); Detector; SIGNAL (programming language); Cost reduction; Sign (mathematics); Sign language; Signal processing; Reduction (mathematics); Machine translation; Computer hardware; Artificial intelligence; Telecommunications; Digital signal processing; Programming language","score_opus":0.044807812458416876,"score_gpt":0.2982043463205578,"score_spread":0.25339653386214095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164574606","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":[0.0003108473,0.00006162585,0.9846532,0.0003795345,0.0002189125,0.00028295896,0.0000039569677,0.00010557171,0.013983426],"genre_scores_gemma":[0.88467324,5.224875e-7,0.11369632,0.0004504696,0.0004423075,0.00005750254,0.000007452163,0.000005993795,0.0006661624],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920917,0.000017572076,0.00014141106,0.00016902418,0.00016185499,0.00030099554],"domain_scores_gemma":[0.99944943,0.000105719795,0.00003558457,0.000168587,0.0000864861,0.00015417361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036694642,0.00007449979,0.00009856754,0.000033951328,0.00006726733,0.00015981768,0.00029541375,0.000044807715,0.000033588913],"category_scores_gemma":[0.000021695769,0.000056278863,0.00003195825,0.00012737318,0.00001650971,0.0008522048,0.000075750664,0.000047796748,0.00025954406],"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":[0.000005805312,0.00014857405,0.00070926605,0.00008709599,0.000010824918,0.0000012991753,0.00048438096,0.000004343438,0.0010256858,0.037472747,0.007633017,0.95241696],"study_design_scores_gemma":[0.0025905697,0.00019784569,0.0028335624,0.00027398492,0.00003535846,0.00033049667,0.00037048594,0.22136913,0.087245286,0.0021188476,0.6813308,0.0013036369],"about_ca_topic_score_codex":0.0000011332708,"about_ca_topic_score_gemma":0.0000011389614,"teacher_disagreement_score":0.95111334,"about_ca_system_score_codex":0.000016608456,"about_ca_system_score_gemma":0.000042518393,"threshold_uncertainty_score":0.33360004},"labels":[],"label_agreement":null},{"id":"W2166974693","doi":"10.1109/robot.2008.4543755","title":"A natural gesture interface for operating robotic systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"McGill University","funders":"","keywords":"Gesture; Computer science; Robot; Operator (biology); Scheme (mathematics); Vocabulary; Interface (matter); Artificial intelligence; Gesture recognition; Human–computer interaction; Computer vision; Human–robot interaction; Point (geometry); Mobile robot","score_opus":0.03219800044853012,"score_gpt":0.265834187640725,"score_spread":0.23363618719219487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166974693","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":[0.0073893606,0.000991584,0.98540425,0.0006095979,0.0021003536,0.0005566602,0.0000010085496,0.0003075274,0.0026396296],"genre_scores_gemma":[0.9614239,0.00000479407,0.03147667,0.00019617337,0.00022801147,0.00008042158,0.000001966895,0.000009661559,0.0065784245],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989164,0.000061791834,0.00025417152,0.00032499546,0.00018926489,0.00025339943],"domain_scores_gemma":[0.99921805,0.00014899942,0.00006352597,0.00030202398,0.0001906221,0.00007680855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020581858,0.00013434833,0.00021082876,0.00006323003,0.0002075556,0.00018736837,0.00047515295,0.000066184504,0.0000035664343],"category_scores_gemma":[0.00006491396,0.00009842975,0.00006781019,0.0002007281,0.000018673149,0.00039380154,0.0000898509,0.00011901705,0.00009897107],"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":[0.00008306803,0.0010597854,0.008626377,0.0026680627,0.0011832425,0.00058292336,0.054983363,0.10073188,0.095023684,0.30511737,0.36531985,0.064620405],"study_design_scores_gemma":[0.001508714,0.0002544205,0.0009976911,0.00028662168,0.000012251262,0.0026014603,0.0005574005,0.9594745,0.005817577,0.00011805759,0.027580097,0.00079121685],"about_ca_topic_score_codex":0.000027804785,"about_ca_topic_score_gemma":0.000007752198,"teacher_disagreement_score":0.9540345,"about_ca_system_score_codex":0.000034238674,"about_ca_system_score_gemma":0.000052445073,"threshold_uncertainty_score":0.4013849},"labels":[],"label_agreement":null},{"id":"W2168054326","doi":"10.1109/tim.2006.887174","title":"A Novel Biometric System for Identification and Verification of Haptic Users","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":53,"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":"Password; Haptic technology; Biometrics; Computer science; Authentication (law); Human–computer interaction; Identification (biology); Identifier; Process (computing); Computer security; Hand geometry; Identity (music); Session (web analytics); Artificial intelligence; World Wide Web; Computer network","score_opus":0.0625388940745402,"score_gpt":0.27409663282300417,"score_spread":0.21155773874846395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168054326","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":[0.037417766,0.00003819722,0.96106774,0.00011195382,0.0006266803,0.00059810065,0.000013779843,0.00005377132,0.0000719909],"genre_scores_gemma":[0.9899827,0.00002010268,0.009846888,0.000024924148,0.00001262476,0.0000851747,0.000002414632,0.0000060886373,0.000019067038],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988158,0.0000264564,0.00040048014,0.0002514634,0.00039006353,0.0001157697],"domain_scores_gemma":[0.9992415,0.00005559756,0.00018599663,0.0001612703,0.0002792517,0.000076412485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011084462,0.00009497666,0.000117374475,0.00052149803,0.00014210168,0.00006709334,0.000081157654,0.000049624527,9.4115103e-7],"category_scores_gemma":[0.000008411643,0.0000943364,0.00003682851,0.00049471494,0.000029722583,0.0002611854,7.7790236e-7,0.000039637547,0.0000029935984],"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":[0.000064216605,0.0001966794,0.000045652203,0.0003385457,0.000079488746,1.6222688e-7,0.0010655975,0.00008049268,0.5386016,0.002572039,0.00001156023,0.45694402],"study_design_scores_gemma":[0.003363029,0.000367792,0.009760825,0.00023397477,0.00009827059,0.000042057884,0.0019882717,0.014270553,0.9690762,0.0000586681,0.0004623876,0.00027794126],"about_ca_topic_score_codex":0.000024585555,"about_ca_topic_score_gemma":0.00002374376,"teacher_disagreement_score":0.95256495,"about_ca_system_score_codex":0.00013599335,"about_ca_system_score_gemma":0.00003135308,"threshold_uncertainty_score":0.38469267},"labels":[],"label_agreement":null},{"id":"W2171516566","doi":"10.1109/icccyb.2010.5491330","title":"A new method for 3D object reconstruction in real-time","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Camera auto-calibration; Image sensor; Digital camera; Gesture; Smart camera; Interface (matter); Computer graphics (images); Camera resectioning","score_opus":0.018394977552825945,"score_gpt":0.30213061967310173,"score_spread":0.2837356421202758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171516566","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":[0.0021270236,0.0000028992092,0.95933926,0.0004757176,0.0009068366,0.00028275177,9.3403753e-7,0.00015548643,0.03670911],"genre_scores_gemma":[0.0029263592,0.0000018806392,0.99266815,0.00007040941,0.00020351491,0.000030135152,0.0000011112942,0.0000052070486,0.004093231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992319,0.00005949748,0.00019606623,0.00026318533,0.000091203474,0.00015814755],"domain_scores_gemma":[0.999328,0.0002213379,0.000054685737,0.00025976484,0.00005935326,0.000076869044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006506096,0.00007438163,0.00013772928,0.00013135411,0.000031223735,0.00008042228,0.00022129522,0.00008751919,0.00012556415],"category_scores_gemma":[0.000069138165,0.0000623486,0.00004488389,0.00026465024,0.0000059136737,0.0002802216,0.00003141302,0.00009587023,0.0001517206],"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":[0.00000364153,0.0000082627,0.00014471718,0.000006088434,0.000004664424,9.4366186e-7,0.00021228756,0.0000018561914,0.024797171,0.0063253837,0.0018448344,0.9666501],"study_design_scores_gemma":[0.0065623773,0.0005093792,0.0077053662,0.00019700696,0.000029110297,0.0021265738,0.00017003487,0.5799454,0.11047411,0.0937382,0.19685604,0.001686398],"about_ca_topic_score_codex":0.00038749172,"about_ca_topic_score_gemma":0.0005076128,"teacher_disagreement_score":0.96496373,"about_ca_system_score_codex":0.000013625389,"about_ca_system_score_gemma":0.000107996515,"threshold_uncertainty_score":0.2542502},"labels":[],"label_agreement":null},{"id":"W2171976581","doi":"10.1109/crv.2010.28","title":"Selecting and Commanding Individual Robots in a Multi-Robot System","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Computer science; Artificial intelligence; Social robot; Gesture; Robot control; Computer vision; Motion (physics); Mobile robot; Human–computer interaction; Robot kinematics; Face (sociological concept)","score_opus":0.04011878227747134,"score_gpt":0.2723964274545166,"score_spread":0.23227764517704524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171976581","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":[0.32817194,0.000049878585,0.66524476,0.00024225391,0.00083162636,0.00031559347,9.771799e-7,0.0003785967,0.0047643757],"genre_scores_gemma":[0.9327134,5.991138e-7,0.06707591,0.000056132998,0.00004795845,0.00001539344,7.057984e-7,0.000005457133,0.0000844721],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999041,0.000077482306,0.00022270091,0.00027377135,0.00016015903,0.00022484767],"domain_scores_gemma":[0.9994776,0.00014140835,0.00006286115,0.00019155322,0.0000404683,0.00008610828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075167674,0.0001001117,0.00015952368,0.0001745765,0.0001042569,0.00026250695,0.00029307886,0.00008430825,0.0000030161564],"category_scores_gemma":[0.000043395445,0.00008478969,0.000018356994,0.0003443196,0.000015497455,0.00030975943,0.00016767529,0.00026497652,0.000027255353],"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":[0.000010213435,0.000353498,0.60971045,0.0006457837,0.00012095365,0.00023204662,0.024462333,0.000316546,0.048334703,0.09584569,0.00048822444,0.21947958],"study_design_scores_gemma":[0.0067715975,0.0001830956,0.4062368,0.0009419769,0.00002615934,0.003047036,0.0036674545,0.5596227,0.015708048,0.00029225493,0.0017836337,0.0017192133],"about_ca_topic_score_codex":0.00011427119,"about_ca_topic_score_gemma":0.0009050172,"teacher_disagreement_score":0.6045414,"about_ca_system_score_codex":0.000017242268,"about_ca_system_score_gemma":0.000024210076,"threshold_uncertainty_score":0.3457623},"labels":[],"label_agreement":null},{"id":"W2179373239","doi":"10.1007/978-3-540-92865-2_5","title":"Improving the Believability of Virtual Characters Using Qualitative Gesture Analysis","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Computer science; Gesture; Human–computer interaction; Qualitative analysis; Artificial intelligence; Computer vision; Computer graphics (images); Qualitative research","score_opus":0.031868758923964284,"score_gpt":0.2954108610700222,"score_spread":0.2635421021460579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179373239","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":[0.002977277,0.00027403733,0.99450123,0.0005490089,0.0008043232,0.0005016933,0.000015760068,0.00007411399,0.00030256395],"genre_scores_gemma":[0.8914431,0.00001285961,0.107118286,0.0008888548,0.0003865783,0.000006293565,0.000007989344,0.000022145674,0.0001139056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99523836,0.0002967787,0.00095844077,0.0015058033,0.0014372211,0.0005633668],"domain_scores_gemma":[0.9950196,0.0014477479,0.0009801096,0.0017662883,0.00064277963,0.00014348558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003580705,0.0005472969,0.0009916215,0.0011607943,0.0003035766,0.00037910714,0.0031565672,0.00036733563,0.000008383419],"category_scores_gemma":[0.000257728,0.0003870545,0.00041852257,0.0021549028,0.00092263974,0.0006071793,0.00075743755,0.00091929577,0.000007890041],"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":[0.000012414522,0.00006054738,0.00010846688,0.00006340748,0.00021892623,0.000026278642,0.024918277,0.03358088,0.00129613,0.006741685,0.0000021750564,0.9329708],"study_design_scores_gemma":[0.00061075186,0.00061276276,0.0030408937,0.0007694735,0.00041521064,0.00009677818,0.00002637608,0.9279672,0.0039962735,0.06033676,0.00026972147,0.0018577757],"about_ca_topic_score_codex":0.00011689091,"about_ca_topic_score_gemma":0.0001276839,"teacher_disagreement_score":0.931113,"about_ca_system_score_codex":0.00032195987,"about_ca_system_score_gemma":0.00059892854,"threshold_uncertainty_score":0.99985814},"labels":[],"label_agreement":null},{"id":"W218034198","doi":"","title":"Recognition, Analysis and Performance with Expressive Conducting Gestures","year":2004,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Gesture; Hidden Markov model; Computer science; Gesture recognition; Speech recognition; Process (computing); Object (grammar); Artificial intelligence; Programming language","score_opus":0.03262746024934667,"score_gpt":0.23321865869644118,"score_spread":0.2005911984470945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W218034198","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":[0.6747468,0.000058165,0.32020032,0.00036855869,0.000044683686,0.000099895435,0.0000011619439,0.00012980876,0.00435063],"genre_scores_gemma":[0.945965,0.000019887782,0.05361246,0.00020927549,0.000041728763,0.000020081226,0.000003977155,0.0000041264448,0.00012347315],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991751,0.000030234254,0.00014589609,0.00031043228,0.00018012944,0.00015824325],"domain_scores_gemma":[0.99941045,0.00004652879,0.00008398867,0.00021640785,0.00016101265,0.00008164033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014831498,0.00010609988,0.00016216382,0.00018551113,0.00013555947,0.00015698638,0.00016019472,0.000039234572,0.000012639785],"category_scores_gemma":[0.000015509031,0.00007277689,0.000029365854,0.0007626563,0.000038439455,0.0005997641,0.00004951821,0.000080049394,0.000032562988],"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":[0.00012823124,0.0005458571,0.2507754,0.00043406643,0.004685757,0.00028130304,0.040138323,0.0031549912,0.012061539,0.009937942,0.00076697726,0.67708963],"study_design_scores_gemma":[0.011519833,0.0024560066,0.47862226,0.0012157148,0.0016725507,0.0027866468,0.006864549,0.00977783,0.46490943,0.011301271,0.0042516594,0.0046222517],"about_ca_topic_score_codex":0.000058349964,"about_ca_topic_score_gemma":0.00008143025,"teacher_disagreement_score":0.67246735,"about_ca_system_score_codex":0.000014233875,"about_ca_system_score_gemma":0.000032712116,"threshold_uncertainty_score":0.29677555},"labels":[],"label_agreement":null},{"id":"W2186775902","doi":"10.1007/978-3-031-23161-2_483","title":"American Sign Language Detection","year":2024,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computer Graphics and Games","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Sign (mathematics); American Sign Language; Computer science; Sign language; Linguistics; Artificial intelligence; Mathematics; Philosophy","score_opus":0.006913625300028842,"score_gpt":0.21818981797947434,"score_spread":0.2112761926794455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186775902","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088493945,0.010983363,0.38391262,0.00045523143,0.003678726,0.00061165367,0.00006552037,0.00054568273,0.5988623],"genre_scores_gemma":[0.30291927,0.048775982,0.03809428,0.0018266223,0.008474988,0.00012539937,0.000112644324,0.0004760764,0.5991947],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984008,0.000028820947,0.00042932265,0.0005846565,0.00036088133,0.00019550647],"domain_scores_gemma":[0.99883753,0.00017062876,0.00029306387,0.00046032612,0.00011476588,0.00012370628],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020718719,0.00034557295,0.0005343121,0.0005321904,0.000048465285,0.00013915267,0.00039797812,0.0001856011,0.000007765269],"category_scores_gemma":[0.000004438607,0.0003048636,0.00020868784,0.00017700365,0.000180702,0.00012767906,0.0002594453,0.00039288742,0.000039420025],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032120658,0.000013105999,0.000018429473,0.00022049638,0.00015722541,0.0000659243,0.0012789684,0.0000015247625,0.000008370842,0.30820975,0.001286781,0.6887362],"study_design_scores_gemma":[0.000223612,0.0004953803,0.00038732035,0.00064924086,0.000097598524,0.00012076654,0.000025986625,0.003872043,0.00007367023,0.06735172,0.92591125,0.0007913886],"about_ca_topic_score_codex":0.00003630134,"about_ca_topic_score_gemma":0.000047882717,"teacher_disagreement_score":0.9246245,"about_ca_system_score_codex":0.000011938667,"about_ca_system_score_gemma":0.000042230415,"threshold_uncertainty_score":0.99994034},"labels":[],"label_agreement":null},{"id":"W2212011099","doi":"10.1109/iros.2015.7353879","title":"Exploring the effect of robot hand configurations in directional gestures for human-robot interaction","year":2015,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gesture; Robot; Nonverbal communication; Human–robot interaction; Computer science; Artificial intelligence; Human–computer interaction; Computer vision; Communication; Psychology","score_opus":0.2027221308892207,"score_gpt":0.3443192343792273,"score_spread":0.1415971034900066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2212011099","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":[0.17312141,0.00005779194,0.816701,0.0010519156,0.0018703418,0.0008237452,0.000003324403,0.00008930297,0.0062811826],"genre_scores_gemma":[0.9979098,0.00000128207,0.0012837221,0.00002547118,0.00013526395,0.00036573547,0.00000496766,0.00000453175,0.00026922591],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991687,0.00015024531,0.00023131126,0.00016851313,0.0001718104,0.00010943298],"domain_scores_gemma":[0.99901277,0.0005578878,0.0000803318,0.00017519704,0.00013304615,0.00004077767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063258724,0.00008385473,0.00014339047,0.00011732582,0.000106098545,0.000110405104,0.00019791687,0.000026335287,0.0000044933827],"category_scores_gemma":[0.00013781138,0.00005236413,0.00005379727,0.00020915981,0.000028262637,0.0005181144,0.000033010132,0.00008051121,0.000008453856],"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":[0.00057734095,0.00072379515,0.020595506,0.0005832188,0.00054787943,0.000016778635,0.04307306,0.08800614,0.37647069,0.11653552,0.019797675,0.3330724],"study_design_scores_gemma":[0.005844729,0.0021617997,0.061921246,0.0004124007,0.000047991383,0.00013185685,0.0013450631,0.018696075,0.8731529,0.003558812,0.03205897,0.0006681565],"about_ca_topic_score_codex":0.00016949138,"about_ca_topic_score_gemma":0.0003583966,"teacher_disagreement_score":0.8247884,"about_ca_system_score_codex":0.000037745343,"about_ca_system_score_gemma":0.000026258445,"threshold_uncertainty_score":0.21353471},"labels":[],"label_agreement":null},{"id":"W2219948256","doi":"10.1109/tim.2015.2498560","title":"Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":265,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Dynamic time warping; Gesture recognition; Computer science; Computer vision; Artificial intelligence; Sign (mathematics); Interface (matter); Sign language; Hidden Markov model; Pixel; Object (grammar); Speech recognition; Mathematics","score_opus":0.14527730662670393,"score_gpt":0.30933058877678243,"score_spread":0.1640532821500785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219948256","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":[0.24016741,0.000094429844,0.7583214,0.00042458656,0.0004046534,0.0004295739,0.00003080741,0.000053821535,0.00007333608],"genre_scores_gemma":[0.98810166,0.00007866571,0.011542587,0.00017983906,0.000008529269,0.000029731516,0.000025239908,0.000011926947,0.000021830569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831736,0.00018318374,0.00033435354,0.00045901205,0.00051173917,0.00019435392],"domain_scores_gemma":[0.9992771,0.000031239775,0.00010486022,0.00029421633,0.0001374449,0.00015513954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082489353,0.00016910744,0.0001774069,0.00029053973,0.00016169404,0.00022881938,0.00016448286,0.0000687903,0.000005629342],"category_scores_gemma":[0.000012244148,0.00016796988,0.000017676191,0.00028442295,0.000045645338,0.0008674471,0.000006758265,0.00015548678,0.000021329262],"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":[0.00007619999,0.00027543947,0.00013328786,0.00012654236,0.00009435355,0.000011747847,0.0044878107,0.0018297542,0.01736365,0.00000915499,0.000044436623,0.9755476],"study_design_scores_gemma":[0.0057960628,0.00034447954,0.0023376753,0.0007981353,0.000106848354,0.00021837483,0.0017444645,0.98036075,0.0065893116,0.00074266637,0.00032376806,0.0006374541],"about_ca_topic_score_codex":0.000093237024,"about_ca_topic_score_gemma":0.0009181669,"teacher_disagreement_score":0.978531,"about_ca_system_score_codex":0.00029610193,"about_ca_system_score_gemma":0.00013423729,"threshold_uncertainty_score":0.68496126},"labels":[],"label_agreement":null},{"id":"W2222266545","doi":"","title":"Comparison of Feature Based Fingerspelling Recognition Algorithms","year":2012,"lang":"en","type":"dissertation","venue":"Eastern Mediterranean University Institutional Repository (Eastern Mediterranean University)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Computer science; Feature (linguistics); Natural language processing; Algorithm; Artificial intelligence; Arithmetic; Pattern recognition (psychology); Linguistics; Mathematics","score_opus":0.051802988438935146,"score_gpt":0.26423087280508306,"score_spread":0.2124278843661479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222266545","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":[0.38742605,0.0028974784,0.3687206,0.00066160655,0.05684702,0.0036952142,0.001043097,0.0017382783,0.17697068],"genre_scores_gemma":[0.95910317,0.000052116487,0.005077246,0.00006746517,0.002620908,0.0000021879725,0.0037876784,0.00008895912,0.029200258],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9936864,0.00078839564,0.0009160108,0.0014691921,0.0022323262,0.0009076833],"domain_scores_gemma":[0.99463624,0.0003404496,0.0017934178,0.001138784,0.00123159,0.0008595238],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005287673,0.0010452339,0.0014056454,0.0024131308,0.0010086837,0.00018756252,0.002700103,0.0012950598,0.00012711552],"category_scores_gemma":[0.000074267926,0.001257093,0.0007742616,0.0014594671,0.00044685774,0.0021705802,0.00026663824,0.0015185808,0.00028685015],"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":[0.011663108,0.00965661,0.030800778,0.017090222,0.0071941,0.02324396,0.14076757,0.0056157983,0.020723116,0.004666925,0.011636672,0.7169411],"study_design_scores_gemma":[0.023271987,0.0024604362,0.009305976,0.02404945,0.004781947,0.0015156853,0.047913928,0.28775057,0.03336686,0.0002039093,0.5539534,0.011425832],"about_ca_topic_score_codex":0.00014806032,"about_ca_topic_score_gemma":0.00015005744,"teacher_disagreement_score":0.7055153,"about_ca_system_score_codex":0.0008314129,"about_ca_system_score_gemma":0.0011520801,"threshold_uncertainty_score":0.9989879},"labels":[],"label_agreement":null},{"id":"W2243124917","doi":"10.1145/2807442.2807489","title":"Gunslinger","year":2015,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Waterloo","funders":"","keywords":"Gesture; Cursor (databases); Computer science; Usability; Focus (optics); Human–computer interaction; Multi-touch; Input device; Gesture recognition; 3D interaction; Interaction technique; Body language; Computer vision; Artificial intelligence; Virtual reality; Communication; Computer hardware; Psychology","score_opus":0.07097478854820906,"score_gpt":0.26415178154372787,"score_spread":0.19317699299551883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2243124917","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":[0.0011768774,0.000046130674,0.7324979,0.0011349615,0.0004924461,0.000033442357,7.496732e-8,0.00019276892,0.26442546],"genre_scores_gemma":[0.9381909,7.829984e-7,0.05266207,0.00088689884,0.0001528049,0.000005749931,3.649428e-7,0.0000026368234,0.00809781],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996286,0.000019127878,0.000060706796,0.00009430953,0.00012386589,0.00007336537],"domain_scores_gemma":[0.999638,0.000013594099,0.000012265639,0.00017393283,0.000072093106,0.00009009117],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018938753,0.000029515877,0.00004033358,0.00002618396,0.000011105889,0.000057278696,0.00020581803,0.000017189424,0.00001334778],"category_scores_gemma":[0.000020552132,0.000021471615,0.00001298046,0.00011746842,0.0000039950164,0.00016521351,0.000045412253,0.000021832855,0.0017586177],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002789858,0.00009149013,0.0023610739,0.000009279175,0.000023731069,0.00004787203,0.0036538343,0.000036799767,0.00041987226,0.33286294,0.3528065,0.3076838],"study_design_scores_gemma":[0.0004712858,0.000051811305,0.00028550692,0.0000083781515,0.0000012291556,0.00007467211,0.00009767573,0.010067091,0.0031990856,0.01212906,0.9734259,0.00018830405],"about_ca_topic_score_codex":0.0000070454676,"about_ca_topic_score_gemma":0.0000026415398,"teacher_disagreement_score":0.937014,"about_ca_system_score_codex":0.000007326643,"about_ca_system_score_gemma":0.000028239887,"threshold_uncertainty_score":0.9990186},"labels":[],"label_agreement":null},{"id":"W2265696903","doi":"10.1145/2799648","title":"Gestures à Go Go","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":53,"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","funders":"","keywords":"Gesture; Computer science; Bootstrapping (finance); Human–computer interaction; Gesture recognition; Quality (philosophy); Artificial intelligence","score_opus":0.04107652253546483,"score_gpt":0.2715265347485359,"score_spread":0.23045001221307104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2265696903","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":[0.0038033722,0.0017630194,0.9853143,0.0041619223,0.0019008144,0.00040414126,0.0000073557403,0.00074777956,0.0018973114],"genre_scores_gemma":[0.9930442,0.00018186767,0.004266393,0.00009424891,0.00006398158,0.00018035852,0.0000014530003,0.00001605332,0.0021514392],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985355,0.00007779131,0.00037063254,0.00046914496,0.00025150052,0.00029543616],"domain_scores_gemma":[0.99844676,0.00010587307,0.00009836021,0.0009656971,0.0002118829,0.00017139556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031583608,0.0002037328,0.00029315279,0.00058779423,0.00014326979,0.00013537586,0.000778863,0.00031128133,0.000007060668],"category_scores_gemma":[0.000060809667,0.00016960612,0.000052334253,0.0007396961,0.000095721734,0.00018321555,0.000031122272,0.00031117653,0.0005129462],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003603077,0.0005851975,0.0010587015,0.00017733,0.0004059089,0.00012567322,0.002632653,0.000945748,0.00077502185,0.18765846,0.008949708,0.7966496],"study_design_scores_gemma":[0.0011265953,0.0012663038,0.00007401757,0.000358492,0.000054816366,0.002162537,0.0032308714,0.0047063776,0.020160437,0.033111062,0.93279654,0.00095193484],"about_ca_topic_score_codex":0.00007209479,"about_ca_topic_score_gemma":0.000030737778,"teacher_disagreement_score":0.9892408,"about_ca_system_score_codex":0.00006217385,"about_ca_system_score_gemma":0.00005690973,"threshold_uncertainty_score":0.6916337},"labels":[],"label_agreement":null},{"id":"W2268351550","doi":"10.1109/ccece.2014.6901162","title":"Notice of Violation of IEEE Publication Principles: Hand gesture recognition framework for recognizing sign gestures and handling movement epenthesis using Level Building nested dynamic programming approach","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gesture; Computer science; Notice; Sign (mathematics); Sign language; Movement (music); Sentence; Gesture recognition; Feature (linguistics); Permission; Speech recognition; Artificial intelligence; Linguistics; Law; Mathematics","score_opus":0.09629791187075125,"score_gpt":0.2986114663468265,"score_spread":0.20231355447607524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2268351550","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":[0.22830072,0.0000724501,0.77012205,0.00007750931,0.00020504648,0.0010642008,0.00000919385,0.00007400119,0.00007483565],"genre_scores_gemma":[0.52945596,0.000005787725,0.4703323,0.000035093923,0.00006123091,0.00006138996,0.000024851608,0.000015670112,0.000007690091],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975705,0.00023035571,0.00084775157,0.00062413514,0.00040352598,0.00032369996],"domain_scores_gemma":[0.9964543,0.0010800309,0.00094475696,0.00038224837,0.0010375257,0.00010116771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018698756,0.00026299723,0.0004577046,0.0003763788,0.00027388782,0.0003120605,0.0003208116,0.00027567672,0.0000014412255],"category_scores_gemma":[0.0012934381,0.00023553165,0.00010586561,0.0006023778,0.0000667209,0.000744575,0.000071282935,0.00017140184,4.804541e-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":[0.00010783723,0.00062934804,0.00770935,0.0029805403,0.00031729,4.034153e-7,0.008127015,0.0046182047,0.13233854,0.016400773,0.000012672221,0.826758],"study_design_scores_gemma":[0.001201314,0.00028916268,0.008559519,0.0017423377,0.0001910324,0.000014291987,0.001115022,0.8480625,0.12960595,0.008377787,0.00018135672,0.0006597022],"about_ca_topic_score_codex":0.000069323316,"about_ca_topic_score_gemma":0.000018545232,"teacher_disagreement_score":0.8434443,"about_ca_system_score_codex":0.0000709786,"about_ca_system_score_gemma":0.00007265669,"threshold_uncertainty_score":0.9604702},"labels":[],"label_agreement":null},{"id":"W2275579731","doi":"10.1109/tce.2015.7389796","title":"Gesture recognition using Markov Systems and wearable wireless inertial sensors","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Hidden Markov model; Gesture; Computer science; Gesture recognition; Wearable computer; Markov chain; Context (archaeology); Artificial intelligence; Inertial measurement unit; Wireless; Markov model; Computer vision; Markov process; Speech recognition; Pattern recognition (psychology); Machine learning; Embedded system; Mathematics","score_opus":0.03826790268156793,"score_gpt":0.2547362436911369,"score_spread":0.21646834100956897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2275579731","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":[0.27199954,0.0021420915,0.72284687,0.00022817095,0.0016321018,0.00047326725,0.000022458926,0.00029409002,0.00036140633],"genre_scores_gemma":[0.99676,0.00042998747,0.0022751242,0.0001023232,0.00008869644,0.0000523518,0.0000030181598,0.000032007396,0.0002564824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978553,0.0003099828,0.0003681459,0.00052911026,0.00042282676,0.0005146197],"domain_scores_gemma":[0.9987424,0.00013630705,0.00013170767,0.000399813,0.00031053415,0.00027921854],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004900991,0.00027569567,0.0003220421,0.00025462525,0.00027013896,0.000285429,0.00023792167,0.00022943637,0.000006063388],"category_scores_gemma":[0.000008931159,0.0002690466,0.00007404304,0.00052937,0.00006350597,0.0005118271,0.000003582875,0.00050713756,0.00009563486],"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":[0.00055508706,0.0011087366,0.00024508234,0.0004437672,0.0011189746,0.0001745423,0.003942609,0.028053917,0.030904444,0.0008018571,0.001896109,0.9307549],"study_design_scores_gemma":[0.0061968905,0.0010696999,0.000045978737,0.0008041327,0.00040771443,0.0036714769,0.0007544455,0.8972516,0.06718468,0.0006950493,0.01961246,0.0023058897],"about_ca_topic_score_codex":0.00016413743,"about_ca_topic_score_gemma":0.0000918259,"teacher_disagreement_score":0.928449,"about_ca_system_score_codex":0.00022969847,"about_ca_system_score_gemma":0.00037798178,"threshold_uncertainty_score":0.99997616},"labels":[],"label_agreement":null},{"id":"W2278621937","doi":"10.1007/978-0-387-36594-7_30","title":"A Vision-Based Approach af Bare-Hand Interface Design in Virtual Assembly","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Human–computer interaction; Computer science; Artificial intelligence; Engineering; Robot; Computer vision; Engineering drawing; Simulation","score_opus":0.054413529599726077,"score_gpt":0.2840718658432355,"score_spread":0.22965833624350945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278621937","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004202788,0.00017258289,0.6153852,0.00006317613,0.00031808353,0.0005039984,0.0000039136116,0.00013513259,0.38341373],"genre_scores_gemma":[0.18361486,0.00003426154,0.19089994,0.0016879964,0.0006252088,0.00012995723,0.000060488645,0.00022458933,0.6227227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675846,0.00014671808,0.0007628057,0.001075265,0.00079436833,0.00046238382],"domain_scores_gemma":[0.9978362,0.0006590652,0.00017888595,0.00088233897,0.00021407567,0.00022945367],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015862362,0.0005594167,0.00067997584,0.0008345774,0.0000841234,0.00044624228,0.0011829722,0.00069117144,0.00014062146],"category_scores_gemma":[0.00004342365,0.00047670776,0.0001864553,0.00019421382,0.00008847337,0.0002917563,0.00023674982,0.00063807226,0.00077957025],"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":[0.00041314555,0.0010405758,0.000034481134,0.00061533216,0.0004377542,0.0010555022,0.0033763254,0.013888877,0.0016639183,0.39774773,0.037223574,0.54250276],"study_design_scores_gemma":[0.007152583,0.0031060548,0.000077510536,0.004466835,0.00009472492,0.0003714093,0.00020427394,0.49571696,0.013277912,0.008481063,0.46081018,0.0062404824],"about_ca_topic_score_codex":0.000023938612,"about_ca_topic_score_gemma":0.000053977135,"teacher_disagreement_score":0.5362623,"about_ca_system_score_codex":0.00018395389,"about_ca_system_score_gemma":0.00033458526,"threshold_uncertainty_score":0.99999845},"labels":[],"label_agreement":null},{"id":"W2295533521","doi":"10.1109/iros.1994.407384","title":"Deictic teleassistance","year":2002,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"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":"McGill University; University of Rochester","keywords":"Computer science; Teleoperation; Artificial intelligence; Robot; Computer vision; Context (archaeology); Voice command device; Task (project management); Human–computer interaction; Utterance; Speech recognition; Engineering","score_opus":0.02643036749550389,"score_gpt":0.19943055602492,"score_spread":0.1730001885294161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295533521","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":[0.00044538628,0.00017540496,0.7661535,0.00075995084,0.00022037597,0.000043100485,1.5538198e-7,0.00022019204,0.23198195],"genre_scores_gemma":[0.9742388,0.000006390161,0.015136935,0.0004936467,0.000051994226,0.0000074504146,5.6592928e-8,0.0000024394228,0.010062291],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994854,0.000023956773,0.00008984218,0.00015108378,0.00013090836,0.000118806696],"domain_scores_gemma":[0.9995819,0.00004956602,0.000021540765,0.00025448514,0.000037560356,0.00005499442],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006205132,0.00004728321,0.00005802639,0.00003227566,0.000041905398,0.000080696445,0.00027141126,0.000021608885,0.00028647514],"category_scores_gemma":[0.000017368138,0.000037174526,0.000024423778,0.0002156064,0.000008235356,0.00021747235,0.000029311192,0.00003718764,0.002604295],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.67717e-7,0.00017528092,0.0017642459,0.00003085124,0.000029387245,0.000072873074,0.0011702619,0.000086491134,0.00036699907,0.11742207,0.059503384,0.8193772],"study_design_scores_gemma":[0.00043802697,0.000057354187,0.0029384517,0.000033372344,0.0000034969594,0.00012338883,0.000038517243,0.22167754,0.0013250869,0.0021346891,0.77083415,0.0003959419],"about_ca_topic_score_codex":0.0000017564511,"about_ca_topic_score_gemma":0.0000036457457,"teacher_disagreement_score":0.9737934,"about_ca_system_score_codex":0.000010810641,"about_ca_system_score_gemma":0.0000031791233,"threshold_uncertainty_score":0.9981723},"labels":[],"label_agreement":null},{"id":"W2296657386","doi":"10.1007/978-3-319-20801-5_28","title":"Dynamic Perceptual Attribute-Based Hidden Conditional Random Fields for Gesture Recognition","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Conditional random field; Speech recognition; Gesture; Perception; Gesture recognition; Artificial intelligence; Pattern recognition (psychology); Neuroscience","score_opus":0.0432341136597365,"score_gpt":0.2764038120661503,"score_spread":0.23316969840641377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296657386","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":[0.00009399893,0.00047234457,0.9919002,0.0020784542,0.0027258499,0.0013340709,0.0002522784,0.00025161763,0.0008911624],"genre_scores_gemma":[0.4166419,0.00003439516,0.5734973,0.0048738755,0.0019563036,0.0003041589,0.0013578136,0.000119755925,0.0012145069],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99534464,0.00009784337,0.0007425164,0.0016940979,0.0013824668,0.000738409],"domain_scores_gemma":[0.9956285,0.0014047116,0.00042351993,0.00096904486,0.0012572386,0.00031700407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016794056,0.00066834455,0.0008194142,0.0008825121,0.00032877843,0.00057760184,0.0021432815,0.00081315933,0.00007406625],"category_scores_gemma":[0.00030492837,0.0006102867,0.00030635824,0.00048244296,0.0005096349,0.00061684067,0.0003753455,0.00088092155,0.00015374653],"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":[0.00012455092,0.00012419955,0.000035900888,0.00023861651,0.00006930875,0.00012145804,0.0012686277,0.014242423,0.00011562462,0.002842782,0.002094487,0.97872204],"study_design_scores_gemma":[0.0055722604,0.0008257191,0.00015379905,0.0011102618,0.000059490943,0.00029095888,0.0000018021792,0.6095577,0.0004259672,0.36695063,0.013089873,0.001961515],"about_ca_topic_score_codex":0.000009789654,"about_ca_topic_score_gemma":0.00012854018,"teacher_disagreement_score":0.9767605,"about_ca_system_score_codex":0.0005261398,"about_ca_system_score_gemma":0.0013501854,"threshold_uncertainty_score":0.99963486},"labels":[],"label_agreement":null},{"id":"W2305532800","doi":"10.14288/1.0167492","title":"Touchless gesture recognition system for imaging controls in sterile environment","year":2014,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Zoom; Usability; Gesture; DICOM; Human–computer interaction; Plug-in; User interface; Gesture recognition; Process (computing); Window (computing); Medical imaging; Focus (optics); Artificial intelligence; Operating system; Engineering","score_opus":0.008222787926728268,"score_gpt":0.16172512269116582,"score_spread":0.15350233476443756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2305532800","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":[0.61864585,0.00010813892,0.3784406,0.0002261645,0.00036359922,0.0007751714,0.00013687716,0.00012956982,0.0011740305],"genre_scores_gemma":[0.996087,0.000013394181,0.0036321264,0.000047714544,0.000055162967,0.000006900695,0.00002191606,0.00000921248,0.00012656096],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99877787,0.00014657933,0.00018014142,0.00043933856,0.0002087531,0.00024729923],"domain_scores_gemma":[0.9992189,0.00012150066,0.00017236943,0.00029429165,0.00010152572,0.00009145296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048510282,0.000051549552,0.0003018195,0.00006552758,0.000136084,0.00016253858,0.00043000956,0.00008262804,0.000009427501],"category_scores_gemma":[0.000019550878,0.00016563969,0.000100384306,0.00014586849,0.0000579488,0.00047723658,0.00010906589,0.00008714224,0.0000356146],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007875117,0.000065892185,0.0027378828,0.00018986188,0.000014412814,0.000036689242,0.0002367741,0.000015069101,0.0005873205,0.000008133155,0.0004970058,0.9956031],"study_design_scores_gemma":[0.0056763003,0.00013747548,0.96643776,0.0011912755,0.00003828818,0.00023144936,0.0022975844,0.016763963,0.000028339957,0.00077466306,0.005871677,0.0005512491],"about_ca_topic_score_codex":0.005962104,"about_ca_topic_score_gemma":0.020397516,"teacher_disagreement_score":0.99505186,"about_ca_system_score_codex":0.00011990503,"about_ca_system_score_gemma":0.000027147049,"threshold_uncertainty_score":0.99747765},"labels":[],"label_agreement":null},{"id":"W2328841831","doi":"10.1109/embc.2014.6944082","title":"Online tracking of the lower body joint angles using IMUs for gait rehabilitation","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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 Waterloo","funders":"","keywords":"Wearable computer; Kinematics; Accelerometer; Kalman filter; Gait; Computer science; Gyroscope; Physical medicine and rehabilitation; Rehabilitation; Inertial measurement unit; Tracking (education); Gait analysis; Motion capture; Artificial intelligence; Joint (building); Computer vision; Motion (physics); Medicine; Physical therapy; Engineering; Psychology","score_opus":0.038034657489966324,"score_gpt":0.2792164910418172,"score_spread":0.2411818335518509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328841831","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":[0.2751536,0.000010525114,0.7229434,0.0007746041,0.0004357365,0.0002414306,0.000003182173,0.00003427677,0.00040322985],"genre_scores_gemma":[0.9086181,2.2298151e-7,0.091052204,0.00015312794,0.000102366736,0.0000056042018,9.64521e-7,0.000004606615,0.00006283239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992371,0.00008906996,0.00024896924,0.00016315232,0.00015537422,0.00010634108],"domain_scores_gemma":[0.9990318,0.00031020984,0.00012233178,0.00029828603,0.00021320269,0.000024188012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050367124,0.00006245029,0.00012110228,0.000046456138,0.00006171835,0.000040288345,0.00021687464,0.000036076246,0.0000047184344],"category_scores_gemma":[0.00029491648,0.000038983246,0.00009929928,0.00016150885,0.000025755933,0.0001763694,0.000048872105,0.000036190908,0.0000022653953],"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":[0.000045570832,0.0022377593,0.007822475,0.0009384461,0.00012453731,0.0000012074069,0.010245207,0.003049492,0.36046153,0.14481685,0.0029158294,0.4673411],"study_design_scores_gemma":[0.0016563187,0.0008881883,0.051263873,0.00066637906,0.000036751568,0.000032715634,0.0006129667,0.85431534,0.052593004,0.022662085,0.014779857,0.00049253966],"about_ca_topic_score_codex":0.000023009197,"about_ca_topic_score_gemma":0.00003035838,"teacher_disagreement_score":0.85126585,"about_ca_system_score_codex":0.000018032155,"about_ca_system_score_gemma":0.000025641992,"threshold_uncertainty_score":0.15896906},"labels":[],"label_agreement":null},{"id":"W2342843801","doi":"10.3390/s16050605","title":"Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Wearable computer; Computer science; Motion capture; Gesture recognition; Motion (physics); Artificial intelligence; Interactivity; Inertial measurement unit; Quaternion; Support vector machine; Wireless; Computer vision; Population; Embedded system; Mathematics; Multimedia","score_opus":0.03153256442997454,"score_gpt":0.2435401714950088,"score_spread":0.21200760706503427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342843801","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":[0.8141141,0.00006962691,0.18159226,0.0018722927,0.0009903562,0.0003180752,0.00002685053,0.00041003225,0.00060641364],"genre_scores_gemma":[0.9938491,0.000017084958,0.0049174824,0.00021479756,0.00028822833,0.000013036814,0.000009424083,0.000033117576,0.00065770175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763495,0.00039513045,0.0003733448,0.00063857646,0.0004901725,0.00046781328],"domain_scores_gemma":[0.9985153,0.00020846217,0.00022878796,0.0005484574,0.00031529655,0.00018369137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044840566,0.0002813651,0.0002840762,0.00024004851,0.00019043332,0.00015013757,0.00032735712,0.00026647898,0.000051416002],"category_scores_gemma":[0.00008179844,0.00019942412,0.00013526708,0.0004548449,0.000059142614,0.00042683745,0.000046559988,0.00017828439,0.00074012385],"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":[0.00015673402,0.0005256371,0.011473982,0.00040743113,0.0002588698,0.00049661804,0.004517545,0.008637241,0.15494336,0.0006744545,0.0019154594,0.81599265],"study_design_scores_gemma":[0.009415625,0.00038232733,0.010866131,0.005369986,0.00021687309,0.0012416914,0.001022168,0.6059296,0.3379342,0.004894256,0.01804852,0.0046786056],"about_ca_topic_score_codex":0.00015397907,"about_ca_topic_score_gemma":0.000032141194,"teacher_disagreement_score":0.81131405,"about_ca_system_score_codex":0.0001489589,"about_ca_system_score_gemma":0.00008061835,"threshold_uncertainty_score":0.95130414},"labels":[],"label_agreement":null},{"id":"W2375068441","doi":"","title":"The Research and Realization In Hand Language Animation Of 3D Virtual Human Based On OpenGL","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Animation; Key frame; Gesture; OpenGL; Computer facial animation; Computer animation; Key (lock); Frame (networking); Motion (physics); Artificial intelligence; Computer graphics (images); Computer vision; Virtual actor; Facial motion capture; Skeletal animation; Realization (probability); Virtual reality; Visualization","score_opus":0.06122790399235081,"score_gpt":0.32646021481358667,"score_spread":0.2652323108212359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2375068441","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":[0.02341403,0.000053165313,0.9713716,0.00015742364,0.000012012869,0.0008367745,0.000004752351,0.00003539177,0.0041148597],"genre_scores_gemma":[0.97208846,0.000005658544,0.027367199,0.000056529538,0.000041502146,0.0003121705,0.000015733163,0.0000073469305,0.00010541017],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990325,0.00015551015,0.00024321057,0.0002520036,0.00017541004,0.0001413439],"domain_scores_gemma":[0.9991699,0.00019096445,0.00007676963,0.00036537042,0.00016121617,0.00003579515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007521592,0.00006950931,0.00008901324,0.00019885882,0.0002851828,0.00012548693,0.0004344555,0.0000465715,0.0000034106142],"category_scores_gemma":[0.0000024771891,0.000054909513,0.000013247376,0.0005368694,0.00010017665,0.00011858849,0.00010759568,0.00010710498,0.000017761276],"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":[0.000025179152,0.00072427595,0.0018481425,0.000082309874,0.000019019733,0.0000036704582,0.017207494,0.000114506176,0.05145381,0.3486778,0.001486638,0.5783571],"study_design_scores_gemma":[0.007826299,0.0028361243,0.18026505,0.0009937888,0.000028197386,0.00006776081,0.0024440996,0.21809477,0.29006302,0.04075316,0.25490966,0.0017180577],"about_ca_topic_score_codex":0.000095794756,"about_ca_topic_score_gemma":0.00010600734,"teacher_disagreement_score":0.94867444,"about_ca_system_score_codex":0.000025461053,"about_ca_system_score_gemma":0.00002906688,"threshold_uncertainty_score":0.2239145},"labels":[],"label_agreement":null},{"id":"W2400017509","doi":"10.1061/9780784479827.080","title":"Action Recognition Using a Wristband-Type Activity Tracker: Case Study of Masonry Work","year":2016,"lang":"en","type":"article","venue":"Construction Research Congress 2016","topic":"Hand Gesture Recognition Systems","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 Waterloo","funders":"","keywords":"Computer science; Accelerometer; Novelty; Activity recognition; Artificial intelligence; Action recognition; Classifier (UML); Work (physics); Machine learning; Engineering","score_opus":0.24887107835907496,"score_gpt":0.41074948758731905,"score_spread":0.1618784092282441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400017509","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":[0.9662748,0.000065913766,0.030215269,0.00016728691,0.0020522175,0.00083545834,0.000013141983,0.00012299279,0.00025291258],"genre_scores_gemma":[0.9972276,0.00006338309,0.0021431819,0.0000039024067,0.00021980489,0.000054971737,9.856122e-7,0.000017206637,0.00026897053],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959199,0.0015717258,0.00045925513,0.00064972905,0.0009309833,0.0004684054],"domain_scores_gemma":[0.99601144,0.0007797569,0.00031587022,0.0007126522,0.001971708,0.00020855319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001984443,0.00019260553,0.00032873705,0.00066239794,0.00039313335,0.00017535737,0.00037278014,0.00017774865,0.00013216565],"category_scores_gemma":[0.00036613084,0.00013955805,0.00007064379,0.0014342827,0.00036122053,0.0012765264,0.0001961085,0.00032939255,0.00014786766],"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":[0.0001985613,0.00033209941,0.00704534,0.00004484217,0.00009632547,0.00042798335,0.0004690066,0.0000015151174,0.012274644,0.00005176312,0.0005488306,0.97850907],"study_design_scores_gemma":[0.07976665,0.01738381,0.044145662,0.017300792,0.00082337717,0.13091545,0.086011544,0.012160047,0.5456562,0.023024078,0.032527298,0.0102850795],"about_ca_topic_score_codex":0.00035840846,"about_ca_topic_score_gemma":0.00011399457,"teacher_disagreement_score":0.968224,"about_ca_system_score_codex":0.00020972505,"about_ca_system_score_gemma":0.00034383775,"threshold_uncertainty_score":0.5691012},"labels":[],"label_agreement":null},{"id":"W2402979485","doi":"10.1061/9780784479827.096","title":"Silhouette-Based On-Site Human Action Recognition in Single-View Video","year":2016,"lang":"en","type":"article","venue":"Construction Research Congress 2016","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Alberta","funders":"","keywords":"Silhouette; Artificial intelligence; Computer vision; Computer science; RGB color model; Task (project management); Action (physics); Monocular; Engineering","score_opus":0.14074854612143,"score_gpt":0.3688562129941395,"score_spread":0.2281076668727095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402979485","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":[0.723204,0.00038487336,0.2047618,0.025090033,0.008998956,0.0036841878,0.00014654075,0.001378455,0.032351155],"genre_scores_gemma":[0.99634206,0.00008137287,0.0018769192,0.00016731826,0.0003519394,0.00024650898,0.0000136669305,0.000026180303,0.00089402625],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99554473,0.0013306751,0.0005434785,0.00082172576,0.0010946951,0.00066471926],"domain_scores_gemma":[0.99680454,0.0010293933,0.00019247246,0.00077802705,0.0009561305,0.00023945254],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002202529,0.00022729956,0.0002950699,0.0012940275,0.00032639306,0.00031666816,0.0005484794,0.00021685932,0.00031520674],"category_scores_gemma":[0.00047817457,0.00016615023,0.00009763606,0.0010794147,0.00044402466,0.0010149955,0.00012277796,0.00041448843,0.0020698728],"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":[0.00006537863,0.00015185695,0.0029149184,0.00006630739,0.000014915399,0.00004360168,0.000055690958,0.0000031577845,0.04668841,0.0022206847,0.0043878085,0.94338727],"study_design_scores_gemma":[0.026986418,0.0034309828,0.024280168,0.020506198,0.000039733277,0.00065662956,0.00047791906,0.005121842,0.47838822,0.0651659,0.3711337,0.0038123077],"about_ca_topic_score_codex":0.000050160037,"about_ca_topic_score_gemma":0.00016619895,"teacher_disagreement_score":0.93957496,"about_ca_system_score_codex":0.00042862134,"about_ca_system_score_gemma":0.00025300303,"threshold_uncertainty_score":0.9987071},"labels":[],"label_agreement":null},{"id":"W2404610055","doi":"","title":"Building a portable gesture-to-audio/visual speech system.","year":2008,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gesture; Computer science; Speech synthesis; Speech recognition; Diva; Human–computer interaction; Artificial intelligence","score_opus":0.019964501715374464,"score_gpt":0.2613085424830658,"score_spread":0.24134404076769134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404610055","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":[0.14461994,0.00010819463,0.80116403,0.00059286976,0.0011579888,0.0004529573,0.0000018080265,0.001020429,0.05088179],"genre_scores_gemma":[0.8932848,0.000003751841,0.102587245,0.0005101802,0.00032439752,0.000036035228,0.0000010649765,0.000014812218,0.0032377383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979878,0.00008197716,0.0003767293,0.000547411,0.00054915226,0.0004569164],"domain_scores_gemma":[0.9988434,0.00007759218,0.000092170885,0.0005048853,0.00018668105,0.0002952751],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043969712,0.0001981068,0.00031267566,0.0002165197,0.00023742396,0.00014833006,0.0006882667,0.00009989683,0.000028471784],"category_scores_gemma":[0.00004260821,0.00016292611,0.00008984683,0.00085316086,0.000018748155,0.0004193601,0.00022215462,0.00013426342,0.0013980524],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069717775,0.0011331369,0.046888776,0.0009771129,0.0005594858,0.008634982,0.007077538,0.00077370374,0.05993061,0.41720015,0.24631348,0.2104413],"study_design_scores_gemma":[0.005035342,0.0014726403,0.066480294,0.0016051093,0.000077867415,0.037930977,0.0012097685,0.04196773,0.1877895,0.0010987385,0.64996946,0.0053625843],"about_ca_topic_score_codex":0.00017151546,"about_ca_topic_score_gemma":0.000015994636,"teacher_disagreement_score":0.74866486,"about_ca_system_score_codex":0.00008537307,"about_ca_system_score_gemma":0.00010928794,"threshold_uncertainty_score":0.99937946},"labels":[],"label_agreement":null},{"id":"W2407605217","doi":"10.1007/978-3-319-27863-6_29","title":"A Study of Hand Motion/Posture Recognition in Two-Camera Views","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Gesture recognition; Scale-invariant feature transform; Trajectory; Gesture; Invariant (physics); Motion (physics); Support vector machine; Set (abstract data type); Feature (linguistics); Pattern recognition (psychology); Feature extraction; Mathematics","score_opus":0.07524949486069304,"score_gpt":0.3085320895191546,"score_spread":0.23328259465846157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407605217","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":[0.004542502,0.00074631406,0.9875241,0.00024838882,0.0019434191,0.0015112157,0.000013099783,0.000083840496,0.003387151],"genre_scores_gemma":[0.9492195,0.000024305644,0.049559787,0.00037817025,0.00051784626,0.000053629297,0.000016103542,0.00004225082,0.00018839474],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950023,0.00021288886,0.0011196639,0.0016190925,0.0014992214,0.0005467975],"domain_scores_gemma":[0.99667174,0.00036110784,0.0006424655,0.0012965121,0.0008256389,0.00020251636],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023014117,0.000580736,0.0009864214,0.0016605917,0.00013857913,0.0004027554,0.0021946284,0.0002857434,0.00001844421],"category_scores_gemma":[0.00015756753,0.0005119271,0.00011737069,0.0013609605,0.0003803242,0.0007239397,0.0008028639,0.00093264284,0.00008150782],"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":[0.000017200584,0.00037014548,0.00045643141,0.00007715525,0.000024225365,0.00019283318,0.010905224,0.00824647,0.00014184527,0.0004021467,0.000045949204,0.9791204],"study_design_scores_gemma":[0.022181079,0.007694141,0.0067721712,0.012002575,0.0001968161,0.0018190326,0.00009283183,0.32076454,0.005328428,0.60428727,0.009933652,0.008927472],"about_ca_topic_score_codex":0.00012720613,"about_ca_topic_score_gemma":0.002061758,"teacher_disagreement_score":0.9701929,"about_ca_system_score_codex":0.00032964334,"about_ca_system_score_gemma":0.00051154615,"threshold_uncertainty_score":0.9997332},"labels":[],"label_agreement":null},{"id":"W2407727755","doi":"10.1002/sdtp.10653","title":"21‐2: Exploring 3D Interactive Performance Animation for VR/AR Applications Using Low‐cost Motion Capture","year":2016,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Hand Gesture Recognition Systems","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 British Columbia","funders":"","keywords":"Animation; Motion capture; Computer science; Motion (physics); Computer animation; Human–computer interaction; Virtual reality; Interactive media; Interactive design; Computer graphics (images); Interactive skeleton-driven simulation; Computer facial animation; Multimedia; Artificial intelligence","score_opus":0.03158030141569047,"score_gpt":0.2638003301458501,"score_spread":0.23222002873015962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407727755","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":[0.061181046,0.000083854706,0.85486144,0.0032514047,0.0010547076,0.0044763912,0.00009252613,0.00089502754,0.07410358],"genre_scores_gemma":[0.99479616,0.000053822198,0.004031744,0.00005980623,0.00014426174,0.00080895313,0.000008771299,0.000020536443,0.00007595958],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844337,0.00004880742,0.00045617553,0.00047078537,0.00029401734,0.00028683382],"domain_scores_gemma":[0.9985807,0.00027385142,0.00029450635,0.0005005843,0.00024641224,0.00010392755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924799,0.00019234988,0.00025773625,0.0001499503,0.00015238942,0.00005390566,0.000494049,0.00013816499,0.000005354756],"category_scores_gemma":[0.00007196825,0.00014455798,0.00012506354,0.0003395057,0.00007719949,0.0011089406,0.00011510648,0.00012008159,0.000022087694],"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":[0.000022535847,0.00008963808,0.00052239135,0.00010512668,0.00001617774,4.3058597e-7,0.00020102721,0.00021031145,0.9800916,0.0016539394,0.000016238284,0.017070623],"study_design_scores_gemma":[0.0036850674,0.0006116038,0.018265264,0.0034172265,0.00016247675,0.00019382527,0.0004281676,0.0053358655,0.9061865,0.00046341348,0.059208307,0.0020422365],"about_ca_topic_score_codex":0.000008338082,"about_ca_topic_score_gemma":0.000006800549,"teacher_disagreement_score":0.9336151,"about_ca_system_score_codex":0.0002370731,"about_ca_system_score_gemma":0.00005705809,"threshold_uncertainty_score":0.58949035},"labels":[],"label_agreement":null},{"id":"W2469784314","doi":"10.1145/2897824.2925965","title":"Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":296,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Initialization; Computer science; Discriminative model; Range (aeronautics); Artificial intelligence; Tracking (education); Convergence (economics); Computer vision; Augmented reality; Pose","score_opus":0.026583553772144032,"score_gpt":0.2587782965155166,"score_spread":0.23219474274337257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469784314","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":[0.11156403,0.00016021135,0.88692975,0.0006612469,0.0003373129,0.00021160206,0.000021092908,0.000043285298,0.00007146566],"genre_scores_gemma":[0.9868515,0.00028721034,0.01271837,0.000058746882,0.000013774831,0.000017746837,4.808293e-7,0.000007381578,0.000044762586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989512,0.000120667704,0.00026584035,0.0003160529,0.00021198895,0.00013424363],"domain_scores_gemma":[0.9986598,0.0006196803,0.00014715036,0.00031209475,0.0002002787,0.000061032733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002409968,0.00012815972,0.00019875937,0.00021998363,0.00016687148,0.00010044117,0.00018821233,0.00008025535,0.000010201942],"category_scores_gemma":[0.00009222136,0.00009040325,0.000052413412,0.00035001253,0.00018995992,0.0003584402,0.000012829757,0.000103615115,0.0000019087074],"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":[0.00060573494,0.0012641988,0.0016272042,0.00023767403,0.0004455969,0.000034044682,0.035166353,0.015323639,0.028748276,0.0060675135,0.000107289554,0.9103725],"study_design_scores_gemma":[0.022235282,0.007549328,0.03379137,0.010686875,0.0007479275,0.00197272,0.0070116413,0.31144363,0.549991,0.046227124,0.0039629685,0.0043801116],"about_ca_topic_score_codex":0.000017982187,"about_ca_topic_score_gemma":0.000020698088,"teacher_disagreement_score":0.9059924,"about_ca_system_score_codex":0.000012120195,"about_ca_system_score_gemma":0.000027133454,"threshold_uncertainty_score":0.36865374},"labels":[],"label_agreement":null},{"id":"W2511779974","doi":"10.1007/s40869-016-0024-1","title":"Design of a Set of Foot Movements for a Soccer Game on a Mobile Phone","year":2016,"lang":"en","type":"article","venue":"The Computer Games Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dynamic time warping; Gesture; Computer science; Accelerometer; Android (operating system); Foot (prosody); Mobile phone; Gesture recognition; Movement (music); Artificial intelligence; Speech recognition; Computer vision; Human–computer interaction; Simulation; Telecommunications","score_opus":0.04732920223969435,"score_gpt":0.28137922719494496,"score_spread":0.23405002495525062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511779974","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":[0.022236498,0.00011632933,0.97600967,0.00054808584,0.00060495996,0.00042207615,0.0000098721275,0.000020067904,0.000032446413],"genre_scores_gemma":[0.95790195,0.000051317038,0.04076051,0.00038521356,0.0005441018,0.000053026186,3.4190776e-7,0.000014947303,0.0002885926],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985102,0.00025201976,0.00046367434,0.00017736285,0.0003622968,0.00023447031],"domain_scores_gemma":[0.9982717,0.0005321832,0.00046195212,0.00037915583,0.00027878245,0.00007620224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001011108,0.00013392663,0.0002859644,0.0001272672,0.00005594598,0.000058802616,0.0008911482,0.00004319978,0.000017764049],"category_scores_gemma":[0.000018535287,0.00006649189,0.00015264822,0.00015475354,0.00005874088,0.00016819697,0.00013049146,0.00009220557,0.000019763136],"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":[0.000335114,0.00029667036,0.00012741891,0.000076875105,0.00043263842,0.000011591245,0.0075973268,0.0024477632,0.021776417,0.0013574028,0.013046225,0.95249456],"study_design_scores_gemma":[0.041387577,0.0232533,0.0065022297,0.008819906,0.00024396543,0.0031876527,0.000524962,0.30919996,0.36570174,0.12814413,0.110337906,0.0026966669],"about_ca_topic_score_codex":8.605779e-7,"about_ca_topic_score_gemma":1.8878183e-7,"teacher_disagreement_score":0.94979787,"about_ca_system_score_codex":0.000033320055,"about_ca_system_score_gemma":0.00007706554,"threshold_uncertainty_score":0.2711461},"labels":[],"label_agreement":null},{"id":"W2528594515","doi":"10.1109/startup.2016.7583978","title":"Wavelet-based tolerance near set approach in classifying hand images: A review","year":2016,"lang":"en","type":"review","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Wavelet; Artificial intelligence; Measure (data warehouse); Closeness; Pattern recognition (psychology); Set (abstract data type); Computer science; Bounded function; Image (mathematics); Matching (statistics); Computer vision; Orientation (vector space); Position (finance); Boundary (topology); Mathematics; Data mining","score_opus":0.08875701594204623,"score_gpt":0.33589612683251985,"score_spread":0.24713911089047363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528594515","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":[5.648724e-9,0.73318607,0.25739715,0.0001866217,0.00023966943,0.0013637438,0.000028347678,0.000119709446,0.0074787093],"genre_scores_gemma":[0.0000010955024,0.9690024,0.028476236,0.0007765932,0.00014975147,0.0006672291,0.000052391373,0.00004232881,0.0008319741],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955453,0.0008813781,0.001312837,0.0011906128,0.00049309334,0.0005767528],"domain_scores_gemma":[0.9973034,0.0004157282,0.00057587307,0.0013845125,0.00013683534,0.00018363855],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015637135,0.0006421131,0.0027199015,0.00026782157,0.00012065179,0.0004888699,0.0015936386,0.00034600153,0.0000871779],"category_scores_gemma":[0.0000999985,0.00039072233,0.00057990104,0.0012744926,0.000095022515,0.0003671977,0.00020925309,0.0004939138,0.0009634017],"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":[2.7541734e-7,0.000025373065,3.9654313e-7,0.06577527,0.00001516946,0.000022377493,0.000016025579,7.1609996e-8,4.734895e-8,0.00011359372,0.0051091257,0.9289223],"study_design_scores_gemma":[0.00022970013,0.00001311163,4.0479236e-7,0.18803056,0.000035881043,0.00008725867,9.77233e-7,0.0003926078,5.787202e-7,0.000013301887,0.81073403,0.00046160145],"about_ca_topic_score_codex":0.000013895646,"about_ca_topic_score_gemma":0.000004160599,"teacher_disagreement_score":0.92846066,"about_ca_system_score_codex":0.00017952819,"about_ca_system_score_gemma":0.00084629824,"threshold_uncertainty_score":0.99985445},"labels":[],"label_agreement":null},{"id":"W2541570138","doi":"10.1109/icat.2006.68","title":"Hand Gesture Interaction for Virtual Training of SPG","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Theoretical Astrophysics","keywords":"Gesture; Computer science; Wired glove; Virtual reality; Human–computer interaction; Interface (matter); Interaction technique; Gesture recognition; Training (meteorology); Artillery; Virtual machine; Artificial intelligence; Simulation","score_opus":0.03257942971522361,"score_gpt":0.26439707243415955,"score_spread":0.23181764271893596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541570138","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":[0.024711419,0.00002019106,0.96322227,0.0003315416,0.00056254835,0.0001444559,0.0000027870526,0.000057411136,0.010947378],"genre_scores_gemma":[0.98345095,3.649085e-7,0.014997388,0.00006529009,0.00019320362,0.000015416415,0.000004413466,0.000003830495,0.0012691144],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993998,0.000020354031,0.00021772845,0.00014960901,0.00010753898,0.00010499361],"domain_scores_gemma":[0.99953884,0.0001222635,0.00009152902,0.00012477077,0.000100074394,0.000022499542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018159687,0.000061104285,0.00012300983,0.00006665695,0.000045275407,0.000068114845,0.00014838678,0.000045183497,0.000011327295],"category_scores_gemma":[0.00002175143,0.000048718564,0.000054105465,0.00011468683,0.000015293215,0.00024795526,0.00002029058,0.00004048972,0.00001184631],"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":[0.000044927758,0.00022944434,0.0007128525,0.00010006473,0.00006479566,0.000006201254,0.006551603,0.0006123851,0.071224414,0.25276655,0.021197641,0.64648914],"study_design_scores_gemma":[0.005523081,0.001554882,0.013388888,0.00039823123,0.000047193007,0.00032578385,0.0036139502,0.10011012,0.3864556,0.022139048,0.46524125,0.0012019618],"about_ca_topic_score_codex":0.00003593463,"about_ca_topic_score_gemma":0.00006357632,"teacher_disagreement_score":0.9587396,"about_ca_system_score_codex":0.000010352156,"about_ca_system_score_gemma":0.000021470916,"threshold_uncertainty_score":0.19866854},"labels":[],"label_agreement":null},{"id":"W2542643231","doi":"10.1109/gem.2014.7048102","title":"A real-time reconstructed 3D environment augmented with virtual objects rendered with correct occlusion","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Augmented reality; Computer vision; Virtual reality; Rendering (computer graphics); Artificial intelligence; Optical head-mounted display; Immersion (mathematics); Computer graphics (images); Stereoscopy; Match moving; Wearable computer; Motion (physics)","score_opus":0.0052679688542792945,"score_gpt":0.17843229866744292,"score_spread":0.17316432981316363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542643231","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":[0.15714273,0.0000061100463,0.76690936,0.00031049314,0.00028241574,0.0006971611,0.0000054631964,0.00073421,0.07391205],"genre_scores_gemma":[0.9311348,0.00001314102,0.06445002,0.00019533915,0.00007641452,0.00003375565,0.000022751889,0.00002615186,0.0040475857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790514,0.00026207004,0.0002852343,0.0006474488,0.00054659485,0.00035350598],"domain_scores_gemma":[0.9986883,0.00015962076,0.00018824906,0.00069213624,0.00006875132,0.00020294792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034159402,0.0002629177,0.00031755172,0.00013509845,0.00016533321,0.00012690059,0.00039103738,0.000089680616,0.00024683808],"category_scores_gemma":[0.000016835833,0.00016977613,0.000035580953,0.0003305079,0.00007933972,0.00030674064,0.00015313031,0.0001263299,0.000527278],"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":[0.0017173878,0.0013938638,0.011288261,0.0001351013,0.0010579056,0.00055747747,0.011502527,0.003852769,0.112258494,0.007582348,0.01531826,0.8333356],"study_design_scores_gemma":[0.03453054,0.030455673,0.024748584,0.002502314,0.00033894743,0.010629236,0.0024733995,0.7452835,0.10860065,0.0008335906,0.032152817,0.007450741],"about_ca_topic_score_codex":0.00007920795,"about_ca_topic_score_gemma":0.000058153317,"teacher_disagreement_score":0.8258849,"about_ca_system_score_codex":0.00009025154,"about_ca_system_score_gemma":0.00008972018,"threshold_uncertainty_score":0.69232696},"labels":[],"label_agreement":null},{"id":"W2545812078","doi":"10.1109/gem.2014.7048108","title":"Quaternion based gesture recognition using worn inertial sensors in a motion tracking system","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Hidden Markov model; Computer science; Gesture recognition; Gesture; Quaternion; Wearable computer; Markov chain; Computer vision; Artificial intelligence; Context (archaeology); Inertial measurement unit; Orientation (vector space); Activity recognition; Speech recognition; Machine learning; Embedded system; Mathematics","score_opus":0.039312399740096496,"score_gpt":0.24561002651374939,"score_spread":0.20629762677365288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545812078","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":[0.3608896,0.000008121249,0.63704026,0.00015063351,0.0005095554,0.00020818174,0.0000010751135,0.00026326097,0.00092931895],"genre_scores_gemma":[0.9838442,4.9023015e-7,0.015704745,0.00012231096,0.00026631865,0.000016756852,0.000011602834,0.000014825025,0.000018714312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791944,0.0005597027,0.00044179647,0.00045356972,0.00033528975,0.0002901733],"domain_scores_gemma":[0.9991744,0.0001239044,0.00015991168,0.00030272777,0.00015114371,0.000087920555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010469464,0.0001805334,0.00024235115,0.00035388977,0.00008586131,0.0002177572,0.00022866375,0.00016729887,0.000009809656],"category_scores_gemma":[0.00007482525,0.00015915431,0.00007090389,0.0005422154,0.000012700403,0.00055795175,0.00003698064,0.00017362667,0.00012515993],"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":[0.0000879092,0.00030339736,0.013509876,0.0007232702,0.00003148741,0.000081762744,0.002218835,0.017988602,0.024367932,0.0015258152,0.00010631562,0.9390548],"study_design_scores_gemma":[0.00092095084,0.0000448273,0.005951005,0.00058152096,0.0000075999883,0.00007736623,0.00012702988,0.98193794,0.009796833,0.00006775901,0.00019548924,0.00029167152],"about_ca_topic_score_codex":0.00027536118,"about_ca_topic_score_gemma":0.00014178091,"teacher_disagreement_score":0.9639493,"about_ca_system_score_codex":0.00017112655,"about_ca_system_score_gemma":0.000031357522,"threshold_uncertainty_score":0.64901245},"labels":[],"label_agreement":null},{"id":"W2552247836","doi":"10.1145/2980179.2980226","title":"Sphere-meshes for real-time hand modeling and tracking","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Initialization; Tracking (education); Discriminative model; Polygon mesh; Embedding; Generative model; Artificial intelligence; Computer vision; Frame rate; Frame (networking); Representation (politics); Video tracking; Generative grammar; Algorithm; Computer graphics (images); Video processing","score_opus":0.03662785974230269,"score_gpt":0.2626951632727153,"score_spread":0.2260673035304126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552247836","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":[0.022645447,0.00005611241,0.9747389,0.0017835484,0.00020680777,0.00023805496,0.000021300102,0.00015786724,0.00015196009],"genre_scores_gemma":[0.97090375,0.0003141386,0.028281556,0.00012062058,0.000050492163,0.00006849236,9.70768e-7,0.000017199402,0.00024279856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989877,0.00004466446,0.00021657503,0.00035795273,0.00017079042,0.00022231434],"domain_scores_gemma":[0.9987737,0.0004640337,0.000047966652,0.00048035337,0.00013481469,0.00009912526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027476862,0.00013890036,0.00016338413,0.00014874559,0.0003333913,0.00015479399,0.0003283595,0.000106968546,0.000013091847],"category_scores_gemma":[0.000033951754,0.00010083638,0.000101002996,0.00025507985,0.000054571232,0.0004506913,0.000006019043,0.000079836405,0.000018397144],"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":[0.0000623237,0.00018490755,0.00011729447,0.00009800443,0.00016785192,0.0000059017607,0.0014506365,0.0006071988,0.028415697,0.0074375905,0.00022254513,0.96123004],"study_design_scores_gemma":[0.012095401,0.0019889246,0.0014252163,0.0025052344,0.0003723538,0.00042929078,0.0004995028,0.53675765,0.075582474,0.3415639,0.023277696,0.0035023394],"about_ca_topic_score_codex":0.000020550531,"about_ca_topic_score_gemma":0.00005197793,"teacher_disagreement_score":0.95772773,"about_ca_system_score_codex":0.000014642002,"about_ca_system_score_gemma":0.00003434602,"threshold_uncertainty_score":0.41119885},"labels":[],"label_agreement":null},{"id":"W2553825232","doi":"10.1111/1556-4029.13248","title":"Measuring the Frequency Occurrence of Handwriting and Handprinting Characteristics<sup>,</sup>","year":2016,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":22,"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. Thomas Hospital","funders":"National Institute of Justice","keywords":"Handwriting; Demographics; Population; Demography; Correlation; Independence (probability theory); Psychology; Statistics; Mathematics; Computer science; Artificial intelligence","score_opus":0.04469540838710795,"score_gpt":0.2560346360667339,"score_spread":0.21133922767962593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553825232","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":[0.9265894,0.00050122465,0.070172705,0.0016182194,0.00046907834,0.00006935047,0.0000035819542,0.000011851229,0.0005645564],"genre_scores_gemma":[0.99179465,0.00006473632,0.007901292,0.000046141915,0.00018196742,9.3680137e-7,2.7028062e-8,0.0000018522918,0.0000083940095],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99827063,0.000113820875,0.00058800937,0.00018001047,0.0006230677,0.00022446555],"domain_scores_gemma":[0.9979834,0.000595072,0.0007571931,0.00015944873,0.00042211247,0.00008280505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030371896,0.00009880336,0.0002440367,0.00016290115,0.00025398933,0.00019276373,0.00077551923,0.000030687752,0.0000044191033],"category_scores_gemma":[0.00080534635,0.000046087527,0.00007403741,0.00037613013,0.000466971,0.0008037347,0.00012155439,0.00010856107,0.0000029962246],"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":[0.0000059760846,0.000025379672,0.085701875,0.000051079554,0.000034696997,0.000022203549,0.0031949838,0.000012350989,0.014719951,0.006760582,0.00012574824,0.88934517],"study_design_scores_gemma":[0.007893987,0.004165421,0.46141198,0.027340962,0.00028074541,0.016084366,0.013111278,0.028105099,0.33991164,0.093939304,0.004824544,0.002930663],"about_ca_topic_score_codex":0.000007051154,"about_ca_topic_score_gemma":0.0000024117285,"teacher_disagreement_score":0.8864145,"about_ca_system_score_codex":0.000016523007,"about_ca_system_score_gemma":0.00014483536,"threshold_uncertainty_score":0.19535062},"labels":[],"label_agreement":null},{"id":"W2556282887","doi":"","title":"Handwavey: A Gestural Motion Password Interface","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Password; Computer science; Dynamic time warping; Motion (physics); Set (abstract data type); Kinesthetic learning; Password strength; Computer security; Cognitive password; Human–computer interaction; One-time password; Biometrics; Artificial intelligence; Programming language; Psychology","score_opus":0.011001994240669919,"score_gpt":0.24585283767021837,"score_spread":0.23485084342954846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556282887","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":[0.094620794,0.000014807122,0.8812151,0.00226442,0.001967444,0.00013349735,6.3820255e-7,0.0003527892,0.019430514],"genre_scores_gemma":[0.9801991,0.0000011010007,0.016877716,0.00020842711,0.00017045581,0.000012169327,0.0000010210302,0.0000049109763,0.0025251105],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920166,0.000040985637,0.00015320476,0.00025211705,0.00017453199,0.00017751488],"domain_scores_gemma":[0.9993502,0.00004997444,0.000043088723,0.00037119194,0.00009338004,0.000092151684],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024195721,0.00009421455,0.000100855046,0.00006562663,0.000063649895,0.00020907774,0.0004166063,0.00007362514,0.00010449852],"category_scores_gemma":[0.00003877937,0.00007046253,0.000044020995,0.00019897317,0.000020090314,0.000406617,0.0001129834,0.00019695515,0.00082248583],"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":[0.0000070220594,0.00014375219,0.007565008,0.000026866126,0.000040043346,0.000021327372,0.0019395534,0.0000086785685,0.0875817,0.06400172,0.012489333,0.826175],"study_design_scores_gemma":[0.003241397,0.00040144238,0.11419443,0.00013280025,0.000026260348,0.0012555685,0.00032690977,0.08124028,0.24480927,0.014976039,0.53734905,0.0020465422],"about_ca_topic_score_codex":0.00002721503,"about_ca_topic_score_gemma":0.00014079073,"teacher_disagreement_score":0.8855783,"about_ca_system_score_codex":0.000009019026,"about_ca_system_score_gemma":0.000018978162,"threshold_uncertainty_score":0.9999555},"labels":[],"label_agreement":null},{"id":"W2559026119","doi":"10.1109/cec.2016.7744331","title":"Multispectral hand recognition using the Kinect v2 sensor","year":2016,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RGB color model; Principal component analysis; Multispectral image; Artificial intelligence; Pattern recognition (psychology); Support vector machine; Computer science; Biometrics; Near-infrared spectroscopy; Computer vision","score_opus":0.05469251163408952,"score_gpt":0.26822466850466714,"score_spread":0.2135321568705776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559026119","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":[0.08588529,0.000025652189,0.9076986,0.0022296435,0.00053015474,0.00019141112,0.0000044492617,0.0001497159,0.0032851007],"genre_scores_gemma":[0.9606071,0.0000073158512,0.037811797,0.00033260701,0.00029031158,0.000010582899,6.6378124e-7,0.000008061987,0.00093156175],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990416,0.00012812261,0.00016952284,0.00025212325,0.00019015055,0.00021847055],"domain_scores_gemma":[0.9992453,0.00021510865,0.00006282856,0.00030895707,0.00010803306,0.000059798294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028252386,0.00009915478,0.000099038116,0.000056282875,0.00016842083,0.00018270455,0.00026925755,0.0000468235,0.00009216231],"category_scores_gemma":[0.00005938333,0.000044532702,0.000059382102,0.0001977885,0.00004846355,0.00035002295,0.000057732104,0.0000511099,0.00046360504],"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":[0.000013448169,0.00006734091,0.0011102047,0.000015209365,0.000058355392,0.000036117217,0.0009937689,0.0000062464655,0.23742047,0.0017843808,0.0018479237,0.7566465],"study_design_scores_gemma":[0.0056928354,0.00035595577,0.013114306,0.0007484897,0.000066701185,0.002257445,0.00042836316,0.050847184,0.85778505,0.013134353,0.053757798,0.001811535],"about_ca_topic_score_codex":0.000051368344,"about_ca_topic_score_gemma":0.000033813623,"teacher_disagreement_score":0.8747218,"about_ca_system_score_codex":0.000033347304,"about_ca_system_score_gemma":0.000028729291,"threshold_uncertainty_score":0.59588593},"labels":[],"label_agreement":null},{"id":"W2574199794","doi":"10.1109/ism.2016.0098","title":"Spatio-Temporally Optimized Multi-sensor Motion Fusion","year":2016,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Ground truth; Optical flow; Sensor fusion; Probabilistic logic; Focus (optics); Tracking (education); Motion (physics); Motion estimation; Image (mathematics)","score_opus":0.03313312033429925,"score_gpt":0.25682485341219585,"score_spread":0.2236917330778966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574199794","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":[0.00632003,0.000012960036,0.98551846,0.0028582958,0.00050276174,0.00022528465,0.0000020125221,0.0003622807,0.0041978923],"genre_scores_gemma":[0.66674966,0.000014068353,0.32135308,0.00024401752,0.00010762173,0.00002250509,0.000003086248,0.000009087579,0.011496852],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998897,0.00011511676,0.00024410195,0.0003232971,0.00023962901,0.00018081415],"domain_scores_gemma":[0.9991714,0.00008892415,0.00009276822,0.00038108387,0.00016516868,0.000100668505],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031599135,0.000109243425,0.00013881605,0.00009669158,0.00006753946,0.000081534985,0.00030794067,0.00007138581,0.00017263986],"category_scores_gemma":[0.00006377992,0.00006192408,0.000061585204,0.00017116148,0.000016713433,0.00047733862,0.00009687557,0.00003521083,0.0013091274],"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":[0.000037082704,0.00034778903,0.006556366,0.000033773158,0.00004500756,0.000044181223,0.0007263275,0.00006063866,0.06372832,0.010233473,0.0071701393,0.9110169],"study_design_scores_gemma":[0.048197757,0.0010222193,0.08901611,0.0013262884,0.0000599616,0.00057183404,0.00032913155,0.31213528,0.25021008,0.005664761,0.2866751,0.0047914786],"about_ca_topic_score_codex":0.00002131195,"about_ca_topic_score_gemma":0.00001815646,"teacher_disagreement_score":0.90622544,"about_ca_system_score_codex":0.00003139078,"about_ca_system_score_gemma":0.000026791626,"threshold_uncertainty_score":0.99946845},"labels":[],"label_agreement":null},{"id":"W2576764985","doi":"10.15353/vsnl.v1i1.49","title":"Colour-based gesture recognition for American Sign Language via Hidden Markov Models","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Sign (mathematics); Gesture recognition; Speech recognition; Centroid; Gesture; Feature (linguistics); Pattern recognition (psychology); Sign language; Artificial intelligence; Computer science; Mathematics; Linguistics","score_opus":0.03183539062265412,"score_gpt":0.27561723798208093,"score_spread":0.24378184735942682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576764985","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":[0.07353454,0.00004581638,0.91158926,0.013007595,0.0005202557,0.0006089465,0.00002745773,0.000289037,0.00037707848],"genre_scores_gemma":[0.8581033,0.000001550052,0.12890317,0.012345816,0.0002999154,0.00016004861,0.00007741957,0.000029849178,0.00007895322],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982507,0.00021371209,0.00027800616,0.00048462735,0.00043764166,0.00033528224],"domain_scores_gemma":[0.99867326,0.00025904353,0.00020296115,0.00043652407,0.00019478855,0.00023343042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061660144,0.00019810972,0.00026154978,0.00020886929,0.00009031295,0.00017578145,0.00047444904,0.000077780554,0.0000075663615],"category_scores_gemma":[0.00006765618,0.00017420712,0.000115305855,0.0004477271,0.000056085835,0.00046331686,0.0000618081,0.00013390073,0.00015077933],"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":[0.00012996183,0.00014684306,0.00008452708,0.00005436384,0.00004009392,0.00006162338,0.0022558826,0.0006060479,0.029195262,0.00006258692,0.13226113,0.83510166],"study_design_scores_gemma":[0.012016036,0.003133621,0.0015254738,0.0006398639,0.00013343742,0.00017612292,0.0016334109,0.8872972,0.030993564,0.006062049,0.05317334,0.0032158538],"about_ca_topic_score_codex":0.00007064721,"about_ca_topic_score_gemma":0.000012687445,"teacher_disagreement_score":0.88669115,"about_ca_system_score_codex":0.000075909105,"about_ca_system_score_gemma":0.00006756485,"threshold_uncertainty_score":0.71039605},"labels":[],"label_agreement":null},{"id":"W2581177282","doi":"10.1109/inventive.2016.7830112","title":"Online hand gesture recognition &amp; classification for deaf &amp; dumb","year":2016,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gesture; Sign language; Computer science; Interpreter; Gesture recognition; Sign (mathematics); Speech recognition; American Sign Language; Language interpretation; Cued speech; Natural language processing; Artificial intelligence; Linguistics; Programming language; Mathematics","score_opus":0.11142839149828883,"score_gpt":0.308176258178505,"score_spread":0.1967478666802162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581177282","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":[0.02280337,0.000049955757,0.9646751,0.009378179,0.0006307941,0.00052035844,0.000080027465,0.00028735065,0.0015748641],"genre_scores_gemma":[0.6770888,0.00009011381,0.29387262,0.0014090661,0.0011588989,0.00036563652,0.00038883006,0.000041496845,0.025584538],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99851674,0.000090476045,0.00034894564,0.0005130296,0.00024717304,0.00028363316],"domain_scores_gemma":[0.9983901,0.00030325179,0.00015865933,0.0005557101,0.00045913193,0.00013317594],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040126618,0.0001673106,0.00018532645,0.00013512062,0.00016427356,0.0001880694,0.00038753322,0.00016396258,0.000079037134],"category_scores_gemma":[0.00024372977,0.000106360865,0.00009972452,0.00025464664,0.0000368222,0.000540386,0.00005520428,0.00006948791,0.0012710162],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029085651,0.00028988963,0.0006179322,0.000056361434,0.00004490779,8.964952e-7,0.00039727497,5.289587e-7,0.06642798,0.0043917517,0.04284521,0.8848982],"study_design_scores_gemma":[0.002050662,0.0001061187,0.009361096,0.00023462882,0.000024866264,0.000079319696,0.00004739638,0.0003748141,0.007568606,0.017191907,0.962376,0.00058455835],"about_ca_topic_score_codex":0.000012184644,"about_ca_topic_score_gemma":0.00057536585,"teacher_disagreement_score":0.9195308,"about_ca_system_score_codex":0.00005755634,"about_ca_system_score_gemma":0.00006360818,"threshold_uncertainty_score":0.9995066},"labels":[],"label_agreement":null},{"id":"W258297","doi":"","title":"Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding","year":2011,"lang":"en","type":"article","venue":"ACM Multimedia","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gesture; Joint (building); Computer science; Human–computer interaction; Joint attention; Artificial intelligence; Engineering; Psychology; Architectural engineering","score_opus":0.18841462973161732,"score_gpt":0.29785362649043284,"score_spread":0.10943899675881552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W258297","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":[0.9895866,0.000109516484,0.00096803287,0.0010869219,0.0009480544,0.0008223335,0.000005738687,0.00016107387,0.0063117277],"genre_scores_gemma":[0.9886724,0.000010362958,0.010826393,0.000106202504,0.00008754477,0.000038197573,6.71814e-7,0.000012441608,0.00024583648],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988925,0.00002294285,0.0002447,0.00032617315,0.00029815504,0.0002155561],"domain_scores_gemma":[0.99888927,0.000113436465,0.00018601278,0.00061936054,0.00009222479,0.00009969951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028741866,0.00015308028,0.00019048252,0.000096959346,0.0001457847,0.000057366393,0.001136763,0.00012688374,0.000032904984],"category_scores_gemma":[0.00025876533,0.000101450336,0.00006401004,0.00017688384,0.00011228696,0.0002307234,0.0006135846,0.00022761361,0.000032334803],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081529804,0.0022032438,0.41905996,0.0005646083,0.0003473346,0.00007406932,0.20120385,9.743638e-7,0.12696558,0.06507598,0.039943706,0.14447917],"study_design_scores_gemma":[0.0017508587,0.00029091156,0.9273111,0.0007530799,0.00011129243,0.000088985296,0.0033244733,0.00023266657,0.049058422,0.015831461,0.000561867,0.00068485166],"about_ca_topic_score_codex":0.000024434481,"about_ca_topic_score_gemma":0.0000170652,"teacher_disagreement_score":0.5082512,"about_ca_system_score_codex":0.000042983193,"about_ca_system_score_gemma":0.000017661963,"threshold_uncertainty_score":0.41370246},"labels":[],"label_agreement":null},{"id":"W2593600134","doi":"10.5220/0006151001080115","title":"Optimized Limited Memory and Warping LCSS for Online Gesture Recognition or Overlearning?","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Dynamic time warping; Computer science; Cluster analysis; Robustness (evolution); Gesture; Gesture recognition; Artificial intelligence; Sliding window protocol; Pattern recognition (psychology); Quantization (signal processing); Algorithm; Speech recognition; Window (computing)","score_opus":0.0748132679497707,"score_gpt":0.3111506178143553,"score_spread":0.23633734986458463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593600134","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":[0.0686205,0.00018315237,0.91509366,0.006078046,0.0012987475,0.0013185581,0.000045359466,0.00059505214,0.0067669386],"genre_scores_gemma":[0.22611578,0.00022066706,0.7609256,0.0018375672,0.00090494147,0.00016701032,0.00009009043,0.00004712558,0.009691242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878216,0.0000724832,0.0002572822,0.00045187652,0.00017729182,0.00025890308],"domain_scores_gemma":[0.99849784,0.00031869835,0.00025790907,0.00055823097,0.0002373958,0.00012989731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045929346,0.00016852008,0.00027217026,0.000098727076,0.00054818904,0.0006344872,0.0004918133,0.0001404857,0.000035898967],"category_scores_gemma":[0.00066956616,0.00012351762,0.00007010762,0.00007467769,0.00004515554,0.0007726472,0.0001969896,0.00014734606,0.000028192799],"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":[0.00023147718,0.00015580289,0.00023260064,0.00018453988,0.00010554613,0.00003335052,0.0009857649,0.000046499736,0.0009423789,0.0003801052,0.004191007,0.9925109],"study_design_scores_gemma":[0.040922057,0.002442869,0.040030107,0.0026641928,0.00030881786,0.0014156908,0.0019883735,0.66526973,0.015790964,0.008150589,0.21656166,0.004454951],"about_ca_topic_score_codex":0.000030530908,"about_ca_topic_score_gemma":0.000049381626,"teacher_disagreement_score":0.988056,"about_ca_system_score_codex":0.000019196086,"about_ca_system_score_gemma":0.00005276427,"threshold_uncertainty_score":0.6118375},"labels":[],"label_agreement":null},{"id":"W2594346122","doi":"10.1177/0278364917692865","title":"Automated detection of handovers using kinematic features","year":2017,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Kinematics; Computer science; Artificial intelligence; Computer vision; Physics","score_opus":0.13727451479057037,"score_gpt":0.433872788833705,"score_spread":0.29659827404313466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594346122","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":[0.23290795,0.00039064858,0.74800414,0.010977789,0.0050764177,0.00033448313,0.000004596021,0.000051210896,0.0022527818],"genre_scores_gemma":[0.99171394,0.00003296305,0.007875924,0.000014993502,0.00026524445,6.635211e-7,1.5157192e-7,0.000005017664,0.000091092435],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975914,0.00021771516,0.0003552477,0.00008187441,0.0016112081,0.00014256149],"domain_scores_gemma":[0.99625814,0.0003950298,0.0006008617,0.0003604334,0.0023307872,0.000054774056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027203453,0.000060185375,0.0001471847,0.0003202974,0.0002616664,0.0005288129,0.0026564226,0.0000434084,0.0000044080375],"category_scores_gemma":[0.0010550056,0.000038187933,0.00008978723,0.00012268231,0.00013549728,0.00043885008,0.00034228078,0.00033465936,0.000008001054],"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":[0.0004876499,0.0004938991,0.0021089693,0.00018774785,0.0018227651,0.0005204238,0.005210398,0.19255428,0.6946173,0.02065494,0.003434728,0.07790696],"study_design_scores_gemma":[0.0021385574,0.0002830479,0.015652226,0.0010814464,0.000034866323,0.002671041,0.00042114066,0.83112335,0.13573802,0.010181667,0.00047058906,0.00020402398],"about_ca_topic_score_codex":0.000085127846,"about_ca_topic_score_gemma":0.000023440893,"teacher_disagreement_score":0.758806,"about_ca_system_score_codex":0.000105472165,"about_ca_system_score_gemma":0.00016842784,"threshold_uncertainty_score":0.5099355},"labels":[],"label_agreement":null},{"id":"W2595039132","doi":"10.1109/thms.2017.2664358","title":"Guest Editorial Special Issue on Drawing and Handwriting Processing for User-Centered Systems","year":2017,"lang":"en","type":"editorial","venue":"IEEE Transactions on Human-Machine Systems","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Handwriting; Focus (optics); Frontier; Computer science; Field (mathematics); Special section; Human–computer interaction; Data science; Artificial intelligence; Engineering; History","score_opus":0.0328404144930048,"score_gpt":0.32143325574590265,"score_spread":0.28859284125289786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595039132","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010456407,0.00043025936,0.20772709,0.000046830264,0.7862048,0.002342987,0.001128109,0.00044219237,0.0016672551],"genre_scores_gemma":[0.024666172,0.00005357842,0.00011950155,0.000010693311,0.96937615,0.0009901134,0.00020020417,0.00019577293,0.0043878127],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99260235,0.0005470265,0.0016802185,0.0019829334,0.0022053712,0.0009821046],"domain_scores_gemma":[0.9932891,0.0019197591,0.0015626168,0.0015396825,0.0012840171,0.00040480684],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0019709717,0.0011937753,0.0018351871,0.0008073436,0.0032749637,0.0054001804,0.0017776199,0.0016884534,0.0000055923174],"category_scores_gemma":[0.00025024245,0.0011362508,0.00040153266,0.00026888103,0.00014449032,0.0011129031,0.000020330024,0.0019336243,0.00008851705],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012774335,0.00025034268,0.0000013705632,0.0028476866,0.00023366012,0.00002818389,0.00072144566,0.00072231825,0.00009543363,0.00005950873,0.9892429,0.005669369],"study_design_scores_gemma":[0.003164683,0.00069737935,5.8676187e-7,0.0065053646,0.00013899877,0.000035053545,0.0001456444,0.0037972282,0.00013965153,0.000008102084,0.98424953,0.0011177722],"about_ca_topic_score_codex":0.0007660305,"about_ca_topic_score_gemma":0.00032361128,"teacher_disagreement_score":0.2076076,"about_ca_system_score_codex":0.00044273734,"about_ca_system_score_gemma":0.00047654385,"threshold_uncertainty_score":0.99960756},"labels":[],"label_agreement":null},{"id":"W2607925037","doi":"","title":"Usability Study of Static/Dynamic Gestures and Haptic Input as Interfaces to 3D Games","year":2013,"lang":"en","type":"article","venue":"Advances in Computer-Human Interaction","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Usability; Gesture; Haptic technology; Computer science; Human–computer interaction; Computer graphics (images); Simulation; Multimedia; Artificial intelligence","score_opus":0.012960375158840963,"score_gpt":0.3254658415092743,"score_spread":0.31250546635043336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607925037","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":[0.79803556,0.00013561056,0.19976267,0.00014275913,0.0008613712,0.0007776071,9.433662e-7,0.00007448343,0.00020896611],"genre_scores_gemma":[0.9832634,0.000030182915,0.0163578,0.0001243115,0.00005293108,0.00012573764,0.0000016768612,0.000010779177,0.000033193897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979178,0.00027415637,0.00067221775,0.00061408704,0.00028815112,0.00023361178],"domain_scores_gemma":[0.9985344,0.00042767075,0.0002679407,0.0004790735,0.00019444541,0.000096466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028607063,0.00022476901,0.00037976322,0.00035602483,0.00008021654,0.00023073038,0.00048044694,0.00004977328,0.000027745446],"category_scores_gemma":[0.00005627619,0.00020261917,0.000031944604,0.000325553,0.000056606044,0.0020561013,0.00034196166,0.00022038983,0.000063835556],"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":[0.000038977683,0.0013179432,0.017873669,0.00024023218,0.00006755483,0.000013497377,0.025070166,0.010469253,0.0021721052,0.00036687191,0.0001464727,0.94222325],"study_design_scores_gemma":[0.005930943,0.01822834,0.4081206,0.0030030722,0.0000672976,0.0003875731,0.013540727,0.50089264,0.003964639,0.033464264,0.009745117,0.0026547692],"about_ca_topic_score_codex":0.0005016549,"about_ca_topic_score_gemma":0.0011763441,"teacher_disagreement_score":0.93956846,"about_ca_system_score_codex":0.00010146623,"about_ca_system_score_gemma":0.000014946798,"threshold_uncertainty_score":0.826257},"labels":[],"label_agreement":null},{"id":"W2611366544","doi":"10.14288/1.0344007","title":"Data-driven design of expressive robot hands and hand gestures : applications for collaborative human-robot interaction","year":2017,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gesture; Human–computer interaction; Human–robot interaction; Robot; Computer science; Social robot; Artificial intelligence; Communication; Psychology; Robot control; Mobile robot","score_opus":0.05795255809722117,"score_gpt":0.26780739321600977,"score_spread":0.2098548351187886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611366544","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":[0.062471736,0.00012555094,0.93535495,0.000121969424,0.00009655485,0.000984621,0.00046535305,0.00003378691,0.0003455006],"genre_scores_gemma":[0.97680384,0.00005429739,0.022806073,0.000008203285,0.000039411094,0.000010818405,0.000046806766,0.0000065342,0.00022403982],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99904126,0.000073980664,0.00014335482,0.00045156304,0.00015700712,0.00013283921],"domain_scores_gemma":[0.99797964,0.00014202576,0.0004838857,0.00078442815,0.00053229905,0.00007773826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002137381,0.000049044676,0.000269532,0.00004900894,0.00090369815,0.0004860799,0.0010821219,0.00008649538,0.000004600977],"category_scores_gemma":[0.0000488852,0.00013496255,0.000039304192,0.000105611274,0.00032796036,0.0012181243,0.00043434906,0.00007226874,0.0000020016312],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058267127,0.00044584522,0.004227292,0.00047858278,0.00038626758,0.000045550554,0.00391768,0.00060615735,0.024839032,0.00012014685,0.016723346,0.9481518],"study_design_scores_gemma":[0.0042485846,0.00047596736,0.97373515,0.0006554075,0.0001320849,0.00007739749,0.002606837,0.012200525,0.00026118683,0.001580049,0.0035482328,0.00047857862],"about_ca_topic_score_codex":0.004183107,"about_ca_topic_score_gemma":0.028167488,"teacher_disagreement_score":0.9695079,"about_ca_system_score_codex":0.00002261385,"about_ca_system_score_gemma":0.000082141596,"threshold_uncertainty_score":0.9895659},"labels":[],"label_agreement":null},{"id":"W2616886632","doi":"10.1177/0278364917709941","title":"Cooperative gestures for industry: Exploring the efficacy of robot hand configurations in expression of instructional gestures for human–robot interaction","year":2017,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Hand Gesture Recognition Systems","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":"University of British Columbia","funders":"","keywords":"Gesture; Nonverbal communication; Robot; Set (abstract data type); Computer science; Human–computer interaction; Human–robot interaction; Task (project management); Context (archaeology); Artificial intelligence; Action (physics); Human communication; Engineering; Communication; Psychology","score_opus":0.30864732514577775,"score_gpt":0.46609926997794515,"score_spread":0.1574519448321674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2616886632","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":[0.5122761,0.0001785839,0.45870456,0.022952704,0.0038119406,0.0015347789,0.00005601319,0.000010001671,0.00047531477],"genre_scores_gemma":[0.9920747,0.000036907997,0.0070134145,0.000021797188,0.0006491968,0.00006740508,0.000005217103,0.000010435438,0.000120903554],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99752223,0.00027777065,0.0007388555,0.00017313617,0.0010897913,0.00019819835],"domain_scores_gemma":[0.9925941,0.002494329,0.0008638622,0.00038273184,0.003609402,0.000055566958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026133463,0.00011752767,0.0002619433,0.00040373753,0.0006187189,0.00047482888,0.0023161354,0.00010157709,0.00000666327],"category_scores_gemma":[0.0022131845,0.00007070963,0.00014378304,0.00014684083,0.00035180463,0.000757607,0.0002845771,0.0007635457,6.633644e-7],"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":[0.0014675449,0.00083771226,0.0021423309,0.000115224575,0.00065391813,0.000015373253,0.012272164,0.37470788,0.44663915,0.12773868,0.0037117854,0.029698232],"study_design_scores_gemma":[0.010604817,0.0018384046,0.1100014,0.0035448833,0.00007280522,0.00044273856,0.005642461,0.023341147,0.80888206,0.028390206,0.0067414977,0.00049756496],"about_ca_topic_score_codex":0.00006977258,"about_ca_topic_score_gemma":0.000093155395,"teacher_disagreement_score":0.47979861,"about_ca_system_score_codex":0.000107804546,"about_ca_system_score_gemma":0.0002896064,"threshold_uncertainty_score":0.4758748},"labels":[],"label_agreement":null},{"id":"W2617500919","doi":"10.1145/3033701.3033744","title":"Text input in Smartwatches Based Gestures Using Geometric Shape","year":2016,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gesture; Computer science; Smartwatch; Alphabet; Classifier (UML); Naive Bayes classifier; Gesture recognition; Artificial intelligence; Computer vision; Speech recognition; Natural language processing; Wearable computer; Support vector machine","score_opus":0.039307336178052696,"score_gpt":0.2585149935095676,"score_spread":0.21920765733151487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617500919","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":[0.20411627,0.00012348704,0.78766185,0.002277516,0.00038119126,0.00019903183,0.0000018293113,0.00020373816,0.0050350665],"genre_scores_gemma":[0.97733104,0.000004693761,0.021542776,0.000588284,0.00008056944,0.000011206553,3.6083844e-7,0.000008292899,0.00043280039],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986721,0.000106379,0.000268217,0.00036621647,0.000286301,0.00030073864],"domain_scores_gemma":[0.99899304,0.00039498345,0.00006895613,0.00036960086,0.000078308505,0.00009509418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000464975,0.00013350557,0.00018313267,0.00067616947,0.00004415839,0.00010706728,0.0004793475,0.0000877469,0.00014063438],"category_scores_gemma":[0.00013185825,0.00007975614,0.00005379728,0.0014669839,0.000026196058,0.00040820095,0.000110567205,0.00006639036,0.00027756486],"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":[0.000011521594,0.00021453726,0.09576748,0.000050312603,0.000024441817,0.000092427814,0.0003753702,0.000102027676,0.018575214,0.0021488336,0.0022991821,0.88033867],"study_design_scores_gemma":[0.010331932,0.0005175317,0.4833119,0.0015599274,0.000031144,0.0003182231,0.00020796874,0.24891329,0.1364448,0.0056226444,0.10940632,0.0033343085],"about_ca_topic_score_codex":0.00008913128,"about_ca_topic_score_gemma":0.000058270663,"teacher_disagreement_score":0.8770043,"about_ca_system_score_codex":0.00006986306,"about_ca_system_score_gemma":0.000088882996,"threshold_uncertainty_score":0.35676274},"labels":[],"label_agreement":null},{"id":"W2623417428","doi":"10.1007/978-3-319-60922-5_24","title":"Operating Virtual Panels with Hand Gestures in Immersive VR Games","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Headset; Desk; Laptop; Gesture; Virtual reality; Human–computer interaction; Android (operating system); Input device; Computer graphics (images); Computer vision; Computer hardware; Operating system","score_opus":0.02149652071282891,"score_gpt":0.252782191653321,"score_spread":0.2312856709404921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623417428","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":[0.0007038622,0.00043756663,0.9885958,0.0005845501,0.0014336756,0.0006188187,0.000007647801,0.00006590172,0.0075521735],"genre_scores_gemma":[0.9207239,0.00004006886,0.07623818,0.0009623671,0.00077071355,0.00003343241,0.000007615627,0.000053367723,0.0011703463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954664,0.00008119275,0.00059382047,0.001880976,0.0011720208,0.0008056063],"domain_scores_gemma":[0.99675196,0.00056929776,0.0004960221,0.0016171909,0.00035270612,0.00021284823],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010424908,0.00068859605,0.000834929,0.00094454194,0.0005503467,0.002019226,0.003663094,0.00041198832,0.000014064766],"category_scores_gemma":[0.00015137829,0.00053389464,0.00010571007,0.00032671387,0.0010857397,0.0010730341,0.0010610683,0.0011021497,0.00005475925],"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":[0.000013504513,0.00005662689,0.0006442552,0.00007692991,0.000042418087,0.0010243608,0.012497809,0.021830846,0.0009615757,0.006028683,0.0000308572,0.9567921],"study_design_scores_gemma":[0.012485473,0.007212105,0.018642537,0.033777196,0.00015643828,0.006066316,0.000060678245,0.64497584,0.071079,0.1453114,0.043013625,0.017219367],"about_ca_topic_score_codex":0.000107245185,"about_ca_topic_score_gemma":0.00049895124,"teacher_disagreement_score":0.93957275,"about_ca_system_score_codex":0.00023510346,"about_ca_system_score_gemma":0.00081609184,"threshold_uncertainty_score":0.9997113},"labels":[],"label_agreement":null},{"id":"W2624845660","doi":"10.1109/tcsvt.2017.2721108","title":"BoMW: Bag of Manifold Words for One-Shot Learning Gesture Recognition From Kinect","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":29,"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 Key Research and Development Program of China; Engineering and Physical Sciences Research Council; Key Technology Research and Development Program of Shandong; Major State Basic Research Development Program of China; Queen's University; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Queen's University Belfast","keywords":"Artificial intelligence; Computer science; Gesture recognition; Feature extraction; Pattern recognition (psychology); Gesture; RGB color model; Nonlinear dimensionality reduction; Computer vision; Neural coding; Feature vector; k-nearest neighbors algorithm; Euclidean distance; Euclidean space; Feature learning; Mathematics; Dimensionality reduction","score_opus":0.08206302087350371,"score_gpt":0.2885304786289863,"score_spread":0.2064674577554826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624845660","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":[0.038157687,0.00040016673,0.9569445,0.0006429959,0.0016871865,0.001345383,0.00027149904,0.0003115436,0.00023900886],"genre_scores_gemma":[0.9970798,0.00010017547,0.0016133671,0.000034465276,0.00013415224,0.00075649255,0.000010900783,0.000030775467,0.00023987076],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981509,0.000082692335,0.00054337847,0.0006589744,0.0002125252,0.00035157072],"domain_scores_gemma":[0.99781764,0.00045714996,0.0005218432,0.00078495144,0.00033229502,0.00008614999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005989778,0.00024594017,0.00057886716,0.00046735574,0.0007596626,0.00024217147,0.00065004424,0.00045024403,0.000004837959],"category_scores_gemma":[0.00009971276,0.00023904358,0.00016828114,0.00018723088,0.00009259851,0.00038697032,0.000006790691,0.00031684586,0.000010511415],"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":[0.000102748774,0.000426785,0.00052602263,0.0010183987,0.0006735031,0.000013217115,0.0010055319,0.000435072,0.050628804,0.0071124383,0.00029460748,0.93776286],"study_design_scores_gemma":[0.028154219,0.011632328,0.0039975718,0.010190889,0.0016009184,0.0011348022,0.004551158,0.12413832,0.6115242,0.06254702,0.13460524,0.0059233247],"about_ca_topic_score_codex":0.00019863948,"about_ca_topic_score_gemma":0.00014732171,"teacher_disagreement_score":0.9589221,"about_ca_system_score_codex":0.00004594224,"about_ca_system_score_gemma":0.000050733168,"threshold_uncertainty_score":0.97479147},"labels":[],"label_agreement":null},{"id":"W2656438608","doi":"10.1109/ccece.2017.7946587","title":"A sensorized glove for therapist skill performance assessment during neck manipulation","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Western University","funders":"","keywords":"Computer science; Simulation; Rehabilitation; Instrumentation (computer programming); Process (computing); Human–computer interaction; Physical medicine and rehabilitation; Artificial intelligence; Physical therapy; Medicine","score_opus":0.03048689459080146,"score_gpt":0.3053946961728286,"score_spread":0.27490780158202716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2656438608","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":[0.48596844,0.000009464456,0.49135426,0.0008998082,0.00072090747,0.0005333023,0.0000021803655,0.00014650554,0.020365147],"genre_scores_gemma":[0.9529012,0.0000083219775,0.043933935,0.00006417324,0.00018502708,0.00005448099,0.000002518969,0.000007399076,0.0028429464],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99913824,0.000023197446,0.00018522334,0.00026787203,0.00019115931,0.00019429871],"domain_scores_gemma":[0.9989806,0.000036172663,0.00017206762,0.00063663267,0.00012082625,0.00005369177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023359316,0.0001043022,0.00013650005,0.00004170444,0.0005191849,0.0004998722,0.00047084366,0.000047062713,0.000016305632],"category_scores_gemma":[0.00001757768,0.00008343569,0.000061774954,0.00003681974,0.000014063106,0.0007182824,0.00008716223,0.000052211984,0.00005293651],"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":[0.00029493577,0.0008481168,0.19770274,0.0018763873,0.0006412295,0.000057756835,0.0075883884,0.0008590988,0.06988266,0.17129241,0.0027256408,0.5462306],"study_design_scores_gemma":[0.0033941076,0.00014584225,0.7354095,0.0001242768,0.00000874443,0.000049985243,0.00005235188,0.24079078,0.012656794,0.0010660258,0.0058699,0.00043166734],"about_ca_topic_score_codex":0.000016074786,"about_ca_topic_score_gemma":0.000011880276,"teacher_disagreement_score":0.54579896,"about_ca_system_score_codex":0.0000490804,"about_ca_system_score_gemma":0.00002868573,"threshold_uncertainty_score":0.48202792},"labels":[],"label_agreement":null},{"id":"W2736737128","doi":"10.1109/smc.2017.8122975","title":"Real-time head gesture recognition on head-mounted displays using cascaded hidden Markov models","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gesture; Computer science; Hidden Markov model; Gesture recognition; Head (geology); Computer vision; Artificial intelligence; Virtual reality; Speech recognition; Interface (matter); Context (archaeology)","score_opus":0.06984552115005388,"score_gpt":0.3324111742110956,"score_spread":0.26256565306104174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736737128","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":[0.66635364,0.00004614362,0.13881123,0.003192425,0.0016041407,0.0012033257,0.00007790484,0.000916051,0.18779515],"genre_scores_gemma":[0.95730144,0.000040793464,0.038019072,0.00043535893,0.0005040973,0.000040396626,0.000044701992,0.000043989072,0.0035701664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974343,0.00021733108,0.00044614397,0.0008019405,0.00057536917,0.0005249123],"domain_scores_gemma":[0.99748164,0.000150881,0.0003557949,0.0014852283,0.00027277408,0.0002536763],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000577825,0.00034508356,0.00042611,0.00019564267,0.0007772638,0.0009031635,0.001113039,0.00028687168,0.00008662427],"category_scores_gemma":[0.000084642954,0.00028903518,0.00015176802,0.00018219721,0.00006744214,0.0016573766,0.00027224285,0.00028622925,0.0008855462],"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":[0.0006639374,0.0016023121,0.0018611057,0.0005082375,0.000705514,0.0011237471,0.0060968604,0.000922242,0.12494136,0.016433964,0.032687068,0.8124536],"study_design_scores_gemma":[0.0033317544,0.00042544166,0.009046134,0.0013896974,0.0000757192,0.0006747493,0.00009642412,0.95570976,0.011612279,0.014165082,0.0016983697,0.001774586],"about_ca_topic_score_codex":0.001178273,"about_ca_topic_score_gemma":0.00020544366,"teacher_disagreement_score":0.9547875,"about_ca_system_score_codex":0.00015355235,"about_ca_system_score_gemma":0.000103358965,"threshold_uncertainty_score":0.9999562},"labels":[],"label_agreement":null},{"id":"W2741713923","doi":"10.4018/ijmdem.2017100101","title":"Multi-Sensor Motion Fusion Using Deep Neural Network Learning","year":2017,"lang":"en","type":"article","venue":"International Journal of Multimedia Data Engineering and Management","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Gesture; Pose; Deep learning; Computer vision; Artificial neural network; Recurrent neural network; Sensor fusion; Sequence (biology); Gesture recognition; Machine learning","score_opus":0.04823099369006916,"score_gpt":0.2945539856150802,"score_spread":0.24632299192501106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741713923","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":[0.011434146,0.00022404223,0.9845451,0.00033773485,0.0033177885,0.00007795314,0.0000037468785,0.000028258557,0.00003119584],"genre_scores_gemma":[0.55296046,0.00030200905,0.4458983,0.000028128614,0.0007608955,0.0000012210627,0.000013843873,0.000009040316,0.000026091484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990222,0.000027493626,0.00028020024,0.0001780454,0.0003575025,0.00013457997],"domain_scores_gemma":[0.99905896,0.000045992023,0.00033130095,0.00034034217,0.00014367359,0.00007972703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005484176,0.000098517114,0.00012573879,0.00014417619,0.00012055199,0.0004189111,0.0013109458,0.00003072104,0.00000279978],"category_scores_gemma":[0.00014178199,0.00008849635,0.00003301549,0.0000319049,0.000013619969,0.0009524179,0.00085646164,0.00015627591,0.000004046806],"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":[0.000019756424,0.00006798656,0.0030571744,0.000055292847,0.00037514672,0.00044548896,0.00032238886,0.28201777,0.0014035378,0.00018316822,0.00020487088,0.7118474],"study_design_scores_gemma":[0.00068789267,0.000013967052,0.012171647,0.00014087255,0.000017295582,0.000143034,0.000023447692,0.9803691,0.000026342204,0.000006090342,0.0063112755,0.00008904459],"about_ca_topic_score_codex":0.000013239652,"about_ca_topic_score_gemma":0.0000028106904,"teacher_disagreement_score":0.7117584,"about_ca_system_score_codex":0.00003311165,"about_ca_system_score_gemma":0.0000049706423,"threshold_uncertainty_score":0.40395695},"labels":[],"label_agreement":null},{"id":"W275054966","doi":"","title":"Online Hand Holding in Fixing Computer Glitches.","year":2005,"lang":"en","type":"article","venue":"Teacher librarian","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Internet privacy; Psychology; Multimedia","score_opus":0.029906237513375702,"score_gpt":0.24644309667166378,"score_spread":0.21653685915828808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W275054966","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":[0.49614498,0.00075385807,0.47510275,0.011454032,0.00085316563,0.00041787012,0.0000066099283,0.0007328902,0.014533828],"genre_scores_gemma":[0.9281211,0.0000031021523,0.06897229,0.0009173153,0.0011474879,0.0000088919805,0.000009409954,0.000016863034,0.0008035442],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99859846,0.00013568558,0.00033304672,0.0004021067,0.0001999911,0.000330701],"domain_scores_gemma":[0.9993026,0.0000690658,0.000076440236,0.0004104127,0.000018648814,0.0001227999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035420406,0.00016025161,0.00021657105,0.000202405,0.00007476216,0.00044253253,0.0006173088,0.0001116077,0.00007712004],"category_scores_gemma":[0.000014813116,0.00014968548,0.000056695513,0.00042041414,0.000028268414,0.0014302171,0.00021771748,0.00028248449,0.00017719495],"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":[0.000009149167,0.00080733746,0.029713262,0.00004720497,0.000049589053,0.00010365773,0.017426768,0.0004978414,0.0015713975,0.008375849,0.008676573,0.9327214],"study_design_scores_gemma":[0.0043384293,0.00015391033,0.12858127,0.00061494927,0.000013981521,0.00018446923,0.00036156413,0.33560497,0.007438497,0.0025745395,0.51859593,0.001537517],"about_ca_topic_score_codex":0.000035687277,"about_ca_topic_score_gemma":0.00006346361,"teacher_disagreement_score":0.9311839,"about_ca_system_score_codex":0.00004867242,"about_ca_system_score_gemma":0.00005841739,"threshold_uncertainty_score":0.61039966},"labels":[],"label_agreement":null},{"id":"W2765583527","doi":"10.1115/detc2017-67415","title":"Body Gesture-Based User Interaction in Design Review","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gesture; Naturalness; Computer science; Human–computer interaction; Process (computing); User interface; Interface (matter); Artificial intelligence; Programming language","score_opus":0.0687259402324585,"score_gpt":0.3306537846997938,"score_spread":0.2619278444673353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765583527","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":[0.00028972197,0.00071167835,0.97661126,0.0066753803,0.00068930443,0.00047205834,2.87904e-7,0.00010751013,0.014442792],"genre_scores_gemma":[0.9603818,0.00043501603,0.03332383,0.004156224,0.00011320944,0.00010868695,0.000001907855,0.000010248376,0.0014690888],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906695,0.00015942128,0.0002145229,0.000249454,0.00016670085,0.00014292475],"domain_scores_gemma":[0.99885243,0.00010135137,0.00014653316,0.0007724193,0.000073552284,0.00005369482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070849934,0.00009257455,0.00016938521,0.000070096874,0.000099040815,0.00026860778,0.00068052753,0.00004571812,0.00005668107],"category_scores_gemma":[0.00017004536,0.00007111961,0.000045519722,0.00008615854,0.0000130942835,0.00067854323,0.000068651905,0.0001120698,0.00042215319],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006701825,0.00092226756,0.03170586,0.002600136,0.000095317686,0.00045600542,0.0006673803,0.00031385472,0.004007865,0.023996137,0.21926159,0.71590656],"study_design_scores_gemma":[0.0040152203,0.00041035737,0.13689604,0.016415734,0.000044339497,0.00019492646,0.00003534069,0.06893109,0.026998306,0.0021208567,0.74221987,0.0017179503],"about_ca_topic_score_codex":0.00006391239,"about_ca_topic_score_gemma":0.00005027369,"teacher_disagreement_score":0.96009207,"about_ca_system_score_codex":0.000033886558,"about_ca_system_score_gemma":0.000050839666,"threshold_uncertainty_score":0.5426066},"labels":[],"label_agreement":null},{"id":"W2767120470","doi":"10.1155/2017/2162078","title":"Hand Motion and Posture Recognition in a Network of Calibrated Cameras","year":2017,"lang":"en","type":"article","venue":"Advances in Multimedia","topic":"Hand Gesture Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer vision; Gesture recognition; Computer science; Trajectory; Gesture; Motion (physics); Feature (linguistics); Tracking (education); Kalman filter; Pattern recognition (psychology)","score_opus":0.015091563268653917,"score_gpt":0.2747001742552807,"score_spread":0.2596086109866268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767120470","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":[0.8698884,0.008214462,0.11379883,0.0008059923,0.0023584529,0.0010425386,0.000027621942,0.00008499488,0.0037786602],"genre_scores_gemma":[0.98178446,0.0003938454,0.017645052,0.000039610964,0.00008836934,0.000022863023,0.000008984842,0.000005224108,0.000011565608],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99906975,0.00009826393,0.00026349365,0.0002610675,0.00012859727,0.00017881262],"domain_scores_gemma":[0.9992349,0.00014277291,0.0002210531,0.00027913906,0.00007515811,0.000046977417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031640686,0.00009846221,0.00020398892,0.000074461626,0.00008031882,0.0000871588,0.00026286423,0.00008523648,0.0000041711496],"category_scores_gemma":[0.00021786451,0.00009020233,0.00001681628,0.00017197605,0.0001104639,0.0012471043,0.0000800442,0.00012907156,0.000007931167],"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":[0.00002047661,0.00004531067,0.116605006,0.00004556378,0.0000043279583,0.000026974636,0.0011778694,0.00019106193,0.0007212039,0.0000687912,0.000016497786,0.88107693],"study_design_scores_gemma":[0.004660188,0.00017446633,0.8082632,0.0011657759,0.000009683337,0.000060665734,0.00017894838,0.15978745,0.0068820966,0.01606087,0.0021619163,0.00059475994],"about_ca_topic_score_codex":0.00015149152,"about_ca_topic_score_gemma":0.0010803214,"teacher_disagreement_score":0.88048214,"about_ca_system_score_codex":0.000018014744,"about_ca_system_score_gemma":0.000016671756,"threshold_uncertainty_score":0.36783445},"labels":[],"label_agreement":null},{"id":"W2770028763","doi":"10.1007/978-3-319-70353-4_5","title":"Real Time Continuous Tracking of Dynamic Hand Gestures on a Mobile GPU","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Gesture; Expansive; Computer vision; Tracking (education); Mobile device; Encoding (memory); Scheme (mathematics); Artificial intelligence; Computer graphics (images); Limiting; Gesture recognition; Operating system","score_opus":0.014285082835909445,"score_gpt":0.26059110947467884,"score_spread":0.24630602663876938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770028763","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":[0.0016066078,0.00038613414,0.97831005,0.00020851499,0.0020427096,0.0008837492,0.00002388389,0.00016986365,0.016368503],"genre_scores_gemma":[0.9128413,0.00011569579,0.082951374,0.0003156492,0.00062821293,0.000037419475,0.000013601087,0.00007550741,0.0030211937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958981,0.000067437526,0.0006898404,0.0015328004,0.0011980691,0.0006137314],"domain_scores_gemma":[0.9958412,0.0006858781,0.0008357358,0.002033029,0.00042771327,0.0001764338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010680354,0.00059136347,0.0009799779,0.0008318545,0.00039187924,0.0009092527,0.0037641262,0.00046826678,0.00001803066],"category_scores_gemma":[0.0001221361,0.00050531083,0.00021815624,0.00020592636,0.0009993776,0.00049270084,0.0007324134,0.0007302797,0.00011739986],"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":[0.000014484424,0.0000703274,0.000022491968,0.000080169615,0.000028553739,0.0001614932,0.0017542894,0.0021930835,0.0029123006,0.0013837678,0.000038510498,0.9913405],"study_design_scores_gemma":[0.0057138647,0.006166422,0.003704031,0.01742281,0.00014830018,0.0015806152,0.0000031118914,0.67544687,0.041958105,0.22039594,0.019935083,0.0075248796],"about_ca_topic_score_codex":0.000043462886,"about_ca_topic_score_gemma":0.00007546247,"teacher_disagreement_score":0.98381567,"about_ca_system_score_codex":0.00020053731,"about_ca_system_score_gemma":0.00046372673,"threshold_uncertainty_score":0.9997398},"labels":[],"label_agreement":null},{"id":"W2773371143","doi":"10.1109/smc.2017.8122603","title":"Multimodal interaction in augmented reality","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Augmented reality; Modalities; Computer science; Human–computer interaction; Gesture; Multimodal interaction; Everyday life; Virtual reality; Modality (human–computer interaction); Assisted living; Multimedia; Computer-mediated reality; Mobile interaction; Cloud computing; Mobile device; Mixed reality; World Wide Web; Artificial intelligence","score_opus":0.057993324303211725,"score_gpt":0.3409582011009458,"score_spread":0.2829648767977341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773371143","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":[0.118675925,0.000007793556,0.6625827,0.0060009942,0.0018823404,0.00029546305,0.0000016559026,0.0002184897,0.21033466],"genre_scores_gemma":[0.9963717,0.0000016411371,0.0028178452,0.00007900159,0.000044496675,0.000010054827,9.4862247e-7,0.0000016592912,0.0006726831],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994382,0.00004781803,0.00013361266,0.00017771109,0.00010192488,0.00010073073],"domain_scores_gemma":[0.99930394,0.00002784156,0.00008116391,0.0005126492,0.00003736045,0.00003705005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027427025,0.000048960217,0.0000753959,0.000050325234,0.00009383799,0.0002334781,0.0004225632,0.00003239027,0.000024154835],"category_scores_gemma":[0.00006413225,0.000040465617,0.00002220722,0.00003803977,0.000012117758,0.0006972815,0.000117438605,0.00006831159,0.00017365749],"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":[0.000030549403,0.00037057025,0.07285987,0.00004005544,0.00003115043,0.00008125486,0.0018744223,0.00005174171,0.003689934,0.025092322,0.00537372,0.8905044],"study_design_scores_gemma":[0.0018698671,0.000059978094,0.7890793,0.00012938467,0.0000022862737,0.000046777866,0.00013824052,0.16766682,0.009527135,0.002673949,0.028455574,0.0003506925],"about_ca_topic_score_codex":0.0010225524,"about_ca_topic_score_gemma":0.000810511,"teacher_disagreement_score":0.8901537,"about_ca_system_score_codex":0.000033433833,"about_ca_system_score_gemma":0.000013601429,"threshold_uncertainty_score":0.22514348},"labels":[],"label_agreement":null},{"id":"W2774809150","doi":"10.1109/smc.2017.8122753","title":"Pilot study on fine finger movement regression, using FMG","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Computer science; Movement (music); Physics","score_opus":0.13669395827748151,"score_gpt":0.3469618084627248,"score_spread":0.21026785018524327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774809150","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":[0.7886244,0.000030687017,0.17174281,0.0021036041,0.0024585081,0.00082304777,0.0000017565974,0.00023768541,0.03397749],"genre_scores_gemma":[0.9936447,5.510792e-7,0.0033673814,0.00037375503,0.00015194493,0.00001407015,2.6872857e-7,0.000006291629,0.0024410398],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988604,0.000069328584,0.00019384472,0.00034116383,0.0003562589,0.00017897476],"domain_scores_gemma":[0.9984808,0.000049209644,0.00015818902,0.0011470591,0.0000877283,0.00007699701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040174092,0.00012118881,0.0001482946,0.00006566517,0.00045967518,0.00042313908,0.00077205145,0.000014998032,0.00004885873],"category_scores_gemma":[0.00007416035,0.00008022103,0.000026368843,0.00005740267,0.000015441568,0.00029771964,0.00030954907,0.00008142631,0.00020873858],"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":[0.00028804713,0.021023376,0.32165197,0.00017108326,0.0007228754,0.0011355366,0.016788268,0.00054550025,0.08023795,0.043982815,0.10067814,0.41277444],"study_design_scores_gemma":[0.012378079,0.016510716,0.82719195,0.0019572398,0.00007349468,0.00006362188,0.0016076473,0.051094495,0.06171057,0.0080146,0.016499955,0.0028976512],"about_ca_topic_score_codex":0.00023364821,"about_ca_topic_score_gemma":0.000098976656,"teacher_disagreement_score":0.50553995,"about_ca_system_score_codex":0.00002912421,"about_ca_system_score_gemma":0.000027735154,"threshold_uncertainty_score":0.40803397},"labels":[],"label_agreement":null},{"id":"W2775357457","doi":"10.1109/roman.2017.8172430","title":"Towards the use of consumer-grade electromyographic armbands for interactive, artistic robotics performances","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Université Laval","funders":"","keywords":"Computer science; Artificial intelligence; Leverage (statistics); Inertial measurement unit; Robot; Robotics; Random forest; Computer vision; Human–computer interaction; Gesture recognition; Gesture; Dimensionality reduction; Support vector machine","score_opus":0.10521858976039929,"score_gpt":0.3060967756636234,"score_spread":0.20087818590322415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775357457","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":[0.016765926,0.000103490456,0.9788537,0.0019792952,0.00067225733,0.00043020144,0.000009161378,0.000058118047,0.0011278291],"genre_scores_gemma":[0.9716103,0.000076768076,0.027805928,0.00011654273,0.000055147822,0.000045579218,0.0000018770613,0.0000055819637,0.0002822489],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991119,0.000048638627,0.00024365482,0.00020007425,0.0001839787,0.00021175231],"domain_scores_gemma":[0.9984141,0.00036690003,0.0002752984,0.0006849805,0.00020854255,0.000050162103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025957482,0.000111108675,0.000192182,0.00007935733,0.0003437248,0.0004371351,0.000839214,0.00004262209,0.0000049159976],"category_scores_gemma":[0.00019964323,0.00006630752,0.00010577401,0.00010186924,0.00014262545,0.00069450826,0.00010702964,0.000100129444,0.000006829045],"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":[0.00016757926,0.0006640995,0.046208486,0.0005035879,0.0010224669,0.000013931312,0.0032363462,0.0008440136,0.004779815,0.2618974,0.022710223,0.6579521],"study_design_scores_gemma":[0.0035844108,0.0016008195,0.109775886,0.00050002465,0.00023071446,0.00024383896,0.00023759456,0.5979496,0.055487137,0.017324802,0.21176003,0.001305123],"about_ca_topic_score_codex":0.000056546116,"about_ca_topic_score_gemma":0.00007663786,"teacher_disagreement_score":0.9548444,"about_ca_system_score_codex":0.0000092549035,"about_ca_system_score_gemma":0.000057820816,"threshold_uncertainty_score":0.4215304},"labels":[],"label_agreement":null},{"id":"W2775790100","doi":"10.1145/3150165.3150169","title":"ThirdLight","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Engineering and Physical Sciences Research Council; Royal Society","keywords":"Computer science; Robustness (evolution); Computer vision; Scalability; Artificial intelligence; Gesture; Virtual reality; Gesture recognition; Inverse kinematics; Robot","score_opus":0.025621659236093098,"score_gpt":0.27506532441955783,"score_spread":0.24944366518346472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775790100","genre_codex":"other","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":[0.00071133336,0.000013192423,0.3825165,0.0032887508,0.00051413587,0.000034181976,1.2135537e-7,0.00011018898,0.61281157],"genre_scores_gemma":[0.97829294,0.0000019317429,0.010433067,0.000278253,0.00008825903,0.0000034715936,8.590179e-8,0.0000014503318,0.010900563],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999671,0.0000097753755,0.0000505888,0.00010830864,0.00008537564,0.000074990036],"domain_scores_gemma":[0.9992155,0.000011990772,0.000037708094,0.00066653994,0.00003060235,0.000037682723],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011229803,0.000031297244,0.000043437685,0.0000151569,0.0002043908,0.00037287816,0.00076335727,0.000019907895,0.000036680703],"category_scores_gemma":[0.000025413307,0.000021957954,0.00001946014,0.000014951574,0.000011553607,0.00038238743,0.00012496192,0.000025550358,0.0014469773],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.944324e-7,0.000027551147,0.0046486175,0.0000072966554,0.000014419272,0.000030910218,0.00034406784,3.1465154e-7,0.000651554,0.6937468,0.036678657,0.26384914],"study_design_scores_gemma":[0.00053330645,0.00003848382,0.06341903,0.00002952838,0.000002379824,0.000079087964,0.000016132788,0.0031812198,0.01980892,0.022910422,0.8896594,0.000322105],"about_ca_topic_score_codex":0.000013209925,"about_ca_topic_score_gemma":0.000009798612,"teacher_disagreement_score":0.97758156,"about_ca_system_score_codex":0.0000030995027,"about_ca_system_score_gemma":0.000011135524,"threshold_uncertainty_score":0.9993305},"labels":[],"label_agreement":null},{"id":"W2781010022","doi":"10.1109/iccv.2017.277","title":"Low-Dimensionality Calibration through Local Anisotropic Scaling for Robust Hand Model Personalization","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Curse of dimensionality; Calibration; Differentiable function; Tracking (education); Convergence (economics); Algorithm; Pipeline (software); Computer vision; Artificial intelligence; Mathematics","score_opus":0.06594983517673404,"score_gpt":0.29152696214237334,"score_spread":0.2255771269656393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781010022","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":[0.0043836595,0.000029642395,0.9925532,0.0012721417,0.00032856863,0.00031889303,0.000008718748,0.00009830743,0.0010069069],"genre_scores_gemma":[0.84947944,0.0000032520254,0.14925845,0.00044327189,0.00013159639,0.000034792563,0.0000219302,0.000009100777,0.0006181923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988652,0.00004266307,0.00023396389,0.00038694494,0.00027379696,0.00019741752],"domain_scores_gemma":[0.99897397,0.000054850923,0.00016135469,0.00050115923,0.00024092576,0.0000677473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002435769,0.00012398147,0.00015902685,0.000027004258,0.00090701616,0.00069942075,0.000414878,0.00009921159,0.000011590859],"category_scores_gemma":[0.000060893806,0.00010492618,0.00007538934,0.00005187663,0.000082401544,0.0014784816,0.00010952613,0.000051225128,0.00001484248],"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":[0.000080881095,0.000424962,0.001339804,0.00055622356,0.000119394914,0.000010451895,0.0053294045,0.22322212,0.008849751,0.7023752,0.01002929,0.047662567],"study_design_scores_gemma":[0.0005230362,0.000023038338,0.0002189853,0.00004718325,0.0000062070617,0.000006091235,0.000037104568,0.9814751,0.008077723,0.009128883,0.00030978268,0.00014684927],"about_ca_topic_score_codex":0.00006308705,"about_ca_topic_score_gemma":0.00006135765,"teacher_disagreement_score":0.84509575,"about_ca_system_score_codex":0.000043741067,"about_ca_system_score_gemma":0.00010171439,"threshold_uncertainty_score":0.6976127},"labels":[],"label_agreement":null},{"id":"W2783130215","doi":"10.1145/3161412","title":"SHOW","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China","keywords":"Computer science; Speech recognition; Task (project management); Handwriting; Word error rate; Input method; Character (mathematics); Gesture; Computer vision; Artificial intelligence","score_opus":0.013499525287456057,"score_gpt":0.25598521645805583,"score_spread":0.24248569117059976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783130215","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":[0.98053896,0.00025912377,0.0002090745,0.0031236147,0.000515529,0.0005131514,0.0000032839903,0.000707593,0.014129689],"genre_scores_gemma":[0.9968057,0.00009363712,0.0024005573,0.0000976853,0.000060232935,0.00014339457,5.5702884e-8,0.000009096137,0.0003896079],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989242,0.000008275224,0.00021166386,0.00038985696,0.00022487772,0.00024114907],"domain_scores_gemma":[0.9986159,0.00014156176,0.00023903817,0.0005958655,0.00038445005,0.000023187484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021424082,0.00016510344,0.00022656943,0.0001762891,0.00018764088,0.00013926165,0.002562665,0.00012542798,0.0000051284233],"category_scores_gemma":[0.000918327,0.00010377534,0.00006613639,0.00046478483,0.00033328356,0.0004959085,0.0019598692,0.00026334077,0.000032760396],"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":[0.00017756762,0.0003936583,0.0063878014,0.00023581594,0.00021805684,0.0000029007679,0.004458955,0.0000020157383,0.3601148,0.03738811,0.020912746,0.5697076],"study_design_scores_gemma":[0.00017202135,0.0008393788,0.00062709866,0.00039490213,0.000007969123,0.000048435188,0.0025378522,0.00018155645,0.9463439,0.03956295,0.009120845,0.0001631016],"about_ca_topic_score_codex":0.000010207239,"about_ca_topic_score_gemma":0.0000022391405,"teacher_disagreement_score":0.5862291,"about_ca_system_score_codex":0.000034846387,"about_ca_system_score_gemma":0.000016574124,"threshold_uncertainty_score":0.47621113},"labels":[],"label_agreement":null},{"id":"W2786611825","doi":"10.1109/crv.2017.30","title":"A Computer Vision System for Virtual Rehabilitation","year":2017,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Centre for Interdisciplinary Research in Rehabilitation; Université Laval","funders":"","keywords":"Context (archaeology); Computer science; Robot; Computer vision; Ground truth; Rehabilitation; Virtual reality; Artificial intelligence; Human–computer interaction; Machine vision; Pose; Movement (music); Psychology","score_opus":0.0165828872953879,"score_gpt":0.28504610499348176,"score_spread":0.2684632176980939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786611825","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":[0.006998303,0.0000045088113,0.98522085,0.0014994794,0.0016777333,0.00039354365,0.0000024119129,0.00021765205,0.003985519],"genre_scores_gemma":[0.86689854,1.7374913e-7,0.13227125,0.000065244945,0.00024750797,0.000052574273,0.0000016249406,0.000004863067,0.00045823425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991695,0.000048117778,0.00019192527,0.0002882827,0.00016594269,0.00013619887],"domain_scores_gemma":[0.99863875,0.00026258445,0.00013165473,0.0007248177,0.0001805656,0.00006164147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005128405,0.000078876576,0.0001322853,0.000053809857,0.00037593156,0.0005676763,0.00061646645,0.000053967782,0.0000021298554],"category_scores_gemma":[0.000066504115,0.00006132068,0.00007456641,0.000033251712,0.000026019792,0.0006756908,0.00013480551,0.000031414056,0.00015321463],"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":[0.000025032246,0.000081921775,0.0004562884,0.00021307301,0.000027034046,0.0000055831815,0.0013026533,0.000043958204,0.0008203971,0.4053837,0.018212182,0.57342815],"study_design_scores_gemma":[0.0043047476,0.0038013626,0.026831957,0.00076960545,0.000017792687,0.00013163277,0.00052677677,0.83853775,0.0028607983,0.0034183047,0.117936715,0.0008625658],"about_ca_topic_score_codex":0.0000124984745,"about_ca_topic_score_gemma":0.000008629758,"teacher_disagreement_score":0.85990024,"about_ca_system_score_codex":0.00003319865,"about_ca_system_score_gemma":0.000021700374,"threshold_uncertainty_score":0.54741156},"labels":[],"label_agreement":null},{"id":"W2790851295","doi":"10.1049/iet-smt.2017.0373","title":"Towards a classification of surgical skills using affine velocity","year":2018,"lang":"en","type":"article","venue":"IET Science Measurement & Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Affine transformation; Computer science; Artificial intelligence; Medicine; Medical physics; Mathematics; Geometry","score_opus":0.06565883547245406,"score_gpt":0.3083097243239566,"score_spread":0.24265088885150254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790851295","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":[0.4758081,0.00011314789,0.50979114,0.0044272994,0.0012493765,0.00049203343,0.000002797207,0.0005248946,0.0075912103],"genre_scores_gemma":[0.97593313,0.0000063604566,0.023912538,0.000029689998,0.000084549756,0.000015311884,2.2395382e-7,0.0000048861234,0.000013286904],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970434,0.00006608957,0.00042486738,0.00061956956,0.0014023748,0.00044370143],"domain_scores_gemma":[0.9971012,0.000016775954,0.00026076342,0.0008022366,0.0017269301,0.00009211949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031220396,0.00014432136,0.0002550598,0.00074160146,0.0002655716,0.00007285714,0.0016805293,0.00015302328,0.00001533721],"category_scores_gemma":[0.00032356835,0.00012319301,0.00005046278,0.003858675,0.0014414167,0.0004315354,0.00032330776,0.0001421186,0.000052346106],"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":[0.000008586035,0.00034726143,0.0031384025,0.000023804489,0.000020618936,0.00000950129,0.00063346163,0.0000056597983,0.7456807,0.03929888,0.00017596433,0.21065716],"study_design_scores_gemma":[0.0012289974,0.0006789179,0.01862654,0.00025162837,0.000022088063,0.0003274801,0.0002336965,0.028964328,0.9181118,0.012207456,0.018763201,0.0005838724],"about_ca_topic_score_codex":0.000021824715,"about_ca_topic_score_gemma":0.000022908313,"teacher_disagreement_score":0.50012505,"about_ca_system_score_codex":0.0003030897,"about_ca_system_score_gemma":0.0006354593,"threshold_uncertainty_score":0.531096},"labels":[],"label_agreement":null},{"id":"W2792397721","doi":"10.1007/s10055-018-0339-2","title":"Virtual grasps recognition using fusion of Leap Motion and force myography","year":2018,"lang":"en","type":"article","venue":"Virtual Reality","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Linear discriminant analysis; Wearable computer; Virtual reality; Motion capture; Motion (physics); Classifier (UML); Pattern recognition (psychology)","score_opus":0.05604011449351804,"score_gpt":0.2896654805709181,"score_spread":0.2336253660774001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792397721","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":[0.47925436,0.000020196805,0.5190953,0.0000787114,0.00034882114,0.00015544261,0.000019068111,0.000073980096,0.0009541452],"genre_scores_gemma":[0.99717706,0.000021931979,0.0024369147,0.00008397963,0.00021999636,0.00000598613,0.000017657798,0.000008734434,0.00002774625],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982849,0.00027171697,0.0004042374,0.00045477675,0.00036563864,0.00021876334],"domain_scores_gemma":[0.99877536,0.00012331505,0.0002416662,0.00039441965,0.0003430497,0.00012220182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009211305,0.00015208955,0.0002347683,0.00021518413,0.00017583823,0.00007193445,0.0002211242,0.00013536075,0.00001311192],"category_scores_gemma":[0.00013340729,0.00014007532,0.00007973131,0.00060415553,0.00020993136,0.0005579222,0.00015826513,0.00010569222,0.000021985694],"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":[0.000072524526,0.00016590473,0.0019654194,0.000055758774,0.000043707132,0.0000035029439,0.0020520762,0.000013188544,0.042238947,0.0077757295,0.00022338235,0.94538987],"study_design_scores_gemma":[0.009529746,0.011381701,0.18637906,0.002211069,0.00034270986,0.00065686816,0.0037329155,0.273241,0.3939072,0.10672763,0.007768354,0.0041217203],"about_ca_topic_score_codex":0.00024054536,"about_ca_topic_score_gemma":0.000058311347,"teacher_disagreement_score":0.94126815,"about_ca_system_score_codex":0.0000280285,"about_ca_system_score_gemma":0.000039522256,"threshold_uncertainty_score":0.57121056},"labels":[],"label_agreement":null},{"id":"W2795097182","doi":"","title":"Robust Gesture Recognition For Non-Contact Augmentative Communication Using a Dynamic Binary Frame of Reference","year":2005,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Gesture; Frame of reference; Computer vision; Computer science; Artificial intelligence; Frame (networking); Reference frame; Augmentative and alternative communication; Communication; Gesture recognition; Binary number; Speech recognition; Human–computer interaction; Psychology; Mathematics; Arithmetic; Telecommunications","score_opus":0.07179519914365558,"score_gpt":0.30276508489835113,"score_spread":0.23096988575469557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795097182","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":[0.86490184,0.0002588713,0.13019016,0.0007240077,0.00013698569,0.0010818965,0.000036938574,0.00015481067,0.0025144874],"genre_scores_gemma":[0.85054886,0.000051122362,0.14901319,0.00012263996,0.00005419941,0.00010547715,0.00003208003,0.000014768942,0.00005765158],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987776,0.00002216096,0.00039348513,0.00035688895,0.00021728197,0.00023256027],"domain_scores_gemma":[0.9984675,0.00012328109,0.0004004646,0.00020678437,0.00073163724,0.00007035294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004102897,0.0001759865,0.00026371083,0.00017826851,0.00017467837,0.00011857361,0.00055052683,0.00015166133,0.000008161034],"category_scores_gemma":[0.000067787274,0.00016847251,0.00007334043,0.0003823948,0.00004776555,0.0012269575,0.00012729887,0.00018966911,0.000021048616],"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":[0.00038462295,0.0011907349,0.0043436848,0.0020994195,0.00039867358,0.0000034544576,0.04684938,0.00033143157,0.66229105,0.009147965,0.004311742,0.26864782],"study_design_scores_gemma":[0.0055467584,0.0016239797,0.022776285,0.0047424203,0.00023621897,0.000238745,0.0073134406,0.8209947,0.11890478,0.0096994685,0.0059709502,0.0019522703],"about_ca_topic_score_codex":0.000023105338,"about_ca_topic_score_gemma":0.000013260764,"teacher_disagreement_score":0.8206633,"about_ca_system_score_codex":0.00011535905,"about_ca_system_score_gemma":0.00006120622,"threshold_uncertainty_score":0.687011},"labels":[],"label_agreement":null},{"id":"W2797984302","doi":"10.1016/j.patcog.2018.04.012","title":"Personal digital bodyguards for e-security, e-learning and e-health: A prospective survey","year":2018,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Handwriting; Computer science; Human–computer interaction; Artificial intelligence; Handwriting recognition; Multimedia; Feature extraction","score_opus":0.051200546123156575,"score_gpt":0.31288313686257163,"score_spread":0.2616825907394151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797984302","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":[0.18365264,0.0001618592,0.80972236,0.0012320152,0.00083864445,0.0012509372,0.00037481502,0.00033671246,0.0024299999],"genre_scores_gemma":[0.998067,0.000014597844,0.0007811351,0.00028655442,0.0004656204,0.00011617003,0.00018050244,0.000016630058,0.0000717582],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99856234,0.0001423195,0.0002527001,0.0004940373,0.00023409576,0.00031448185],"domain_scores_gemma":[0.9989375,0.00019625307,0.00016489225,0.00009878629,0.00046702745,0.00013554725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009306386,0.00015617059,0.000205072,0.000114842616,0.00027076,0.00043252084,0.00013604705,0.000076071046,0.000025541736],"category_scores_gemma":[0.0001584573,0.00015220523,0.00005572439,0.00019581546,0.000060105565,0.0005765783,0.00009252053,0.00014158047,0.00019070973],"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":[0.000020830683,0.000069209105,0.11214403,0.00009168476,0.000036485995,0.0000018831324,0.003937812,1.4563355e-8,0.000019395591,0.00001724656,0.0009355039,0.8827259],"study_design_scores_gemma":[0.008799059,0.007624242,0.86359936,0.0015276022,0.000049655435,0.0007716764,0.0016625508,0.051250014,0.003266714,0.033193573,0.025433298,0.0028222578],"about_ca_topic_score_codex":0.00009132147,"about_ca_topic_score_gemma":0.00023426677,"teacher_disagreement_score":0.8799036,"about_ca_system_score_codex":0.000059363418,"about_ca_system_score_gemma":0.00006275112,"threshold_uncertainty_score":0.6206749},"labels":[],"label_agreement":null},{"id":"W2798961521","doi":"10.1145/3170427.3188498","title":"Kalgan","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.01425229694948276,"score_gpt":0.24361595580155454,"score_spread":0.22936365885207177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798961521","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":[0.0016692554,0.0000061553264,0.68284935,0.00059992814,0.0003529736,0.000025942438,6.6002215e-8,0.00015553695,0.31434077],"genre_scores_gemma":[0.97019416,3.8024854e-7,0.024357077,0.0007964669,0.00024128219,0.000002411117,1.2434583e-7,0.0000015358191,0.0044065653],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996675,0.000014959223,0.000054308224,0.00010463063,0.0000793264,0.000079266145],"domain_scores_gemma":[0.9996822,0.0000130967555,0.0000112687,0.00019567528,0.00005976555,0.000038026927],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000092905284,0.000028161745,0.000033698867,0.000025852296,0.00003693798,0.000057379406,0.0002314769,0.000015827947,0.00011497145],"category_scores_gemma":[0.000007865181,0.000020595335,0.000012768111,0.00013868524,0.000014773796,0.00013551596,0.000046511734,0.000016625676,0.0037114425],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001185073,0.000039938353,0.0011077446,0.0000056596396,0.000014725398,0.000009458386,0.0013264247,9.8860326e-8,0.0029688054,0.47145393,0.0766764,0.4463956],"study_design_scores_gemma":[0.00028596635,0.00014869231,0.004142201,0.000013140913,0.0000013177652,0.00008333453,0.000032963308,0.0043830373,0.045382593,0.01095948,0.9343467,0.00022057623],"about_ca_topic_score_codex":0.00000563887,"about_ca_topic_score_gemma":0.000009447788,"teacher_disagreement_score":0.9685249,"about_ca_system_score_codex":0.0000036724614,"about_ca_system_score_gemma":0.000011158477,"threshold_uncertainty_score":0.9970643},"labels":[],"label_agreement":null},{"id":"W2799176589","doi":"10.1109/icit.2018.8352174","title":"Hand gesture recognition using force myography of the forearm activities and optimized features","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":30,"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 Windsor","funders":"Simon Fraser University","keywords":"Gesture; Computer science; Gesture recognition; Feature extraction; Artificial intelligence; Graph; Pattern recognition (psychology); Computer vision","score_opus":0.02249332448376333,"score_gpt":0.24453060573758925,"score_spread":0.22203728125382594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799176589","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":[0.58634996,0.000362009,0.39826638,0.00058942597,0.000814125,0.00057663897,0.000014589407,0.000113021415,0.012913836],"genre_scores_gemma":[0.9626785,0.000010026497,0.036708012,0.0001933485,0.0001285825,0.0000080760565,0.0000011194415,0.0000067203923,0.00026562327],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991828,0.00009514704,0.00015142554,0.00021876706,0.00019862878,0.00015321565],"domain_scores_gemma":[0.9993104,0.000102517566,0.0001233749,0.00027170253,0.00014808826,0.00004396336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020842727,0.00011282607,0.00016265368,0.00010235936,0.00023119182,0.00016341747,0.00024310892,0.00008609999,0.000012063714],"category_scores_gemma":[0.000026870635,0.00006846007,0.000078676334,0.00034834718,0.00023566725,0.00035731294,0.0001285053,0.00008718638,0.0000015262261],"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":[0.0003845256,0.0004676701,0.01644258,0.0006659505,0.0009155878,0.00001185407,0.036118455,0.00008801282,0.21039313,0.011638749,0.010750549,0.7121229],"study_design_scores_gemma":[0.00463429,0.000655818,0.042979266,0.0008881306,0.00016188438,0.0007092773,0.0022262,0.019000573,0.89887464,0.025846694,0.0028708135,0.0011524064],"about_ca_topic_score_codex":0.00005391689,"about_ca_topic_score_gemma":0.000055882476,"teacher_disagreement_score":0.7109705,"about_ca_system_score_codex":0.000007748402,"about_ca_system_score_gemma":0.000026152444,"threshold_uncertainty_score":0.27917206},"labels":[],"label_agreement":null},{"id":"W2800899095","doi":"10.1093/sleep/zsy061.677","title":"0678 Contactless 3D Detection Of Leg Movements In Sleep","year":2018,"lang":"en","type":"article","venue":"SLEEP","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 British Columbia","funders":"","keywords":"Sleep (system call); Physical medicine and rehabilitation; Polysomnography; Medicine; Neuroscience; Psychology; Computer science; Electroencephalography","score_opus":0.013500583704152731,"score_gpt":0.24372668419468407,"score_spread":0.23022610049053135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800899095","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":[0.5420742,0.0000712014,0.44487035,0.00007790557,0.0016682451,0.0002668656,0.0000031275356,0.000075354896,0.010892712],"genre_scores_gemma":[0.9988519,0.0000020351276,0.0007602436,0.00011970299,0.00012809891,0.000015948272,9.737705e-7,0.0000052522314,0.000115842064],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990571,0.00007389441,0.00025325693,0.00022160854,0.00021980506,0.00017431968],"domain_scores_gemma":[0.9994113,0.000025958001,0.00010815777,0.0002886633,0.000119719305,0.000046187834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027341116,0.00008232319,0.00014422783,0.0001426357,0.00003696602,0.00003196223,0.00029435658,0.00005988587,0.00002482851],"category_scores_gemma":[0.000022334782,0.00007700588,0.00003313029,0.00035065782,0.0000300109,0.00024739504,0.00007886476,0.000068292655,0.00018603815],"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":[0.000042498235,0.00029881552,0.019578135,0.00007531934,0.000060984057,0.000019912874,0.0052918787,0.0000314063,0.09216896,0.0019459665,0.000093978306,0.88039213],"study_design_scores_gemma":[0.0053008515,0.0011696062,0.14418039,0.0003304743,0.000016129428,0.000045382374,0.0005592553,0.08777831,0.7217181,0.0034330494,0.034542087,0.0009263677],"about_ca_topic_score_codex":0.00014061072,"about_ca_topic_score_gemma":0.0004166637,"teacher_disagreement_score":0.87946576,"about_ca_system_score_codex":0.000041069932,"about_ca_system_score_gemma":0.000012346963,"threshold_uncertainty_score":0.31402087},"labels":[],"label_agreement":null},{"id":"W2801263210","doi":"10.1007/s12652-018-0796-1","title":"A novel depth image analysis for sleep posture estimation","year":2018,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Feature selection; Support vector machine; Frequency domain; Feature extraction; Feature vector; Feature (linguistics); Curse of dimensionality; Data mining; Point cloud; Domain (mathematical analysis); Computer vision; Mathematics","score_opus":0.04406537426225268,"score_gpt":0.31288344675482876,"score_spread":0.26881807249257605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801263210","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":[0.05653992,0.00013068365,0.9423184,0.00019210755,0.00046961659,0.00015073745,0.0000013188779,0.00002776869,0.00016948792],"genre_scores_gemma":[0.7331322,0.0000069104462,0.2663443,0.00015967031,0.00033145034,9.80447e-7,0.0000013401857,0.0000054433845,0.000017690685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984719,0.000046320318,0.0006998927,0.00024198003,0.0003129933,0.00022687713],"domain_scores_gemma":[0.99766415,0.00023877602,0.0006981921,0.00018890329,0.001099022,0.00011096986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010513185,0.00014431035,0.0003679757,0.00046900698,0.00029327284,0.0003923366,0.00046969636,0.00005906938,0.000008446713],"category_scores_gemma":[0.00014212726,0.00011756897,0.00021052247,0.00063521904,0.00006538984,0.00046902135,0.00010738846,0.00014881721,0.000009998534],"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":[0.00023552816,0.00069708016,0.0028847673,0.00023201555,0.0025529992,0.000043226544,0.031257737,0.0077213473,0.030601406,0.03711385,0.0007351381,0.88592494],"study_design_scores_gemma":[0.0006105654,0.00095636764,0.004766074,0.00020230658,0.00030708712,0.00033260253,0.0004836692,0.97320753,0.013943057,0.003961905,0.0009181331,0.00031072786],"about_ca_topic_score_codex":0.000007769247,"about_ca_topic_score_gemma":0.000016635315,"teacher_disagreement_score":0.96548617,"about_ca_system_score_codex":0.000037900307,"about_ca_system_score_gemma":0.00003932426,"threshold_uncertainty_score":0.47943234},"labels":[],"label_agreement":null},{"id":"W2810206268","doi":"10.1109/icpcsi.2017.8392033","title":"Design and implementation of a sign-to-speech/text system for deaf and dumb people","year":2017,"lang":"en","type":"article","venue":"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Patient Safety Institute","keywords":"Gesture; Sign language; Interpreter; Computer science; American Sign Language; Gesture recognition; Sign (mathematics); Assistive technology; Wired glove; Speech synthesis; Human–computer interaction; Manual communication; Multimedia; Speech recognition; Artificial intelligence; Linguistics; Programming language","score_opus":0.03490298165725236,"score_gpt":0.30382016837561765,"score_spread":0.2689171867183653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810206268","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":[0.22243719,0.000042277195,0.7748297,0.00082227046,0.0007410814,0.0008284898,0.00006786025,0.000046874906,0.0001842577],"genre_scores_gemma":[0.99064237,0.000060897033,0.008961126,0.000090129884,0.00005750566,0.0001356095,0.0000068360514,0.000013489466,0.000032065578],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986158,0.000047442973,0.00042432698,0.00039617956,0.00031367634,0.00020254689],"domain_scores_gemma":[0.9986832,0.0001911556,0.00037579203,0.00024117396,0.0003627681,0.00014590299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005460197,0.0002108995,0.0003075951,0.00022615578,0.00020319631,0.0006401789,0.00036748318,0.000071042006,0.000015154233],"category_scores_gemma":[0.0000594804,0.00020587076,0.000041817068,0.000043191227,0.00003213115,0.0007556189,0.00005780914,0.0000725333,0.000005220386],"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":[0.00072157173,0.00018721895,0.0059768516,0.00078282296,0.0010891773,0.000024019588,0.00881278,0.0032187286,0.4555095,0.2552408,0.00093928055,0.26749724],"study_design_scores_gemma":[0.015556334,0.0030553974,0.064102374,0.0014087869,0.00012620568,0.00019702004,0.0019266707,0.8182841,0.09109585,0.0017327617,0.0011045954,0.0014099287],"about_ca_topic_score_codex":0.00011781965,"about_ca_topic_score_gemma":0.000027232038,"teacher_disagreement_score":0.8150653,"about_ca_system_score_codex":0.0000499265,"about_ca_system_score_gemma":0.00005623129,"threshold_uncertainty_score":0.83951664},"labels":[],"label_agreement":null},{"id":"W2838525274","doi":"10.1109/crv.2019.00028","title":"HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Scale (ratio); Quality (philosophy); Market segmentation; Computer vision; Geography; Cartography","score_opus":0.04233180545687948,"score_gpt":0.3218101160723537,"score_spread":0.27947831061547423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2838525274","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":[0.0059624924,0.00008162681,0.9840122,0.00046912167,0.0013122384,0.0015242696,0.005916053,0.00025072077,0.00047129177],"genre_scores_gemma":[0.19509926,0.00002036642,0.7455485,0.0010742067,0.00054973294,0.0006075102,0.05612843,0.000047679765,0.0009243132],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976594,0.00018483827,0.00050977786,0.0009681319,0.00039496648,0.0002829032],"domain_scores_gemma":[0.9976358,0.00034915475,0.00028460636,0.0013473348,0.00022277136,0.00016035352],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00049065455,0.00031197438,0.00046645728,0.00012828768,0.000112313246,0.0014577822,0.0011785997,0.00027614651,0.0000772638],"category_scores_gemma":[0.00006304032,0.0002608119,0.00009441139,0.000091596885,0.000033477256,0.00060599454,0.0006707381,0.00020787449,0.00032857186],"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":[0.00016809175,0.001438933,0.0029892903,0.0024306534,0.0015889865,0.000056315486,0.0067503247,0.0020505593,0.053705197,0.004033949,0.52068853,0.40409917],"study_design_scores_gemma":[0.007418211,0.00067834015,0.0065929014,0.0010007488,0.00032604206,0.000031280437,0.0003403228,0.7868213,0.14167856,0.02376707,0.028470486,0.0028747132],"about_ca_topic_score_codex":0.00021051525,"about_ca_topic_score_gemma":0.00027571296,"teacher_disagreement_score":0.7847708,"about_ca_system_score_codex":0.00005191768,"about_ca_system_score_gemma":0.00021378699,"threshold_uncertainty_score":0.9999844},"labels":[],"label_agreement":null},{"id":"W2884123940","doi":"10.1016/b978-0-12-811810-8.00007-5","title":"Wearable Technologies and Force Myography for Healthcare","year":2018,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Wearable computer; Electrical impedance myography; Exoskeleton; Accelerometer; Computer science; Human–computer interaction; Physical medicine and rehabilitation; Wearable technology; Simulation; Medicine; Embedded system","score_opus":0.022327708986525207,"score_gpt":0.25125705604066056,"score_spread":0.22892934705413537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884123940","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010700309,0.010908309,0.0068841623,0.0010111814,0.0007594059,0.0016286704,0.00004446888,0.0008130915,0.97794],"genre_scores_gemma":[0.0007381453,0.0003984359,0.012670996,0.00036120196,0.00026480036,0.00024655848,0.0000068809422,0.000053541957,0.9852594],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820673,0.00002004505,0.00037902364,0.00077743013,0.00025113788,0.00036563753],"domain_scores_gemma":[0.9983805,0.00011298902,0.00026436057,0.0008678208,0.0002776039,0.000096749616],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003569996,0.00036292896,0.00051327026,0.00033942432,0.00021532857,0.00017979734,0.000673824,0.0005283413,0.0000071197665],"category_scores_gemma":[0.000021697055,0.00031729933,0.0001932635,0.00003344366,0.00020091372,0.00010056431,0.00029262397,0.000275805,0.000062946354],"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":[0.000003150376,0.0000016642404,0.0000022585652,0.00022859378,0.00004331427,0.0000033732601,0.00029198907,8.696311e-9,0.0000047869325,0.021572841,0.0004439645,0.97740406],"study_design_scores_gemma":[0.0001905993,0.00018399676,0.0000014642798,0.0007308854,0.000018174076,0.00003966124,0.000039952803,0.000022188287,0.0001162159,0.16363479,0.83469576,0.00032632524],"about_ca_topic_score_codex":5.030377e-7,"about_ca_topic_score_gemma":0.00003447964,"teacher_disagreement_score":0.9770777,"about_ca_system_score_codex":0.00003081213,"about_ca_system_score_gemma":0.00008872009,"threshold_uncertainty_score":0.99992794},"labels":[],"label_agreement":null},{"id":"W2886574382","doi":"10.1109/civemsa.2018.8439952","title":"Teaching a Robot Sign Language using Vision-Based Hand Gesture Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":18,"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":"Gesture; Computer science; Artificial intelligence; Gesture recognition; Support vector machine; Speech recognition; Pattern recognition (psychology); Classifier (UML); Sign language; Computer vision; Hidden Markov model; Robot","score_opus":0.02995806555997378,"score_gpt":0.30017135133710426,"score_spread":0.2702132857771305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886574382","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":[0.05567782,0.000051555107,0.93421894,0.00057106686,0.000491052,0.00021742948,0.0000034989569,0.0002996203,0.008469001],"genre_scores_gemma":[0.8777298,3.0468323e-7,0.12019366,0.0011146854,0.0005438975,0.0000083108125,0.00000806632,0.000013796912,0.00038752117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985042,0.00024618144,0.00024050615,0.00042409575,0.0003079155,0.00027710947],"domain_scores_gemma":[0.9990375,0.0001593146,0.00010461761,0.00040202818,0.0001761589,0.000120389326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006351776,0.0001639456,0.00017215605,0.00017758373,0.00038484167,0.0004190138,0.00035909255,0.000115904906,0.00011814459],"category_scores_gemma":[0.00008402819,0.00013222365,0.00007028616,0.0002884328,0.0000609637,0.00048532258,0.00007833127,0.00018990095,0.00045599724],"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":[0.000027592348,0.00025515526,0.00029220938,0.000080178026,0.000048473466,0.00008148582,0.007505813,0.00010611466,0.20695588,0.0005775248,0.0029048994,0.78116465],"study_design_scores_gemma":[0.0038910802,0.0012632108,0.0020079874,0.0013206928,0.00007370979,0.00039932661,0.0012926661,0.7205196,0.24510844,0.0028129742,0.019256515,0.002053779],"about_ca_topic_score_codex":0.00028101433,"about_ca_topic_score_gemma":0.000101572085,"teacher_disagreement_score":0.82205194,"about_ca_system_score_codex":0.00004638256,"about_ca_system_score_gemma":0.00008102785,"threshold_uncertainty_score":0.58610743},"labels":[],"label_agreement":null},{"id":"W2898060811","doi":"10.1121/1.5067624","title":"Computer-vision analysis shows different facial movements for the production of different Mandarin tones","year":2018,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Mandarin Chinese; Tone (literature); Computer science; Vowel; Speech recognition; Feature (linguistics); Artificial intelligence; Computer vision; Acoustics","score_opus":0.01785550969437166,"score_gpt":0.276158149675885,"score_spread":0.2583026399815133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898060811","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":[0.08176448,0.0000569886,0.9126421,0.00427217,0.0009590398,0.00028081783,0.000008280065,0.0000071041145,0.000008976773],"genre_scores_gemma":[0.98653084,0.00008802589,0.012351824,0.0003293311,0.0005945977,0.0000035445164,4.0379186e-7,0.0000058062546,0.00009565192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981606,0.00022189313,0.00057831523,0.00013980934,0.00071397494,0.00018543344],"domain_scores_gemma":[0.99759126,0.00057614676,0.0008241345,0.00047350614,0.0004808679,0.000054092885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007236669,0.00014118211,0.00043497785,0.000035789963,0.00029988447,0.00004192024,0.0011499993,0.000046990393,0.000012935775],"category_scores_gemma":[0.00009084465,0.00005485383,0.00068423245,0.0004286278,0.00050336757,0.00009216156,0.00033045886,0.00018681055,8.722344e-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":[0.0009489144,0.0021903587,0.0037143645,0.00032065736,0.009386093,6.493952e-7,0.017463824,0.015961727,0.10053007,0.00014528503,0.05064402,0.798694],"study_design_scores_gemma":[0.00089744583,0.0021831428,0.043270182,0.00022239091,0.0019265146,0.000031250118,0.0012763698,0.9268189,0.018860927,0.0016700029,0.0025983877,0.00024448454],"about_ca_topic_score_codex":0.000017182776,"about_ca_topic_score_gemma":0.0000037429077,"teacher_disagreement_score":0.9108572,"about_ca_system_score_codex":0.00004589454,"about_ca_system_score_gemma":0.000030809348,"threshold_uncertainty_score":0.23064992},"labels":[],"label_agreement":null},{"id":"W2899190276","doi":"10.1115/detc2018-86186","title":"Vision-Based Hand Gesture Recognition With Deep Machine Learning for Visual Servoing","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Visual servoing; Artificial intelligence; Computer science; Convolutional neural network; Computer vision; Gesture recognition; Robot; Gesture; Motion control","score_opus":0.015514185538545208,"score_gpt":0.26511548395367307,"score_spread":0.24960129841512785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899190276","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":[0.025304096,0.00004255217,0.97126436,0.00068726955,0.00024232458,0.00039757963,0.000002379873,0.00028830432,0.0017711414],"genre_scores_gemma":[0.9036122,0.0000012585348,0.09481001,0.0006796471,0.0003477624,0.00006224946,0.000042813703,0.000021083622,0.00042295366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866885,0.00010386801,0.00021488614,0.000445968,0.0002735197,0.0002928993],"domain_scores_gemma":[0.9988055,0.00027407025,0.000122052894,0.00020032995,0.00048131146,0.0001167342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044061753,0.00017561311,0.00018583197,0.0001409499,0.00045274865,0.00038297533,0.00024158359,0.000096386175,0.000068838075],"category_scores_gemma":[0.0000740238,0.00012557265,0.000057425434,0.00036930456,0.000060138827,0.000400209,0.00004644301,0.00014269937,0.00018057296],"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":[0.00052762864,0.0005125629,0.008041525,0.00038228193,0.00017911046,0.000039824754,0.0034570086,0.00089759356,0.012778884,0.0007948778,0.0016361058,0.9707526],"study_design_scores_gemma":[0.0029800201,0.003004983,0.0011566958,0.0003296176,0.000028342147,0.0000762875,0.00011559818,0.93052065,0.037180454,0.00071592384,0.02329049,0.0006009386],"about_ca_topic_score_codex":0.00003009789,"about_ca_topic_score_gemma":0.0003867866,"teacher_disagreement_score":0.97015166,"about_ca_system_score_codex":0.000027287766,"about_ca_system_score_gemma":0.00006633895,"threshold_uncertainty_score":0.5120704},"labels":[],"label_agreement":null},{"id":"W2902728877","doi":"10.1109/icpr.2018.8545718","title":"CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":113,"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":"Computer science; Recurrent neural network; Gesture; Convolutional neural network; Artificial intelligence; Gesture recognition; Human skeleton; Deep learning; Pattern recognition (psychology); Skeleton (computer programming); Computer vision; Feature extraction; Artificial neural network","score_opus":0.0171564051364419,"score_gpt":0.2490488216906206,"score_spread":0.2318924165541787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902728877","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":[0.095532335,0.00012440751,0.8780183,0.0018410509,0.0007065115,0.00032308834,0.000005757986,0.00032306067,0.023125457],"genre_scores_gemma":[0.97186387,0.00000981783,0.025929172,0.0011224484,0.00018412524,0.000020553593,0.000014417143,0.000010928331,0.0008446859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987882,0.000101011254,0.00019942294,0.00043916504,0.00022563567,0.00024658968],"domain_scores_gemma":[0.9991471,0.000097834134,0.00007444002,0.00033091073,0.00021307272,0.00013660843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002965386,0.00015514059,0.00015916392,0.00013704662,0.00020134624,0.0003147575,0.00024276249,0.00012217987,0.00008894078],"category_scores_gemma":[0.000042263382,0.00012552986,0.000040145253,0.00031026747,0.0001040953,0.00036300058,0.00007072858,0.00008105779,0.0005261829],"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":[0.000024730467,0.0001241461,0.0021807589,0.0000950645,0.000042773212,0.0000283993,0.0010066117,0.000001592413,0.009400271,0.00083723397,0.005713449,0.980545],"study_design_scores_gemma":[0.010668965,0.003365015,0.17213212,0.0011805593,0.00014013269,0.00096688495,0.00051157334,0.38911492,0.14681844,0.025985746,0.24502893,0.004086714],"about_ca_topic_score_codex":0.000039243583,"about_ca_topic_score_gemma":0.0005308161,"teacher_disagreement_score":0.97645825,"about_ca_system_score_codex":0.000027147857,"about_ca_system_score_gemma":0.000053695163,"threshold_uncertainty_score":0.6763192},"labels":[],"label_agreement":null},{"id":"W2906183165","doi":"","title":"The generation of synthetic handwritten data for improving on-line learning","year":2015,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Polytechnique Montréal; École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Line (geometry); Synthetic data; Speech recognition; Pattern recognition (psychology)","score_opus":0.06375087346569779,"score_gpt":0.2870046654697617,"score_spread":0.22325379200406392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906183165","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":[0.002597723,0.0020661282,0.98693305,0.0049021957,0.00085202564,0.0017853209,0.00020176907,0.0004696025,0.00019219001],"genre_scores_gemma":[0.8442411,0.00024833172,0.15061285,0.00044505732,0.0011183475,0.001588334,0.00054856885,0.00010079109,0.0010966705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964376,0.00043092616,0.0008722602,0.0010393468,0.0006394425,0.0005804398],"domain_scores_gemma":[0.9940071,0.00059733837,0.0009415159,0.0035951384,0.0006400787,0.00021880913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004328595,0.00042638922,0.0005742941,0.00032289012,0.00042661384,0.0007352252,0.0035118153,0.00049389555,0.0000012783477],"category_scores_gemma":[0.0017022726,0.00033383802,0.00018713254,0.0002889768,0.00008074138,0.00032021417,0.0026399097,0.0008861039,0.000007849791],"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":[0.00017115254,0.00042127707,0.00061065843,0.00075919385,0.0003462888,0.0000258021,0.0010436604,0.091590315,0.009829381,0.03907044,0.016930794,0.83920103],"study_design_scores_gemma":[0.00035415118,0.00023319903,0.000059733426,0.00019638146,0.000047261507,0.00003308511,0.000046367255,0.98080564,0.006674573,0.0034209304,0.0077544283,0.000374271],"about_ca_topic_score_codex":0.00138462,"about_ca_topic_score_gemma":0.0011052218,"teacher_disagreement_score":0.8892153,"about_ca_system_score_codex":0.00029621125,"about_ca_system_score_gemma":0.0008207046,"threshold_uncertainty_score":0.99991137},"labels":[],"label_agreement":null},{"id":"W2906392928","doi":"10.32920/ryerson.14657838.v1","title":"The classification of gesture interactions and the study of their ergonomic effect on hands","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Computer science; Human–computer interaction; Gesture recognition; Object (grammar); Artificial intelligence","score_opus":0.02619182189393986,"score_gpt":0.27816742735134803,"score_spread":0.25197560545740816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906392928","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":[0.98737794,0.00024104075,0.006581694,0.0009703321,0.00094953965,0.001246556,0.0000031572524,0.000033487875,0.0025962768],"genre_scores_gemma":[0.99936604,0.00006185967,0.000057150213,0.000023994031,0.00006295824,0.0002024432,0.000003997426,0.000006403061,0.00021512453],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979793,0.00094790565,0.00042210252,0.00036346202,0.0001979103,0.000089333174],"domain_scores_gemma":[0.9960727,0.0021763928,0.00044045912,0.0011000129,0.00018442648,0.000025977952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011643468,0.00017034083,0.00037647877,0.000075513475,0.000140638,0.00022701012,0.00071491825,0.00008874294,0.0000030664835],"category_scores_gemma":[0.0000882385,0.000073801704,0.00012276467,0.00014991402,0.00009373528,0.00006620432,0.00056650507,0.00041711383,0.0000023053556],"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":[0.0012966519,0.0030484705,0.012504624,0.0011429741,0.004503857,0.000007909392,0.16004033,0.0015096816,0.006336725,0.057553183,0.0029949925,0.7490606],"study_design_scores_gemma":[0.021655912,0.0058843573,0.7340925,0.003630167,0.0007791589,0.00015792363,0.068053894,0.11664855,0.035704076,0.0053557865,0.006143611,0.0018940947],"about_ca_topic_score_codex":0.00012144201,"about_ca_topic_score_gemma":0.0006049786,"teacher_disagreement_score":0.7471665,"about_ca_system_score_codex":0.000028226372,"about_ca_system_score_gemma":0.00007594132,"threshold_uncertainty_score":0.3009546},"labels":[],"label_agreement":null},{"id":"W2910237648","doi":"10.1109/iros.2018.8593709","title":"Hands and Faces, Fast: Mono-Camera User Detection Robust Enough to Directly Control a UAV in Flight","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Benchmark (surveying); Gesture recognition; RGB color model; Face detection; Detector; Deep learning; Gesture; Robot; Object detection; Human–robot interaction; Facial recognition system; Pattern recognition (psychology)","score_opus":0.011234746639092926,"score_gpt":0.22158073659454858,"score_spread":0.21034598995545564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910237648","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":[0.14463294,0.00009156432,0.8456854,0.0012648357,0.0004317849,0.00044223832,0.000002340172,0.00017671977,0.0072721685],"genre_scores_gemma":[0.99257386,0.000006879747,0.004804782,0.00068424095,0.00018523487,0.00006507909,4.5662884e-7,0.00000870377,0.0016707579],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873,0.00010898367,0.00024586736,0.000441648,0.00019421395,0.0002792934],"domain_scores_gemma":[0.9993124,0.000078495876,0.000052071744,0.00027617335,0.00013241492,0.00014841768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033316112,0.00014842536,0.00022115695,0.00021228672,0.000102691505,0.00024397344,0.00023873549,0.0000878408,0.000039688937],"category_scores_gemma":[0.000039691604,0.00012005049,0.000029224519,0.000516349,0.00003064894,0.00041318595,0.000082660656,0.00009000305,0.00025062345],"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":[0.00015702353,0.00033911265,0.010179762,0.00006259361,0.000121633006,0.000046751626,0.012413362,0.00031890313,0.04167613,0.0028306968,0.0052474285,0.9266066],"study_design_scores_gemma":[0.010693004,0.0027011728,0.054702453,0.0003620546,0.000036600144,0.00031405658,0.0005475261,0.14629647,0.29616106,0.0004711577,0.48575467,0.0019598051],"about_ca_topic_score_codex":0.0002762839,"about_ca_topic_score_gemma":0.0017547631,"teacher_disagreement_score":0.9246468,"about_ca_system_score_codex":0.000040354782,"about_ca_system_score_gemma":0.000024907786,"threshold_uncertainty_score":0.4895517},"labels":[],"label_agreement":null},{"id":"W2911261841","doi":"10.1109/crv.2019.00031","title":"Commodifying Pointing in HRI: Simple and Fast Pointing Gesture Detection from RGB-D Images","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"RGB color model; Computer vision; Artificial intelligence; Computer science; Gesture; Pixel; Ground plane; Point (geometry); Frame rate; Robot; Frame (networking); Ground truth; Detector; Exploit; Mathematics","score_opus":0.01880108468758575,"score_gpt":0.24834145215766984,"score_spread":0.22954036747008408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911261841","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":[0.3896515,0.0005430284,0.60214466,0.0005740837,0.0012417857,0.0006596837,0.00002294668,0.00040253362,0.0047598016],"genre_scores_gemma":[0.9816866,0.000030486015,0.017462624,0.00025697303,0.0003319312,0.000038612518,0.000032119835,0.000032025997,0.00012865118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969934,0.00032651273,0.00068409287,0.0011655375,0.00037879206,0.00045166138],"domain_scores_gemma":[0.9979723,0.00048220312,0.00043887176,0.000849417,0.0001485239,0.000108662694],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009258785,0.00042211523,0.0006453969,0.00037959332,0.0001464207,0.0009792624,0.0007439468,0.0004776027,0.000018075141],"category_scores_gemma":[0.00014477053,0.00039827882,0.00012390997,0.00028866733,0.000029691515,0.00052733975,0.0022580174,0.0012022301,0.00007494059],"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":[0.000027391814,0.00022471302,0.14597997,0.0016083186,0.00026681586,0.0001370522,0.013640392,0.0032341133,0.01816402,0.00044631958,0.00083190296,0.815439],"study_design_scores_gemma":[0.0047367755,0.00018137367,0.32210195,0.0055622575,0.00011051399,0.0003256456,0.005265636,0.57154715,0.05515762,0.025498977,0.00461569,0.0048964447],"about_ca_topic_score_codex":0.0030177818,"about_ca_topic_score_gemma":0.00096827367,"teacher_disagreement_score":0.8105425,"about_ca_system_score_codex":0.00009774445,"about_ca_system_score_gemma":0.00007107556,"threshold_uncertainty_score":0.99984694},"labels":[],"label_agreement":null},{"id":"W2911519575","doi":"10.1109/ssci.2018.8628906","title":"An Evaluation Study of Recognizing Conducting Gesture Using Computational Intelligence Techniques","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity Western University; Western University","funders":"","keywords":"Gesture; Computer science; Hidden Markov model; Gesture recognition; Movement (music); Human–computer interaction; Artificial intelligence; Computational intelligence; Speech recognition","score_opus":0.25074269343589634,"score_gpt":0.4186997404801935,"score_spread":0.16795704704429715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911519575","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":[0.41620475,0.000009097657,0.58221453,0.00002097771,0.00020544797,0.00040345843,4.293484e-7,0.00012329499,0.00081802416],"genre_scores_gemma":[0.80706936,2.736404e-7,0.19271031,0.00004642952,0.00014900143,0.000012154863,0.0000021349956,0.000005890749,0.0000044465814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815524,0.00037572582,0.00041138756,0.00036781857,0.00055121037,0.00013862],"domain_scores_gemma":[0.9978977,0.0001127069,0.00022893834,0.0003180526,0.0013890713,0.000053532407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017750442,0.00011007871,0.00016402961,0.0001881191,0.00015574611,0.0001062082,0.00039821127,0.000058823836,0.00003897133],"category_scores_gemma":[0.00009277858,0.00009687338,0.000023821183,0.0005427546,0.000045499608,0.00073859305,0.00008096865,0.0000877305,0.000011780886],"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":[0.00001198857,0.0009464732,0.00769317,0.00003461183,0.00008104987,0.0000039207575,0.022992065,0.003934657,0.02148631,0.0022212425,0.00005871564,0.9405358],"study_design_scores_gemma":[0.00027253298,0.0011137364,0.0015934649,0.00011816388,0.000030994193,0.00007105271,0.0070487857,0.8943491,0.08945355,0.0056005633,0.000040666124,0.0003073679],"about_ca_topic_score_codex":0.000113752045,"about_ca_topic_score_gemma":0.000058768797,"teacher_disagreement_score":0.9402284,"about_ca_system_score_codex":0.00004496087,"about_ca_system_score_gemma":0.00010173197,"threshold_uncertainty_score":0.39503816},"labels":[],"label_agreement":null},{"id":"W2913330565","doi":"10.22215/etd/2014-10565","title":"A Quaternion-Based Motion Tracking and Gesture Recognition System Using Wireless Inertial Sensors","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Hand Gesture Recognition Systems","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":"Carleton University","funders":"","keywords":"Quaternion; Gesture recognition; Gesture; Hidden Markov model; Computer science; Computer vision; Artificial intelligence; Wearable computer; Match moving; Kalman filter; Inertial measurement unit; Tracking (education); Tracking system; Motion (physics); Augmented reality; Virtual reality; Gyroscope; Engineering; Embedded system; Mathematics","score_opus":0.027369350194304733,"score_gpt":0.2558366445939513,"score_spread":0.22846729439964655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913330565","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":[0.697153,0.00012635888,0.29681677,0.00007586511,0.0025820404,0.00068227615,0.000012769622,0.0006226784,0.0019282941],"genre_scores_gemma":[0.9955102,0.000006260938,0.0028773057,0.000060288807,0.00054325804,0.000047376143,0.0005545365,0.000054087253,0.00034667805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712574,0.0004322026,0.0006703309,0.00085887284,0.00057258294,0.0003402835],"domain_scores_gemma":[0.9981974,0.00015753458,0.0005741212,0.00039880094,0.00051300303,0.00015918753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006132382,0.00046214781,0.00057552365,0.00047074782,0.00024128491,0.00056198984,0.00030653842,0.00066283345,0.000010029327],"category_scores_gemma":[0.000045077188,0.00041927822,0.0001403711,0.00034475143,0.000018690795,0.0004177128,0.000025337988,0.00037066988,0.000074859585],"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":[0.00015487568,0.00017241386,0.000790854,0.007362305,0.00023983211,0.0001049208,0.004106419,0.000881808,0.017393958,0.00096487603,0.00022026406,0.9676075],"study_design_scores_gemma":[0.0024757916,0.00020444868,0.0040578414,0.008253291,0.00033673985,0.00031031537,0.0018354283,0.9095598,0.069929235,0.00015700443,0.0005537066,0.0023264166],"about_ca_topic_score_codex":0.0003825199,"about_ca_topic_score_gemma":0.00028632995,"teacher_disagreement_score":0.96528107,"about_ca_system_score_codex":0.00016023117,"about_ca_system_score_gemma":0.00011824239,"threshold_uncertainty_score":0.9998259},"labels":[],"label_agreement":null},{"id":"W2925112720","doi":"10.3389/frobt.2019.00015","title":"VR Road and Construction Site Safety Conceptual Modeling Based on Hand Gestures","year":2019,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Bentley (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Gesture; Conceptual design; Human–computer interaction; Natural (archaeology); Software; Software engineering; Artificial intelligence; Programming language","score_opus":0.009026866381931592,"score_gpt":0.21077039468824568,"score_spread":0.2017435283063141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925112720","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":[0.046433944,0.0004770276,0.9503517,0.00064096984,0.0012327265,0.00019345625,0.000005011158,0.000027289685,0.00063783803],"genre_scores_gemma":[0.9155592,0.000057082558,0.083770186,0.00047341923,0.000052554173,0.000002763553,0.000005499178,0.000006690213,0.00007261539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912107,0.00006124523,0.00018689003,0.00031094303,0.00015455877,0.00016527517],"domain_scores_gemma":[0.99961495,0.000031237763,0.000063256346,0.00017465655,0.00004447865,0.000071415576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001839267,0.000118847915,0.00020893467,0.00012478062,0.00008613254,0.000160983,0.000097776385,0.000092923685,0.0000020411633],"category_scores_gemma":[0.0000164315,0.000106016276,0.000022648986,0.0001214651,0.000083725005,0.00017180575,0.000043662425,0.00015490317,0.000004665423],"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":[0.000086041575,0.000065583874,0.097042,0.00009117355,0.000036400248,0.000016475069,0.003225029,0.77528214,0.0001665096,0.014934985,0.0009269841,0.108126655],"study_design_scores_gemma":[0.0008376681,0.00007057637,0.0015498818,0.00010079224,0.000004519047,0.0000089726145,0.0002464469,0.99525577,0.000034290653,0.0008695814,0.0008705409,0.00015098105],"about_ca_topic_score_codex":0.000015860209,"about_ca_topic_score_gemma":0.000004921822,"teacher_disagreement_score":0.86912525,"about_ca_system_score_codex":0.000022335314,"about_ca_system_score_gemma":0.00003133105,"threshold_uncertainty_score":0.43232185},"labels":[],"label_agreement":null},{"id":"W2931821131","doi":"10.1109/hri.2019.8673133","title":"Human Robot Interaction Using Diver Hand Signals","year":2019,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"York University","funders":"","keywords":"Computer science; Human–robot interaction; Robot; Artificial intelligence; Computer vision; Human–computer interaction","score_opus":0.0553465872112438,"score_gpt":0.3069605793343199,"score_spread":0.2516139921230761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931821131","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":[0.34191182,0.000017336906,0.6368155,0.00011334364,0.0008509248,0.00016627176,3.5435014e-7,0.000096889664,0.020027554],"genre_scores_gemma":[0.992356,2.3265864e-7,0.0037547594,0.00018567829,0.00007929117,0.0000020507512,7.829145e-7,0.000003903256,0.0036173055],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933064,0.00005064967,0.00013847926,0.00020603246,0.00015425385,0.00011996136],"domain_scores_gemma":[0.99958056,0.000028377382,0.000060042963,0.00021771094,0.00007011363,0.00004318941],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014984338,0.00006882626,0.00010059404,0.00007711435,0.00008763849,0.00025747315,0.00020498085,0.000036883965,0.00036912912],"category_scores_gemma":[0.0000038708754,0.00005545471,0.000039359333,0.000130841,0.000008819696,0.0005864288,0.00009548958,0.000059951984,0.001059354],"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":[0.0000048409993,0.00013158501,0.008965981,0.000062553365,0.000080254955,0.0000165056,0.0022410455,0.0027083226,0.9471304,0.008198889,0.0029390994,0.027520524],"study_design_scores_gemma":[0.0039223568,0.0006687079,0.024616513,0.00082217297,0.000049091388,0.0004864518,0.0008692771,0.36452547,0.5188835,0.0055281594,0.07741193,0.0022163556],"about_ca_topic_score_codex":0.00007632144,"about_ca_topic_score_gemma":0.00001029113,"teacher_disagreement_score":0.6504442,"about_ca_system_score_codex":0.000026848618,"about_ca_system_score_gemma":0.0000125490815,"threshold_uncertainty_score":0.9997184},"labels":[],"label_agreement":null},{"id":"W2935126934","doi":"10.1145/3181672","title":"Bi-Level Thresholding","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thresholding; False positive paradox; Artificial intelligence; Computer science; Pattern recognition (psychology); Balanced histogram thresholding; Accelerometer; Speech recognition; Histogram","score_opus":0.05449439370813911,"score_gpt":0.2935625069466191,"score_spread":0.23906811323848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935126934","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":[0.013785684,0.000121969686,0.9674312,0.00048338357,0.008648983,0.00095419417,0.00003634364,0.0003468383,0.008191431],"genre_scores_gemma":[0.99194777,0.000036432826,0.0019848128,0.00024054277,0.00013524981,0.00020257155,0.0000048987977,0.000034922363,0.0054128156],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99726605,0.00024421775,0.0006635944,0.0007894692,0.00059364556,0.0004430211],"domain_scores_gemma":[0.9968908,0.00081473606,0.0002507833,0.0015365237,0.00032701757,0.00018012962],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043236732,0.00035284398,0.00043552392,0.0005509263,0.00017464368,0.00039178532,0.001469142,0.00015880702,0.00033558594],"category_scores_gemma":[0.00005200417,0.00031007445,0.0002722416,0.00061457267,0.000029476356,0.001022792,0.000034632034,0.0005219724,0.008166786],"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":[0.00079549465,0.0043197866,0.004409799,0.0012354809,0.0042662844,0.00027033672,0.028703747,0.08567878,0.046620853,0.059245035,0.0062787356,0.7581757],"study_design_scores_gemma":[0.0035342977,0.002941648,0.0018083296,0.006211361,0.00016654932,0.0019418214,0.013989092,0.1584391,0.39944446,0.005191296,0.40167072,0.0046613184],"about_ca_topic_score_codex":0.00019895773,"about_ca_topic_score_gemma":0.000016204138,"teacher_disagreement_score":0.97816205,"about_ca_system_score_codex":0.00030174313,"about_ca_system_score_gemma":0.00006851808,"threshold_uncertainty_score":0.99993515},"labels":[],"label_agreement":null},{"id":"W2942380124","doi":"10.1016/j.bspc.2019.04.022","title":"Medical gesture recognition using dynamic arc length warping","year":2019,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Universidad Nacional de Colombia; Université Catholique de Louvain","keywords":"Dynamic time warping; Gesture; Computer science; Gesture recognition; Artificial intelligence; Image warping; Computer vision; Curvature; Multilayer perceptron; Parametrization (atmospheric modeling); Artificial neural network; Pattern recognition (psychology); Speech recognition; Mathematics","score_opus":0.012616280210831913,"score_gpt":0.24770281778450337,"score_spread":0.23508653757367146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942380124","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":[0.08528515,0.0017389025,0.90909475,0.002639069,0.00037840704,0.00029480367,0.000006764179,0.00023627271,0.00032588237],"genre_scores_gemma":[0.9955461,0.000022794642,0.0029147856,0.0011460966,0.00028578777,0.000014885261,0.000010592333,0.000014228666,0.00004471464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975293,0.0001713274,0.00040528036,0.00054274336,0.0009364758,0.0004148537],"domain_scores_gemma":[0.9989273,0.00019900902,0.00015998652,0.0001565414,0.00013642228,0.00042076726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009136812,0.00020667528,0.0003503939,0.00015683765,0.00019668415,0.00024953543,0.0003766461,0.0003061666,0.0001079129],"category_scores_gemma":[0.0000858929,0.00016016715,0.00006661883,0.0003715934,0.00012803037,0.00040386087,0.000084102394,0.00037399534,0.00009520438],"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":[0.000019887717,0.00006851351,0.000265769,0.00015663038,0.000028753871,0.000035648973,0.00020368426,0.0000017489597,0.0050338516,0.000031414897,0.00003110994,0.994123],"study_design_scores_gemma":[0.0033814125,0.0002351889,0.0006490948,0.0012811553,0.000040594387,0.0005487102,0.00012049425,0.98640305,0.00012397382,0.001664699,0.005062173,0.00048945163],"about_ca_topic_score_codex":0.000016394872,"about_ca_topic_score_gemma":0.000002137204,"teacher_disagreement_score":0.9936335,"about_ca_system_score_codex":0.00004700517,"about_ca_system_score_gemma":0.00030248857,"threshold_uncertainty_score":0.6531427},"labels":[],"label_agreement":null},{"id":"W2946039368","doi":"10.1167/19.10.109b","title":"Eye-hand Coordination in Reaching and Grasping Vertically Translating Targets","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gaze; Index finger; Thumb; Computer vision; Artificial intelligence; Computer science; Movement (music); Communication; Psychology; Physics; Anatomy; Medicine; Acoustics","score_opus":0.011716581461163462,"score_gpt":0.27776820218669923,"score_spread":0.2660516207255358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946039368","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":[0.7779247,0.0003652947,0.21912692,0.0018261374,0.00031102414,0.00007504411,7.303076e-8,0.0000065092836,0.00036427838],"genre_scores_gemma":[0.9924355,0.000008071649,0.0074448315,0.00004477294,0.0000475698,2.5617354e-7,1.4334528e-7,0.0000034135678,0.000015407519],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991049,0.00010056156,0.00035021393,0.00010311979,0.00024040222,0.000100789795],"domain_scores_gemma":[0.9995234,0.00009317871,0.00016700632,0.0000707869,0.00009297498,0.00005267026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036374,0.000057091,0.00015636258,0.00018242061,0.000041950927,0.00013171115,0.00012649443,0.0000426457,0.0000025361624],"category_scores_gemma":[0.00007339983,0.0000448869,0.000033793756,0.0001534092,0.000007649767,0.0007327271,0.000025545887,0.00017457125,0.000005373337],"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":[0.000062630315,0.0001363996,0.056529474,0.00014785952,0.000027526863,0.00008876361,0.009431505,0.00044028848,0.43940485,0.002003414,0.000112045906,0.49161527],"study_design_scores_gemma":[0.0056909323,0.0015631011,0.72037905,0.0039795935,0.00001994729,0.00051472645,0.00038978894,0.24682237,0.007040304,0.011011142,0.0021018258,0.00048723558],"about_ca_topic_score_codex":0.0000037728687,"about_ca_topic_score_gemma":0.0000036863744,"teacher_disagreement_score":0.66384953,"about_ca_system_score_codex":0.000029071969,"about_ca_system_score_gemma":0.000024956747,"threshold_uncertainty_score":0.18304347},"labels":[],"label_agreement":null},{"id":"W2948276468","doi":"","title":"Facial EMG signal analysis method and its implementation as a stand-alone system","year":2010,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; SIGNAL (programming language); Computer vision; Speech recognition; Artificial intelligence","score_opus":0.01641506135220188,"score_gpt":0.31277016753005205,"score_spread":0.29635510617785016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948276468","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":[0.8140135,0.00012679554,0.18014662,0.00032798084,0.00034345454,0.0004918029,0.000018595074,0.0003293544,0.004201936],"genre_scores_gemma":[0.98955905,0.0000040146115,0.009963694,0.000053565404,0.00019731437,0.000059435035,0.0000045098477,0.000008859574,0.00014957943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985241,0.000024364426,0.00032369714,0.00047595575,0.00038925515,0.00026257016],"domain_scores_gemma":[0.9990862,0.000051159037,0.00018806508,0.000097907156,0.0004088263,0.00016783098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083395914,0.00016417513,0.00030321683,0.00032994695,0.00020264465,0.00046205998,0.0003056101,0.00010198337,0.00004068826],"category_scores_gemma":[0.00002905044,0.00014535245,0.00008535154,0.0009863508,0.000017845685,0.00070299924,0.00012001944,0.00017088269,0.000057328874],"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":[0.00007297527,0.00014089762,0.03456081,0.0011510213,0.0017156987,0.000043874308,0.030571107,0.000007232546,0.48767298,0.21787624,0.0018989814,0.22428818],"study_design_scores_gemma":[0.008082821,0.0013688131,0.05665609,0.00040358066,0.0027050925,0.0019905868,0.021499151,0.17632543,0.6630818,0.006427063,0.05763851,0.0038210303],"about_ca_topic_score_codex":0.000042719883,"about_ca_topic_score_gemma":0.000028890654,"teacher_disagreement_score":0.22046715,"about_ca_system_score_codex":0.000032754127,"about_ca_system_score_gemma":0.000053903143,"threshold_uncertainty_score":0.59273005},"labels":[],"label_agreement":null},{"id":"W2958496937","doi":"10.1088/1742-6596/1267/1/012056","title":"A Human-Robot Interaction for a Mecanum Wheeled Mobile Robot with Real-Time 3D Two-Hand Gesture Recognition","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Mobile robot; Robot; Computer science; Gesture recognition; Computer vision; Artificial intelligence; Robot control; Mobile robot navigation; Scheme (mathematics); Engineering; Human–computer interaction; Mathematics","score_opus":0.02633425853736656,"score_gpt":0.2752302088606273,"score_spread":0.24889595032326076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958496937","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":[0.34455958,0.000039008155,0.64993674,0.00039769293,0.0010737224,0.0011076735,0.000024594197,0.00008875229,0.0027722132],"genre_scores_gemma":[0.9783756,0.00002299961,0.020052195,0.000051343493,0.0005343638,0.000059299568,0.000018077997,0.000022252576,0.00086383824],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843,0.00012692985,0.00050317694,0.00028944627,0.0003898322,0.00026061785],"domain_scores_gemma":[0.99703413,0.000117847754,0.0009096052,0.00029165923,0.0015340057,0.00011277523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035658962,0.00024269123,0.0005173126,0.00012371733,0.0001562726,0.00051061896,0.00040471004,0.000081498394,0.000056390716],"category_scores_gemma":[0.000017907223,0.00018542206,0.0001431571,0.00025364867,0.000059924292,0.0023464193,0.00005398407,0.00028766846,0.00006924925],"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":[0.00064343115,0.0004525148,0.00027520984,0.00034420882,0.0004692935,0.000032913136,0.008378321,0.0021080652,0.73122364,0.004134437,0.0007871081,0.25115088],"study_design_scores_gemma":[0.013362869,0.01682932,0.0021492015,0.00492736,0.00045463064,0.001768487,0.0046760864,0.00850562,0.8871452,0.04946517,0.008284591,0.002431449],"about_ca_topic_score_codex":0.0000212974,"about_ca_topic_score_gemma":0.000033201803,"teacher_disagreement_score":0.63381606,"about_ca_system_score_codex":0.00006889651,"about_ca_system_score_gemma":0.00030464592,"threshold_uncertainty_score":0.7561292},"labels":[],"label_agreement":null},{"id":"W2969192420","doi":"10.1007/978-3-030-27272-2_36","title":"An End-to-End Deep Learning Based Gesture Recognizer for Vehicle Self Parking System","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Software deployment; Automotive industry; End-to-end principle; Gesture; Robustness (evolution); Gesture recognition; Artificial intelligence; Deep learning; Classifier (UML); Computer vision; Real-time computing; Engineering","score_opus":0.017379542823800102,"score_gpt":0.24456647487266106,"score_spread":0.22718693204886095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969192420","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":[0.00024341149,0.0002700277,0.9910613,0.000453279,0.0036767165,0.001667499,0.000013771506,0.0007213604,0.0018926383],"genre_scores_gemma":[0.6001123,0.0000055856376,0.39661366,0.0015961375,0.001209674,0.00007849289,0.000026291696,0.000101477315,0.0002563705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99383724,0.00019421558,0.00083042996,0.002636301,0.0013887883,0.0011130477],"domain_scores_gemma":[0.9951415,0.0014169848,0.00053515675,0.0017209924,0.0007298252,0.00045552707],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002551883,0.00081270654,0.00097492355,0.0011828251,0.00053260993,0.0012831885,0.0035156321,0.00066092017,0.00001913904],"category_scores_gemma":[0.00013782995,0.0007463013,0.00025718028,0.0008835533,0.00015860781,0.0007910205,0.0005604043,0.0011306846,0.00019930283],"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":[0.00002435984,0.000055031196,0.0002907897,0.00047606687,0.000039767954,0.00007735809,0.002319121,0.11051971,0.0005430138,0.0025905361,0.00002221792,0.88304204],"study_design_scores_gemma":[0.0007409736,0.0006176096,0.00019075914,0.0015070671,0.000028521747,0.000101146616,0.0000027224432,0.9758192,0.0014016125,0.0016088864,0.016760182,0.0012213561],"about_ca_topic_score_codex":0.000017964972,"about_ca_topic_score_gemma":0.00008840094,"teacher_disagreement_score":0.8818207,"about_ca_system_score_codex":0.0006021948,"about_ca_system_score_gemma":0.00082015054,"threshold_uncertainty_score":0.9997536},"labels":[],"label_agreement":null},{"id":"W2969396096","doi":"10.1016/j.bspc.2019.101580","title":"A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals","year":2019,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Computer science; Joint (building); Segmentation; Actigraphy; Knee Joint; Bluetooth; Artificial intelligence; Physical medicine and rehabilitation; Wireless; Medicine; Telecommunications; Engineering","score_opus":0.0236070807994558,"score_gpt":0.29312703818939057,"score_spread":0.2695199573899348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969396096","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":[0.026605709,0.001858704,0.9692729,0.0006827734,0.00037196014,0.00080846535,0.000023931429,0.00029174815,0.00008378336],"genre_scores_gemma":[0.97078586,0.0000027280341,0.028083242,0.00054877513,0.0004253397,0.00011275688,0.000009174133,0.000020291243,0.000011862029],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975573,0.0001886453,0.00058353797,0.0006027821,0.0005164407,0.0005513009],"domain_scores_gemma":[0.9983199,0.0004892057,0.00034864986,0.0002133999,0.00022979877,0.00039904998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009924704,0.0002440149,0.00053961086,0.00026301955,0.0005073998,0.00069378753,0.0002488207,0.00026735826,0.000015058039],"category_scores_gemma":[0.00005328389,0.00019831579,0.0001335387,0.0006074636,0.00015403384,0.00051824684,0.000045738674,0.00025673382,0.000017453985],"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":[0.00010825511,0.00052072195,0.0022196788,0.005805124,0.00032491138,0.00005498478,0.0012346476,0.0001037624,0.07131395,0.030263891,0.00021430945,0.88783574],"study_design_scores_gemma":[0.0055893906,0.0008140406,0.00095316715,0.006318766,0.00015012476,0.0004234128,0.001162462,0.96541566,0.00048472232,0.0125961425,0.0051398724,0.00095222174],"about_ca_topic_score_codex":0.000014285775,"about_ca_topic_score_gemma":3.7203432e-7,"teacher_disagreement_score":0.9653119,"about_ca_system_score_codex":0.000060551953,"about_ca_system_score_gemma":0.00037025436,"threshold_uncertainty_score":0.8087083},"labels":[],"label_agreement":null},{"id":"W2969726585","doi":"10.1007/s11042-019-08080-4","title":"Multi-scale predictions fusion for robust hand detection and classification","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Scale (ratio); Fusion; Pattern recognition (psychology); Image (mathematics); Machine learning; Architecture; Data mining; Computer vision","score_opus":0.044217125443013254,"score_gpt":0.2601064708962498,"score_spread":0.21588934545323651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969726585","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":[0.0108531015,0.00013283089,0.98625654,0.00039519064,0.0001652174,0.0017397272,0.00007274059,0.0001235948,0.00026108036],"genre_scores_gemma":[0.7977081,0.00013072327,0.1982275,0.00007852949,0.00023688942,0.0027692248,0.00008732245,0.000016370208,0.0007453792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991003,0.000025092088,0.00020823417,0.00041673897,0.00010298037,0.00014663688],"domain_scores_gemma":[0.99916995,0.00019907403,0.00009388841,0.00029519526,0.00013173767,0.00011014794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000171209,0.00010831967,0.00012639597,0.000081492944,0.00033880427,0.00025176426,0.0001278071,0.00010123839,0.000005042842],"category_scores_gemma":[0.000020974603,0.00009969944,0.000032709126,0.0001959273,0.000052479692,0.00036574606,0.00005342871,0.00007882671,0.000059217622],"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":[0.000007857014,0.00013480008,0.002487605,0.00008210298,0.000017234332,6.929998e-8,0.0007061174,0.00007011636,0.1650053,0.0006767216,0.00014504598,0.830667],"study_design_scores_gemma":[0.0011116569,0.000060971426,0.07673077,0.000024831268,0.000018501338,0.000015172064,0.00015486043,0.86065656,0.002378217,0.0001687234,0.05848902,0.00019070253],"about_ca_topic_score_codex":0.000009059555,"about_ca_topic_score_gemma":0.000045692996,"teacher_disagreement_score":0.86058646,"about_ca_system_score_codex":0.000019892459,"about_ca_system_score_gemma":0.00001975584,"threshold_uncertainty_score":0.4065625},"labels":[],"label_agreement":null},{"id":"W2969730641","doi":"10.1109/iccsre.2019.8807586","title":"Arab Sign language Recognition with Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Université de Moncton","funders":"","keywords":"Computer science; Convolutional neural network; Robustness (evolution); Support vector machine; Sign language; Sign (mathematics); Artificial intelligence; Machine learning; Natural language processing; Speech recognition; Linguistics","score_opus":0.011680661642690339,"score_gpt":0.2114058748590899,"score_spread":0.19972521321639958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969730641","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":[0.114758454,0.00010888954,0.85226923,0.00045090847,0.0005998031,0.0003628943,0.0000035506862,0.00033481087,0.031111483],"genre_scores_gemma":[0.9921354,0.0000016599508,0.0051612924,0.0006837118,0.00015623131,0.000018420611,0.000024824105,0.000007268939,0.0018111828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904215,0.00007692934,0.00014318088,0.00028993897,0.00023408681,0.00021372072],"domain_scores_gemma":[0.99940234,0.00009894408,0.00006277031,0.0002536894,0.00010561225,0.00007662593],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017890414,0.00010775181,0.00012523073,0.000061222476,0.000044487966,0.00010707878,0.00023484406,0.000058587113,0.00028645294],"category_scores_gemma":[0.000006058339,0.000077932425,0.00003703744,0.00025474344,0.00001825784,0.00040960623,0.00004299583,0.0001172974,0.0009491362],"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":[0.00024419976,0.0005261586,0.03196475,0.0001658348,0.00033299666,0.00028667718,0.003779931,0.01286754,0.0035323072,0.034243803,0.017642934,0.8944129],"study_design_scores_gemma":[0.0023032299,0.0005129256,0.012481038,0.00011142301,0.000014236518,0.0005417121,0.00038522357,0.9780816,0.00085586205,0.0006379019,0.003321221,0.0007536323],"about_ca_topic_score_codex":0.000024630941,"about_ca_topic_score_gemma":0.000018187915,"teacher_disagreement_score":0.9652141,"about_ca_system_score_codex":0.000020593014,"about_ca_system_score_gemma":0.00002817885,"threshold_uncertainty_score":0.99982876},"labels":[],"label_agreement":null},{"id":"W2970342098","doi":"10.1109/tnsre.2020.2968912","title":"An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Rick Hansen Institute","keywords":"Computer science; Wearable computer; Detector; Artificial intelligence; Computer vision; Reset (finance); BitTorrent tracker; Tracking (education); Eye tracking; Psychology; Embedded system","score_opus":0.010616457335916997,"score_gpt":0.27339035034035486,"score_spread":0.2627738930044379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970342098","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":[0.031759962,0.0002396816,0.9587047,0.000535922,0.00088009326,0.0075495294,0.00007767411,0.0002502677,0.0000021534029],"genre_scores_gemma":[0.8880127,0.000007071251,0.10053079,0.000013864758,0.00011472132,0.01127112,0.000008782415,0.000037318037,0.0000036493145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758154,0.0002633487,0.0006851849,0.0009897036,0.00020486575,0.00027534773],"domain_scores_gemma":[0.99389577,0.0051147924,0.00020090911,0.0003612082,0.00025181734,0.0001755137],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010334316,0.00034092172,0.00052858127,0.0006031072,0.00021047847,0.00025631004,0.00019178193,0.00026388728,1.8249607e-7],"category_scores_gemma":[0.00016244128,0.00033941583,0.00016869188,0.00045752592,0.000029468058,0.00021881267,0.000007695734,0.00036501797,5.135552e-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":[0.000043657634,0.00008760947,0.000027628004,0.0033825627,0.000030517618,3.1536365e-7,0.003095332,0.91224295,0.002043521,0.0005730758,0.0000034079387,0.07846942],"study_design_scores_gemma":[0.0010921296,0.00090026733,0.000842044,0.0004351847,0.00003892083,0.000011408568,0.00033479946,0.99515384,0.00038382874,0.00029328425,0.00017449849,0.0003397729],"about_ca_topic_score_codex":0.000064067885,"about_ca_topic_score_gemma":0.000018608238,"teacher_disagreement_score":0.8581739,"about_ca_system_score_codex":0.00019296867,"about_ca_system_score_gemma":0.000032019638,"threshold_uncertainty_score":0.99990577},"labels":[],"label_agreement":null},{"id":"W2977704702","doi":"10.1145/3338286.3340130","title":"WRIST","year":2019,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":39,"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":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Wrist; Inertial measurement unit; Computer science; Smartwatch; Wearable computer; Artificial intelligence; Orientation (vector space); Computer vision; Human–computer interaction; Set (abstract data type); Gesture; Sensor fusion; Embedded system; Medicine; Mathematics","score_opus":0.0056705764428561685,"score_gpt":0.19981978501802725,"score_spread":0.19414920857517107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977704702","genre_codex":"other","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":[0.010519713,0.000018547556,0.40157282,0.0007193637,0.0005686505,0.00007911768,1.4508942e-7,0.0001733967,0.58634824],"genre_scores_gemma":[0.9751784,6.409097e-7,0.007203023,0.00044088514,0.00003094957,0.0000022020213,2.4583466e-7,0.000001415794,0.017142195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967676,0.000012429409,0.00005325563,0.00010550663,0.00008427396,0.00006775189],"domain_scores_gemma":[0.9997032,0.000019769384,0.00001188286,0.00021549905,0.000022281094,0.000027333941],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000077979304,0.000027319069,0.000041968586,0.000021817019,0.000010437003,0.000052305477,0.00020911424,0.000015149871,0.00020095253],"category_scores_gemma":[0.0000025919642,0.000020121151,0.000016711323,0.000097782715,0.000002285964,0.00013871295,0.000040696985,0.000021968703,0.011055211],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001514495,0.000060605886,0.021065729,0.000032463722,0.000021451615,0.0000117992895,0.000651935,0.000008958823,0.0040942114,0.72521985,0.03794387,0.21088764],"study_design_scores_gemma":[0.00062644866,0.000090744106,0.012052273,0.000025551615,0.0000013316222,0.00007976895,0.00006939136,0.010494547,0.008371036,0.0057132663,0.9621375,0.00033814003],"about_ca_topic_score_codex":0.000005239416,"about_ca_topic_score_gemma":0.0000014090764,"teacher_disagreement_score":0.96465874,"about_ca_system_score_codex":0.0000041546796,"about_ca_system_score_gemma":0.000009764426,"threshold_uncertainty_score":0.9897148},"labels":[],"label_agreement":null},{"id":"W2986336325","doi":"10.22215/etd/2017-11949","title":"Human Body Structure Calibration Using Wearable Inertial Sensors","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Calibration; Computer science; Wearable computer; Accelerometer; Torso; Inertial measurement unit; Inertial frame of reference; Computer vision; Simulation; Process (computing); Artificial intelligence; Embedded system","score_opus":0.02540322536467976,"score_gpt":0.30502377604997966,"score_spread":0.2796205506852999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986336325","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":[0.8221827,0.00031270148,0.03781752,0.00018886077,0.010950877,0.0013588495,0.000048886497,0.00084766117,0.1262919],"genre_scores_gemma":[0.94441354,0.0000038602984,0.0027910555,0.000049420858,0.00084942294,0.000008688035,0.00034951736,0.0000359487,0.05149854],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99829745,0.00010178844,0.0003687952,0.0005375056,0.00043841515,0.00025603003],"domain_scores_gemma":[0.9984867,0.000014723854,0.00045330828,0.0007483301,0.00019851128,0.000098433404],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013205815,0.00028779006,0.00034430964,0.00020360388,0.00054239953,0.000919523,0.0007300159,0.00046870013,0.00018419408],"category_scores_gemma":[0.000030764353,0.0002505434,0.0001019041,0.00012044956,0.0000149835405,0.0007797635,0.000036268793,0.00030274966,0.000034004715],"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":[0.000050148738,0.00018006399,0.00062083086,0.0013612529,0.000515994,0.00021404281,0.008115778,0.0015070982,0.90609604,0.025307527,0.0144874705,0.041543737],"study_design_scores_gemma":[0.0036218646,0.00041524842,0.012083657,0.003606512,0.00038061125,0.0004178479,0.0014625196,0.32477444,0.6026768,0.0122191915,0.032024164,0.0063171443],"about_ca_topic_score_codex":0.0004697063,"about_ca_topic_score_gemma":0.0007252225,"teacher_disagreement_score":0.32326734,"about_ca_system_score_codex":0.00005132506,"about_ca_system_score_gemma":0.00018513334,"threshold_uncertainty_score":0.9999947},"labels":[],"label_agreement":null},{"id":"W2989911746","doi":"10.1109/thms.2019.2947576","title":"Natural Human–Robot Interface Using Adaptive Tracking System with the Unscented Kalman Filter","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Hand Gesture Recognition Systems","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":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Kalman filter; Computer science; Robot; Interface (matter); Cartesian coordinate system; Process (computing); Task (project management); Filter (signal processing); Noise (video); Computer vision; Tracking (education); Simulation; Artificial intelligence; Engineering","score_opus":0.04389706553892932,"score_gpt":0.29133492114076276,"score_spread":0.24743785560183346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989911746","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":[0.06979683,0.00033580404,0.9230546,0.000102594255,0.0031830273,0.0016575888,0.000038740225,0.0005854813,0.0012453836],"genre_scores_gemma":[0.99775726,0.0000011202186,0.00033042676,0.000057556466,0.0001923688,0.000107158856,0.000005783762,0.000070085734,0.0014782222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962809,0.0006930878,0.0007166509,0.00088846765,0.00083472295,0.0005861891],"domain_scores_gemma":[0.9976595,0.00023472005,0.00044144093,0.0011990132,0.0003174447,0.00014787618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067145523,0.0005569276,0.00065172865,0.00037639178,0.001014147,0.00059880165,0.0011280314,0.00015952473,0.000025408017],"category_scores_gemma":[0.0000016028033,0.00036312666,0.00024599902,0.0006910052,0.000092913855,0.00079504645,0.000010837872,0.0009050381,0.00022973436],"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":[0.00037948185,0.0010198188,0.00037924643,0.002066175,0.002407627,0.00019725433,0.015569464,0.8124506,0.14609714,0.013677058,0.0005006609,0.0052554975],"study_design_scores_gemma":[0.005937205,0.0018798532,0.00055514404,0.006840782,0.0003230181,0.0020983939,0.008192701,0.9452827,0.024571802,0.000015176934,0.0017609157,0.0025423008],"about_ca_topic_score_codex":0.0010268477,"about_ca_topic_score_gemma":0.0005131855,"teacher_disagreement_score":0.92796046,"about_ca_system_score_codex":0.00039590386,"about_ca_system_score_gemma":0.000063276646,"threshold_uncertainty_score":0.99988204},"labels":[],"label_agreement":null},{"id":"W2990548414","doi":"10.20380/gi2019.09","title":"Eliciting Wrist and Finger Gestures to Guide Recognizer Design","year":2019,"lang":"en","type":"article","venue":"NPARC","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Rendering (computer graphics); Wrist; Modality (human–computer interaction); Set (abstract data type); Human–computer interaction; Artificial intelligence; Computer vision; Speech recognition; Medicine","score_opus":0.019865541548964694,"score_gpt":0.250319703233593,"score_spread":0.2304541616846283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990548414","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":[0.13732733,0.000113681104,0.72142476,0.004365109,0.0007683775,0.0007701945,0.0000029444595,0.00028994607,0.13493769],"genre_scores_gemma":[0.72404075,0.000010667214,0.26943702,0.0025549233,0.00021036717,0.000040917887,0.0000010770297,0.000017266642,0.003687004],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988227,0.000107334185,0.00021008363,0.0003929896,0.00021825261,0.00024867922],"domain_scores_gemma":[0.9991221,0.000245389,0.000057783967,0.0003414031,0.00010090639,0.00013242701],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00059122837,0.000119135635,0.00017231899,0.0000958881,0.00007007375,0.00019508977,0.0003147812,0.000061548046,0.00008747278],"category_scores_gemma":[0.00012521792,0.00010265582,0.00002915008,0.00022091274,0.00001082435,0.00019008263,0.00015380481,0.000094141156,0.0009665804],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016554914,0.00004522069,0.0037373377,0.000062509745,0.00005173992,0.000031098567,0.0041864524,0.00007226855,0.13481003,0.0033239534,0.10759759,0.74606526],"study_design_scores_gemma":[0.0026967109,0.00091033254,0.021143833,0.00086730305,0.000041115647,0.0005158276,0.0006211422,0.021385415,0.08981473,0.042804983,0.81685317,0.0023454526],"about_ca_topic_score_codex":0.000011401572,"about_ca_topic_score_gemma":0.00000284749,"teacher_disagreement_score":0.7437198,"about_ca_system_score_codex":0.000021615626,"about_ca_system_score_gemma":0.000034605957,"threshold_uncertainty_score":0.9998113},"labels":[],"label_agreement":null},{"id":"W2990618083","doi":"10.1109/smc.2019.8914464","title":"Smart Glove and Hand Gesture-based Control Interface For Multi-rotor Aerial Vehicles","year":2019,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Gesture recognition; Wired glove; Computer science; Gesture; Interface (matter); Computer vision; Artificial intelligence; Convolutional neural network; Throttle; Simulation; Engineering","score_opus":0.02162406619136944,"score_gpt":0.26449060093619847,"score_spread":0.24286653474482903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990618083","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":[0.10235032,0.00012556267,0.8936282,0.0008016132,0.0010209382,0.0014829339,0.000018908144,0.000112405454,0.00045913237],"genre_scores_gemma":[0.9760995,8.663789e-7,0.021915792,0.0006552068,0.00012684982,0.00010062751,0.0000020221828,0.000010574671,0.0010885611],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895585,0.00006909277,0.00021200522,0.00038221508,0.00014706675,0.00023377377],"domain_scores_gemma":[0.9991431,0.00025189502,0.000078292695,0.00028566862,0.00013336752,0.00010763286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030316066,0.00014917183,0.00024721754,0.000059053506,0.00006964539,0.00032130952,0.0002752721,0.00009738528,0.000023288245],"category_scores_gemma":[0.000048041627,0.00011457869,0.000063772386,0.00007788001,0.000036984817,0.00021912548,0.000039527764,0.00006788772,0.00014695438],"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":[0.002259527,0.0013499695,0.0867222,0.0014274606,0.0007179807,0.000027353222,0.005838396,0.0005590475,0.6945379,0.022490691,0.018256495,0.16581297],"study_design_scores_gemma":[0.04068524,0.0015219938,0.017673392,0.00028765562,0.000050179122,0.000048055248,0.00017770495,0.55355144,0.22294809,0.00039840536,0.1613834,0.0012744288],"about_ca_topic_score_codex":0.00003606978,"about_ca_topic_score_gemma":0.000060623184,"teacher_disagreement_score":0.8737492,"about_ca_system_score_codex":0.00001934374,"about_ca_system_score_gemma":0.000055065055,"threshold_uncertainty_score":0.46723834},"labels":[],"label_agreement":null},{"id":"W2991127829","doi":"10.48550/arxiv.1911.09840","title":"Real-time Ultrasound-enhanced Multimodal Imaging of Tongue using 3D Printable Stabilizer System: A Deep Learning Approach","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pronunciation; Computer science; Flexibility (engineering); Active listening; Human–computer interaction; Artificial intelligence; Multimedia; Psychology; Linguistics","score_opus":0.035335073526644396,"score_gpt":0.19358361007027905,"score_spread":0.15824853654363466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991127829","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":[0.3598906,0.00003762329,0.6261582,0.0000017187394,0.0004082473,0.00061555376,0.000008703101,0.00032193988,0.012557453],"genre_scores_gemma":[0.9789339,0.000037783575,0.02003801,0.0000044042163,0.000085290005,0.0000026112273,0.00003390644,0.000048749735,0.0008153721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632657,0.0006343883,0.00054938893,0.0016406111,0.00025970172,0.0005893384],"domain_scores_gemma":[0.9965878,0.00029536747,0.0009142921,0.0014645449,0.0005429956,0.00019500316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00080425286,0.0005071865,0.00092066877,0.00045562032,0.00021033689,0.00020657427,0.0016131297,0.00035130334,0.000030297628],"category_scores_gemma":[0.000071171125,0.0005715593,0.0003302083,0.00075864303,0.00011876486,0.00064363657,0.0014736749,0.0007676742,0.00012709126],"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":[0.000035119767,0.00013216566,0.005647022,0.001300277,0.0001764672,0.000052762476,0.0014836063,0.9737533,0.014226438,0.0027892627,0.0000046439936,0.00039899148],"study_design_scores_gemma":[0.00073968037,0.000026496045,0.00043331514,0.00062536966,0.00011006642,0.000052985037,0.00083890377,0.99451494,0.0018425796,0.00015392978,0.000025123467,0.00063661014],"about_ca_topic_score_codex":0.00090411847,"about_ca_topic_score_gemma":0.0000068386717,"teacher_disagreement_score":0.6190433,"about_ca_system_score_codex":0.00063955726,"about_ca_system_score_gemma":0.0003049763,"threshold_uncertainty_score":0.9996736},"labels":[],"label_agreement":null},{"id":"W2996840610","doi":"10.1109/access.2019.2963113","title":"Emotion Recognition From Body Movement","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":152,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Sadness; Feature selection; Biometrics; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Machine learning; Anger; Psychology","score_opus":0.03304959684730779,"score_gpt":0.27708288520509816,"score_spread":0.24403328835779037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996840610","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":[0.6712281,0.000032131597,0.31591976,0.0003577212,0.003382852,0.0003530975,0.000015790194,0.00019912422,0.008511393],"genre_scores_gemma":[0.99713504,0.000011348282,0.0011185466,0.0010186601,0.00031509678,0.000029369337,0.000031497373,0.000008530899,0.0003319002],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998849,0.000080649836,0.0002188443,0.00037327028,0.00030141426,0.00017687146],"domain_scores_gemma":[0.99920547,0.00006760357,0.000118046184,0.0004238295,0.0001171892,0.00006784258],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018672747,0.00010854605,0.00013885323,0.0000917001,0.0000463552,0.00038607293,0.0006670122,0.00006964639,0.00022748053],"category_scores_gemma":[0.000009821609,0.00009969877,0.000051091138,0.00025341372,0.0000068780428,0.0013126973,0.00009511299,0.00008833125,0.0032894944],"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":[0.000035665922,0.00046150567,0.034708112,0.00011511461,0.00015039189,0.00004254815,0.0018109202,0.0001627633,0.0673738,0.0013852682,0.01645931,0.8772946],"study_design_scores_gemma":[0.0054564714,0.00045429618,0.20524055,0.000910041,0.00006017796,0.000028580977,0.00021212315,0.042307723,0.57163686,0.13425022,0.037229504,0.0022134336],"about_ca_topic_score_codex":0.00019980603,"about_ca_topic_score_gemma":0.000014999426,"teacher_disagreement_score":0.8750812,"about_ca_system_score_codex":0.000041820047,"about_ca_system_score_gemma":0.00002589491,"threshold_uncertainty_score":0.99748653},"labels":[],"label_agreement":null},{"id":"W2998501520","doi":"10.1007/978-3-030-37188-3_23","title":"Real Time Static Gesture Detection Using Deep Learning","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Convolutional neural network; Sign (mathematics); Sign language; Artificial intelligence; Alphabet; Set (abstract data type); American Sign Language; Speech recognition; Deep learning; Task (project management); Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.016568320344135287,"score_gpt":0.24321418997554553,"score_spread":0.22664586963141026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998501520","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":[0.0010778153,0.00017156058,0.99134773,0.000108511784,0.0022099726,0.0005418702,0.0000021262583,0.00030749958,0.0042328867],"genre_scores_gemma":[0.49844253,0.00012584991,0.49504587,0.00082265603,0.0019852913,0.000016102658,0.000018982331,0.00017755594,0.003365142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956834,0.00013031434,0.0006123171,0.0016302405,0.0012115729,0.00073216157],"domain_scores_gemma":[0.99728984,0.0006002299,0.0005054524,0.0010478032,0.00036581172,0.00019087641],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011329071,0.0005981783,0.00068708573,0.0010106636,0.0003463555,0.00074160576,0.0019020125,0.00051855965,0.000032608783],"category_scores_gemma":[0.00010151189,0.0005539012,0.00016001567,0.00075919647,0.00027322545,0.0007795774,0.0007767026,0.0012660264,0.00045826865],"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":[0.00000701733,0.000018884957,0.00008501818,0.000104888626,0.000026094094,0.00009899186,0.0014477986,0.1272502,0.0041492074,0.0006941552,0.0000027801118,0.866115],"study_design_scores_gemma":[0.00028439332,0.00021081409,0.00014350055,0.0005561089,0.000015908057,0.00028798674,4.3630604e-7,0.98492205,0.0012643735,0.010455148,0.0010744589,0.0007848311],"about_ca_topic_score_codex":0.000046473135,"about_ca_topic_score_gemma":0.000057591074,"teacher_disagreement_score":0.86533016,"about_ca_system_score_codex":0.00054688007,"about_ca_system_score_gemma":0.0005504593,"threshold_uncertainty_score":0.99969125},"labels":[],"label_agreement":null},{"id":"W2999252716","doi":"10.1162/comj_a_00500","title":"Detecting Hand Posture in Piano Playing Using Depth Data","year":2019,"lang":"en","type":"article","venue":"Computer Music Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence; Histogram; Computer vision; Segmentation; Context (archaeology); Piano; Pixel; Pattern recognition (psychology); Feature (linguistics); Image (mathematics)","score_opus":0.0832130756453166,"score_gpt":0.28382697536570445,"score_spread":0.20061389972038784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999252716","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":[0.39226696,0.00021105206,0.6033308,0.00010259507,0.003690329,0.00012383255,0.000001315246,0.000034369783,0.00023875768],"genre_scores_gemma":[0.8969355,0.0000040884597,0.101341635,0.0005057164,0.0011710149,6.421844e-7,0.0000027306073,0.000015609781,0.000023046496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797124,0.00021675185,0.00047592894,0.0005008249,0.00039188008,0.00044335186],"domain_scores_gemma":[0.99851495,0.00014641605,0.0002629321,0.00080587156,0.000117332165,0.00015250676],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011043772,0.00019055331,0.000296524,0.00032865023,0.00025471393,0.0011015916,0.0017028196,0.0001006911,0.000022703622],"category_scores_gemma":[0.000027083128,0.00016697172,0.000060274506,0.0004999249,0.000019441437,0.0013922296,0.0009347077,0.0006506905,0.00008820161],"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":[0.000030630017,0.00018745067,0.09914837,0.00016011983,0.00017643144,0.0012947991,0.0098078875,0.0060576648,0.02176179,0.00028957103,0.002823677,0.8582616],"study_design_scores_gemma":[0.0019277357,0.00010997057,0.03208662,0.0008663125,0.000011886388,0.008222208,0.00011470392,0.9511482,0.00022342484,0.00042308282,0.00434945,0.00051637506],"about_ca_topic_score_codex":0.00002012349,"about_ca_topic_score_gemma":0.000053224114,"teacher_disagreement_score":0.9450906,"about_ca_system_score_codex":0.00009272717,"about_ca_system_score_gemma":0.00017700047,"threshold_uncertainty_score":0.9999354},"labels":[],"label_agreement":null},{"id":"W3001759680","doi":"","title":"Framed Guessability: Improving the Discoverability of Gestures and Body Movements for Full-Body Interaction","year":2018,"lang":"en","type":"book-chapter","venue":"Author eBooks","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Discoverability; Gesture; Movement (music); Computer science; Frame (networking); Human–computer interaction; Multimedia; Artificial intelligence; Aesthetics; Art; Telecommunications","score_opus":0.032743509404572935,"score_gpt":0.2868148493572224,"score_spread":0.25407133995264947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001759680","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":[0.0426637,0.00069379306,0.61384314,0.0018509806,0.010556668,0.012673685,0.000576034,0.0006378462,0.31650415],"genre_scores_gemma":[0.7887637,0.000009591099,0.003926399,0.00037447683,0.0010981063,0.00038586877,0.000043872027,0.0000925812,0.2053054],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99773854,0.00010500859,0.00069751806,0.00077836553,0.00041948014,0.00026107606],"domain_scores_gemma":[0.9972775,0.0005335643,0.00067167194,0.0010751365,0.00034680773,0.000095346426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011332514,0.00036465997,0.0005101398,0.000110730434,0.00020018473,0.00022117866,0.000639731,0.00034822995,0.00002180049],"category_scores_gemma":[0.00018021934,0.00025457868,0.00021834043,0.000019195517,0.000277368,0.00019272653,0.00041766433,0.00036853948,0.000011783576],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051997113,0.00018259804,0.0002660969,0.0030410094,0.0013501622,0.00001063713,0.020857392,0.0000019920312,0.02679445,0.31204057,0.004113459,0.63082165],"study_design_scores_gemma":[0.0020035207,0.0021778627,0.0010230042,0.0016240377,0.00039099067,0.00008381212,0.00056753954,0.0059207305,0.014195982,0.59880406,0.37134337,0.0018651314],"about_ca_topic_score_codex":0.00004758031,"about_ca_topic_score_gemma":0.0000544455,"teacher_disagreement_score":0.7461,"about_ca_system_score_codex":0.0000780627,"about_ca_system_score_gemma":0.00010176174,"threshold_uncertainty_score":0.99999064},"labels":[],"label_agreement":null},{"id":"W3010954129","doi":"10.22215/etd/2016-11292","title":"Quaternion-Based Human Gesture Recognition System Using Multiple Body-Worn Intertial Sensors","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Gesture; Hidden Markov model; Computer science; Gesture recognition; Support vector machine; Artificial intelligence; Set (abstract data type); Pattern recognition (psychology); Data set; Quaternion; Speech recognition; Motion (physics); Gyroscope; Computer vision; Engineering; Mathematics","score_opus":0.03066006133175471,"score_gpt":0.27792758372433535,"score_spread":0.24726752239258065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010954129","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":[0.5678199,0.00017396935,0.39571518,0.00015437893,0.012772301,0.0022670089,0.00016666557,0.0025107889,0.018419832],"genre_scores_gemma":[0.99072105,0.0000030091758,0.0031796568,0.000070174676,0.0010076704,0.000109711895,0.0009936441,0.00009017478,0.003824894],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960848,0.00047105894,0.000995796,0.0011333275,0.0007626798,0.00055234507],"domain_scores_gemma":[0.99721795,0.0002512598,0.0007850462,0.00081825414,0.000700085,0.000227403],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005141266,0.00065093784,0.00070299057,0.00061314355,0.00040403224,0.0004694407,0.00084587437,0.00080785155,0.000088677785],"category_scores_gemma":[0.00007726709,0.00050496624,0.00035727967,0.00033691028,0.000027661978,0.0004650702,0.00005981062,0.00042112486,0.00088491134],"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":[0.001522897,0.0016740318,0.0061130547,0.018495396,0.0022743035,0.0014446507,0.013029294,0.00033749064,0.37107462,0.0031201777,0.008609406,0.57230467],"study_design_scores_gemma":[0.014946665,0.0011525516,0.0066951336,0.054640025,0.0008915414,0.00073728425,0.0061123306,0.25431487,0.63708127,0.000903071,0.010873373,0.011651888],"about_ca_topic_score_codex":0.0004557868,"about_ca_topic_score_gemma":0.0005295638,"teacher_disagreement_score":0.5606528,"about_ca_system_score_codex":0.00037836516,"about_ca_system_score_gemma":0.00022517995,"threshold_uncertainty_score":0.999893},"labels":[],"label_agreement":null},{"id":"W3011489390","doi":"10.1109/gcwkshps45667.2019.9024607","title":"Sign Language Gesture Recognition Using Doppler Radar and Deep Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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 Ottawa","funders":"","keywords":"Spectrogram; Computer science; Gesture; Convolutional neural network; Doppler effect; Artificial intelligence; Speech recognition; Doppler radar; Radar; Pattern recognition (psychology); Gesture recognition; Deep learning; Computer vision; Telecommunications; Physics","score_opus":0.016100923235297566,"score_gpt":0.23628989730648622,"score_spread":0.22018897407118865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011489390","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":[0.6569309,0.00054897193,0.32872283,0.000201396,0.00040511048,0.00033314255,0.0000012090521,0.00031987342,0.012536526],"genre_scores_gemma":[0.9575403,0.00001776823,0.041178677,0.00022858877,0.00011179149,0.000004166239,0.0000070247884,0.000011376563,0.0009003337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903727,0.00012359378,0.00014786774,0.00031490423,0.00018566908,0.00019071174],"domain_scores_gemma":[0.999509,0.000097416756,0.000069567745,0.00018473751,0.00006339873,0.000075907665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028826355,0.000113941314,0.00014575939,0.00009122132,0.00007966518,0.00016957289,0.00014652939,0.000086251115,0.00016334091],"category_scores_gemma":[0.000024482331,0.000094737894,0.00003219036,0.0002025428,0.000011848474,0.00043562753,0.0000862833,0.00016616406,0.0004231926],"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":[0.000016001026,0.00006922409,0.009220419,0.00017289512,0.00007911491,0.000072330855,0.010754742,0.00011225795,0.0658731,0.0011394974,0.0003867181,0.9121037],"study_design_scores_gemma":[0.01475238,0.0018797322,0.054757036,0.0018637753,0.00022630692,0.0077403425,0.025525007,0.7079594,0.08810425,0.00741504,0.0820737,0.0077030123],"about_ca_topic_score_codex":0.000048940754,"about_ca_topic_score_gemma":0.000012455183,"teacher_disagreement_score":0.9044007,"about_ca_system_score_codex":0.000019626726,"about_ca_system_score_gemma":0.000016382774,"threshold_uncertainty_score":0.5439426},"labels":[],"label_agreement":null},{"id":"W3017737189","doi":"","title":"Transposing Formal Annotations of 2D Video Data into a 3D Environment for Gesture Research","year":2016,"lang":"en","type":"article","venue":"Revista de Estudos Anglo-Portugueses/Journal of Anglo-Portuguese Studies","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Canadian Linguistic Association","funders":"","keywords":"Gesture; Computer science; Human–computer interaction; Computer vision; Artificial intelligence","score_opus":0.11827365197607434,"score_gpt":0.37411903867741086,"score_spread":0.2558453867013365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017737189","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":[0.15654662,0.20478168,0.6209128,0.009895358,0.0020701448,0.004239476,0.00074175425,0.00025584543,0.00055629935],"genre_scores_gemma":[0.9119509,0.026720142,0.059356224,0.00018839253,0.0012238187,0.0001505733,0.000046755857,0.000091427355,0.00027180254],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99237496,0.00063721626,0.0026648017,0.0010167754,0.0021298388,0.0011764064],"domain_scores_gemma":[0.99076444,0.0020779318,0.0018842012,0.0019525661,0.0028034486,0.00051741133],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009048978,0.0006124927,0.0016233141,0.00087780936,0.0005629435,0.00016212258,0.0028861603,0.00024765317,0.00001840159],"category_scores_gemma":[0.002134916,0.0004179315,0.00051750476,0.0011283632,0.00069274125,0.002745206,0.0009548748,0.00055999996,0.000019979916],"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":[0.00078686624,0.0022355015,0.020559968,0.0032421474,0.005000532,0.0015270057,0.026838861,0.000092408074,0.02091916,0.013404884,0.040296327,0.86509633],"study_design_scores_gemma":[0.021175168,0.013615116,0.1290247,0.023833498,0.003174614,0.005137094,0.027870364,0.0017174291,0.010221637,0.013906532,0.7446175,0.0057063387],"about_ca_topic_score_codex":0.00003024201,"about_ca_topic_score_gemma":0.000042778367,"teacher_disagreement_score":0.85939,"about_ca_system_score_codex":0.0004676813,"about_ca_system_score_gemma":0.0007992288,"threshold_uncertainty_score":0.99982727},"labels":[],"label_agreement":null},{"id":"W3026369469","doi":"10.13140/rg.2.2.32861.97767","title":"MAPPING A CONTINUOUS VOWEL SPACE TO HAND GESTURES","year":2019,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 British Columbia","funders":"","keywords":"Formant; Gesture; Diphthong; Computer science; Speech recognition; Vowel; Coarticulation; Kinematics; Artificial intelligence","score_opus":0.010852469766941342,"score_gpt":0.2240752221805173,"score_spread":0.21322275241357594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026369469","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":[0.15332809,0.00008873549,0.75407505,0.0050899093,0.0011030661,0.0005551592,0.0000011162532,0.00028201504,0.08547687],"genre_scores_gemma":[0.9485103,0.00000130255,0.02292124,0.0017089369,0.00010884878,0.000017438806,5.8770826e-7,0.0000076091446,0.026723776],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99898255,0.000043340297,0.00015390021,0.00034368093,0.00022910154,0.0002474398],"domain_scores_gemma":[0.99921995,0.00007558368,0.000042449694,0.0004316409,0.00009643722,0.00013395833],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022966448,0.000110466484,0.00018123687,0.00012830613,0.000055500535,0.00032493557,0.00044231792,0.00005361094,0.00007587029],"category_scores_gemma":[0.000029217821,0.000086990476,0.00004700665,0.000341994,0.000009425169,0.00019723728,0.00016564761,0.00007329952,0.004212022],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044369943,0.0003783751,0.050250005,0.00034569227,0.00029308104,0.00021979006,0.08844717,0.00033231315,0.18109144,0.107403174,0.33119673,0.23999786],"study_design_scores_gemma":[0.0011463389,0.00023307474,0.038960334,0.00021006513,0.0000035166117,0.00015418835,0.0010107768,0.0023533693,0.016823918,0.0011144901,0.93726325,0.00072666525],"about_ca_topic_score_codex":0.000071758855,"about_ca_topic_score_gemma":0.000043278444,"teacher_disagreement_score":0.79518217,"about_ca_system_score_codex":0.000017081986,"about_ca_system_score_gemma":0.000029278008,"threshold_uncertainty_score":0.9965633},"labels":[],"label_agreement":null},{"id":"W3032705480","doi":"10.1145/3396339.3396381","title":"An interactive sensory system to record and monitor human motion in physical rehabilitation","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Graphical user interface; Orientation (vector space); Interface (matter); Sensory system; Software; Tracking (education); Motion capture; SIGNAL (programming language); Computer vision; Match moving; Simulation; Degrees of freedom (physics and chemistry); Motion (physics); Human–computer interaction; Artificial intelligence","score_opus":0.020365263993473614,"score_gpt":0.2938745971434032,"score_spread":0.2735093331499296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032705480","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":[0.87208575,0.000001537209,0.12518872,0.0013400441,0.00012990917,0.0002972626,0.0000013334081,0.00016371482,0.0007917446],"genre_scores_gemma":[0.9914993,8.927631e-8,0.008120177,0.00014314911,0.00018515496,0.000030149831,0.0000012836526,0.000004957853,0.00001575543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902457,0.0002298055,0.00017269215,0.00034736193,0.00012786426,0.0000976974],"domain_scores_gemma":[0.9994667,0.00012320853,0.000041745614,0.00015472282,0.00007522747,0.0001384056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014550526,0.00008007583,0.00014482996,0.00008424697,0.00003729524,0.00009533933,0.0001286212,0.00003061044,0.000001329611],"category_scores_gemma":[0.000046071964,0.000071527415,0.000020843074,0.00024487716,0.000009107531,0.0005450497,0.000044648335,0.00007992524,0.00006637441],"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":[0.00013000809,0.0006183296,0.030808145,0.0006554728,0.000039579365,0.000054088512,0.16387257,0.00043870523,0.18689424,0.029688481,0.0007549069,0.5860455],"study_design_scores_gemma":[0.0023095421,0.005045398,0.23955421,0.0006556267,0.000015479289,0.000041163137,0.030319696,0.6983999,0.020039253,0.0012632569,0.0012282737,0.0011281733],"about_ca_topic_score_codex":0.000071055794,"about_ca_topic_score_gemma":0.000029365008,"teacher_disagreement_score":0.6979612,"about_ca_system_score_codex":0.000059021288,"about_ca_system_score_gemma":0.000006067119,"threshold_uncertainty_score":0.29168034},"labels":[],"label_agreement":null},{"id":"W3033212464","doi":"10.1016/j.procs.2020.04.165","title":"Comparative Analysis of Convolution Neural Network Models for Continuous Indian Sign Language Classification","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Hand Gesture Recognition Systems","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":"Carleton University","funders":"Science and Engineering Research Board","keywords":"Computer science; Classifier (UML); Convolutional neural network; Artificial intelligence; Sign language; Sign (mathematics); Pattern recognition (psychology); Speech recognition; Natural language processing; Mathematics","score_opus":0.05605353777993627,"score_gpt":0.288091800585051,"score_spread":0.23203826280511475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033212464","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":[0.051242176,0.00011133242,0.94673765,0.00069668883,0.0003186385,0.00057019934,0.000013538926,0.00013557929,0.00017419322],"genre_scores_gemma":[0.9262981,0.0000017913165,0.072812475,0.00058975024,0.00022321165,0.00005286133,0.000014138961,0.000003754524,0.0000039165657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979755,0.00007258533,0.00042142958,0.00068098895,0.000482296,0.00036715585],"domain_scores_gemma":[0.99829084,0.0002068903,0.0003527854,0.00031798022,0.00061283284,0.00021867496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006443608,0.0001558299,0.0004642787,0.00028000868,0.0001825427,0.0002145542,0.0011601424,0.000052409276,0.0000015098008],"category_scores_gemma":[0.000042685075,0.00014225092,0.0001251547,0.0042335065,0.00020178276,0.0010225001,0.00018347961,0.00009480915,0.0000071748264],"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":[0.0000940859,0.0002646762,0.0059189377,0.00031122068,0.0006188733,0.000011626856,0.13182698,0.63684356,0.014465842,0.074933514,0.0031828315,0.13152784],"study_design_scores_gemma":[0.00024214914,0.00015387987,0.0042530303,0.000015244358,0.00005128956,0.0000031077807,0.00013978797,0.9934254,0.001106458,0.0003807289,0.00006556727,0.00016334308],"about_ca_topic_score_codex":0.000008809847,"about_ca_topic_score_gemma":0.000008445433,"teacher_disagreement_score":0.8750559,"about_ca_system_score_codex":0.000039206407,"about_ca_system_score_gemma":0.00020058347,"threshold_uncertainty_score":0.5800824},"labels":[],"label_agreement":null},{"id":"W3034583872","doi":"10.1109/radar42522.2020.9114818","title":"A Linguistic Perspective on Radar Micro-Doppler Analysis of American Sign Language","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"American Sign Language; Perspective (graphical); Computer science; Sign (mathematics); Work (physics); Sign language; Radar; Work flow; Linguistic analysis; Speech recognition; Linguistics; Human–computer interaction; Artificial intelligence; Telecommunications; Engineering","score_opus":0.016870123542372155,"score_gpt":0.2789147218375349,"score_spread":0.26204459829516275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034583872","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":[0.05305527,0.00019961041,0.85810876,0.0049450602,0.00015356425,0.00031751583,0.000040436382,0.00034165007,0.08283812],"genre_scores_gemma":[0.9854319,0.0000019442398,0.0129571,0.0013748284,0.00008234322,0.000003797388,0.000003027076,0.0000049444857,0.00014009951],"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990596,0.000089058674,0.000178593,0.00032497782,0.00022090106,0.00012684074],"domain_scores_gemma":[0.9991979,0.0001584205,0.0001152543,0.0002607679,0.00016248033,0.000105169755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009789215,0.00009607638,0.00031386118,0.000193123,0.000024669642,0.000036421334,0.00033306092,0.000018408891,0.00007090078],"category_scores_gemma":[0.00017708006,0.00007710838,0.00014443322,0.0019120697,0.0000402142,0.000035114637,0.00005422606,0.00007397288,0.00009335695],"study_design_candidate":"qualitative","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":[0.0002076548,0.00096240075,0.007121949,0.00013616626,0.009292845,0.0005306868,0.6039855,0.0010449849,0.12124397,0.17131685,0.010833461,0.07332357],"study_design_scores_gemma":[0.0071852193,0.0087416265,0.086276166,0.0003168571,0.005126446,0.00007618329,0.11670674,0.3193787,0.39417186,0.001849301,0.05423874,0.0059321676],"about_ca_topic_score_codex":0.0004940825,"about_ca_topic_score_gemma":0.00004468302,"teacher_disagreement_score":0.9323766,"about_ca_system_score_codex":0.000035590358,"about_ca_system_score_gemma":0.000035840447,"threshold_uncertainty_score":0.31443885},"labels":[],"label_agreement":null},{"id":"W3037936842","doi":"10.1109/jbhi.2020.3004319","title":"Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Gyroscope; Orientation (vector space); Computer science; Kinematics; Wearable computer; Kalman filter; Computer vision; Sensor fusion; Artificial intelligence; Inertial measurement unit; Angular velocity; Motion capture; Rotation (mathematics); Simulation; Motion (physics); Engineering; Mathematics","score_opus":0.055900132278253176,"score_gpt":0.31482589327282795,"score_spread":0.25892576099457476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037936842","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":[0.11036627,0.00062110083,0.87066746,0.016455421,0.0014512019,0.00029578918,0.000021512356,0.000035604957,0.00008566325],"genre_scores_gemma":[0.8674317,0.0007717915,0.12838988,0.002280488,0.0010907263,0.000007657808,0.0000010407497,0.0000081684275,0.000018572937],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99824965,0.000014446569,0.0009889433,0.00006895474,0.0004089111,0.0002690774],"domain_scores_gemma":[0.9984307,0.000100593046,0.0006185931,0.00006489758,0.00011152901,0.0006736707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031778,0.00011715316,0.00039125606,0.00012732165,0.00015251974,0.00014919329,0.0002143872,0.00009089675,0.0000013469823],"category_scores_gemma":[0.00008104801,0.00007949207,0.00005172943,0.00017539649,0.000089007575,0.00086795114,0.000049515984,0.00024965405,0.000001525491],"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":[0.00011015244,0.000083643856,0.0012294856,0.00365638,0.0001345695,0.000015077022,0.029706398,0.0000062663057,0.000164997,0.0011029886,0.008333564,0.9554565],"study_design_scores_gemma":[0.037556313,0.034650333,0.0073746634,0.010973702,0.00018053903,0.0066317623,0.06381646,0.11124018,0.012385563,0.008493177,0.7040088,0.00268847],"about_ca_topic_score_codex":0.0000053226327,"about_ca_topic_score_gemma":7.2361826e-7,"teacher_disagreement_score":0.952768,"about_ca_system_score_codex":0.000036328896,"about_ca_system_score_gemma":0.0004494732,"threshold_uncertainty_score":0.32415923},"labels":[],"label_agreement":null},{"id":"W3080437961","doi":"10.1109/rbme.2020.3019769","title":"Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review","year":2020,"lang":"en","type":"review","venue":"IEEE Reviews in Biomedical Engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":138,"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":"Sign language; Gesture; Wearable computer; Computer science; Gesture recognition; Human–computer interaction; Variation (astronomy); Sign (mathematics); American Sign Language; Artificial intelligence; Speech recognition; Natural language processing; Embedded system; Linguistics","score_opus":0.07036499578637223,"score_gpt":0.3228353500198016,"score_spread":0.2524703542334294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080437961","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":[2.6883068e-8,0.92258096,0.0729011,0.00032389522,0.0012441166,0.0024701038,0.000040808718,0.00032301055,0.00011597879],"genre_scores_gemma":[1.238704e-7,0.98056626,0.016699241,0.001044838,0.00062067894,0.0008630324,0.00010901302,0.0000719139,0.000024873767],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945336,0.00082559534,0.0022312165,0.0010822195,0.0006768066,0.00065056083],"domain_scores_gemma":[0.9970652,0.00077793567,0.0005916258,0.0009544021,0.00008618891,0.0005246588],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012695802,0.0008633288,0.0047494266,0.0005219342,0.000040939514,0.00010014882,0.0013153283,0.00048145634,0.00008816626],"category_scores_gemma":[0.00064213533,0.00068278407,0.00095956755,0.0032701832,0.00005144928,0.00018117718,0.00013994373,0.0012464403,0.0024571589],"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":[2.5139659e-7,0.00002919911,1.1144125e-8,0.15865418,0.000031810723,0.0003221202,0.000021457485,0.000001929581,0.0000021856074,0.0000033920353,0.0027058045,0.8382277],"study_design_scores_gemma":[0.00015275515,0.00003334902,3.3896058e-8,0.29012436,0.000118869044,0.0001968659,0.0000014691708,0.0008426477,0.0000011381372,0.0000015143953,0.7080137,0.0005132602],"about_ca_topic_score_codex":0.0000069317007,"about_ca_topic_score_gemma":3.2960185e-7,"teacher_disagreement_score":0.8377144,"about_ca_system_score_codex":0.00024774903,"about_ca_system_score_gemma":0.00035358578,"threshold_uncertainty_score":0.9995623},"labels":[],"label_agreement":null},{"id":"W3091761422","doi":"10.1109/ijcnn48605.2020.9207601","title":"An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":14,"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":"Computer science; Gesture; Artificial intelligence; Recurrent neural network; Gesture recognition; Machine learning; Classifier (UML); Artificial neural network; Transformer; Pattern recognition (psychology)","score_opus":0.0671254699398242,"score_gpt":0.2891091284142453,"score_spread":0.22198365847442109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091761422","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":[0.020661067,0.000126403,0.97318846,0.00047032646,0.00015555217,0.0003622459,0.0000142568515,0.0001495598,0.0048721046],"genre_scores_gemma":[0.95913786,0.0000068054815,0.04033759,0.0002107894,0.000084909494,0.00004270408,0.000023113791,0.000011548873,0.00014468875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990144,0.000036566933,0.00025782513,0.00040110046,0.000117827716,0.0001723058],"domain_scores_gemma":[0.9991822,0.00006973677,0.00008708128,0.00025601682,0.00026508945,0.00013987794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002047659,0.00011337322,0.00020292187,0.00006425093,0.00007665236,0.00011252727,0.00044856704,0.00008531747,0.000009819716],"category_scores_gemma":[0.000025641366,0.000100218975,0.000071950264,0.00025411224,0.000016963355,0.00066875323,0.00007484096,0.000069057176,0.000041028183],"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":[0.000103123944,0.00051354227,0.0002342678,0.0011628752,0.0001385864,0.000008012062,0.026717883,0.002279703,0.15243565,0.026606238,0.0023820915,0.787418],"study_design_scores_gemma":[0.00057209557,0.00024129772,0.000064194675,0.000059599137,0.000010403732,0.000007987304,0.000107666274,0.9611137,0.021523464,0.0154697215,0.0006475905,0.00018227617],"about_ca_topic_score_codex":0.000007569389,"about_ca_topic_score_gemma":0.00004342552,"teacher_disagreement_score":0.958834,"about_ca_system_score_codex":0.000015419573,"about_ca_system_score_gemma":0.000043597243,"threshold_uncertainty_score":0.40868112},"labels":[],"label_agreement":null},{"id":"W3092561602","doi":"10.1007/978-3-030-59830-3_13","title":"Automating Stress Detection from Handwritten Documents","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Handwriting; Computer science; Stress (linguistics); Focus (optics); Automation; Process (computing); Handwriting recognition; Artificial intelligence; Field (mathematics); Speech recognition; Natural language processing; Feature extraction; Programming language; Engineering","score_opus":0.01565083413286273,"score_gpt":0.23883267184358536,"score_spread":0.22318183771072264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092561602","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":[0.0001919279,0.000323343,0.9920448,0.00055727415,0.0028146054,0.0004188348,0.000021628657,0.00041484894,0.003212712],"genre_scores_gemma":[0.7641709,0.00002677448,0.23195101,0.0016952431,0.0018509377,0.000026858277,0.00002562296,0.00005965584,0.00019300566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957682,0.000071564435,0.0006493708,0.0017517862,0.0012394817,0.0005195893],"domain_scores_gemma":[0.99763715,0.000448048,0.0004269409,0.0010130213,0.0002179769,0.00025683237],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00047789232,0.0005338562,0.0005941125,0.0005295864,0.0002886442,0.0011124908,0.0026089416,0.00036513203,0.000031440337],"category_scores_gemma":[0.00010413382,0.00049635133,0.00014369398,0.000609576,0.00022513229,0.000846119,0.0011646919,0.0008306722,0.00023837633],"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":[0.0000031449908,0.000013447717,0.00008243146,0.00004095767,0.000023246354,0.000094366434,0.0008428504,0.0018333949,0.00067744596,0.00056828395,0.000009901556,0.9958105],"study_design_scores_gemma":[0.00092903024,0.00028790595,0.0006307038,0.002230069,0.000029914303,0.00009689174,8.857833e-7,0.8445801,0.024248956,0.12266033,0.0025816287,0.0017235428],"about_ca_topic_score_codex":0.0000886829,"about_ca_topic_score_gemma":0.00019271609,"teacher_disagreement_score":0.994087,"about_ca_system_score_codex":0.00028231886,"about_ca_system_score_gemma":0.00027345217,"threshold_uncertainty_score":0.9999244},"labels":[],"label_agreement":null},{"id":"W3098950550","doi":"10.1061/9780784482865.102","title":"Conceptual Framework for Safety Improvement in Mobile Cranes","year":2020,"lang":"en","type":"article","venue":"Construction Research Congress 2020","topic":"Hand Gesture Recognition Systems","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 Alberta","funders":"","keywords":"Operator (biology); Computer science; Communications system; Engineering; Systems engineering; Telecommunications","score_opus":0.06371021152906624,"score_gpt":0.35687639719049336,"score_spread":0.2931661856614271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098950550","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":[0.031389587,0.0012302062,0.94034755,0.01672987,0.0034315416,0.004403805,0.000120549936,0.00037775977,0.001969117],"genre_scores_gemma":[0.9472393,0.00026180487,0.049366344,0.0007382871,0.0009057299,0.0012712567,0.00001995999,0.000026652355,0.00017071253],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99732465,0.00033458098,0.00046888756,0.00066494296,0.0006876633,0.0005192928],"domain_scores_gemma":[0.99760973,0.00097434915,0.00009912356,0.000351463,0.00064224476,0.00032306387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009776864,0.00015218451,0.00029313046,0.00014972281,0.00021598137,0.0002490779,0.00071931526,0.00016697646,0.00016258656],"category_scores_gemma":[0.000995838,0.00014722114,0.00008449965,0.0010422267,0.00041495095,0.00036785493,0.00027639949,0.0005718121,0.0001288017],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027524921,0.00008780052,0.0028824876,0.00023082319,0.00007343499,0.00007182107,0.00438786,0.00015766187,0.0034569923,0.32682332,0.006939318,0.65461326],"study_design_scores_gemma":[0.005747332,0.0020793714,0.00077403535,0.00047398964,0.000010995137,0.00008939536,0.013395802,0.027460603,0.02357928,0.030687308,0.8947137,0.0009881904],"about_ca_topic_score_codex":0.000013721578,"about_ca_topic_score_gemma":0.000011883341,"teacher_disagreement_score":0.9158497,"about_ca_system_score_codex":0.00009636109,"about_ca_system_score_gemma":0.00027274902,"threshold_uncertainty_score":0.60035044},"labels":[],"label_agreement":null},{"id":"W3106439297","doi":"10.1115/detc2001/dac-21013","title":"Posture Prediction Versus Inverse Kinematics","year":2001,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Kinematics; Inverse kinematics; Torso; Position (finance); Computer science; Degrees of freedom (physics and chemistry); Point (geometry); Orientation (vector space); Inverse; Robot kinematics; Kinematics equations; Control theory (sociology); Artificial intelligence; Robot; Mathematics; Geometry; Mobile robot","score_opus":0.03276094881377407,"score_gpt":0.25307295772508887,"score_spread":0.2203120089113148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106439297","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":[0.027906999,0.000032604683,0.7892984,0.001988382,0.0028622616,0.00022962251,0.0000027078906,0.00064704876,0.177032],"genre_scores_gemma":[0.91719687,0.000064168365,0.07252516,0.0012223707,0.0007358181,0.000031097374,0.00001616471,0.000015485275,0.008192894],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993125,0.000034762343,0.00014512298,0.00016566081,0.0002095966,0.00013235994],"domain_scores_gemma":[0.9994602,0.000053975367,0.000039945186,0.00028667029,0.00008278682,0.00007638277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012950264,0.00007353518,0.00008010483,0.00006496929,0.000052753778,0.000079768775,0.00024436755,0.00006080645,0.00007609483],"category_scores_gemma":[0.000036329493,0.000058262267,0.000034120647,0.00032095728,0.000010206829,0.00031757748,0.000056437755,0.000067888126,0.0005648652],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002444327,0.0008537754,0.018253904,0.00023037102,0.00043240492,0.0004757714,0.01039077,0.0019185572,0.007261458,0.17731032,0.510433,0.27219525],"study_design_scores_gemma":[0.009588011,0.0011362811,0.019265285,0.00019915847,0.0000684952,0.0009244395,0.0013491274,0.35142136,0.0029355842,0.006174448,0.60567206,0.0012657478],"about_ca_topic_score_codex":0.000010450347,"about_ca_topic_score_gemma":0.000030982508,"teacher_disagreement_score":0.88928986,"about_ca_system_score_codex":0.000022481077,"about_ca_system_score_gemma":0.000021529599,"threshold_uncertainty_score":0.72603875},"labels":[],"label_agreement":null},{"id":"W3107070394","doi":"10.1109/ccece47787.2020.9255828","title":"Hand Gesture-Based Control of a Front-End Loader","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Loader; Gesture; Wired glove; Payload (computing); Inertial measurement unit; Computer science; Process (computing); Interface (matter); Frame (networking); Front and back ends; Gesture recognition; Accelerometer; Engineering; Embedded system; Simulation; Artificial intelligence; Operating system","score_opus":0.020363288830067872,"score_gpt":0.21882910423299506,"score_spread":0.19846581540292718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107070394","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":[0.0011724107,0.00018721237,0.98144907,0.009202184,0.00014446552,0.00019801702,0.000006487217,0.000099733435,0.007540423],"genre_scores_gemma":[0.98934704,5.485162e-7,0.005921398,0.0044812583,0.0000935088,0.000011372169,0.0000012753256,0.000005100551,0.00013849777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904656,0.00007612111,0.00022533118,0.00024656378,0.0002556543,0.00014977322],"domain_scores_gemma":[0.99931586,0.00011879982,0.0000832917,0.00022750814,0.00011211991,0.00014239326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001291238,0.000098288016,0.00022370287,0.00003930432,0.000035301964,0.000080119076,0.00036547726,0.00005824821,0.00017997809],"category_scores_gemma":[0.00005005521,0.00007458927,0.00007421749,0.00015929232,0.000039376588,0.00013735855,0.000024476905,0.00007680737,0.00023696577],"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":[0.00064944493,0.0014399011,0.047282945,0.0015244394,0.001122409,0.00041150514,0.025809988,0.0033937895,0.35232872,0.06320793,0.14284502,0.35998392],"study_design_scores_gemma":[0.01715783,0.0012332228,0.01196359,0.00017683482,0.00008268625,0.000037373826,0.0002717423,0.42087996,0.35030788,0.0010022621,0.1956269,0.0012597442],"about_ca_topic_score_codex":0.000022465994,"about_ca_topic_score_gemma":0.000011506626,"teacher_disagreement_score":0.9881746,"about_ca_system_score_codex":0.0000072356356,"about_ca_system_score_gemma":0.00006537599,"threshold_uncertainty_score":0.30457947},"labels":[],"label_agreement":null},{"id":"W3107930498","doi":"10.1111/cgf.14149","title":"Personalized Hand Modeling from Multiple Postures with Multi‐View Color Images","year":2020,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Parameterized complexity; Artificial intelligence; Fidelity; Machine learning; Pose; Deep learning; High fidelity; Computer vision; Algorithm","score_opus":0.03660836793274246,"score_gpt":0.2376908208821341,"score_spread":0.20108245294939164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107930498","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":[0.02511225,0.0011948319,0.9686754,0.0037331264,0.00039324744,0.00045441528,0.000049892966,0.00035346884,0.000033369877],"genre_scores_gemma":[0.86229575,0.000061845894,0.13175671,0.0054833423,0.0002714078,0.000040106905,0.000042138836,0.000031949294,0.00001673401],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977949,0.00014595344,0.0003624815,0.0007969373,0.00044335207,0.0004563377],"domain_scores_gemma":[0.9986532,0.0001582673,0.00014023595,0.00044173037,0.0002880144,0.0003186002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015004675,0.0003358565,0.0004578655,0.00011919033,0.00033345493,0.00059079874,0.0009314265,0.00012629967,0.000010203264],"category_scores_gemma":[0.000024910834,0.00026408923,0.00016764335,0.0005761693,0.00012144125,0.00048879243,0.00036556862,0.0002982747,0.000075400785],"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":[0.0025260127,0.003994201,0.1724559,0.0025752864,0.00760615,0.0033832162,0.12528297,0.056020904,0.0256902,0.12692732,0.08443084,0.389107],"study_design_scores_gemma":[0.0021475537,0.00021448421,0.00069821486,0.00013752852,0.000022593815,0.000024284123,0.000084745174,0.9848381,0.0004985335,0.00038118896,0.010535062,0.00041766593],"about_ca_topic_score_codex":0.00013276997,"about_ca_topic_score_gemma":0.000094255396,"teacher_disagreement_score":0.9288172,"about_ca_system_score_codex":0.0000152585835,"about_ca_system_score_gemma":0.000072708965,"threshold_uncertainty_score":0.9999811},"labels":[],"label_agreement":null},{"id":"W3108204456","doi":"10.23919/cnsm50824.2020.9269056","title":"Deep Learning Models for Gesture-controlled Drone Operation","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Drone; Computer science; Gesture; Convolutional neural network; Artificial intelligence; Gesture recognition; Deep learning; Artificial neural network; Scheme (mathematics); Machine learning; Computer vision","score_opus":0.04247780139743395,"score_gpt":0.24405620204772657,"score_spread":0.20157840065029262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108204456","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":[0.00026747413,0.00013932755,0.9854478,0.00535042,0.000143821,0.0006407949,2.9191756e-7,0.00027884168,0.00773123],"genre_scores_gemma":[0.9254694,0.000008502881,0.0713599,0.0017807719,0.0002679845,0.0001594545,0.0000064495603,0.000009326163,0.0009382154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990694,0.000080029466,0.00022600255,0.00029384563,0.00016564917,0.00016506446],"domain_scores_gemma":[0.9994661,0.00012806218,0.00006008419,0.00012030867,0.00011536753,0.00011005292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024803297,0.00009933694,0.0002290332,0.00003271903,0.00011842248,0.00020940378,0.00026160933,0.000058506204,0.000020678805],"category_scores_gemma":[0.000084127416,0.000077004675,0.00008112307,0.00012840127,0.000005740156,0.00049736496,0.00004939405,0.00008857398,0.00011060176],"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":[0.0003249087,0.00013221591,0.00015490784,0.00013258147,0.00023474594,0.00001212506,0.015778664,0.40595916,0.00762253,0.35592157,0.0048266314,0.20889996],"study_design_scores_gemma":[0.0025178718,0.000120384226,0.000012212472,0.0000044583576,0.0000060409034,0.0000033550368,0.000060527822,0.9911358,0.00042066976,0.0012559625,0.004349402,0.00011331355],"about_ca_topic_score_codex":0.000004877537,"about_ca_topic_score_gemma":0.0000072211974,"teacher_disagreement_score":0.9252019,"about_ca_system_score_codex":0.000012283753,"about_ca_system_score_gemma":0.000025870288,"threshold_uncertainty_score":0.31401595},"labels":[],"label_agreement":null},{"id":"W3108253735","doi":"10.1145/3384419.3430712","title":"Sensing finger input using an RFID transmission line","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"National Research Council Canada; University of Waterloo","funders":"","keywords":"RSS; Computer science; Transmission (telecommunications); Key (lock); Radio-frequency identification; Radio frequency; Wireless; Transmission line; Antenna (radio); Line (geometry); Computer hardware; Chip; SIGNAL (programming language); Real-time computing; Embedded system; Telecommunications","score_opus":0.08789310079451829,"score_gpt":0.2866340521246106,"score_spread":0.19874095133009234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108253735","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":[0.029664157,0.0000382565,0.96531856,0.0029244418,0.00014367324,0.00008378506,4.3375226e-7,0.00026167082,0.0015650253],"genre_scores_gemma":[0.79631704,0.0000022710315,0.20093305,0.002406618,0.00026993957,2.5684585e-7,0.0000015783589,0.000008104652,0.000061165534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908626,0.00008492906,0.0001849323,0.00029576008,0.0001913492,0.00015675976],"domain_scores_gemma":[0.9994688,0.000029266213,0.000039426497,0.0001787542,0.00007073387,0.00021299873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000148037,0.000094423754,0.00012950516,0.00004169565,0.00008368426,0.00013809251,0.00020375827,0.00005496674,0.000033278546],"category_scores_gemma":[0.000014420395,0.00007590305,0.00003942382,0.00031896116,0.000009268334,0.00042616416,0.000045670557,0.00009108333,0.000060809263],"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":[0.000009628562,0.00005927487,0.00007606182,0.00006283087,0.000017359087,0.000081259386,0.007417012,0.001309542,0.18068919,0.0005565211,0.00047212723,0.8092492],"study_design_scores_gemma":[0.00024106774,0.0000795867,0.000034424702,0.00004501125,0.000004247844,0.000053091786,0.00004990967,0.9421485,0.040004957,0.00014016176,0.01702183,0.00017721999],"about_ca_topic_score_codex":0.000019194504,"about_ca_topic_score_gemma":0.0000021852188,"teacher_disagreement_score":0.94083893,"about_ca_system_score_codex":0.000010864891,"about_ca_system_score_gemma":0.000042666015,"threshold_uncertainty_score":0.30952364},"labels":[],"label_agreement":null},{"id":"W3109877058","doi":"10.1109/ccece47787.2020.9255783","title":"ES2ISL: An Advancement in Speech to Sign Language Translation using 3D Avatar Animator","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Sign language; Avatar; Bridge (graph theory); Semantics (computer science); Natural language processing; Exploit; Speech recognition; Artificial intelligence; Sign (mathematics); Speech translation; Machine translation; Human–computer interaction; Linguistics; Programming language","score_opus":0.06967932700482808,"score_gpt":0.31420335943432764,"score_spread":0.24452403242949955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109877058","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":[0.106434084,0.000050701674,0.89049333,0.0012819875,0.0000921458,0.00032408535,0.000003015809,0.00012976032,0.001190876],"genre_scores_gemma":[0.7117454,0.0000012940826,0.28672764,0.0013945082,0.00009027681,0.00000732259,0.0000038288913,0.000007100648,0.00002266756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999029,0.000071488575,0.00021344869,0.00030820663,0.00021358872,0.00016426525],"domain_scores_gemma":[0.99959224,0.000022485006,0.000031290714,0.00017902293,0.000030544164,0.00014442914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018627882,0.00009015882,0.000126386,0.0000747947,0.000026496853,0.00007867535,0.0002448637,0.000031618933,0.000059447462],"category_scores_gemma":[0.000012344348,0.00008323452,0.000019267425,0.0004587459,0.0000038294284,0.00051984,0.000034496596,0.0000546304,0.00013124535],"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":[0.000038118666,0.0001471107,0.001044544,0.000084995714,0.000013328857,0.00015018482,0.038315862,0.002470935,0.22600077,0.0014822098,0.00018079166,0.7300711],"study_design_scores_gemma":[0.002199694,0.00074680755,0.0014211145,0.00016221369,0.000011026903,0.000048072296,0.0028168715,0.8523373,0.102020405,0.00015611085,0.03706457,0.0010158245],"about_ca_topic_score_codex":0.000072346425,"about_ca_topic_score_gemma":0.00012431924,"teacher_disagreement_score":0.84986633,"about_ca_system_score_codex":0.000030369625,"about_ca_system_score_gemma":0.000030210773,"threshold_uncertainty_score":0.33942053},"labels":[],"label_agreement":null},{"id":"W3112541994","doi":"10.18280/ts.370521","title":"Gesture Feature Extraction and Recognition Based on Image Processing","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"People's Government of Jilin Province","keywords":"Gesture recognition; Gesture; Computer science; Artificial intelligence; Convolutional neural network; Preprocessor; Feature extraction; Computer vision; Pattern recognition (psychology); Sketch recognition; Feature (linguistics); Workflow; Speech recognition; Database","score_opus":0.028217413792847746,"score_gpt":0.24914903091172141,"score_spread":0.22093161711887366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112541994","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":[0.020284124,0.0001400104,0.9545501,0.01999832,0.00018290029,0.0005994434,0.000020286168,0.0004322322,0.003792579],"genre_scores_gemma":[0.9746987,0.0000049822775,0.021086806,0.0037015118,0.00039885216,0.000039795992,0.000029928604,0.000012816573,0.000026595157],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875313,0.0001008664,0.0001790398,0.00042303186,0.00035984733,0.00018410609],"domain_scores_gemma":[0.9994563,0.00006354093,0.00011445099,0.00010033418,0.0001065337,0.00015888017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021964173,0.00016703103,0.00014654802,0.000075829535,0.00013940198,0.0003070549,0.0001612947,0.00008758815,0.0000650264],"category_scores_gemma":[0.000023012108,0.00014539479,0.000045251436,0.0002708584,0.000021935162,0.00056086865,0.000023596913,0.00022048454,0.00007042035],"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":[0.00016464351,0.00026290424,0.0003482676,0.00038995818,0.00002945446,0.000111044996,0.0028410745,0.00016630765,0.06109282,0.00017276229,0.00940216,0.9250186],"study_design_scores_gemma":[0.007129175,0.0023580568,0.022173433,0.0011000063,0.00011719167,0.00019744903,0.0005713256,0.8578855,0.05704805,0.0014102756,0.04821849,0.0017910517],"about_ca_topic_score_codex":0.0000017186126,"about_ca_topic_score_gemma":0.0000012156029,"teacher_disagreement_score":0.9544146,"about_ca_system_score_codex":0.000026206695,"about_ca_system_score_gemma":0.000043146618,"threshold_uncertainty_score":0.5929027},"labels":[],"label_agreement":null},{"id":"W3113740551","doi":"10.1109/access.2020.3045858","title":"Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Gesture; Artificial intelligence; Gesture recognition; Frame (networking); Support vector machine; Computer vision; Interface (matter); Pattern recognition (psychology)","score_opus":0.08005369473308244,"score_gpt":0.31404938469589033,"score_spread":0.2339956899628079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113740551","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":[0.17390607,0.00021603618,0.8226954,0.0012120524,0.0004434903,0.0014173969,0.00004267549,0.000057998368,0.000008903879],"genre_scores_gemma":[0.98964906,0.000006417561,0.008671014,0.0011194427,0.0001707126,0.00034716396,0.0000024843032,0.000016813441,0.000016895166],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984922,0.0001428398,0.00033031532,0.0005554718,0.00018912302,0.00029006042],"domain_scores_gemma":[0.9992093,0.0002138794,0.00013157126,0.00023392543,0.00003946468,0.00017187979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026666428,0.00020831253,0.00033196708,0.00007251852,0.00008116787,0.0004609424,0.00061783247,0.00011066367,0.0000057278976],"category_scores_gemma":[0.00008118289,0.00018474413,0.000066054155,0.00013703381,0.00006306329,0.00041945686,0.000080663536,0.00013407547,0.000027417333],"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":[0.0040942733,0.0027312222,0.17082947,0.0016994643,0.0004812447,0.00028775935,0.022461621,0.013953039,0.62738234,0.00018538971,0.0036731523,0.15222101],"study_design_scores_gemma":[0.029766262,0.0005837822,0.03616505,0.00020589685,0.00003840774,0.000011910874,0.00007553554,0.6868414,0.2310538,0.00004290083,0.014357624,0.000857412],"about_ca_topic_score_codex":0.00008414807,"about_ca_topic_score_gemma":0.00014736173,"teacher_disagreement_score":0.81574297,"about_ca_system_score_codex":0.00004633873,"about_ca_system_score_gemma":0.000053093772,"threshold_uncertainty_score":0.7533647},"labels":[],"label_agreement":null},{"id":"W3119138272","doi":"10.1109/icpr48806.2021.9412916","title":"Context Matters: Self-Attention for Sign Language Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Sign language; Context (archaeology); Sign (mathematics); Benchmark (surveying); Language model; Aggregate (composite); Traffic sign; Modalities; Natural language processing; Artificial intelligence; Modality (human–computer interaction); Linguistics","score_opus":0.030355586733718555,"score_gpt":0.26847088537132013,"score_spread":0.23811529863760159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119138272","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":[0.009423106,0.0003624615,0.97991353,0.0030770937,0.002639796,0.0013125162,0.00005921244,0.0007909291,0.002421379],"genre_scores_gemma":[0.71631277,0.00007723521,0.26910037,0.0072048185,0.0011795732,0.0016151511,0.0016513424,0.00007438694,0.0027843446],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99785876,0.00021534518,0.00047485082,0.0008536956,0.0003046357,0.0002927236],"domain_scores_gemma":[0.99822986,0.00019995835,0.0002878229,0.0006997722,0.00047535292,0.00010721563],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056189473,0.00028700667,0.0004041834,0.00017863013,0.000083574756,0.00084053905,0.00058459176,0.00034327718,0.00011880863],"category_scores_gemma":[0.000043750515,0.00027647705,0.0003203955,0.00016074989,0.000011833029,0.00036160348,0.00046710993,0.00027917034,0.00031912167],"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":[0.00002051167,0.0005518422,0.00008866301,0.002711642,0.0006860325,0.00010595685,0.010880302,0.0000144944215,0.0054237214,0.0012448083,0.051622182,0.92664987],"study_design_scores_gemma":[0.032790955,0.0025743362,0.0074504823,0.029530056,0.0027214896,0.0026353379,0.08002422,0.20481023,0.2523841,0.073885545,0.28478432,0.02640892],"about_ca_topic_score_codex":0.00008484496,"about_ca_topic_score_gemma":0.000076378354,"teacher_disagreement_score":0.9002409,"about_ca_system_score_codex":0.00009774214,"about_ca_system_score_gemma":0.00014804905,"threshold_uncertainty_score":0.99996877},"labels":[],"label_agreement":null},{"id":"W3130787839","doi":"10.1016/j.medengphy.2021.02.005","title":"Estimation of spatial-temporal hand motion parameters in rehabilitation using a low-cost noncontact measurement system","year":2021,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interface (matter); Computer science; Process (computing); Measure (data warehouse); Graphical user interface; Wavelet; Computer vision; Motion (physics); Motion capture; SIGNAL (programming language); Motion analysis; Artificial intelligence; Data collection; Software; Joint (building); Simulation; Engineering; Data mining; Mathematics","score_opus":0.020722972830525217,"score_gpt":0.23476781392215118,"score_spread":0.21404484109162597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130787839","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":[0.17823742,0.000030231697,0.8209949,0.00007524391,0.00042389595,0.00017586448,0.0000014572023,0.00004993705,0.000011045647],"genre_scores_gemma":[0.9733184,7.2676494e-7,0.026561154,0.000009595222,0.00006598418,0.000024783798,0.000009503922,0.000009337461,5.313686e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815774,0.000107943306,0.00044275925,0.00024008693,0.0008878413,0.0001636176],"domain_scores_gemma":[0.9992121,0.00014974583,0.00011412908,0.00023820165,0.00018636437,0.000099494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008492334,0.00011742835,0.00026174323,0.000075238495,0.000024733059,0.000048439877,0.00014154984,0.00008254395,0.0000012507676],"category_scores_gemma":[0.0005782795,0.0001199265,0.00006581638,0.00042259746,0.000018755149,0.00023589948,0.00004097156,0.00013813957,0.000004126849],"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":[0.000008112574,0.00026666652,0.0014215802,0.0017038932,0.000044582954,0.00005638718,0.001937336,0.7108415,0.0068456316,0.0018734734,0.0000075826356,0.2749933],"study_design_scores_gemma":[0.00046383083,0.000022701886,0.0017099081,0.0014779322,0.0000069491857,0.000011341236,0.000037738166,0.98198384,0.014112707,0.00005104425,0.0000134380725,0.00010858919],"about_ca_topic_score_codex":0.0002354356,"about_ca_topic_score_gemma":0.000036016096,"teacher_disagreement_score":0.79508096,"about_ca_system_score_codex":0.00028294337,"about_ca_system_score_gemma":0.00018750402,"threshold_uncertainty_score":0.4890461},"labels":[],"label_agreement":null},{"id":"W3133540979","doi":"10.1115/1.4050828","title":"Human Temporomandibular Joint Motion: A Synthesis Approach for Designing a Six-Bar Kinematic Simulator","year":2021,"lang":"en","type":"article","venue":"Journal of Mechanisms and Robotics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Kinematics; Linkage (software); Computer science; Simulation; Hinge; Four-bar linkage; Mechanism (biology); Nonlinear system; Motion (physics); Rotation (mathematics); Temporomandibular joint; Engineering; Computer vision; Mechanical engineering; Physics; Orthodontics","score_opus":0.038797556484162804,"score_gpt":0.2530777712275411,"score_spread":0.2142802147433783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133540979","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":[0.00061227963,0.00031089043,0.99792445,0.00059888884,0.0002867532,0.00017974252,0.000001544399,0.000020313964,0.00006515365],"genre_scores_gemma":[0.3416611,0.000010336172,0.6580351,0.00007435621,0.00015032446,0.0000060749076,7.1462074e-7,0.000010984306,0.000051001167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984813,0.00013741995,0.0006186583,0.0002072877,0.00035936548,0.00019600418],"domain_scores_gemma":[0.998579,0.0001489809,0.00046851396,0.00021232877,0.00042729752,0.00016384028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010977013,0.00015088292,0.00046017085,0.0001271549,0.00022069336,0.00022457092,0.00021404307,0.000105113315,0.000005750502],"category_scores_gemma":[0.00018045951,0.00011735811,0.00017560327,0.00015364456,0.000013653446,0.0002397245,0.000072591465,0.00014278844,0.0000013798419],"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":[0.000026119596,0.0011905297,0.000070536466,0.0023860529,0.00090709614,0.0007439637,0.0025429337,0.5885817,0.1529082,0.23806049,0.000995902,0.011586461],"study_design_scores_gemma":[0.0027655927,0.0009100268,0.00018195795,0.0012926764,0.00043314576,0.004350102,0.0012769781,0.9020492,0.04362524,0.042078238,0.00029487908,0.000741991],"about_ca_topic_score_codex":5.8513643e-7,"about_ca_topic_score_gemma":3.613846e-7,"teacher_disagreement_score":0.3410488,"about_ca_system_score_codex":0.000030949293,"about_ca_system_score_gemma":0.00008135727,"threshold_uncertainty_score":0.4785725},"labels":[],"label_agreement":null},{"id":"W3135876039","doi":"10.3390/s21051750","title":"Comparison of the Performance of the Leap Motion ControllerTM with a Standard Marker-Based Motion Capture System","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":27,"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":"EIT Health","keywords":"Motion capture; Gold standard (test); Kinematics; Computer science; Motion analysis; Interphalangeal Joint; Motion (physics); Middle finger; Wrist; Thumb; Artificial intelligence; Computer vision; Mathematics; Medicine; Anatomy; Physics; Statistics","score_opus":0.013088711113265738,"score_gpt":0.22960507945237935,"score_spread":0.2165163683391136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135876039","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":[0.96815515,0.00009511604,0.029086143,0.00066201686,0.00047739278,0.00036254773,0.000016046524,0.000037781,0.0011078326],"genre_scores_gemma":[0.9992486,8.6255477e-7,0.00047130487,0.00003491378,0.000027171054,0.0000073647607,0.0000015566166,0.0000068814284,0.00020131662],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833584,0.00042850172,0.00032644035,0.00020451161,0.0005669402,0.00013777163],"domain_scores_gemma":[0.9984759,0.0000878049,0.00036380388,0.0005916734,0.0004506229,0.0000302066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003385009,0.000114040144,0.000301878,0.000035390636,0.00011550278,0.00003172419,0.00032144162,0.000069107824,0.0000040686464],"category_scores_gemma":[0.000037359645,0.00005878521,0.00010822533,0.00051352906,0.00006825686,0.00007148539,0.000049699145,0.00014314875,0.0000031649245],"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":[0.0006610629,0.00050910783,0.6719436,0.003192525,0.0003873529,0.000018785353,0.011977897,0.26976064,0.017010292,0.004403085,0.0009355191,0.019200109],"study_design_scores_gemma":[0.0029865261,0.00018953477,0.11497278,0.0017923923,0.000081487364,0.000081958664,0.0027932697,0.61397326,0.26181084,0.000011505111,0.0010328882,0.00027353785],"about_ca_topic_score_codex":0.000020252868,"about_ca_topic_score_gemma":0.000043177126,"teacher_disagreement_score":0.55697083,"about_ca_system_score_codex":0.00007570228,"about_ca_system_score_gemma":0.00013080392,"threshold_uncertainty_score":0.23971912},"labels":[],"label_agreement":null},{"id":"W3151038801","doi":"10.1109/hri.2010.5453167","title":"Selecting and commanding individual robots in a vision-based multi-robot system","year":2010,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"Simon Fraser University","funders":"","keywords":"Robot; Gesture; Computer science; Computer vision; Task (project management); Artificial intelligence; Face (sociological concept); Motion (physics); Human–computer interaction; Social robot; Mobile robot; Robot control; Engineering","score_opus":0.021846564145499587,"score_gpt":0.27454076861052235,"score_spread":0.2526942044650228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151038801","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":[0.20995916,0.00003300145,0.78668994,0.00033506815,0.0006547697,0.00033125354,0.0000013652623,0.00035070052,0.0016447234],"genre_scores_gemma":[0.9152285,2.3613067e-7,0.08455095,0.00010796316,0.000047196612,0.000018874696,0.0000014563494,0.0000071852437,0.000037677775],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987947,0.000114246664,0.00027451938,0.00033958702,0.00022468535,0.00025229857],"domain_scores_gemma":[0.9991908,0.00031084535,0.000083173974,0.00024599454,0.00005971375,0.000109442524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000955687,0.00012581264,0.00019148951,0.0002461772,0.00013742977,0.00032833984,0.00033541807,0.00010364919,0.0000047074896],"category_scores_gemma":[0.00006307943,0.00010496853,0.000026578158,0.00043893696,0.000021251524,0.0002863109,0.0001263901,0.00028995905,0.00002694371],"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":[0.000028859184,0.00080383813,0.5821191,0.0010761555,0.00011606143,0.00031140744,0.011858504,0.0029710063,0.06753074,0.03192345,0.00071473833,0.30054614],"study_design_scores_gemma":[0.0029426727,0.0001022053,0.094552554,0.00048690595,0.00000848287,0.00024575338,0.00049244077,0.89393324,0.006286921,0.000043578846,0.00035933475,0.0005459395],"about_ca_topic_score_codex":0.00011872337,"about_ca_topic_score_gemma":0.0007952612,"teacher_disagreement_score":0.8909622,"about_ca_system_score_codex":0.000024282403,"about_ca_system_score_gemma":0.000050001818,"threshold_uncertainty_score":0.42804924},"labels":[],"label_agreement":null},{"id":"W3158050854","doi":"10.1016/j.patrec.2021.04.017","title":"Object recognition in performed basic daily activities with a handcrafted data glove prototype","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Object (grammar); Computer science; Computer vision; Artificial intelligence; Wired glove; Computer graphics (images); Engineering drawing; Engineering; Virtual reality","score_opus":0.046512752141074636,"score_gpt":0.2504616354469917,"score_spread":0.20394888330591704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158050854","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":[0.8174839,0.00006628058,0.17374422,0.004682283,0.00062164315,0.0014157442,0.00033580503,0.00032676192,0.0013233342],"genre_scores_gemma":[0.98197985,0.000044461234,0.00806167,0.0066489843,0.00034968334,0.0006441742,0.00215535,0.000056376495,0.000059429112],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965226,0.0005180292,0.00054407906,0.001201191,0.0006263723,0.000587714],"domain_scores_gemma":[0.997972,0.00027677338,0.00025872336,0.0011028709,0.00023593997,0.00015365599],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005413157,0.0003754589,0.0004283585,0.0003877818,0.00014315883,0.0004882663,0.0007138718,0.00013858342,0.00025929068],"category_scores_gemma":[0.000111071175,0.00035111912,0.00007619109,0.0010291154,0.000072004,0.0021819416,0.0002831945,0.00044811863,0.000595317],"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":[0.00022007317,0.00045720176,0.021070462,0.00043720173,0.00019865138,0.001020764,0.0025496578,0.000011165599,0.011143425,0.0000015212007,0.0021851333,0.96070474],"study_design_scores_gemma":[0.06314549,0.004440776,0.2501677,0.023710268,0.00095463125,0.013434738,0.010819657,0.06575117,0.51337975,0.0032553053,0.03341985,0.017520688],"about_ca_topic_score_codex":0.00012266992,"about_ca_topic_score_gemma":0.0009228345,"teacher_disagreement_score":0.9431841,"about_ca_system_score_codex":0.00013569488,"about_ca_system_score_gemma":0.00022769203,"threshold_uncertainty_score":0.9998941},"labels":[],"label_agreement":null},{"id":"W3160068080","doi":"10.1109/smc52423.2021.9658982","title":"A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Gesture; Sign language; Gesture recognition; Artificial intelligence; Speech recognition; Transformer; Encoder; Natural language processing","score_opus":0.08625669195234964,"score_gpt":0.3080878288388225,"score_spread":0.22183113688647288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160068080","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":[0.10670946,0.0005776322,0.8350886,0.0039794478,0.0036809857,0.0015783829,0.00012782923,0.000093659175,0.04816402],"genre_scores_gemma":[0.99451053,0.00014443927,0.002256334,0.0002704067,0.00057648495,0.00022677716,0.00022800721,0.000014718115,0.0017722817],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769276,0.00030923402,0.0005350543,0.0006150442,0.00058718736,0.00026069558],"domain_scores_gemma":[0.9984686,0.0002897151,0.00023211146,0.00027522296,0.0006679428,0.0000664133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008743688,0.00022125908,0.00027474063,0.00019068825,0.00011062568,0.0008372462,0.0004835795,0.00013840746,0.000021871556],"category_scores_gemma":[0.00009979986,0.00018671739,0.00010082677,0.000282201,0.000039878247,0.00023641842,0.00005573785,0.00022149221,0.000052675066],"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":[0.0003959833,0.002984481,0.0050406307,0.0015081621,0.0009314216,0.0004110022,0.012784647,0.002481292,0.014666665,0.7191142,0.013739702,0.22594184],"study_design_scores_gemma":[0.0034200973,0.00042659993,0.0018731414,0.0014490244,0.00006808097,0.00045471595,0.0057698423,0.9557009,0.0013645061,0.0076736785,0.020755187,0.0010442581],"about_ca_topic_score_codex":0.00006408162,"about_ca_topic_score_gemma":0.00011569963,"teacher_disagreement_score":0.9532196,"about_ca_system_score_codex":0.00006110625,"about_ca_system_score_gemma":0.00010055164,"threshold_uncertainty_score":0.8073584},"labels":[],"label_agreement":null},{"id":"W3172764674","doi":"10.1002/cav.2016","title":"ExpressGesture: Expressive gesture generation from speech through database matching","year":2021,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":50,"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":"ADAPT - Centre for Digital Content Technology; University of California, Davis; University of Toronto; Science Foundation Ireland","keywords":"Gesture; Computer science; Speech recognition; Gesture recognition; Motion capture; Artificial intelligence; Motion (physics)","score_opus":0.03317050590792655,"score_gpt":0.2683416722137332,"score_spread":0.23517116630580662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172764674","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":[0.06539421,0.00075667247,0.9303897,0.0011963582,0.0011490043,0.0001632572,0.000039310584,0.0002071756,0.0007042882],"genre_scores_gemma":[0.7073489,0.00012595073,0.28675595,0.002815811,0.0018258697,0.000028669065,0.0004924276,0.00002232063,0.00058412115],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980604,0.00026587892,0.00036552316,0.0006982811,0.00038681758,0.00022307606],"domain_scores_gemma":[0.9988169,0.00015917019,0.00015037725,0.0005419849,0.00020818353,0.00012337953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001754754,0.00023845643,0.0002690659,0.00006639427,0.00025488538,0.00074906193,0.00033519362,0.00011552213,0.000064906286],"category_scores_gemma":[0.000021068758,0.00022476651,0.00006356337,0.00033895203,0.000028388822,0.0014771153,0.00042994355,0.00022956842,0.0000839276],"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":[0.000031331772,0.0005015747,0.0006452021,0.000103298946,0.00024087934,0.00061631267,0.028547978,0.0010158856,0.34055668,0.22726077,0.116644934,0.28383514],"study_design_scores_gemma":[0.0072985627,0.0005297472,0.021676738,0.0015471756,0.00015597584,0.0011445627,0.0017932833,0.40155488,0.2703487,0.02336105,0.26677725,0.003812067],"about_ca_topic_score_codex":0.000026244988,"about_ca_topic_score_gemma":0.000042490403,"teacher_disagreement_score":0.6436338,"about_ca_system_score_codex":0.000026876345,"about_ca_system_score_gemma":0.00005673687,"threshold_uncertainty_score":0.91657126},"labels":[],"label_agreement":null},{"id":"W3178259030","doi":"10.1016/j.jvcir.2021.103200","title":"A CNN model for real time hand pose estimation","year":2021,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Convolutional neural network; Pose; Artificial intelligence; Joint (building); Pattern recognition (psychology); Machine learning; Computer vision","score_opus":0.03991462739930919,"score_gpt":0.37676524098813585,"score_spread":0.3368506135888267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178259030","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":[0.032339334,0.00029507763,0.96437186,0.0018734623,0.000074500065,0.00014852876,0.000002413541,0.000018816481,0.00087599707],"genre_scores_gemma":[0.6718911,0.0006265063,0.32671258,0.00010303982,0.000070995666,0.0000112965345,0.00003642487,0.000008705435,0.00053938583],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884963,0.00025934842,0.0004552878,0.00012473763,0.0002260681,0.00008491847],"domain_scores_gemma":[0.9978421,0.00031217973,0.00044635416,0.00030024763,0.0010263372,0.0000727876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006644857,0.000074872056,0.00017614092,0.00011213652,0.00017507123,0.00039184705,0.00022576278,0.000047074896,0.0000064668943],"category_scores_gemma":[0.00033156102,0.000069984424,0.00007581992,0.00019746648,0.000042053252,0.001273211,0.000094574694,0.00007831579,0.000007319748],"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":[0.00020912269,0.00082918105,0.0002804142,0.00017656201,0.00020217842,0.000019732714,0.012757349,0.0029875431,0.6173563,0.007699811,0.009160925,0.3483209],"study_design_scores_gemma":[0.0009062799,0.000094262636,0.0006461994,0.00007262658,0.000023194807,0.00017025444,0.0001746369,0.9759193,0.016950015,0.004748525,0.00020915853,0.0000855498],"about_ca_topic_score_codex":0.0000050901053,"about_ca_topic_score_gemma":0.00000233105,"teacher_disagreement_score":0.97293174,"about_ca_system_score_codex":0.00003048612,"about_ca_system_score_gemma":0.0001217596,"threshold_uncertainty_score":0.37785903},"labels":[],"label_agreement":null},{"id":"W3182673766","doi":"10.1061/(asce)cp.1943-5487.0000981","title":"Automatic Identification of Idling Reasons in Excavation Operations Based on Excavator–Truck Relationships","year":2021,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":22,"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":"Excavator; Truck; Earthworks; Engineering; Identification (biology); Excavation; Productivity; Work (physics); Transport engineering; Automotive engineering; Civil engineering","score_opus":0.02074527809500524,"score_gpt":0.24652295545353517,"score_spread":0.22577767735852994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182673766","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":[0.41010034,0.000060360355,0.5891867,0.00018621536,0.0002971895,0.00005685468,4.16708e-7,0.000021650181,0.000090227775],"genre_scores_gemma":[0.9626093,0.0000039842403,0.037288778,0.000015823869,0.00006531029,0.000001981029,0.0000018810509,0.000009069059,0.000003877644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980531,0.0002072683,0.0010958918,0.00014910172,0.00035628807,0.00013835845],"domain_scores_gemma":[0.9983789,0.0006898717,0.0003400991,0.00022873381,0.00031559324,0.000046828183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022023022,0.00009977895,0.00025088384,0.00067034364,0.000044993503,0.00009103095,0.00023202853,0.00006901156,0.0000040271425],"category_scores_gemma":[0.00135974,0.00010762721,0.00006604363,0.0011586851,0.00000617019,0.00035511932,0.00003026356,0.00036985005,0.0000022710633],"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":[9.1447265e-7,0.0000620716,0.0071447026,0.000071749084,0.000006593034,0.000018897272,0.00076882355,0.98469186,0.0039270213,0.0016946609,0.0000062973286,0.0016063864],"study_design_scores_gemma":[0.00036741188,0.000016825634,0.10872467,0.0012050439,0.0000038354606,0.000044849865,0.00005538712,0.886658,0.0027398602,0.00008650019,0.000013953029,0.000083636056],"about_ca_topic_score_codex":0.0000034975017,"about_ca_topic_score_gemma":0.000042642085,"teacher_disagreement_score":0.55250895,"about_ca_system_score_codex":0.00014265637,"about_ca_system_score_gemma":0.00017966458,"threshold_uncertainty_score":0.43889102},"labels":[],"label_agreement":null},{"id":"W3190786081","doi":"10.13140/rg.2.1.3586.1527","title":"Computer Vision Methods for Guitarist Left-Hand Fingering Recognition","year":2006,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"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":"Università degli Studi di Genova; McGill University","keywords":"Computer vision; Artificial intelligence; Computer science; Computer graphics (images)","score_opus":0.029202686469452722,"score_gpt":0.2914676615747959,"score_spread":0.2622649751053432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190786081","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":[0.27529386,0.0006619573,0.64417183,0.00046043686,0.0085357,0.0036972677,0.00084817206,0.0023545527,0.06397623],"genre_scores_gemma":[0.50633997,0.000012966187,0.4921721,0.000398214,0.0002650939,0.00012017058,0.000100825164,0.00007162192,0.00051903597],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99592197,0.000673145,0.00093879126,0.0012096296,0.0004749338,0.00078152],"domain_scores_gemma":[0.99706256,0.0008881847,0.0003931438,0.0008067314,0.0005945845,0.0002547796],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0026664513,0.0004915835,0.0005705124,0.0003822217,0.0014466991,0.0005597563,0.000907483,0.00034856258,0.00004641278],"category_scores_gemma":[0.00026699906,0.0004942949,0.00034052462,0.00054965005,0.00007325173,0.0017443706,0.00040736664,0.00047458283,0.0003141522],"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":[0.0000268278,0.00013824846,0.000012774157,0.00010328485,0.000042873584,0.000017603721,0.000005697013,0.00005889985,0.048748367,0.02087154,0.000056389512,0.9299175],"study_design_scores_gemma":[0.002779763,0.000688243,0.00087916286,0.0005558458,0.00008470641,0.00041523867,0.000023502265,0.0146686295,0.267802,0.15771697,0.55264807,0.0017378739],"about_ca_topic_score_codex":0.00018212308,"about_ca_topic_score_gemma":0.000169399,"teacher_disagreement_score":0.9281796,"about_ca_system_score_codex":0.00042610426,"about_ca_system_score_gemma":0.000028574947,"threshold_uncertainty_score":0.99985325},"labels":[],"label_agreement":null},{"id":"W3196085244","doi":"10.3390/s21175743","title":"Image Generation for 2D-CNN Using Time-Series Signal Features from Foot Gesture Applied to Select Cobot Operating Mode","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Cegep de Sept Iles; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Foundation for Innovation","keywords":"Computer science; Artificial intelligence; Gesture; Gesture recognition; Convolutional neural network; Computer vision; Robot; Robotics; Modality (human–computer interaction); Representation (politics); Inertial measurement unit; Pattern recognition (psychology)","score_opus":0.02444352356204602,"score_gpt":0.26723495163075356,"score_spread":0.24279142806870754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196085244","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":[0.36708838,0.00014058662,0.62932247,0.0010678187,0.00045373058,0.0006711315,0.000078490724,0.00020394498,0.00097345863],"genre_scores_gemma":[0.4452869,0.0000023261553,0.55174553,0.0006506689,0.0011837663,0.00005474154,0.00013226041,0.000033509343,0.0009102675],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984326,0.00012742376,0.00026415428,0.0005944958,0.00027285094,0.00030849746],"domain_scores_gemma":[0.9990323,0.00012418255,0.00008088189,0.00032445785,0.00032062575,0.00011756163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017766879,0.00020885828,0.00028108162,0.00007030388,0.00031061858,0.00055302616,0.00022586988,0.00012593757,0.000024468134],"category_scores_gemma":[0.000076432225,0.00019842402,0.00007796812,0.00034103933,0.000016569733,0.00023737762,0.00009402629,0.00013564827,0.00006297297],"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":[0.000010222312,0.000018356817,0.000013050003,0.000013769084,0.00003816365,0.000014543076,0.0017758395,0.01677694,0.97579306,0.00033477257,0.002049392,0.0031618602],"study_design_scores_gemma":[0.00044626129,0.000060849005,0.00011169874,0.000051258903,0.000025932575,0.00006105467,0.00023974189,0.34191516,0.654432,0.00027153897,0.0019674466,0.00041705093],"about_ca_topic_score_codex":0.00004789827,"about_ca_topic_score_gemma":0.00009688545,"teacher_disagreement_score":0.3251382,"about_ca_system_score_codex":0.000059363505,"about_ca_system_score_gemma":0.00013531312,"threshold_uncertainty_score":0.8091497},"labels":[],"label_agreement":null},{"id":"W3198694343","doi":"10.32920/ryerson.14655699.v1","title":"Engagement Detection Framework for Hand Gesture and Posture Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Disengagement theory; Computer science; Action (physics); Artificial intelligence; Feature (linguistics); Object (grammar); Human–computer interaction; Gesture recognition; Discriminative model; Gaze; Computer vision; Pattern recognition (psychology)","score_opus":0.04885436298260926,"score_gpt":0.2851612602853466,"score_spread":0.23630689730273735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198694343","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":[0.016530422,0.0011666251,0.97592,0.0013019326,0.0029214334,0.0012581814,0.000031032363,0.00024656998,0.00062381016],"genre_scores_gemma":[0.703574,0.00024582117,0.29379383,0.0007220352,0.0007249969,0.000583683,0.00013807116,0.000027775277,0.00018982851],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99781305,0.00024467037,0.0003638505,0.0010027838,0.0003006371,0.00027501455],"domain_scores_gemma":[0.99813914,0.0003873358,0.00023353357,0.00061865745,0.00048858236,0.00013273329],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00066475815,0.00033410717,0.00039127388,0.00016125436,0.0003016708,0.0014579695,0.00035371125,0.0008031078,0.000025553825],"category_scores_gemma":[0.00028284817,0.00030263636,0.00016050703,0.00019230384,0.000031033243,0.00023907375,0.00073870237,0.0009100972,0.000014169156],"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":[0.00003225594,0.00015937627,0.00009821114,0.0014974123,0.00035993604,0.000036027337,0.00573446,0.00003288941,0.0038715652,0.0027917116,0.000755296,0.9846309],"study_design_scores_gemma":[0.00531795,0.0015177092,0.016405335,0.010464228,0.00087030086,0.001383902,0.0051406254,0.030150212,0.2096133,0.6274328,0.08437081,0.0073328433],"about_ca_topic_score_codex":0.00002767288,"about_ca_topic_score_gemma":0.00008815185,"teacher_disagreement_score":0.977298,"about_ca_system_score_codex":0.00006397126,"about_ca_system_score_gemma":0.000120661396,"threshold_uncertainty_score":0.9999426},"labels":[],"label_agreement":null},{"id":"W3202070111","doi":"10.18280/ts.380402","title":"Function to Flatten Gesture Data for Specific Feature Selection Methods to Improve Classification","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature selection; Gesture; Computer science; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence; Function (biology); Feature (linguistics); Machine learning; Biology","score_opus":0.07884785000310972,"score_gpt":0.3383465185940114,"score_spread":0.25949866859090165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202070111","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":[0.0010747481,0.00010090432,0.9830672,0.0129284505,0.0012672106,0.0010321052,0.000077444325,0.00016822656,0.00028371302],"genre_scores_gemma":[0.114625014,0.000009037986,0.87674356,0.0030228281,0.0023017935,0.0005524627,0.0006211708,0.00003924501,0.0020848927],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99777156,0.000242473,0.00032200446,0.0009908163,0.000360001,0.00031312802],"domain_scores_gemma":[0.99830616,0.00016054501,0.00010714306,0.0007680914,0.00045380444,0.00020426462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011400458,0.00019753369,0.00022252831,0.00015679211,0.00022400943,0.00036701618,0.0006455603,0.00012900976,0.000066330664],"category_scores_gemma":[0.00006986138,0.00018623624,0.00007131162,0.00086181925,0.000006981178,0.000503956,0.00019606622,0.00015531972,0.00010679631],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006189874,0.00009957206,0.00006246017,0.000039444894,0.000063928666,0.0000021810808,0.00044480813,0.00010697784,0.4503484,0.0027454696,0.09527804,0.45074683],"study_design_scores_gemma":[0.00080968265,0.00043247544,0.009888017,0.0000569028,0.00004854517,0.000033808265,0.00019149874,0.022908207,0.035373155,0.0006001017,0.92923886,0.00041873328],"about_ca_topic_score_codex":0.0000030295294,"about_ca_topic_score_gemma":0.000030200088,"teacher_disagreement_score":0.83396083,"about_ca_system_score_codex":0.00014285756,"about_ca_system_score_gemma":0.00010214304,"threshold_uncertainty_score":0.75944936},"labels":[],"label_agreement":null},{"id":"W3204597830","doi":"10.1109/tai.2022.3195968","title":"Multiview Video-Based 3-D Hand Pose Estimation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pose; Artificial intelligence; Computer vision; Computer science; Estimation; Engineering","score_opus":0.058803631670398704,"score_gpt":0.29923958715074195,"score_spread":0.24043595548034324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204597830","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":[0.0018459143,0.000043000186,0.9936877,0.0011844445,0.0023107978,0.0004083513,0.000031437798,0.00025378304,0.00023458328],"genre_scores_gemma":[0.98705447,0.0000045287616,0.011857687,0.0005651032,0.000044366523,0.00029486735,0.0000035583466,0.000015203244,0.0001602294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790674,0.00029617266,0.0004692147,0.0004972472,0.0005492263,0.00028142333],"domain_scores_gemma":[0.9988192,0.0003057472,0.0001232534,0.0005154352,0.000116040064,0.000120335455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005389241,0.00018599647,0.00019154258,0.00031183645,0.0009931581,0.00024351964,0.00058622664,0.000050946648,0.0004458746],"category_scores_gemma":[0.000016812337,0.00019593477,0.00014705848,0.00097616116,0.00007690671,0.0003177166,0.000005214187,0.00036405824,0.0006567132],"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":[0.000029620813,0.0003555861,6.0431205e-7,0.000013315779,0.000012277836,0.000013433165,0.000596908,0.34217656,0.0024497658,0.001441636,0.00006129324,0.652849],"study_design_scores_gemma":[0.000057593432,0.00023201949,0.0000049507935,0.00003158808,0.000011959254,0.000025096908,0.0001133083,0.71100557,0.28311047,0.0030102467,0.0021507498,0.0002464285],"about_ca_topic_score_codex":0.000069447706,"about_ca_topic_score_gemma":0.000076493445,"teacher_disagreement_score":0.9852086,"about_ca_system_score_codex":0.0001390577,"about_ca_system_score_gemma":0.00013973037,"threshold_uncertainty_score":0.84409386},"labels":[],"label_agreement":null},{"id":"W3207108977","doi":"10.3390/app11219787","title":"A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Discretization; Feature selection; Dimensionality reduction; Computer science; Dynamic time warping; Pattern recognition (psychology); Artificial intelligence; Benchmark (surveying); Algorithm; Mathematical optimization; Mathematics","score_opus":0.016804780747618507,"score_gpt":0.2523224487273044,"score_spread":0.2355176679796859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207108977","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":[0.029102009,0.00017846371,0.96405756,0.0022140015,0.0003236985,0.0006818494,0.0000177747,0.00020916863,0.003215459],"genre_scores_gemma":[0.93785864,0.000010297869,0.06115358,0.0005241484,0.00013882475,0.0001394606,0.000033186905,0.0000073235597,0.00013455526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998569,0.00006830832,0.00015135328,0.0006519798,0.0003113322,0.0002479914],"domain_scores_gemma":[0.9989313,0.00047408117,0.0001140288,0.00013004504,0.00027352123,0.000077042765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041943937,0.00014518181,0.00016079399,0.00011768077,0.00052161596,0.00042092518,0.00020500412,0.00012090229,0.0000060830635],"category_scores_gemma":[0.00014084853,0.00012418015,0.000040932304,0.0010402565,0.00008646861,0.0002899778,0.00004480789,0.000110597786,0.000012058522],"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":[0.00015563135,0.00043228982,0.0016678221,0.00043531292,0.000113570626,0.0000445338,0.0057996726,0.0073998696,0.120223776,0.090333536,0.0028917028,0.77050227],"study_design_scores_gemma":[0.0031495336,0.0007871767,0.0027769383,0.00028919827,0.00009473938,0.00036143075,0.002330885,0.6535502,0.2578682,0.058719154,0.018508932,0.0015636372],"about_ca_topic_score_codex":0.000004377458,"about_ca_topic_score_gemma":0.00006893683,"teacher_disagreement_score":0.9087566,"about_ca_system_score_codex":0.00003617683,"about_ca_system_score_gemma":0.00017733239,"threshold_uncertainty_score":0.50639194},"labels":[],"label_agreement":null},{"id":"W3212534344","doi":"10.48550/arxiv.2111.03635","title":"BBC-Oxford British Sign Language Dataset","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"","keywords":"Sign (mathematics); Linguistics; History; Computer science; Natural language processing; Mathematics; Philosophy","score_opus":0.05956021653571327,"score_gpt":0.1916315840599191,"score_spread":0.1320713675242058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212534344","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":[0.15339854,0.0005152103,0.83576083,0.00010753953,0.0016064347,0.00045123292,0.0013027422,0.00046966108,0.0063877995],"genre_scores_gemma":[0.9939734,0.0002718152,0.0014962112,0.00027123236,0.00017840405,0.0000018625373,0.0016922591,0.00002037574,0.002094451],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976438,0.0002728546,0.0002329329,0.0013286123,0.00014402147,0.00037774918],"domain_scores_gemma":[0.99769926,0.000101057565,0.0002190392,0.0015870209,0.00016304699,0.00023057133],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028852638,0.00028204863,0.00040698846,0.00016491956,0.00013365633,0.0006407378,0.0019418655,0.00032871985,0.00016854826],"category_scores_gemma":[0.000044890745,0.00038929543,0.00019782713,0.0005930169,0.00006136037,0.0005350847,0.0024474664,0.0005797815,0.00018195242],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015033329,0.003280914,0.017197296,0.0047055553,0.0039822822,0.21798132,0.014029608,0.102054514,0.0014390203,0.098271936,0.31283063,0.22407658],"study_design_scores_gemma":[0.010270794,0.0005423379,0.016576463,0.006912066,0.0011868852,0.0027850575,0.00926961,0.47596112,0.0035139685,0.029123599,0.4300495,0.013808566],"about_ca_topic_score_codex":0.0008215552,"about_ca_topic_score_gemma":0.0007630541,"teacher_disagreement_score":0.84057486,"about_ca_system_score_codex":0.00012012089,"about_ca_system_score_gemma":0.00025209552,"threshold_uncertainty_score":0.9998559},"labels":[],"label_agreement":null},{"id":"W3213356319","doi":"10.1109/ismar-adjunct54149.2021.00097","title":"Designing a Multi-Modal Communication System for the Deaf and Hard-of-Hearing Users","year":2021,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Sign language; Computer science; Interpreter; Gesture; Spoken language; American Sign Language; Human–computer interaction; Population; Cued speech; Multimedia; Natural language processing; Artificial intelligence; Linguistics; Programming language","score_opus":0.08124216141907392,"score_gpt":0.2880454403060269,"score_spread":0.206803278886953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213356319","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":[0.005843639,0.0007281403,0.9921971,0.0004223583,0.00012505676,0.0002339891,0.0000010844519,0.000065044616,0.00038357935],"genre_scores_gemma":[0.7334143,0.00001373669,0.26635164,0.000039459122,0.000012048498,0.00003653314,8.619418e-7,0.0000036187164,0.00012779536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936545,0.00011804095,0.00017086565,0.0001500795,0.00009644415,0.00009911885],"domain_scores_gemma":[0.9987326,0.0005920731,0.00006815091,0.00040803401,0.00016744826,0.000031648404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005527139,0.000055823544,0.00010855467,0.000026038586,0.00016731503,0.00012700625,0.00027254544,0.000033615663,8.9993256e-7],"category_scores_gemma":[0.00005942735,0.00003904147,0.000036138084,0.00013684465,0.000020314097,0.00016001574,0.0001370515,0.00005031498,0.0000030768347],"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":[0.00005824559,0.00034420073,0.040482957,0.0029168366,0.0008843642,0.000024631863,0.03701097,0.0016968455,0.23131578,0.29854998,0.0015035144,0.38521168],"study_design_scores_gemma":[0.0025479854,0.000069692556,0.016266938,0.00090018107,0.00006475555,0.00027856824,0.009664243,0.7522014,0.21239778,0.00027461033,0.004909047,0.000424802],"about_ca_topic_score_codex":0.000042921525,"about_ca_topic_score_gemma":0.00004630999,"teacher_disagreement_score":0.75050455,"about_ca_system_score_codex":0.00001699455,"about_ca_system_score_gemma":0.000043545628,"threshold_uncertainty_score":0.1592065},"labels":[],"label_agreement":null},{"id":"W3214575505","doi":"10.1145/3486952","title":"Typealike","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Laptop; Computer science; Artificial intelligence; Rotation (mathematics); Computer vision; Typing; Key (lock); Human–computer interaction; Speech recognition","score_opus":0.06057261144803624,"score_gpt":0.3182253181540785,"score_spread":0.2576527067060423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214575505","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":[0.9033777,0.00007575354,0.018578473,0.011732175,0.009113941,0.00053196016,0.0000042026395,0.00048588542,0.05609987],"genre_scores_gemma":[0.98785007,0.0000036902975,0.009945939,0.0007373791,0.0004910517,0.000012924707,0.0000013725033,0.0000116016145,0.0009459953],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988178,0.000021469314,0.0003035635,0.00036176766,0.00033479487,0.0001605878],"domain_scores_gemma":[0.99850905,0.000077744546,0.00027847596,0.0005785915,0.00051145395,0.000044696506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021189949,0.00013375882,0.00017709505,0.000101119265,0.00013741924,0.00023154212,0.001646847,0.000062167484,0.00002617165],"category_scores_gemma":[0.00013697206,0.000099778816,0.00014349488,0.00035980897,0.000021904167,0.0005442854,0.00090362644,0.00022280838,0.00009569418],"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":[0.00007263715,0.0012687449,0.0042307023,0.0006512863,0.00044721307,0.00002105985,0.008296048,0.00018434689,0.29443195,0.21886592,0.23242885,0.23910123],"study_design_scores_gemma":[0.0011820339,0.0004396221,0.018133126,0.0013721222,0.00004382534,0.000595169,0.000318548,0.0057206247,0.8422775,0.038696665,0.090532385,0.00068832195],"about_ca_topic_score_codex":0.0000064064257,"about_ca_topic_score_gemma":0.0000022207366,"teacher_disagreement_score":0.5478456,"about_ca_system_score_codex":0.00005969037,"about_ca_system_score_gemma":0.000021850869,"threshold_uncertainty_score":0.4068862},"labels":[],"label_agreement":null},{"id":"W3217373359","doi":"10.1109/ssrr53300.2021.9597855","title":"Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Defence Research and Development Canada","keywords":"Computer science; Lidar; Artificial intelligence; Gesture recognition; Convolutional neural network; Computer vision; Gesture; Teleoperation; Robustness (evolution); Mobile robot; Modular design; Pose; Robot; Classifier (UML); Remote sensing","score_opus":0.09389762036205669,"score_gpt":0.1979715686401101,"score_spread":0.1040739482780534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217373359","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":[0.06053172,0.0001336194,0.9346875,0.0004449603,0.0019814007,0.0010363392,0.000045628574,0.00038723525,0.000751604],"genre_scores_gemma":[0.97765034,0.00005708216,0.019784456,0.00052020774,0.0007372077,0.000018134768,0.00065773306,0.000033194232,0.00054163067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721,0.00042763905,0.00034479017,0.001405479,0.000139792,0.00047225016],"domain_scores_gemma":[0.9974122,0.00028597575,0.00037079296,0.0010028065,0.000721703,0.00020651647],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005006672,0.00040126327,0.00048592075,0.00020086353,0.00030629226,0.0004543599,0.00090382586,0.00057065947,0.000036990536],"category_scores_gemma":[0.000082084036,0.0004619558,0.00036090057,0.0007282994,0.00004152009,0.0005515109,0.00047549765,0.0005897435,0.00005707141],"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":[0.00009707988,0.00020181198,0.0015467838,0.00042920632,0.0001843823,0.00033534112,0.00032753355,0.9757166,0.00014015475,0.0024817926,0.0022341858,0.016305143],"study_design_scores_gemma":[0.0011180354,0.00012852684,0.0016318308,0.00045595627,0.00013512197,0.000019363664,0.00006689096,0.98902804,0.00039639862,0.0048019313,0.0014065108,0.000811374],"about_ca_topic_score_codex":0.000073152194,"about_ca_topic_score_gemma":0.00017146849,"teacher_disagreement_score":0.9171186,"about_ca_system_score_codex":0.00015357342,"about_ca_system_score_gemma":0.00039815978,"threshold_uncertainty_score":0.9997832},"labels":[],"label_agreement":null},{"id":"W389677024","doi":"","title":"Real Time Hand Gesture Recognition Using Sift","year":2012,"lang":"en","type":"preprint","venue":"viXra","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gesture; Scale-invariant feature transform; Gesture recognition; Sign language; Computer science; American Sign Language; Speech recognition; Sign (mathematics); Feature (linguistics); Artificial intelligence; Invariant (physics); Computer vision; Natural language processing; Feature extraction; Linguistics; Mathematics","score_opus":0.05516236181718224,"score_gpt":0.2777915414390949,"score_spread":0.22262917962191267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W389677024","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":[0.3958604,0.0025374428,0.51658475,0.0013774196,0.016360888,0.0029406673,0.00037778955,0.002209479,0.061751157],"genre_scores_gemma":[0.8192148,0.00039270692,0.17009825,0.0005144374,0.0067311963,0.00020672962,0.0006249686,0.00017616538,0.0020407129],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975777,0.0002801972,0.00044628052,0.0007402337,0.00047647307,0.00047907684],"domain_scores_gemma":[0.99807936,0.00010454476,0.00038634124,0.0009235884,0.00027565306,0.00023049446],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006317041,0.00038309282,0.0004803651,0.00023822517,0.00018872676,0.00060347887,0.00079229084,0.00066439866,0.0001242046],"category_scores_gemma":[0.00004413922,0.00036151047,0.00019266213,0.00025915983,0.000054019256,0.0004129018,0.0009265598,0.0006455154,0.0021087574],"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":[0.000082064646,0.0010641768,0.0018711145,0.0023120306,0.0010551544,0.00037005346,0.019251734,0.0006584603,0.11760455,0.0010097611,0.026269712,0.82845116],"study_design_scores_gemma":[0.010896254,0.00091446313,0.03650966,0.021745564,0.002431744,0.00507329,0.0005153364,0.31752184,0.113871224,0.10494002,0.36103112,0.024549475],"about_ca_topic_score_codex":0.0001687556,"about_ca_topic_score_gemma":0.000008431261,"teacher_disagreement_score":0.80390173,"about_ca_system_score_codex":0.00013283863,"about_ca_system_score_gemma":0.00021562318,"threshold_uncertainty_score":0.9998837},"labels":[],"label_agreement":null},{"id":"W41271234","doi":"10.1007/978-3-642-23623-5_24","title":"Augmented Virtuality for Arthroscopic Knee Surgery","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston General Hospital; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; California HIV/AIDS Research Program","keywords":"Computer science; Knee surgery; Virtuality (gaming); Artificial intelligence; Osteoarthritis; Medicine; Pathology","score_opus":0.05302927964703044,"score_gpt":0.27287737808734475,"score_spread":0.2198480984403143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W41271234","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":[0.024692396,0.00008089134,0.97147197,0.00034320442,0.0027345873,0.0004290642,0.0000027411443,0.00018369734,0.000061460996],"genre_scores_gemma":[0.7347517,0.000002223236,0.264216,0.0008308499,0.00014424676,0.00004554135,0.0000012345818,0.0000066047533,0.0000016262929],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750984,0.00011133246,0.00043333325,0.0008780169,0.00045704635,0.00061042357],"domain_scores_gemma":[0.9978726,0.0007982302,0.00014665972,0.0008005008,0.00022314978,0.00015886119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017160272,0.00021359042,0.00032362944,0.000423779,0.00021962413,0.00023975754,0.0011624569,0.00009442105,0.000011610425],"category_scores_gemma":[0.00028219342,0.00018367187,0.00010385776,0.0017189095,0.00025758287,0.0007697395,0.0003274334,0.0001586738,0.000035103523],"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":[0.000014419483,0.00017200015,0.011215224,0.000056778685,0.000011865876,0.000020320022,0.003384068,0.00046683746,0.0011143264,0.0016998908,0.00006287185,0.9817814],"study_design_scores_gemma":[0.0020120917,0.00080229284,0.07200904,0.00062579813,0.000014714179,0.0001918737,0.0000062601866,0.63054186,0.18122314,0.109087415,0.0016504442,0.0018350523],"about_ca_topic_score_codex":0.000042664913,"about_ca_topic_score_gemma":0.00005754561,"teacher_disagreement_score":0.9799463,"about_ca_system_score_codex":0.000087637105,"about_ca_system_score_gemma":0.00030106783,"threshold_uncertainty_score":0.7489922},"labels":[],"label_agreement":null},{"id":"W4200240718","doi":"10.1155/2021/9023010","title":"Intelligent Malaysian Sign Language Translation System Using Convolutional‐Based Attention Module with Residual Network","year":2021,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Hand Gesture Recognition Systems","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":"Centennial College","funders":"Research Institute of Electrical Communication, Tohoku University; Universiti Malaysia Sarawak; King Saud University","keywords":"Computer science; Residual; Classifier (UML); Confusion; Convolutional neural network; Sign language; Confusion matrix; Artificial intelligence; Speech recognition; Residual neural network; Algorithm; Psychology","score_opus":0.06583588349893778,"score_gpt":0.2880973675296436,"score_spread":0.22226148403070584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200240718","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":[0.034599673,0.00041695285,0.963703,0.0003690602,0.00040790468,0.00020891793,0.000009020233,0.00012116489,0.00016431946],"genre_scores_gemma":[0.9447721,0.0000090913745,0.054645978,0.00038296622,0.00010806164,0.000013087557,0.000020140522,0.000009757654,0.000038786664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977938,0.0002229396,0.00036821334,0.00069178187,0.00061480823,0.00030845345],"domain_scores_gemma":[0.99887997,0.00024907695,0.00015476931,0.00024059795,0.00033566874,0.00013989805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035913737,0.00018490248,0.00018832447,0.0001250634,0.00037883915,0.00032893376,0.00031769255,0.00005751119,0.000005943819],"category_scores_gemma":[0.000031324686,0.00016950753,0.00004696727,0.0010350704,0.00015956794,0.0005559021,0.000070978684,0.00013903012,0.000015913556],"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":[0.000011997159,0.000051638825,0.0009227897,0.00005646014,0.0000058895785,0.0000975563,0.00022660922,0.9686431,0.0022036445,0.019207666,0.00001736516,0.00855525],"study_design_scores_gemma":[0.0001186989,0.00009033855,0.0036598488,0.00020386642,0.000011258207,0.00041991152,0.00021990511,0.9892817,0.004870574,0.00080307754,0.00009394902,0.00022684279],"about_ca_topic_score_codex":0.000011750581,"about_ca_topic_score_gemma":0.00000843905,"teacher_disagreement_score":0.91017246,"about_ca_system_score_codex":0.00005972521,"about_ca_system_score_gemma":0.00024095297,"threshold_uncertainty_score":0.69123167},"labels":[],"label_agreement":null},{"id":"W4200466983","doi":"10.1109/iscc53001.2021.9631255","title":"Hand Gesture Recognition Using CNN &amp; Publication of World's Largest ASL Database","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Symposium on Computers and Communications (ISCC)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Sign language; Gesture; RGB color model; American Sign Language; Gesture recognition; Artificial intelligence; Alphabet; Sign (mathematics); Layer (electronics); Natural language processing; Speech recognition; Pattern recognition (psychology); Database","score_opus":0.06683967639533489,"score_gpt":0.2926921146411678,"score_spread":0.2258524382458329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200466983","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":[0.033728503,0.0026142371,0.9215593,0.023872925,0.0021055886,0.0009275253,0.0003009795,0.00022758913,0.014663379],"genre_scores_gemma":[0.7286604,0.0016622886,0.26502755,0.0017305881,0.00037215019,0.000113047856,0.0014098351,0.00005074649,0.0009734132],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997479,0.0006327657,0.0006254318,0.00060963654,0.00037174823,0.0002813793],"domain_scores_gemma":[0.99516565,0.00048471979,0.00038652946,0.0028891684,0.0008964424,0.00017747207],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005871677,0.00025769338,0.0003728079,0.0004048167,0.0005351397,0.00060075184,0.0011894939,0.00011967072,0.00003512718],"category_scores_gemma":[0.000057633573,0.00026577117,0.000119485805,0.001421833,0.00016675978,0.00068647077,0.0006777033,0.00036315044,0.00008165606],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012149374,0.0069596563,0.00538704,0.00097042805,0.00128579,0.00008415958,0.011054082,0.0018588961,0.27791238,0.0719498,0.05807433,0.56434196],"study_design_scores_gemma":[0.0069779614,0.0005318073,0.0042254375,0.0048361705,0.00049311813,0.001128181,0.00064281095,0.36687467,0.07167935,0.0041867634,0.53463626,0.003787488],"about_ca_topic_score_codex":0.00007634302,"about_ca_topic_score_gemma":0.00024791443,"teacher_disagreement_score":0.69493186,"about_ca_system_score_codex":0.000066786466,"about_ca_system_score_gemma":0.00018462236,"threshold_uncertainty_score":0.99997944},"labels":[],"label_agreement":null},{"id":"W4205718953","doi":"10.1007/978-3-030-90439-5_4","title":"DeepSolfège: Recognizing Solfège Hand Signs Using Convolutional Neural Networks","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; RGB color model; Deep learning; Sign (mathematics); Pixel; Domain (mathematical analysis); Image (mathematics); Computer vision; Pattern recognition (psychology); Speech recognition","score_opus":0.04383600625549706,"score_gpt":0.26004821374262643,"score_spread":0.21621220748712938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205718953","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":[0.00017303083,0.0022566065,0.9899216,0.0003291211,0.005690776,0.00044453872,0.000009663749,0.00016975962,0.0010048832],"genre_scores_gemma":[0.557955,0.00007241331,0.43407226,0.0032745565,0.0039197826,0.000023397228,0.00005077685,0.00012096694,0.00051080267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943468,0.00013170278,0.00093585585,0.0021718363,0.0013240613,0.0010897601],"domain_scores_gemma":[0.996385,0.000758678,0.0005068415,0.0012436718,0.0007616673,0.00034416176],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012599186,0.00075843866,0.00087246014,0.0007980253,0.00071646675,0.0016780095,0.002431652,0.00061746035,0.000059564514],"category_scores_gemma":[0.00013748922,0.0007445953,0.00027664212,0.0010481102,0.0008396926,0.00091303856,0.0015344962,0.0013464144,0.00003299539],"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":[0.000008018418,0.000059066944,0.00020332038,0.00009616466,0.00006755739,0.0006820879,0.0008077797,0.41624925,0.0011572838,0.0059832856,0.000050576764,0.5746356],"study_design_scores_gemma":[0.00037026356,0.00009773662,0.000050423878,0.00074403605,0.000018711899,0.00073473214,4.6132698e-7,0.98506516,0.00048994384,0.010566021,0.00091783056,0.00094466977],"about_ca_topic_score_codex":0.00002784485,"about_ca_topic_score_gemma":0.000098516095,"teacher_disagreement_score":0.57369095,"about_ca_system_score_codex":0.00047702118,"about_ca_system_score_gemma":0.0010122489,"threshold_uncertainty_score":0.9995005},"labels":[],"label_agreement":null},{"id":"W4210647848","doi":"10.1109/access.2022.3146838","title":"Intuitiveness Level: Frustration-Based Methodology for Human–Robot Interaction Gesture Elicitation","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Computer science; Robustness (evolution); Vocabulary; Robot; Artificial intelligence; Human–robot interaction; Human–computer interaction; Linguistics","score_opus":0.408940731366379,"score_gpt":0.45231525151574525,"score_spread":0.04337452014936627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210647848","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":[0.037019506,0.000020422633,0.95801747,0.0012924498,0.0026424625,0.00057061657,0.00003362217,0.0001480775,0.0002553817],"genre_scores_gemma":[0.98239356,6.771433e-7,0.015216967,0.00094851304,0.0002763688,0.0009392629,0.00008040058,0.000015464477,0.00012875615],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99806744,0.00068809,0.0003272892,0.0004267157,0.00028313394,0.00020730769],"domain_scores_gemma":[0.998132,0.00097320834,0.00026608247,0.00029722293,0.00027990885,0.000051612078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001033508,0.00013914998,0.00020674443,0.00026385783,0.0004991125,0.00026244446,0.00083768513,0.00007194629,0.000059476017],"category_scores_gemma":[0.000108452296,0.0001444945,0.000090146445,0.00048426248,0.000020738598,0.00079508993,0.00009571146,0.00021688109,0.000010650323],"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":[0.00042680252,0.0011245155,0.004891116,0.00055926,0.00029609268,0.00005838301,0.008696442,0.12224432,0.20933117,0.081435144,0.021882964,0.5490538],"study_design_scores_gemma":[0.008702227,0.002286178,0.0422642,0.00023600076,0.00017359704,0.00025058052,0.0021324977,0.10385752,0.5541415,0.14022183,0.14294818,0.002785649],"about_ca_topic_score_codex":0.00013430424,"about_ca_topic_score_gemma":0.00013831667,"teacher_disagreement_score":0.9453741,"about_ca_system_score_codex":0.00013296564,"about_ca_system_score_gemma":0.0001207841,"threshold_uncertainty_score":0.5892315},"labels":[],"label_agreement":null},{"id":"W4214810854","doi":"10.1016/j.compeleceng.2022.107836","title":"Hand gesture recognition based on a Harris Hawks optimized Convolution Neural Network","year":2022,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Gesture; Convolutional neural network; Computer science; Artificial intelligence; Key (lock); Convolution (computer science); Gesture recognition; Speech recognition; Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.011437153954341497,"score_gpt":0.19289328494055077,"score_spread":0.18145613098620927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214810854","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":[0.0054111215,0.0002935451,0.9905003,0.0006955068,0.0019530418,0.0004018768,0.0000058268843,0.000619058,0.000119757366],"genre_scores_gemma":[0.9463947,0.0000039424076,0.05142131,0.0013025969,0.00055303896,0.00019911256,0.00005965148,0.000037161324,0.000028493125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997686,0.00022826542,0.00035540698,0.00058224524,0.0005347627,0.0006132615],"domain_scores_gemma":[0.998774,0.00044970168,0.00011626446,0.0003770581,0.00008244219,0.0002005582],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043744134,0.00028158544,0.00035653406,0.00030727996,0.00042448624,0.00025271787,0.00060220115,0.00009313638,0.000023633875],"category_scores_gemma":[0.00006039341,0.00029984958,0.00016727927,0.0014091134,0.000015641996,0.0002013574,0.00017268918,0.000708547,0.000026948645],"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":[0.000038540053,0.00006340632,0.00001698533,0.000012340467,0.00002281142,0.00004921285,0.000069019094,0.956312,0.00029988468,0.00023804508,0.0033539792,0.0395238],"study_design_scores_gemma":[0.0011979943,0.00037718113,0.00031873825,0.000036599802,0.000012221526,0.000085902604,0.0000016603883,0.9906366,0.0002161262,0.00007552101,0.006682688,0.00035878055],"about_ca_topic_score_codex":0.00000720042,"about_ca_topic_score_gemma":2.7739037e-7,"teacher_disagreement_score":0.9409836,"about_ca_system_score_codex":0.00028095863,"about_ca_system_score_gemma":0.000062571155,"threshold_uncertainty_score":0.99994534},"labels":[],"label_agreement":null},{"id":"W4214938822","doi":"10.1109/csde53843.2021.9718421","title":"Hand Gesture Recognition System for Games","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","topic":"Hand Gesture Recognition Systems","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Gesture; Laptop; Computer vision; Artificial intelligence; Computer graphics (images); Thresholding; Grayscale; Gesture recognition; Interface (matter); Video game; Human–computer interaction; Multimedia; Image (mathematics)","score_opus":0.04958759318305748,"score_gpt":0.25982541089941785,"score_spread":0.21023781771636038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214938822","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":[0.004452281,0.00019367054,0.98870534,0.0011483574,0.00377044,0.00042053743,0.00017920979,0.00023623423,0.00089390884],"genre_scores_gemma":[0.8991677,0.000102308775,0.09927588,0.00013512479,0.0008775542,0.00008335563,0.00017030246,0.000028148368,0.00015961245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964114,0.00006734922,0.00043749117,0.0016860401,0.0007708208,0.00062689447],"domain_scores_gemma":[0.99644303,0.00024182964,0.00013700358,0.0018404181,0.0009961254,0.00034156733],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013152075,0.0003571358,0.00043651627,0.00034058612,0.00043122118,0.0023189913,0.0017664401,0.00013686292,0.00000931514],"category_scores_gemma":[0.00015735347,0.00033556455,0.00006176881,0.001210599,0.00016145576,0.0016271517,0.0006116012,0.0002636186,0.00007008371],"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":[0.000017512728,0.000151839,0.000046529683,0.00055682054,0.00008513519,0.00018725096,0.0011933714,0.0004815981,0.02708534,0.012983633,0.007365671,0.9498453],"study_design_scores_gemma":[0.0009332922,0.00023773093,0.00042481098,0.0010188529,0.00003231248,0.00058553094,0.00038300524,0.93034965,0.032765727,0.00022442904,0.03215163,0.0008930085],"about_ca_topic_score_codex":0.0000072806506,"about_ca_topic_score_gemma":0.0000065014397,"teacher_disagreement_score":0.9489523,"about_ca_system_score_codex":0.000092422364,"about_ca_system_score_gemma":0.00056040473,"threshold_uncertainty_score":0.99990964},"labels":[],"label_agreement":null},{"id":"W4220801845","doi":"10.1145/3490100.3516476","title":"Integrating in-hand physical objects in mixed reality interactions","year":2022,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Human–computer interaction; Computer science; Gesture; Headset; Mixed reality; Modalities; Virtual reality; Augmented reality; Context (archaeology); Multimedia; Artificial intelligence","score_opus":0.030834418556614073,"score_gpt":0.2993770285809041,"score_spread":0.26854261002429003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220801845","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":[0.8103394,0.000019851077,0.11271098,0.0023743538,0.001420292,0.00040091996,0.0000061064616,0.0001695415,0.07255855],"genre_scores_gemma":[0.9981803,3.0940248e-7,0.0011891075,0.00013532,0.00004728454,0.0000964746,0.00000318886,0.0000032613602,0.00034471435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988787,0.00031065175,0.00020692441,0.00025752163,0.00018791691,0.00015825154],"domain_scores_gemma":[0.9994579,0.00021553604,0.0000494148,0.00021436071,0.00002593439,0.00003686116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004067902,0.00007235143,0.0001391997,0.00017608564,0.00010201291,0.000086411004,0.00028846602,0.000012218949,0.000035649322],"category_scores_gemma":[0.000060895974,0.000064824475,0.00003809569,0.00076809106,0.000012674477,0.00027009347,0.00025119027,0.0003091668,0.00002446211],"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":[0.00008027011,0.004434261,0.039462905,0.0001357238,0.00007434213,0.00057126116,0.15625472,0.00413099,0.05208067,0.20679906,0.009040601,0.5269352],"study_design_scores_gemma":[0.004511683,0.00060509576,0.13268973,0.00030689305,0.000009990355,0.00048129153,0.030950971,0.72224575,0.021447325,0.039152414,0.04598492,0.0016139094],"about_ca_topic_score_codex":0.0007210717,"about_ca_topic_score_gemma":0.0042814915,"teacher_disagreement_score":0.7181148,"about_ca_system_score_codex":0.00013102284,"about_ca_system_score_gemma":0.00006233962,"threshold_uncertainty_score":0.26434654},"labels":[],"label_agreement":null},{"id":"W4223523719","doi":"10.1109/icears53579.2022.9751926","title":"IoT Based Low-Cost Robotic Agent Design for Covid-19 affected people","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Gesture; Heading (navigation); Computer science; Accelerometer; Robot; Internet of Things; Human–computer interaction; Artificial intelligence; Gyroscope; Field (mathematics); Computer security; Engineering; Operating system","score_opus":0.052483765658536186,"score_gpt":0.288812038588842,"score_spread":0.23632827293030584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223523719","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":[0.0022329977,0.00080593606,0.9874924,0.0030264973,0.0026177727,0.0021782855,0.0001263679,0.00020407506,0.0013156915],"genre_scores_gemma":[0.99192494,0.00012377156,0.0009062216,0.0008791468,0.00016730248,0.0017647814,0.00017772465,0.000032791773,0.0040233163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967221,0.0005718166,0.00047315803,0.0007985396,0.00088177255,0.00055259233],"domain_scores_gemma":[0.9982141,0.0004114661,0.0003367067,0.00043854763,0.00031516852,0.00028395874],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015517198,0.00028581484,0.00039174617,0.0003757788,0.0006279152,0.0005067356,0.0010279686,0.00009348667,0.00020979212],"category_scores_gemma":[0.00014668175,0.0002778187,0.00012393478,0.0004805062,0.000026518828,0.00013329834,0.00020434939,0.00031072597,0.000018514624],"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":[0.00025778505,0.0005144201,0.00011660189,0.00033850357,0.000322593,0.00004304996,0.0007433039,0.8063294,0.010241859,0.16440749,0.013588272,0.0030966932],"study_design_scores_gemma":[0.0015269386,0.00056635065,0.000028464172,0.00006712677,0.000018635503,0.000087066954,0.00024221954,0.9415216,0.000544826,0.001517718,0.053498037,0.00038103235],"about_ca_topic_score_codex":0.00042548263,"about_ca_topic_score_gemma":0.00022772915,"teacher_disagreement_score":0.989692,"about_ca_system_score_codex":0.0008165081,"about_ca_system_score_gemma":0.0014616112,"threshold_uncertainty_score":0.9999674},"labels":[],"label_agreement":null},{"id":"W4229069922","doi":"10.1007/s11042-022-12870-8","title":"Long Hands gesture recognition system: 2 step gesture recognition with machine learning and geometric shape analysis","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"National Research Council Canada; Mitacs","keywords":"Computer science; AdaBoost; Artificial intelligence; Gesture recognition; Gesture; Support vector machine; Pattern recognition (psychology); Computer vision; Artificial neural network; Machine learning","score_opus":0.020513705370452356,"score_gpt":0.23141834532513325,"score_spread":0.2109046399546809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229069922","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":[0.18808787,0.0047291666,0.7984152,0.0011478699,0.00019520926,0.0033672205,0.00095390715,0.001052493,0.002051061],"genre_scores_gemma":[0.9804062,0.00028704415,0.014728499,0.00011750238,0.000200046,0.0023612126,0.0016268442,0.000028542818,0.00024405959],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978366,0.00023648598,0.00037915425,0.0007778036,0.00046493462,0.00030501655],"domain_scores_gemma":[0.99853307,0.0004052478,0.00030358808,0.00032031204,0.00022074285,0.00021703707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005738083,0.00026267185,0.00039663838,0.0007213297,0.0010120174,0.000419404,0.00026990278,0.00010930026,0.000081997816],"category_scores_gemma":[0.00004187976,0.00023117164,0.000082917635,0.0033673157,0.00005900551,0.0004118532,0.00022604087,0.00051003136,0.000035803514],"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":[0.000020469704,0.0001184451,0.021603644,0.000120374854,0.00029095478,0.000015331447,0.0004650643,0.00012599803,0.00009596007,0.00005217716,0.00008728022,0.9770043],"study_design_scores_gemma":[0.009003932,0.0015616175,0.25637302,0.0003315031,0.0033393444,0.0018118105,0.0040839734,0.6220289,0.00070869736,0.00030674177,0.09715624,0.003294232],"about_ca_topic_score_codex":0.00007930276,"about_ca_topic_score_gemma":0.000081226994,"teacher_disagreement_score":0.97371006,"about_ca_system_score_codex":0.00007773239,"about_ca_system_score_gemma":0.00003859711,"threshold_uncertainty_score":0.9426906},"labels":[],"label_agreement":null},{"id":"W4231222293","doi":"10.1145/1734454.1734575","title":"Selecting and commanding individual robots in a vision-based multi-robot system","year":2010,"lang":"en","type":"article","venue":"Proceeding of the 5th ACM/IEEE international conference on Human-robot interaction - HRI '10","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gesture; Computer science; Robot; Computer vision; Artificial intelligence; Task (project management); Face (sociological concept); Motion (physics); Human–computer interaction; Social robot; Mobile robot; Robot control; Engineering","score_opus":0.2635375949057934,"score_gpt":0.43108510003340333,"score_spread":0.1675475051276099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231222293","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":[0.8828705,0.000030260559,0.07683813,0.0055556973,0.012552091,0.0018490606,0.000044175675,0.0006161181,0.019643985],"genre_scores_gemma":[0.99371225,0.0000015565212,0.0053196116,0.00011411302,0.0002616634,0.00008576414,0.000011838287,0.000023967063,0.00046925456],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972371,0.00012381915,0.00083781756,0.000652251,0.0008109718,0.00033803546],"domain_scores_gemma":[0.9971792,0.000427195,0.00085175224,0.00043444894,0.0009884477,0.00011896953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010638147,0.00033585465,0.00038325286,0.0007027748,0.00034207117,0.00069619244,0.0019574803,0.0001711375,0.00015087276],"category_scores_gemma":[0.00059494126,0.0002797783,0.000119581244,0.00049785955,0.000098041215,0.00093609595,0.0004041494,0.0009318209,0.000051689534],"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":[0.00066036667,0.0031036881,0.061133325,0.0014239153,0.00085025054,0.00005060585,0.01781098,0.011584496,0.7432589,0.1222573,0.004198717,0.03366743],"study_design_scores_gemma":[0.006006251,0.00070940494,0.032431766,0.008500754,0.00008417504,0.00044067515,0.0037269338,0.8142018,0.12963054,0.0018169638,0.00081397063,0.001636795],"about_ca_topic_score_codex":0.00010750224,"about_ca_topic_score_gemma":0.00020334244,"teacher_disagreement_score":0.80261725,"about_ca_system_score_codex":0.00017935186,"about_ca_system_score_gemma":0.0001379514,"threshold_uncertainty_score":0.9999654},"labels":[],"label_agreement":null},{"id":"W4232456436","doi":"10.32920/ryerson.14648475","title":"Trajectory Analysis on Spherical Self-Organizing Maps With Application to Gesture Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Gesture recognition; Computer science; Set (abstract data type); Trajectory; Computer vision; Visualization; Artificial intelligence; Hidden Markov model; Sign language","score_opus":0.015332763727081181,"score_gpt":0.23515791564560398,"score_spread":0.2198251519185228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232456436","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":[0.025250133,0.000057107067,0.9666508,0.0009709188,0.0003168077,0.0008446291,0.000016718748,0.00082542433,0.005067474],"genre_scores_gemma":[0.6706184,0.000022504468,0.32629335,0.001509721,0.00039577152,0.00044333245,0.00043558143,0.000043208125,0.00023810558],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9966705,0.00027686707,0.00045380963,0.0015451546,0.0007284973,0.0003252088],"domain_scores_gemma":[0.9974747,0.00013006554,0.0002586308,0.001325605,0.0005370607,0.0002739596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039327296,0.00041986906,0.0006640125,0.00041993396,0.00011213124,0.0005990227,0.00077664846,0.00041138186,0.000071920666],"category_scores_gemma":[0.000030572217,0.000348678,0.00024083411,0.0025949806,0.000012679779,0.00017060888,0.0004361509,0.0006618265,0.00034308652],"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":[0.000318813,0.004823435,0.012931768,0.0021994202,0.018298423,0.00070106395,0.034264695,0.059371922,0.009165474,0.0020014143,0.0129099665,0.8430136],"study_design_scores_gemma":[0.011552401,0.006561445,0.35117283,0.009563188,0.022534994,0.0015538346,0.014435417,0.22346598,0.1803472,0.0077320808,0.13403474,0.037045896],"about_ca_topic_score_codex":0.00011935908,"about_ca_topic_score_gemma":0.0004163885,"teacher_disagreement_score":0.8059677,"about_ca_system_score_codex":0.00024445952,"about_ca_system_score_gemma":0.00023508984,"threshold_uncertainty_score":0.9998965},"labels":[],"label_agreement":null},{"id":"W4234330444","doi":"10.32920/ryerson.14655699","title":"Engagement Detection Framework for Hand Gesture and Posture Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Disengagement theory; Computer science; Action (physics); Artificial intelligence; Feature (linguistics); Object (grammar); Human–computer interaction; Gesture recognition; Gaze; Discriminative model; Computer vision; Pattern recognition (psychology)","score_opus":0.04885436298260926,"score_gpt":0.2851612602853466,"score_spread":0.23630689730273735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234330444","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":[0.016530422,0.0011666251,0.97592,0.0013019326,0.0029214334,0.0012581814,0.000031032363,0.00024656998,0.00062381016],"genre_scores_gemma":[0.703574,0.00024582117,0.29379383,0.0007220352,0.0007249969,0.000583683,0.00013807116,0.000027775277,0.00018982851],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99781305,0.00024467037,0.0003638505,0.0010027838,0.0003006371,0.00027501455],"domain_scores_gemma":[0.99813914,0.0003873358,0.00023353357,0.00061865745,0.00048858236,0.00013273329],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00066475815,0.00033410717,0.00039127388,0.00016125436,0.0003016708,0.0014579695,0.00035371125,0.0008031078,0.000025553825],"category_scores_gemma":[0.00028284817,0.00030263636,0.00016050703,0.00019230384,0.000031033243,0.00023907375,0.00073870237,0.0009100972,0.000014169156],"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":[0.00003225594,0.00015937627,0.00009821114,0.0014974123,0.00035993604,0.000036027337,0.00573446,0.00003288941,0.0038715652,0.0027917116,0.000755296,0.9846309],"study_design_scores_gemma":[0.00531795,0.0015177092,0.016405335,0.010464228,0.00087030086,0.001383902,0.0051406254,0.030150212,0.2096133,0.6274328,0.08437081,0.0073328433],"about_ca_topic_score_codex":0.00002767288,"about_ca_topic_score_gemma":0.00008815185,"teacher_disagreement_score":0.977298,"about_ca_system_score_codex":0.00006397126,"about_ca_system_score_gemma":0.000120661396,"threshold_uncertainty_score":0.9999426},"labels":[],"label_agreement":null},{"id":"W4235340546","doi":"10.1109/haptic.2006.1627094","title":"Haptic-Based Biometrics: A Feasibility Study","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"University of Ottawa","funders":"","keywords":"Haptic technology; Biometrics; Password; Computer science; Session (web analytics); Authentication (law); Task (project management); Human–computer interaction; Artificial intelligence; Computer vision; Computer security; Engineering; World Wide Web","score_opus":0.03847942573476557,"score_gpt":0.28094922435812636,"score_spread":0.2424697986233608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235340546","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":[0.15241235,0.000028364262,0.8293977,0.0003103481,0.00027341064,0.00052785105,0.0000016215366,0.00037095218,0.016677443],"genre_scores_gemma":[0.98514885,6.743729e-8,0.014054743,0.00013117926,0.00006284959,0.00002295985,0.0000013194282,0.0000040229106,0.00057401333],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998702,0.00012250248,0.000250127,0.00037615056,0.00036264697,0.0001865582],"domain_scores_gemma":[0.9990115,0.00014414093,0.000055094875,0.0005806639,0.00014515064,0.00006342841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063568633,0.00009960784,0.0001424806,0.0003492358,0.000068877984,0.00019396139,0.0004427886,0.000036277637,0.000040459177],"category_scores_gemma":[0.00005119899,0.00007662869,0.000054732838,0.0017760698,0.000016432217,0.00017974635,0.00006658909,0.000052749343,0.00036581047],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027385802,0.012804289,0.86253923,0.00008926447,0.00008123778,0.00020176727,0.0006658736,0.00018967155,0.004105884,0.02405436,0.014019732,0.081221275],"study_design_scores_gemma":[0.0063522854,0.0015289565,0.8844006,0.000030232426,0.000029717103,0.000032672328,0.00035899557,0.07673614,0.0060720877,0.006992526,0.016298663,0.001167149],"about_ca_topic_score_codex":0.00028806063,"about_ca_topic_score_gemma":0.0001328982,"teacher_disagreement_score":0.8327365,"about_ca_system_score_codex":0.00004616779,"about_ca_system_score_gemma":0.000052891963,"threshold_uncertainty_score":0.47018757},"labels":[],"label_agreement":null},{"id":"W4236088569","doi":"10.22215/etd/2012-06854","title":"Depth sensitive vision-based human-computer interaction using natural arm/finger gestures : an empirical investigation","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Carleton University; Library and Archives Canada","funders":"","keywords":"Gesture; Natural (archaeology); Computer science; Computer graphics (images); Artificial intelligence; Geography","score_opus":0.051397148164657444,"score_gpt":0.3682212814814242,"score_spread":0.31682413331676673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236088569","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":[0.76549006,0.000111962734,0.22068734,0.00022843886,0.009008416,0.0008005748,0.00000500302,0.000692886,0.0029753128],"genre_scores_gemma":[0.960146,8.5164953e-7,0.03537503,0.0008991727,0.0016092288,0.000025393407,0.0012448095,0.000055583336,0.0006439038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965135,0.0006096327,0.0006893038,0.0009576052,0.00076130865,0.00046868695],"domain_scores_gemma":[0.9975676,0.00026433333,0.0005976472,0.0005926018,0.00067913654,0.00029869602],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052496896,0.00056417164,0.00052465836,0.00064471783,0.00036740367,0.0006367874,0.0004751476,0.00057665765,0.000037369602],"category_scores_gemma":[0.000036774516,0.0004858913,0.00022027065,0.0004909179,0.000041116455,0.0017422952,0.00006162784,0.00081308204,0.00010832309],"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":[0.00057322875,0.0035639175,0.016934969,0.0022034822,0.0013444327,0.0003868358,0.09425433,0.0060543222,0.27676213,0.0037520523,0.023908438,0.57026184],"study_design_scores_gemma":[0.0016994101,0.00081159285,0.26354676,0.0022540437,0.0002904182,0.00026924376,0.0016497073,0.6373879,0.084746666,0.00091682724,0.0029444017,0.003483042],"about_ca_topic_score_codex":0.00022327111,"about_ca_topic_score_gemma":0.00095214846,"teacher_disagreement_score":0.6313336,"about_ca_system_score_codex":0.00024663875,"about_ca_system_score_gemma":0.00024362771,"threshold_uncertainty_score":0.99975926},"labels":[],"label_agreement":null},{"id":"W4236451683","doi":"10.32920/ryerson.14657508","title":"Machine recognition of human gestures through principal joint variable analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Gesture recognition; Computer science; Benchmark (surveying); Artificial intelligence; Principal (computer security); Speech recognition; Machine learning; Human–computer interaction; Computer vision","score_opus":0.06096488061768561,"score_gpt":0.29330883477803305,"score_spread":0.23234395416034742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236451683","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":[0.014781528,0.00061903923,0.9215663,0.00025089833,0.000814464,0.0004640997,0.000074060015,0.0002884646,0.061141122],"genre_scores_gemma":[0.7618256,0.00010764986,0.23531477,0.00026611172,0.00022598117,0.00011383527,0.0009076501,0.000025193407,0.0012131997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963617,0.00042514017,0.001069442,0.0011095535,0.0007067272,0.00032746122],"domain_scores_gemma":[0.99674445,0.00009766835,0.00073474733,0.001563649,0.000746668,0.000112794136],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086294534,0.00039869157,0.0011675333,0.00046740047,0.00011555513,0.000389005,0.00093621644,0.00045835561,0.00061906123],"category_scores_gemma":[0.00008501732,0.00035185218,0.00061711104,0.001391932,0.00004647403,0.00032376236,0.001723012,0.0006264334,0.000030934236],"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":[0.00014333823,0.014225414,0.03382683,0.02347558,0.106325254,0.0014822901,0.09506945,0.060460556,0.08894316,0.35996914,0.016692767,0.19938622],"study_design_scores_gemma":[0.007662746,0.0014743949,0.06452279,0.0082222605,0.016402073,0.0005236706,0.0027990849,0.1795391,0.2823407,0.4008842,0.022089407,0.01353958],"about_ca_topic_score_codex":0.0021365995,"about_ca_topic_score_gemma":0.00038685193,"teacher_disagreement_score":0.7470441,"about_ca_system_score_codex":0.000078629695,"about_ca_system_score_gemma":0.00022653995,"threshold_uncertainty_score":0.99989337},"labels":[],"label_agreement":null},{"id":"W4240047005","doi":"10.1109/crv.2019.00004","title":"Table of Contents","year":2019,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Table (database); Computer science; Database","score_opus":0.019137291862517163,"score_gpt":0.22837862877867487,"score_spread":0.2092413369161577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240047005","genre_codex":"other","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":[0.09253936,0.00006683583,0.40369233,0.0003432685,0.0009113881,0.00023019823,0.0000014142763,0.00010498357,0.50211024],"genre_scores_gemma":[0.98588806,8.4332646e-7,0.0031604995,0.000112224414,0.000007979172,0.0000014660064,2.484924e-7,0.0000011059332,0.010827559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996624,0.000012305304,0.00008388035,0.00008270245,0.00009777428,0.0000609633],"domain_scores_gemma":[0.9996812,0.00002195252,0.000029626653,0.00018523962,0.000061469145,0.000020549727],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007433466,0.000025359028,0.000069867674,0.00002326751,0.0000051014053,0.000013832487,0.0001935458,0.000015274403,0.00012320396],"category_scores_gemma":[0.0000050048275,0.0000186722,0.000015831725,0.00011147601,0.0000034600425,0.00013580392,0.000040020634,0.000015672447,0.00082776335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011580106,0.00030579462,0.172441,0.00023006937,0.00011502227,0.000008771774,0.0015011919,0.000043095908,0.13146064,0.5658594,0.033688575,0.09433491],"study_design_scores_gemma":[0.0041600605,0.00052439945,0.04631852,0.00026093787,0.000008309284,0.000082090184,0.00040257146,0.02816151,0.43324378,0.006249853,0.4798294,0.0007585526],"about_ca_topic_score_codex":0.00002255532,"about_ca_topic_score_gemma":0.0000018574467,"teacher_disagreement_score":0.8933487,"about_ca_system_score_codex":0.0000020189805,"about_ca_system_score_gemma":0.000009129824,"threshold_uncertainty_score":0.99995023},"labels":[],"label_agreement":null},{"id":"W4241463658","doi":"10.1007/978-981-32-9945-0_7","title":"Conclusion","year":2019,"lang":"en","type":"book-chapter","venue":"Cognitive intelligence and robotics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.04339881004719913,"score_gpt":0.26788542113882735,"score_spread":0.2244866110916282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241463658","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000017631023,0.0018346782,0.6027528,0.00018967857,0.0008095601,0.00035324477,0.000018206645,0.000078588804,0.3939615],"genre_scores_gemma":[0.046673145,0.0069465847,0.007419523,0.0018080188,0.0006902178,0.000011967133,0.00008748979,0.00010098433,0.9362621],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998276,0.000031079544,0.0004061577,0.0006686682,0.00036094687,0.00025717163],"domain_scores_gemma":[0.99823594,0.00048101583,0.00024727016,0.00034729159,0.00053784775,0.00015063773],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020423598,0.00038006902,0.00046429515,0.0001894155,0.0001332426,0.00017019875,0.00045121825,0.00042038236,0.00007941965],"category_scores_gemma":[0.000059351994,0.00033345743,0.0001242302,0.000058012403,0.0001619054,0.00020422741,0.0005502515,0.00045232003,0.0025498075],"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":[0.000007998856,0.000019685149,0.000024435896,0.00014059039,0.000108101034,0.000098587545,0.00051840703,0.00007408569,0.000008059087,0.6285693,0.0004969281,0.36993384],"study_design_scores_gemma":[0.0011487585,0.0021007154,0.00013963807,0.016776964,0.0006756323,0.0014454542,0.0006919022,0.030644666,0.0033100415,0.49718803,0.43936616,0.0065120375],"about_ca_topic_score_codex":0.0000021567398,"about_ca_topic_score_gemma":0.0000046528967,"teacher_disagreement_score":0.5953333,"about_ca_system_score_codex":0.000032847023,"about_ca_system_score_gemma":0.00016811505,"threshold_uncertainty_score":0.9999117},"labels":[],"label_agreement":null},{"id":"W4245967622","doi":"10.1109/vr46266.2020.00048","title":"Disambiguation Techniques for Freehand Object Manipulations in Virtual Reality","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Toronto","funders":"","keywords":"Computer science; Virtual reality; Object (grammar); Human–computer interaction; Gaze; Gesture; Context (archaeology); Set (abstract data type); Computer vision; Ambiguity; Augmented reality; Virtual image; Artificial intelligence; Optical head-mounted display; Modalities; Interaction technique","score_opus":0.11603531248083788,"score_gpt":0.33371152641115526,"score_spread":0.21767621393031739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245967622","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":[0.05769786,0.000049653554,0.92966473,0.007195106,0.00043765068,0.0012195988,0.0002414492,0.00034675954,0.0031471848],"genre_scores_gemma":[0.99747837,0.00013068639,0.0009906257,0.00078665535,0.00018834943,0.0001771447,0.000055232256,0.000016557997,0.00017638334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742496,0.0003528712,0.0006588278,0.00085176335,0.00037555373,0.00033599648],"domain_scores_gemma":[0.99853057,0.00037129092,0.00022300925,0.0004290614,0.00021491406,0.00023114955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006752635,0.0003135479,0.00048161537,0.00011101712,0.000163579,0.00041302803,0.0005831589,0.00021926401,0.000029460305],"category_scores_gemma":[0.00032234637,0.00028945247,0.00008120521,0.0003924209,0.00011514567,0.00069973816,0.00015246942,0.00032630772,0.000037098565],"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":[0.0011385448,0.0008180637,0.0010844606,0.00056962995,0.00021635287,0.00006022239,0.031025354,0.0014019533,0.02562021,0.1846449,0.0114522455,0.7419681],"study_design_scores_gemma":[0.0043553044,0.008372863,0.010526562,0.0017792318,0.00013446849,0.000047880014,0.0036563037,0.7743478,0.13683462,0.016976414,0.039966285,0.0030023113],"about_ca_topic_score_codex":0.00032106714,"about_ca_topic_score_gemma":0.001274762,"teacher_disagreement_score":0.93978053,"about_ca_system_score_codex":0.000070242,"about_ca_system_score_gemma":0.00013405287,"threshold_uncertainty_score":0.9999558},"labels":[],"label_agreement":null},{"id":"W4249297896","doi":"10.3410/f.1001043.16604","title":"Faculty Opinions recommendation of Optic flow is used to control human walking.","year":2001,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Control (management); Computer science; Psychology; Medical education; Data science; Artificial intelligence; Medicine","score_opus":0.043169994223804575,"score_gpt":0.3586415332783931,"score_spread":0.3154715390545885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249297896","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0053902e-7,0.0003848857,0.004602895,0.228875,0.0012960145,0.001889762,0.7628076,0.00007384633,0.000069766684],"genre_scores_gemma":[0.000019638032,0.000064249325,0.0032661534,0.010108734,0.000465482,0.00043029152,0.98467577,0.000028368715,0.00094132865],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929178,0.00087699416,0.0023055898,0.0011080548,0.0022533934,0.0005381791],"domain_scores_gemma":[0.9870997,0.00025785645,0.0018704231,0.0026828172,0.007527773,0.00056140556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023582084,0.00069194724,0.0014362992,0.0007153131,0.00035893265,0.00041783226,0.004065775,0.0008085044,0.00043003183],"category_scores_gemma":[0.003976881,0.00046721226,0.0008136958,0.003909698,0.00018326825,0.0006194046,0.0007289646,0.0010387554,0.00024318618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004211555,0.00028773307,0.0000024606868,0.002951991,0.00014966533,6.891957e-7,0.00032890023,2.7028645e-7,0.0000025769398,0.00028430505,0.99104905,0.004938134],"study_design_scores_gemma":[0.0009836539,0.000178382,0.00034067745,0.011134684,0.00012474987,0.00007785758,0.000015602376,0.000064919186,0.00001686499,0.0000508435,0.98654443,0.00046732023],"about_ca_topic_score_codex":0.000056164794,"about_ca_topic_score_gemma":0.000010871099,"teacher_disagreement_score":0.22186813,"about_ca_system_score_codex":0.00016599394,"about_ca_system_score_gemma":0.00048124595,"threshold_uncertainty_score":0.999778},"labels":[],"label_agreement":null},{"id":"W4249409697","doi":"10.1109/iembs.2006.4397514","title":"Approximated Mutual Information Training for Speech Recognition Using Myoelectric Signals","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Speech recognition; Computer science; Training (meteorology); Mutual information; Pattern recognition (psychology); Artificial intelligence","score_opus":0.07908535890712068,"score_gpt":0.26629655941522473,"score_spread":0.18721120050810405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249409697","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":[0.22897258,0.00002042496,0.7624154,0.0001676021,0.00019397652,0.00077443395,0.000013387465,0.00038474484,0.007057438],"genre_scores_gemma":[0.92478067,0.0000026315704,0.074661076,0.00011818868,0.00020368006,0.00011940006,0.00005545245,0.000010311463,0.000048575686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848133,0.000012556989,0.000486516,0.00030434816,0.00030248368,0.0004127371],"domain_scores_gemma":[0.9982984,0.00006413116,0.000331136,0.00008634701,0.0011432932,0.00007666895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005509556,0.00019366249,0.00023938288,0.00034608602,0.00019770149,0.0007581755,0.000359608,0.00014317,0.00001462789],"category_scores_gemma":[0.0001298194,0.00019183393,0.000067624234,0.00074347836,0.000028546248,0.00299686,0.000045286666,0.00012719654,0.00007115311],"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":[0.000050637347,0.00008610092,0.0003397745,0.0004037837,0.000035802834,0.0000019920885,0.007163933,0.00001189124,0.06153576,0.014278397,0.0022009907,0.91389096],"study_design_scores_gemma":[0.0019950564,0.00033997744,0.00041364678,0.00044982383,0.000050499795,0.00029754644,0.0014453811,0.80571294,0.12385148,0.057871092,0.0065180776,0.0010545076],"about_ca_topic_score_codex":0.000023140348,"about_ca_topic_score_gemma":0.0000016020034,"teacher_disagreement_score":0.91283643,"about_ca_system_score_codex":0.000071077135,"about_ca_system_score_gemma":0.00016722799,"threshold_uncertainty_score":0.78227603},"labels":[],"label_agreement":null},{"id":"W4249691857","doi":"10.32920/ryerson.14657508.v1","title":"Machine recognition of human gestures through principal joint variable analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Gesture recognition; Computer science; Benchmark (surveying); Artificial intelligence; Principal (computer security); Speech recognition; Machine learning; Computer vision; Human–computer interaction","score_opus":0.06096488061768561,"score_gpt":0.29330883477803305,"score_spread":0.23234395416034742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249691857","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":[0.014781528,0.00061903923,0.9215663,0.00025089833,0.000814464,0.0004640997,0.000074060015,0.0002884646,0.061141122],"genre_scores_gemma":[0.7618256,0.00010764986,0.23531477,0.00026611172,0.00022598117,0.00011383527,0.0009076501,0.000025193407,0.0012131997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963617,0.00042514017,0.001069442,0.0011095535,0.0007067272,0.00032746122],"domain_scores_gemma":[0.99674445,0.00009766835,0.00073474733,0.001563649,0.000746668,0.000112794136],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086294534,0.00039869157,0.0011675333,0.00046740047,0.00011555513,0.000389005,0.00093621644,0.00045835561,0.00061906123],"category_scores_gemma":[0.00008501732,0.00035185218,0.00061711104,0.001391932,0.00004647403,0.00032376236,0.001723012,0.0006264334,0.000030934236],"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":[0.00014333823,0.014225414,0.03382683,0.02347558,0.106325254,0.0014822901,0.09506945,0.060460556,0.08894316,0.35996914,0.016692767,0.19938622],"study_design_scores_gemma":[0.007662746,0.0014743949,0.06452279,0.0082222605,0.016402073,0.0005236706,0.0027990849,0.1795391,0.2823407,0.4008842,0.022089407,0.01353958],"about_ca_topic_score_codex":0.0021365995,"about_ca_topic_score_gemma":0.00038685193,"teacher_disagreement_score":0.7470441,"about_ca_system_score_codex":0.000078629695,"about_ca_system_score_gemma":0.00022653995,"threshold_uncertainty_score":0.99989337},"labels":[],"label_agreement":null},{"id":"W4250204849","doi":"10.4018/978-1-7998-0414-7.ch032","title":"Multi-Sensor Motion Fusion Using Deep Neural Network Learning","year":2019,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Gesture; Pose; Computer vision; Artificial neural network; Sensor fusion; Deep learning; Recurrent neural network; Sequence (biology); Gesture recognition; Real-time computing","score_opus":0.03728364920950301,"score_gpt":0.2589500921977637,"score_spread":0.22166644298826071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250204849","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003447951,0.00052432047,0.45645684,0.000026184245,0.0034267001,0.0006503288,0.000010334552,0.0004429031,0.5381176],"genre_scores_gemma":[0.6423724,0.000018157889,0.14780776,0.0014608386,0.0062398915,0.000026629059,0.0000437348,0.0003043894,0.20172614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973956,0.00012551715,0.00053069287,0.00084349257,0.00057272555,0.0005319793],"domain_scores_gemma":[0.9983724,0.00006128829,0.0005092612,0.000637515,0.00023370802,0.00018584194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027661893,0.0005080302,0.00057279284,0.00009196605,0.0002531358,0.0003072259,0.0006136807,0.0005789475,0.000016833657],"category_scores_gemma":[0.000020359932,0.00049641356,0.00028957624,0.000051166742,0.000042482152,0.00015711697,0.00042346577,0.0005754811,0.0006758398],"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":[0.000045338165,0.000032100124,0.0006507115,0.00023247945,0.0002314524,0.00035776963,0.0004940212,0.028699962,0.0005326481,0.6604227,0.00040067866,0.30790016],"study_design_scores_gemma":[0.0022334387,0.00034269536,0.000499079,0.0018211703,0.00019666422,0.0013070331,0.00004038718,0.8976582,0.000052794618,0.034165002,0.05886241,0.002821104],"about_ca_topic_score_codex":0.000046872356,"about_ca_topic_score_gemma":0.000048884507,"teacher_disagreement_score":0.86895823,"about_ca_system_score_codex":0.00026326487,"about_ca_system_score_gemma":0.00010565208,"threshold_uncertainty_score":0.99974877},"labels":[],"label_agreement":null},{"id":"W4252152308","doi":"10.1109/hri.2013.6483609","title":"Gestures for industry Intuitive human-robot communication from human observation","year":2013,"lang":"en","type":"article","venue":"2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Gesture; Computer science; Human–robot interaction; Task (project management); Human–computer interaction; Context (archaeology); Robot; Set (abstract data type); Lexicon; Artificial intelligence; Engineering; Systems engineering","score_opus":0.3428105174556708,"score_gpt":0.4339543164412463,"score_spread":0.09114379898557551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252152308","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":[0.7115716,0.0001253098,0.18656546,0.036329545,0.013714395,0.0057340935,0.0003465968,0.001640855,0.043972187],"genre_scores_gemma":[0.9833249,0.000031482286,0.0061410973,0.0019614699,0.0012935281,0.0013470288,0.0012422951,0.000062406805,0.0045958012],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99553514,0.0004267517,0.0013767913,0.0011521106,0.0009692743,0.0005399398],"domain_scores_gemma":[0.99312896,0.0007101741,0.0012819907,0.0016365314,0.0029759235,0.00026643527],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005516523,0.0006268046,0.000574941,0.000619739,0.0008517512,0.00167542,0.0033098222,0.000643969,0.0021787426],"category_scores_gemma":[0.00032137107,0.000627012,0.00026333405,0.0003230604,0.00014757733,0.0032536923,0.00045729594,0.0014212407,0.0012485491],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017751289,0.003063389,0.0036633275,0.00013570276,0.001460329,0.00001727321,0.010726201,0.0015765603,0.273525,0.32418805,0.35110816,0.03035849],"study_design_scores_gemma":[0.016288422,0.0040862076,0.20592,0.0073246863,0.0003806139,0.00017655533,0.012827629,0.07212047,0.14070079,0.4042203,0.127297,0.008657295],"about_ca_topic_score_codex":0.0030417312,"about_ca_topic_score_gemma":0.001033469,"teacher_disagreement_score":0.27175334,"about_ca_system_score_codex":0.0005153673,"about_ca_system_score_gemma":0.0001201826,"threshold_uncertainty_score":0.9996181},"labels":[],"label_agreement":null},{"id":"W4256638732","doi":"10.32920/ryerson.14648475.v1","title":"Trajectory Analysis on Spherical Self-Organizing Maps With Application to Gesture Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Gesture recognition; Computer science; Set (abstract data type); Trajectory; Computer vision; Visualization; Artificial intelligence; Hidden Markov model","score_opus":0.015332763727081181,"score_gpt":0.23515791564560398,"score_spread":0.2198251519185228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256638732","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":[0.025250133,0.000057107067,0.9666508,0.0009709188,0.0003168077,0.0008446291,0.000016718748,0.00082542433,0.005067474],"genre_scores_gemma":[0.6706184,0.000022504468,0.32629335,0.001509721,0.00039577152,0.00044333245,0.00043558143,0.000043208125,0.00023810558],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9966705,0.00027686707,0.00045380963,0.0015451546,0.0007284973,0.0003252088],"domain_scores_gemma":[0.9974747,0.00013006554,0.0002586308,0.001325605,0.0005370607,0.0002739596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039327296,0.00041986906,0.0006640125,0.00041993396,0.00011213124,0.0005990227,0.00077664846,0.00041138186,0.000071920666],"category_scores_gemma":[0.000030572217,0.000348678,0.00024083411,0.0025949806,0.000012679779,0.00017060888,0.0004361509,0.0006618265,0.00034308652],"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":[0.000318813,0.004823435,0.012931768,0.0021994202,0.018298423,0.00070106395,0.034264695,0.059371922,0.009165474,0.0020014143,0.0129099665,0.8430136],"study_design_scores_gemma":[0.011552401,0.006561445,0.35117283,0.009563188,0.022534994,0.0015538346,0.014435417,0.22346598,0.1803472,0.0077320808,0.13403474,0.037045896],"about_ca_topic_score_codex":0.00011935908,"about_ca_topic_score_gemma":0.0004163885,"teacher_disagreement_score":0.8059677,"about_ca_system_score_codex":0.00024445952,"about_ca_system_score_gemma":0.00023508984,"threshold_uncertainty_score":0.9998965},"labels":[],"label_agreement":null},{"id":"W4280541453","doi":"10.1109/iotm.002.2200022","title":"Touchless Control of Heavy Equipment Using Low-Cost Hand Gesture Recognition","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Magazine","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Queen's University","funders":"","keywords":"Loader; Gesture; Gesture recognition; Laptop; Computer science; Process (computing); Artificial intelligence; Operating system","score_opus":0.027853727825141953,"score_gpt":0.2544352912104833,"score_spread":0.22658156338534136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280541453","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":[0.3578026,0.00021231467,0.63718855,0.00047513103,0.002297834,0.00065104064,0.000066900095,0.00007878439,0.0012268167],"genre_scores_gemma":[0.99536514,0.0000045062125,0.0035230345,0.0005112588,0.000098008364,0.000051365016,0.000014916601,0.000018283838,0.0004135019],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977605,0.00023248425,0.0006916528,0.0003968872,0.00064444914,0.00027404583],"domain_scores_gemma":[0.99841255,0.00014309383,0.0006522833,0.0003925915,0.00031060685,0.000088877845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008099292,0.00020038887,0.0004598537,0.00025074152,0.00008701574,0.00008731642,0.0008285499,0.0000705731,0.00013398082],"category_scores_gemma":[0.000054050677,0.00019567882,0.00015803786,0.0003467744,0.00007624978,0.00045270132,0.00027797383,0.00030343785,0.000036622394],"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":[0.0010621888,0.00196264,0.0019901323,0.0012916932,0.0007098383,0.0002528507,0.02352739,0.00409038,0.750958,0.0010934756,0.014922505,0.1981389],"study_design_scores_gemma":[0.00875604,0.0014186369,0.00095112494,0.0019688027,0.0001897183,0.00092944276,0.00046406715,0.22555777,0.73119307,0.0030468148,0.024153445,0.001371094],"about_ca_topic_score_codex":0.00014059876,"about_ca_topic_score_gemma":0.000008133916,"teacher_disagreement_score":0.6375625,"about_ca_system_score_codex":0.00013106661,"about_ca_system_score_gemma":0.00007896919,"threshold_uncertainty_score":0.7979551},"labels":[],"label_agreement":null},{"id":"W4285464727","doi":"10.32920/ryerson.14657838","title":"The classification of gesture interactions and the study of their ergonomic effect on hands","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gesture; Computer science; Human–computer interaction; Gesture recognition; Object (grammar); Artificial intelligence","score_opus":0.02619182189393986,"score_gpt":0.27816742735134803,"score_spread":0.25197560545740816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285464727","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":[0.98737794,0.00024104075,0.006581694,0.0009703321,0.00094953965,0.001246556,0.0000031572524,0.000033487875,0.0025962768],"genre_scores_gemma":[0.99936604,0.00006185967,0.000057150213,0.000023994031,0.00006295824,0.0002024432,0.000003997426,0.000006403061,0.00021512453],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979793,0.00094790565,0.00042210252,0.00036346202,0.0001979103,0.000089333174],"domain_scores_gemma":[0.9960727,0.0021763928,0.00044045912,0.0011000129,0.00018442648,0.000025977952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011643468,0.00017034083,0.00037647877,0.000075513475,0.000140638,0.00022701012,0.00071491825,0.00008874294,0.0000030664835],"category_scores_gemma":[0.0000882385,0.000073801704,0.00012276467,0.00014991402,0.00009373528,0.00006620432,0.00056650507,0.00041711383,0.0000023053556],"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":[0.0012966519,0.0030484705,0.012504624,0.0011429741,0.004503857,0.000007909392,0.16004033,0.0015096816,0.006336725,0.057553183,0.0029949925,0.7490606],"study_design_scores_gemma":[0.021655912,0.0058843573,0.7340925,0.003630167,0.0007791589,0.00015792363,0.068053894,0.11664855,0.035704076,0.0053557865,0.006143611,0.0018940947],"about_ca_topic_score_codex":0.00012144201,"about_ca_topic_score_gemma":0.0006049786,"teacher_disagreement_score":0.7471665,"about_ca_system_score_codex":0.000028226372,"about_ca_system_score_gemma":0.00007594132,"threshold_uncertainty_score":0.3009546},"labels":[],"label_agreement":null},{"id":"W4288070401","doi":"10.18280/ts.390307","title":"A Novel Hyperparameter Optimization Aided Hand Gesture Recognition Framework Based on Deep Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gesture; Computer science; Hyperparameter; Artificial intelligence; Gesture recognition; Deep learning; Machine learning; Field (mathematics); Artificial neural network; Architecture; Sketch recognition; Pattern recognition (psychology)","score_opus":0.026381227449356916,"score_gpt":0.23485039639160848,"score_spread":0.20846916894225156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288070401","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":[0.004429017,0.000039531744,0.9928957,0.0007804692,0.00051587616,0.0005192928,0.000022792281,0.00023300806,0.0005642733],"genre_scores_gemma":[0.7619561,0.0000030647811,0.23511451,0.001993861,0.00026945065,0.00040075183,0.00016653654,0.000030504825,0.00006524963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736947,0.00038029937,0.00040078122,0.0006077551,0.0008851477,0.0003565498],"domain_scores_gemma":[0.99884105,0.00039623477,0.00022444903,0.0002688719,0.00014022217,0.00012917824],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007627455,0.0002500344,0.00024355948,0.00028714028,0.0007211381,0.00034840676,0.000446286,0.00010364531,0.0009484419],"category_scores_gemma":[0.00007495333,0.00024916528,0.00012752862,0.00065672904,0.00003271991,0.00029319458,0.00011173389,0.00059737416,0.000062574254],"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":[0.00008011424,0.00050276215,0.00013065831,0.000023984681,0.00004738626,0.000026649677,0.001380629,0.869458,0.0011874508,0.00021340538,0.00018615411,0.1267628],"study_design_scores_gemma":[0.0015149221,0.0007471668,0.00034449392,0.000080687685,0.000024047518,0.00004623417,0.00014943523,0.99184746,0.0008543871,0.00028983512,0.0037247958,0.00037651847],"about_ca_topic_score_codex":0.0000099822655,"about_ca_topic_score_gemma":0.0000015700896,"teacher_disagreement_score":0.7577812,"about_ca_system_score_codex":0.00014850327,"about_ca_system_score_gemma":0.00006188491,"threshold_uncertainty_score":0.99999607},"labels":[],"label_agreement":null},{"id":"W4288075345","doi":"10.18280/ts.390320","title":"Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Mustansiriyah University","keywords":"Gesture; Gesture recognition; Preprocessor; Computer science; Artificial intelligence; Detector; Computer vision; Channel (broadcasting); Rotation (mathematics); Process (computing); Focus (optics); Speech recognition; Physics; Optics","score_opus":0.019697330784101467,"score_gpt":0.21949836456045152,"score_spread":0.19980103377635006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288075345","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":[0.24551639,0.0013733502,0.73823035,0.005201748,0.0031677461,0.0029244854,0.00045998572,0.0011927887,0.0019331872],"genre_scores_gemma":[0.9955581,0.000007821755,0.0021066775,0.0011697917,0.00026215793,0.00041371337,0.0001494697,0.000025709607,0.00030660038],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974265,0.00040082785,0.00039128002,0.0005626488,0.0007772328,0.0004415214],"domain_scores_gemma":[0.99902815,0.00012609122,0.0002318716,0.00029365596,0.00014818141,0.00017202899],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005788341,0.00026439986,0.0002694552,0.00025404128,0.00064956554,0.00027340677,0.00059661246,0.00006801716,0.0006486979],"category_scores_gemma":[0.000017966217,0.0002507063,0.00012138607,0.00066877785,0.000043978325,0.00036653987,0.0002033386,0.00033831634,0.000117379364],"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":[0.00039109398,0.0017979831,0.00027867276,0.00036199277,0.00062303385,0.00073271274,0.022431128,0.0023027952,0.084798746,0.00054758915,0.051622637,0.83411163],"study_design_scores_gemma":[0.036263026,0.014644724,0.040247258,0.0013292292,0.00073633064,0.0060303104,0.006780562,0.2131704,0.2963998,0.037823237,0.3363919,0.010183232],"about_ca_topic_score_codex":0.00004591344,"about_ca_topic_score_gemma":0.00003604131,"teacher_disagreement_score":0.82392836,"about_ca_system_score_codex":0.0001379638,"about_ca_system_score_gemma":0.00007496343,"threshold_uncertainty_score":0.9999945},"labels":[],"label_agreement":null},{"id":"W4288075352","doi":"10.18280/ts.390331","title":"Hand Gesture Recognizing Model Using Optimized Capsule Neural Network","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Softmax function; Gesture; Computer science; Artificial neural network; Artificial intelligence; sort; Interface (matter); Gesture recognition; Hyperparameter; Machine learning","score_opus":0.047146934067694275,"score_gpt":0.24856975981030593,"score_spread":0.20142282574261167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288075352","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":[0.12549838,0.0003890729,0.8710708,0.00061642885,0.00088948704,0.0005451158,0.000022341495,0.00021565623,0.00075273437],"genre_scores_gemma":[0.9365297,0.0000029015691,0.06174109,0.0009815365,0.00043188772,0.00010858153,0.000019780578,0.000024416253,0.00016013709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752903,0.00032036265,0.0004418933,0.00052900513,0.0006480377,0.00053167244],"domain_scores_gemma":[0.9991974,0.00006304703,0.00019630384,0.00030191892,0.00008948669,0.00015184337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087921787,0.00023426498,0.00030222855,0.000114778544,0.000962853,0.00031469844,0.0006844305,0.000051875555,0.00024866086],"category_scores_gemma":[0.000007645925,0.00023213292,0.00014022781,0.0005087297,0.000033121574,0.00039451494,0.000389843,0.0003289317,0.000013130375],"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":[0.00002318311,0.00007181268,0.00007281051,0.000011795515,0.00004240319,0.000037733214,0.0011943582,0.98501456,0.0035694332,0.00031915787,0.0020965785,0.007546156],"study_design_scores_gemma":[0.0012783052,0.0000909233,0.00006510842,0.000020084688,0.000024160237,0.00011642555,0.0000918825,0.99554825,0.0002790937,0.00066745863,0.0015156659,0.00030265277],"about_ca_topic_score_codex":0.000027527383,"about_ca_topic_score_gemma":0.000003976425,"teacher_disagreement_score":0.8110313,"about_ca_system_score_codex":0.00013126862,"about_ca_system_score_gemma":0.00010546016,"threshold_uncertainty_score":0.94661057},"labels":[],"label_agreement":null},{"id":"W4293863377","doi":"10.1109/siu55565.2022.9864859","title":"Improving Keyword Search in Sign Language using Similarity Models","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Cosine similarity; Similarity (geometry); Sign (mathematics); Artificial intelligence; Natural language processing; Word (group theory); Language model; Phoenix; Information retrieval; Pattern recognition (psychology); Mathematics","score_opus":0.07479669533706654,"score_gpt":0.3111369480288207,"score_spread":0.23634025269175415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863377","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":[0.007137788,0.0031211574,0.984488,0.0012423947,0.000018183104,0.0007404092,0.000038268412,0.0001919836,0.0030218079],"genre_scores_gemma":[0.96093816,0.0000875818,0.037419517,0.00018651143,0.000023703462,0.0011374196,0.00004720795,0.000017393426,0.00014248521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769366,0.00049778185,0.0004752994,0.0005627177,0.00042182367,0.00034871727],"domain_scores_gemma":[0.9978113,0.00024155299,0.0002161432,0.0013596229,0.00024835728,0.0001229961],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011426163,0.00019475751,0.00025231024,0.00034942818,0.0016936916,0.0004439992,0.0021561508,0.00006878213,0.000042782365],"category_scores_gemma":[0.000012944486,0.00021951408,0.00004508823,0.0015549447,0.00016260827,0.00066424126,0.0017051898,0.00077208964,0.0000060964703],"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":[0.000010630487,0.00048046332,0.00045795922,0.00013704608,0.000019451707,0.0000043472437,0.01022264,0.005219654,0.010407498,0.05854609,0.000033636454,0.9144606],"study_design_scores_gemma":[0.00025366957,0.000031364707,0.00009267456,0.0000487504,0.0000123981645,0.000038762206,0.004666539,0.9870441,0.00017567459,0.0058868285,0.0014563246,0.00029293957],"about_ca_topic_score_codex":0.00045389708,"about_ca_topic_score_gemma":0.00009274865,"teacher_disagreement_score":0.9818244,"about_ca_system_score_codex":0.00015498944,"about_ca_system_score_gemma":0.0006083279,"threshold_uncertainty_score":0.99960595},"labels":[],"label_agreement":null},{"id":"W4296539348","doi":"","title":"Multi-touch human interface system and device for graphical input, and method for processing image in such a system","year":2011,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interface (matter); Computer science; Computer graphics (images); Graphical user interface; Human interface device; Computer vision; Image processing; Human–computer interaction; Image (mathematics); Multi-touch; Artificial intelligence; Computer hardware; Operating system","score_opus":0.03368883802047142,"score_gpt":0.2964326484491082,"score_spread":0.2627438104286368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296539348","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":[0.016688354,0.0015886385,0.9766636,0.00079820084,0.00019490883,0.0021567931,0.000056621997,0.0003363164,0.0015165864],"genre_scores_gemma":[0.5342906,0.00002319477,0.46477363,0.000015234029,0.00001731579,0.00061679987,0.000041532232,0.00003127248,0.00019043985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99352115,0.0036027809,0.00086756865,0.0013180877,0.00026145234,0.00042894247],"domain_scores_gemma":[0.99412787,0.0014892245,0.0007028954,0.0012825743,0.0021816003,0.00021580848],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00877332,0.00041342987,0.00072072016,0.00041228978,0.00041867947,0.00095068244,0.0012858205,0.00040685333,8.069819e-7],"category_scores_gemma":[0.00050547713,0.0004124041,0.0001474158,0.00039190834,0.00013980777,0.00032503836,0.0013710916,0.00047656393,0.0000020934333],"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":[0.00014805133,0.0018804504,0.0059209885,0.0651599,0.0005833526,0.000035234712,0.13533299,0.00004152852,0.045485508,0.43764144,0.00038293432,0.30738762],"study_design_scores_gemma":[0.0043690214,0.0000058324727,0.0032052975,0.029698148,0.00017703432,0.00022805673,0.0020740689,0.90005946,0.05363756,0.0030285039,0.0019157223,0.0016013039],"about_ca_topic_score_codex":0.0008403468,"about_ca_topic_score_gemma":0.0017646343,"teacher_disagreement_score":0.9000179,"about_ca_system_score_codex":0.00015363024,"about_ca_system_score_gemma":0.00018144463,"threshold_uncertainty_score":0.9998328},"labels":[],"label_agreement":null},{"id":"W4300066272","doi":"","title":"Hand position estimation using inertial sensors","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Position (finance); Inertial frame of reference; Computer science; Inertial measurement unit; Estimation; Inertial reference unit; Inertial navigation system; Artificial intelligence; Engineering; Physics","score_opus":0.018666077174306246,"score_gpt":0.2440984220914218,"score_spread":0.22543234491711556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300066272","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":[0.065432645,0.00026269408,0.91409487,0.008304071,0.0006742207,0.00047018606,0.000044207558,0.00035639387,0.0103607355],"genre_scores_gemma":[0.8485381,0.0000447919,0.1494857,0.00009534224,0.00008564963,0.000050158455,0.00017033043,0.000038865142,0.0014910332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923284,0.0050012935,0.00067463535,0.000977845,0.0006254012,0.00039242094],"domain_scores_gemma":[0.9936685,0.00088704645,0.00072780065,0.0021278376,0.002371928,0.00021690191],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0039013003,0.0003858483,0.0004131086,0.00034695657,0.00048559345,0.0012158818,0.0014169798,0.0005486498,0.00004089288],"category_scores_gemma":[0.00080767553,0.00036128488,0.00021636336,0.00040249483,0.00017758596,0.0004978026,0.0013861032,0.0005999375,0.00012495095],"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":[0.000045326007,0.0013120177,0.0012956242,0.00099551,0.00045703768,0.00007776321,0.034728527,0.0032741835,0.13686001,0.26291424,0.0025842704,0.5554555],"study_design_scores_gemma":[0.0011644444,8.563972e-7,0.001184957,0.0059700743,0.00007399431,0.0001596555,0.000036962887,0.7212677,0.2427464,0.022501517,0.0037574824,0.0011359873],"about_ca_topic_score_codex":0.0003614424,"about_ca_topic_score_gemma":0.00014368849,"teacher_disagreement_score":0.7831055,"about_ca_system_score_codex":0.0002342329,"about_ca_system_score_gemma":0.00033847065,"threshold_uncertainty_score":0.9998839},"labels":[],"label_agreement":null},{"id":"W4310072716","doi":"10.1016/j.displa.2022.102340","title":"Character-level arabic text generation from sign language video using encoder–decoder model","year":2022,"lang":"en","type":"article","venue":"Displays","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; RGB color model; Artificial intelligence; Encoder; Sign language; Decoding methods; Character (mathematics); Feature extraction; Encoding (memory); Feature (linguistics); Sign (mathematics); Speech recognition; Natural language processing; Pattern recognition (psychology); Computer vision; Algorithm","score_opus":0.07411832322335266,"score_gpt":0.27649155780109164,"score_spread":0.20237323457773898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310072716","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":[0.32508776,0.00014519155,0.67310005,0.00017454788,0.0005267859,0.00016107324,0.0001640734,0.000080722515,0.0005597717],"genre_scores_gemma":[0.9801639,0.000002803087,0.017744469,0.0008221571,0.00033891026,0.00008327391,0.00016680149,0.0000198393,0.0006578661],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832183,0.0001824904,0.00029042267,0.00049619516,0.00044467478,0.0002643867],"domain_scores_gemma":[0.9991506,0.00006434061,0.0001433937,0.00049420947,0.00005474094,0.00009274028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003096899,0.00016821145,0.00019785318,0.00010902565,0.00035298613,0.00018151866,0.0005289926,0.00005526597,0.00014553784],"category_scores_gemma":[0.00001849095,0.00016700137,0.00008333375,0.0002657984,0.000012971039,0.0005202749,0.00025476015,0.0001963009,0.00011854528],"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":[0.0000255337,0.00032604937,0.00087296974,0.000028178665,0.00012619972,0.00015788186,0.025767067,0.14929566,0.76569515,0.010501869,0.007361465,0.039842],"study_design_scores_gemma":[0.00030335583,0.000019348692,0.00062794826,0.000010606378,0.0000109226985,0.000028836228,0.00015548065,0.9931371,0.0038947063,0.00047425207,0.0010893001,0.00024812238],"about_ca_topic_score_codex":0.00019269623,"about_ca_topic_score_gemma":0.00004686398,"teacher_disagreement_score":0.8438415,"about_ca_system_score_codex":0.00014369414,"about_ca_system_score_gemma":0.00012363178,"threshold_uncertainty_score":0.6810118},"labels":[],"label_agreement":null},{"id":"W4310790240","doi":"10.1061/jcemd4.coeng-12811","title":"A Deep-Learning Classification Framework for Reducing Communication Errors in Dynamic Hand Signaling for Crane Operation","year":2022,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Deep learning; Set (abstract data type); Machine learning; Field (mathematics); SIGNAL (programming language); Object (grammar); Recurrent neural network; Task (project management); Operator (biology); Artificial neural network; Engineering","score_opus":0.012405953474234451,"score_gpt":0.24613886327813314,"score_spread":0.2337329098038987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310790240","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":[0.029491378,0.00032888353,0.968892,0.00050533016,0.0004468515,0.00030391957,5.364427e-7,0.00001888931,0.00001225893],"genre_scores_gemma":[0.68831545,0.00008977061,0.31146234,0.000010364939,0.000024140165,0.000078992365,0.000003139662,0.000005140532,0.000010671091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992764,0.000055354343,0.00033849667,0.00010906314,0.00013026701,0.00009040257],"domain_scores_gemma":[0.99943787,0.00012804258,0.00023503085,0.00009244515,0.00007933199,0.00002726888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085672946,0.00006367345,0.00011993543,0.0003117525,0.00022077152,0.000108743385,0.00013340444,0.000025197212,0.0000015845528],"category_scores_gemma":[0.00004910561,0.000069223934,0.000039417704,0.00017279951,0.0000096843605,0.00023177288,0.000038567112,0.00016410899,9.179998e-8],"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":[0.000029079845,0.00002452098,0.000054327997,0.00018832064,0.000053356496,0.0000015573052,0.0014098176,0.81991184,0.0027187155,0.040991556,0.0000095849855,0.13460733],"study_design_scores_gemma":[0.0007141627,0.00011066026,0.0005016061,0.00017877547,0.000020803609,0.00011456468,0.0016369517,0.9911887,0.00014390802,0.0016432704,0.0036514697,0.00009512602],"about_ca_topic_score_codex":0.0000010326296,"about_ca_topic_score_gemma":7.223949e-7,"teacher_disagreement_score":0.658824,"about_ca_system_score_codex":0.000106656626,"about_ca_system_score_gemma":0.000012182254,"threshold_uncertainty_score":0.282287},"labels":[],"label_agreement":null},{"id":"W4312304637","doi":"10.1007/978-3-319-08234-9_510-1","title":"Data Gloves for Hand and Finger Motion Interactions","year":2022,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computer Graphics and Games","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Motion (physics); Computer science; Physical medicine and rehabilitation; Artificial intelligence; Medicine","score_opus":0.03434755344701142,"score_gpt":0.2629737894735601,"score_spread":0.2286262360265487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312304637","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":[0.00031582857,0.0077893483,0.9057786,0.0015137993,0.0039863163,0.0011497163,0.0013230599,0.00012774245,0.07801559],"genre_scores_gemma":[0.058397986,0.16724722,0.39972612,0.00436304,0.0134603,0.0007080598,0.0070956172,0.0007468314,0.3482548],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844235,0.00003414205,0.00042997437,0.0006803405,0.00025553218,0.00015767383],"domain_scores_gemma":[0.9982745,0.00044336068,0.00033958672,0.0007125981,0.00013629672,0.000093639166],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032576232,0.00026920377,0.00042835966,0.00034194006,0.00019033664,0.00019077004,0.0005975638,0.00012479188,0.000026230644],"category_scores_gemma":[0.000016296271,0.00025718394,0.000093030336,0.000067875466,0.00011299613,0.00045487896,0.00090906164,0.00026893718,0.0000013452267],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013226581,0.000068833215,0.00010829052,0.00041109815,0.00027556054,0.000009593424,0.0011051187,0.000005629001,0.0000030470048,0.63215995,0.01816558,0.34767407],"study_design_scores_gemma":[0.0002831248,0.00016136876,0.00023582256,0.00015454821,0.000053381293,0.000047752404,0.0000054976404,0.009654074,0.0000021238475,0.030975344,0.9581459,0.00028102132],"about_ca_topic_score_codex":0.000011352128,"about_ca_topic_score_gemma":0.000045210483,"teacher_disagreement_score":0.9399804,"about_ca_system_score_codex":0.000008151258,"about_ca_system_score_gemma":0.0000479843,"threshold_uncertainty_score":0.999988},"labels":[],"label_agreement":null},{"id":"W4312437946","doi":"10.1109/iros47612.2022.9981117","title":"Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture Generation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Computer science; Autoencoder; Artificial intelligence; Gesture recognition; Representation (politics); Context (archaeology); Natural language processing; Cluster analysis; Speech recognition; Deep learning","score_opus":0.19848591019330233,"score_gpt":0.42278306656895315,"score_spread":0.2242971563756508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312437946","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":[0.013390126,0.00050962874,0.9748323,0.0007578403,0.007338867,0.0012281528,0.00004553206,0.00017598667,0.001721541],"genre_scores_gemma":[0.9680666,0.00029150664,0.024416272,0.00040408506,0.0012653675,0.00090911397,0.00028926268,0.00006287279,0.0042949524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956316,0.0010357613,0.0008858992,0.0010520872,0.0009795454,0.0004151557],"domain_scores_gemma":[0.9978914,0.00035784655,0.0006327592,0.00044212918,0.0005040594,0.00017177663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002005876,0.00039502265,0.00049696665,0.00049712975,0.0009704533,0.0010309482,0.00087478623,0.0001607441,0.00012735758],"category_scores_gemma":[0.00016626065,0.00038201464,0.0001790632,0.00033716226,0.000050923052,0.0004668964,0.00030686756,0.00064815907,0.000014332243],"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":[0.00022012998,0.00037097197,0.0017843957,0.00024681046,0.0006213158,0.00007460276,0.008954339,0.5193306,0.21765637,0.08937343,0.004911212,0.1564558],"study_design_scores_gemma":[0.0005018815,0.000305401,0.0000931751,0.00010643624,0.000026824026,0.00036574752,0.001709961,0.97037935,0.0061548646,0.0004380923,0.019446798,0.00047147291],"about_ca_topic_score_codex":0.00023420632,"about_ca_topic_score_gemma":0.000021434551,"teacher_disagreement_score":0.95467645,"about_ca_system_score_codex":0.00036846608,"about_ca_system_score_gemma":0.00012055958,"threshold_uncertainty_score":0.99986315},"labels":[],"label_agreement":null},{"id":"W4312757722","doi":"10.1109/ijcnn55064.2022.9892631","title":"Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Gesture; Computer science; Gesture recognition; Artificial intelligence; Recurrent neural network; Deep learning; Joint (building); Praxis; Pattern recognition (psychology); Speech recognition; Machine learning; Artificial neural network; Engineering","score_opus":0.09535500339808643,"score_gpt":0.31137331760407716,"score_spread":0.2160183142059907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312757722","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":[0.021217812,0.00016074307,0.8363338,0.088191316,0.020575149,0.015495005,0.0034757606,0.0008934628,0.013656968],"genre_scores_gemma":[0.9885355,0.000013008453,0.00055989064,0.0033523785,0.0007064027,0.0047752857,0.0015212442,0.00002823558,0.00050800265],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692166,0.00030814763,0.0005632565,0.00088980614,0.0009256262,0.0003915075],"domain_scores_gemma":[0.9979026,0.00062725437,0.0004577483,0.0006354513,0.00022663693,0.00015035144],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000576257,0.00031117856,0.00028746706,0.00027536022,0.0006167701,0.0006492966,0.0014145422,0.000087565364,0.00069633644],"category_scores_gemma":[0.00018224532,0.0002932195,0.00015524415,0.00031164844,0.000052924246,0.0005594181,0.00034460763,0.0006452455,0.000041469426],"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":[0.0009145272,0.0011109303,0.00024406947,0.000048952337,0.00026116727,0.00015631426,0.0016210357,0.029519487,0.0070616687,0.31340942,0.42140585,0.22424658],"study_design_scores_gemma":[0.0011149629,0.00048564223,0.00059585564,0.000067718785,0.0000073321276,0.00009422738,0.00016780192,0.90484196,0.00032592268,0.0013886676,0.09055156,0.00035831655],"about_ca_topic_score_codex":0.000011194908,"about_ca_topic_score_gemma":0.000013337003,"teacher_disagreement_score":0.96731776,"about_ca_system_score_codex":0.00022774813,"about_ca_system_score_gemma":0.000083823914,"threshold_uncertainty_score":0.999952},"labels":[],"label_agreement":null},{"id":"W4313731371","doi":"10.1016/j.iswa.2022.200174","title":"Fighting for a future free from violence: A framework for real-time detection of “Signal for Help”","year":2023,"lang":"en","type":"article","venue":"Intelligent Systems with Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Politecnico di Torino","keywords":"Computer science; Gesture; SIGNAL (programming language); Isolation (microbiology); Task (project management); Detection theory; Workflow; Artificial intelligence; Shadow (psychology); Computer security; Human–computer interaction; Detector; Telecommunications; Database; Engineering","score_opus":0.02134112298921333,"score_gpt":0.2767328795700351,"score_spread":0.2553917565808218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313731371","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":[0.0016401919,0.0002180909,0.9898262,0.00029738466,0.0005018719,0.006336141,0.0007559047,0.00037698503,0.000047210615],"genre_scores_gemma":[0.6373154,0.00012411109,0.2792624,0.000063502805,0.0051071816,0.077065215,0.00047392308,0.000109851775,0.0004784586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981626,0.000047372643,0.000614922,0.00057467126,0.00027196817,0.00032843408],"domain_scores_gemma":[0.9963304,0.0016443783,0.00047012037,0.000784413,0.0006616377,0.000109084125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054361566,0.00020012846,0.0003643052,0.00019550191,0.0002755273,0.000118089694,0.00073654804,0.0002030634,0.000002503998],"category_scores_gemma":[0.00007588433,0.00016802989,0.00016565154,0.00076353934,0.000028556558,0.00013228718,0.000055045246,0.00008553285,0.000054149343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007368804,0.0006736445,0.00062083063,0.005492351,0.0014316079,0.0000025121549,0.00939182,0.0049783187,0.09413857,0.3932132,0.011352605,0.47796765],"study_design_scores_gemma":[0.0024261102,0.0014546213,0.00031970683,0.0042386954,0.0002832333,0.000039634167,0.005437306,0.3840817,0.16510288,0.21397913,0.22111131,0.0015256723],"about_ca_topic_score_codex":0.000099368866,"about_ca_topic_score_gemma":0.00002667949,"teacher_disagreement_score":0.71056384,"about_ca_system_score_codex":0.000063761836,"about_ca_system_score_gemma":0.00008893201,"threshold_uncertainty_score":0.685206},"labels":[],"label_agreement":null},{"id":"W4315781750","doi":"10.18280/isi.270612","title":"Detecting Hand Gestures Using Machine Learning Techniques","year":2022,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gesture; Computer science; Artificial intelligence; Human–computer interaction; Machine learning","score_opus":0.030875309286690522,"score_gpt":0.2541314353132227,"score_spread":0.22325612602653216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315781750","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":[0.09800927,0.008362754,0.8750299,0.0005052462,0.006544253,0.0011772063,0.00009415295,0.0009294388,0.009347747],"genre_scores_gemma":[0.9672,0.00007577019,0.030341856,0.0003837878,0.00040353343,0.00016854443,0.00007927204,0.000035958128,0.00131127],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99640846,0.00082610716,0.0010607763,0.00030363767,0.0007559721,0.00064506015],"domain_scores_gemma":[0.9979029,0.00019350174,0.00093909586,0.0003623394,0.00045183353,0.00015028396],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021577282,0.0003583236,0.00039819867,0.00061212474,0.003154056,0.0017030707,0.00058131083,0.00018273132,0.0001763384],"category_scores_gemma":[0.00045116502,0.00040991371,0.00015987112,0.0013015509,0.00020404854,0.005562007,0.0007268547,0.0009306699,0.00008549475],"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":[0.000044249584,0.00007142438,0.0028139292,0.0011990615,0.00012623209,0.00004939239,0.06806393,0.011086007,0.0039066826,0.003151562,0.00039142623,0.9090961],"study_design_scores_gemma":[0.0006935573,0.000627822,0.0004886698,0.0009247035,0.00007626348,0.0038812403,0.0058035078,0.39640954,0.021610228,0.0030887367,0.56532764,0.0010681107],"about_ca_topic_score_codex":0.0016304292,"about_ca_topic_score_gemma":0.000048997787,"teacher_disagreement_score":0.908028,"about_ca_system_score_codex":0.0009793362,"about_ca_system_score_gemma":0.00028317398,"threshold_uncertainty_score":0.99983525},"labels":[],"label_agreement":null},{"id":"W4317039797","doi":"10.18280/ria.360613","title":"Recognition of Hand Motion Trajectory Gestures for Novel Input Interfaces","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Gesture; Benchmark (surveying); Character (mathematics); Motion (physics); Word (group theory); Trajectory; SIGNAL (programming language); Artificial intelligence; Plan (archaeology); Computer vision; Frame (networking); Sequence (biology); Human–computer interaction","score_opus":0.07283512743525153,"score_gpt":0.2774146540057215,"score_spread":0.20457952657047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317039797","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":[0.060512908,0.00028952837,0.9362801,0.00040397976,0.0010152975,0.00056512316,0.000061491715,0.00007930589,0.0007923051],"genre_scores_gemma":[0.9891966,0.000014240471,0.009752943,0.00009123922,0.000097190525,0.00025921193,0.000028758632,0.000014624685,0.0005452099],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984038,0.000106996966,0.0005456523,0.00044468796,0.0002526113,0.00024627076],"domain_scores_gemma":[0.9987318,0.0003021843,0.00027747994,0.00040117407,0.00022666434,0.000060693586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007941261,0.00014702033,0.00024086537,0.00022748433,0.0003261762,0.00008383828,0.0006058348,0.000056069926,0.00010720437],"category_scores_gemma":[0.00012393498,0.00015342646,0.00013068943,0.00052122277,0.00006988293,0.00025584467,0.00017039086,0.00017209184,0.00004477861],"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":[0.00009435835,0.00089621625,0.000119347744,0.00037815262,0.00007562932,0.000004984308,0.014764449,0.04170263,0.24270691,0.004843612,0.0018195519,0.6925942],"study_design_scores_gemma":[0.00019595887,0.00059305894,0.00008310335,0.000114177106,0.000020075337,0.000093411865,0.0019749475,0.19062044,0.78390974,0.0037867117,0.018250996,0.0003573949],"about_ca_topic_score_codex":0.000024703924,"about_ca_topic_score_gemma":0.000011754023,"teacher_disagreement_score":0.9286837,"about_ca_system_score_codex":0.00006261097,"about_ca_system_score_gemma":0.000050512856,"threshold_uncertainty_score":0.62565494},"labels":[],"label_agreement":null},{"id":"W4317105980","doi":"10.18280/jesa.550608","title":"Development of a Prototype Sensory Device as a Substitute for Single Sided Deaf People in Developing Nations","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensory system; Sensory substitution; Decibel; Perception; Hearing loss; Audiology; Sensory loss; Computer science; Neuroscience; Psychology; Communication; Medicine","score_opus":0.05178476996466262,"score_gpt":0.28922722894183134,"score_spread":0.2374424589771687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317105980","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":[0.60275877,0.00039822943,0.39193645,0.00047958328,0.000692502,0.0017185704,0.000011789712,0.00019156652,0.0018125246],"genre_scores_gemma":[0.72560257,0.0000069771054,0.27366775,0.00009937219,0.000045819867,0.00037707994,0.0000046605264,0.000023302093,0.00017243698],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9971843,0.00041059038,0.0010671257,0.00030268828,0.00065478013,0.0003805566],"domain_scores_gemma":[0.99815506,0.000415338,0.0006396669,0.00023201501,0.00044491058,0.00011302127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014796973,0.00019414807,0.0003929821,0.0007080186,0.0007855296,0.00019990955,0.000670856,0.000045275614,0.000024777682],"category_scores_gemma":[0.00051465357,0.00019034508,0.00010344583,0.0014146495,0.000025405177,0.00048394676,0.00025123204,0.00023251973,0.000025597865],"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":[0.00024225717,0.0013903538,0.0075969812,0.0033273876,0.0007141975,0.00068906497,0.112599365,0.0062145274,0.06869007,0.07206929,0.00068230816,0.7257842],"study_design_scores_gemma":[0.013724424,0.0029863806,0.5289933,0.008358429,0.00017860267,0.037789337,0.009602819,0.105300754,0.084691666,0.030309869,0.17328133,0.004783102],"about_ca_topic_score_codex":0.000020425627,"about_ca_topic_score_gemma":0.00035230233,"teacher_disagreement_score":0.7210011,"about_ca_system_score_codex":0.00066713657,"about_ca_system_score_gemma":0.0012834979,"threshold_uncertainty_score":0.7762047},"labels":[],"label_agreement":null},{"id":"W4318605464","doi":"10.1109/aivr56993.2022.00025","title":"Capture and Recognition of Bead Weaving Activities using Hand Skeletal Data and an LSTM Deep Neural Network","year":2022,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Computer science; Craft; Weaving; Workflow; Artificial intelligence; Gesture; Context (archaeology); Augmented reality; Artificial neural network; Deep learning; Process (computing); Human–computer interaction; Machine learning; Engineering; Database; Visual arts","score_opus":0.05590826842443038,"score_gpt":0.2803613952621253,"score_spread":0.22445312683769492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318605464","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":[0.87032163,0.00083791936,0.1276694,0.00010357901,0.0004075492,0.00019240493,0.00006287648,0.00005928625,0.00034537763],"genre_scores_gemma":[0.98789245,0.000008860791,0.011705909,0.00013588711,0.00015540665,0.0000058343176,0.000060694038,0.000008341991,0.000026644999],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878085,0.00022846869,0.00019038917,0.00039269187,0.00021992254,0.00018765377],"domain_scores_gemma":[0.99928504,0.00011391437,0.00011730897,0.0003741298,0.00003516082,0.0000744651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046735734,0.00011166599,0.00018113956,0.000073347364,0.00036577802,0.00019764391,0.00032419732,0.000037006575,0.000023466115],"category_scores_gemma":[0.000014477433,0.00010556733,0.000016156298,0.0002140498,0.00006839022,0.0011026241,0.00078189716,0.00014841728,3.396795e-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":[0.000062979045,0.00022040895,0.010493423,0.00025252346,0.00015925089,0.0000751553,0.014744967,0.002334692,0.01679257,0.0008278098,0.0007091834,0.95332706],"study_design_scores_gemma":[0.00072308106,0.00024814575,0.0021916786,0.00005690635,0.000045645284,0.0008558379,0.0031408411,0.9890014,0.0007691958,0.0015314848,0.0009861467,0.00044963823],"about_ca_topic_score_codex":0.00017379146,"about_ca_topic_score_gemma":0.00017905483,"teacher_disagreement_score":0.9866667,"about_ca_system_score_codex":0.000013425312,"about_ca_system_score_gemma":0.000025062984,"threshold_uncertainty_score":0.4304911},"labels":[],"label_agreement":null},{"id":"W4318832773","doi":"10.3390/s23031555","title":"PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Annotation; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Pose; Encoder; Encoding (memory); Partial least squares regression; RGB color model; Automatic image annotation; Embedding; Deep learning; Set (abstract data type); Image (mathematics); Machine learning; Image retrieval; Computer vision","score_opus":0.016991161608986456,"score_gpt":0.2804752527647815,"score_spread":0.2634840911557951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318832773","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":[0.88774693,0.000007191359,0.108871214,0.0012326911,0.00037558802,0.00021196455,0.0000019609438,0.0004968956,0.0010555774],"genre_scores_gemma":[0.9889053,0.0000014609076,0.009463795,0.00008128087,0.00013351502,0.000017181648,0.000008094234,0.000012811712,0.0013765574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989649,0.000096153235,0.00013823436,0.00028622005,0.00025965253,0.00025483078],"domain_scores_gemma":[0.9994927,0.00007776039,0.00004824186,0.00017791276,0.00008910178,0.00011430724],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024606727,0.00010062223,0.00011959174,0.00013541804,0.00016864079,0.00019915398,0.00014141561,0.000037920527,0.0000064005344],"category_scores_gemma":[0.000056766447,0.000082538536,0.000023426046,0.00074383605,0.000019788964,0.00015000165,0.000036967987,0.00009411581,0.0011615838],"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":[0.00013780856,0.00008335379,0.008142376,0.000116068695,0.00010708856,0.0009029927,0.0518544,0.35025516,0.021848168,0.0016613882,0.0029740755,0.5619171],"study_design_scores_gemma":[0.0019470064,0.000974655,0.040890988,0.00044023557,0.000032881973,0.00025835825,0.00107265,0.80251485,0.038929626,0.0003067255,0.1115438,0.0010881921],"about_ca_topic_score_codex":0.000017866327,"about_ca_topic_score_gemma":0.00002686253,"teacher_disagreement_score":0.5608289,"about_ca_system_score_codex":0.000013237687,"about_ca_system_score_gemma":0.00002487223,"threshold_uncertainty_score":0.99961615},"labels":[],"label_agreement":null},{"id":"W4319752461","doi":"10.1007/978-3-319-08234-9_483-1","title":"American Sign Language Detection","year":2023,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computer Graphics and Games","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Sign (mathematics); Sign language; Linguistics; Computer science; Philosophy; Mathematics","score_opus":0.010093385362215119,"score_gpt":0.22220649303797108,"score_spread":0.21211310767575595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319752461","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014927142,0.00243957,0.647013,0.00048803532,0.004275579,0.00088897,0.000086393695,0.0009984148,0.3423173],"genre_scores_gemma":[0.21055153,0.06786781,0.049423873,0.00235061,0.009339921,0.00019757377,0.00021290375,0.00072985765,0.6593259],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983415,0.000046054683,0.00044378656,0.0005486863,0.0004004474,0.00021955396],"domain_scores_gemma":[0.99852574,0.00027882008,0.0004254309,0.00050023216,0.00014035642,0.00012943715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002628335,0.00033024664,0.0005657159,0.0005503723,0.00006605055,0.00009134118,0.00044722584,0.00019077602,0.0000048101265],"category_scores_gemma":[0.000010065263,0.00030567538,0.00018921832,0.00021900614,0.0001798673,0.00012865853,0.00026279618,0.0003267628,0.000039690913],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040507484,0.000014239966,0.000054667096,0.00012899548,0.0001347272,0.000053855332,0.0012115635,0.0000028144837,0.000007821808,0.138025,0.001527288,0.858835],"study_design_scores_gemma":[0.0008704393,0.0012805313,0.005447134,0.0010678909,0.00015216954,0.00015394586,0.0000787983,0.007911935,0.00015247185,0.08036067,0.9004417,0.0020822927],"about_ca_topic_score_codex":0.00004890654,"about_ca_topic_score_gemma":0.000077258024,"teacher_disagreement_score":0.89891446,"about_ca_system_score_codex":0.000009537355,"about_ca_system_score_gemma":0.000043575288,"threshold_uncertainty_score":0.99993956},"labels":[],"label_agreement":null},{"id":"W4322492046","doi":"10.4028/p-oswg04","title":"Sign Language Detection Using Action Recognition","year":2023,"lang":"en","type":"article","venue":"Advances in science and technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Sign language; Gesture; Computer science; Gesture recognition; Sign (mathematics); Task (project management); Meaning (existential); Action (physics); Key (lock); Position (finance); Human–computer interaction; Artificial intelligence; Natural language processing; Speech recognition; Linguistics; Engineering; Computer security; Mathematics","score_opus":0.03193655781275823,"score_gpt":0.329246003478619,"score_spread":0.29730944566586076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322492046","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":[0.92610115,0.00041698944,0.07134875,0.00034137443,0.0005784022,0.0001457905,7.608181e-7,0.0005156528,0.00055115565],"genre_scores_gemma":[0.99674314,0.00029835248,0.0028657205,0.000029731296,0.00002564648,0.000022600483,4.6088115e-7,0.0000027813596,0.000011538968],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990373,0.000021984924,0.00013144549,0.00035350578,0.00021181974,0.00024390052],"domain_scores_gemma":[0.999566,0.00004421701,0.00006146108,0.00018345623,0.00011579682,0.000029054585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006776245,0.000064975,0.00008705476,0.0011963584,0.0001761467,0.000058455982,0.00028633027,0.00007406558,0.0000011644429],"category_scores_gemma":[0.00022851686,0.000060904156,0.000007653101,0.0059916186,0.00025220396,0.0016274017,0.00012088687,0.00011156458,0.000046539968],"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":[8.392743e-7,0.0000047367816,0.00043853,0.000006557724,4.1381614e-7,0.0000091221245,0.00015313845,0.000009531906,0.07040833,0.00051702367,9.552208e-7,0.9284508],"study_design_scores_gemma":[0.0009661244,0.00033536585,0.0038103152,0.00022150952,0.0000068023537,0.00063892844,0.0063789706,0.12575787,0.7078696,0.14359951,0.00971682,0.0006982001],"about_ca_topic_score_codex":0.0000119120505,"about_ca_topic_score_gemma":0.00008842364,"teacher_disagreement_score":0.9277526,"about_ca_system_score_codex":0.000061176215,"about_ca_system_score_gemma":0.000047669542,"threshold_uncertainty_score":0.2878774},"labels":[],"label_agreement":null},{"id":"W4366967686","doi":"10.1109/wi-iat55865.2022.00039","title":"Gesture Recognition Using MediaPipe for Online Realtime Gameplay","year":2022,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Climb; Human–computer interaction; Video game; Multimedia; Artificial intelligence; Speech recognition; Engineering","score_opus":0.07997679137679171,"score_gpt":0.2978998282116307,"score_spread":0.217923036834839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366967686","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":[0.03829806,0.00020376359,0.9508553,0.0032665203,0.0022901783,0.0010471932,0.00027243275,0.00054360414,0.0032229403],"genre_scores_gemma":[0.30406734,0.000024366438,0.6858186,0.005186983,0.0013066899,0.0005006813,0.0006481363,0.000059321716,0.0023878454],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855816,0.00014847319,0.00028147467,0.000393731,0.00035032694,0.0002678537],"domain_scores_gemma":[0.99908584,0.0002127444,0.00013004505,0.00031707017,0.00015369902,0.00010061815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055945024,0.00012617085,0.00018072235,0.00015523008,0.00030630783,0.00007430299,0.00044353696,0.000052306426,0.00022033557],"category_scores_gemma":[0.000062779654,0.00011695943,0.00008938892,0.0004335775,0.0000132015875,0.00026816662,0.00020292826,0.00017204185,0.000035354467],"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":[0.00007945233,0.0007755715,0.00024652504,0.00014334175,0.00012776253,0.000058742356,0.0028382365,0.00075140846,0.010006082,0.0040906332,0.038879726,0.94200253],"study_design_scores_gemma":[0.0042879577,0.0010526524,0.00083452516,0.00010829725,0.000079526995,0.0013556811,0.0012863013,0.44926548,0.005371132,0.025937023,0.50884247,0.001578969],"about_ca_topic_score_codex":0.000042877447,"about_ca_topic_score_gemma":0.000029989435,"teacher_disagreement_score":0.94042355,"about_ca_system_score_codex":0.000091682814,"about_ca_system_score_gemma":0.000106225765,"threshold_uncertainty_score":0.4769467},"labels":[],"label_agreement":null},{"id":"W4376456990","doi":"10.2316/j.2023.206-0909","title":"A STUDY OF ELECTRONIC SIGNATURE HANDWRITING UNDER FORCE FEEDBACK PERCEPTION, 471-480.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Handwriting; Signature (topology); Computer science; Perception; Mathematics; Artificial intelligence; Psychology; Neuroscience; Geometry","score_opus":0.017356386049392276,"score_gpt":0.28269214249029684,"score_spread":0.26533575644090457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376456990","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":[0.6761686,0.00007678789,0.32098436,0.0018463727,0.00063430105,0.00012443136,0.0000016182815,0.0000460045,0.00011746503],"genre_scores_gemma":[0.9976098,0.000058539437,0.0019850014,0.000059501384,0.00018294816,0.0000017219612,0.000003847293,0.000005890343,0.000092767346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986665,0.000071780676,0.00046105633,0.000113487804,0.00057001633,0.00011715973],"domain_scores_gemma":[0.9986149,0.000106507294,0.0004395591,0.00008025397,0.0007178587,0.00004089683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005506998,0.000080409634,0.00015366882,0.00032999014,0.000053228297,0.00014485042,0.00032492017,0.000050000308,0.0000048514494],"category_scores_gemma":[0.000054331525,0.00006869586,0.000054333363,0.00027788265,0.000013094903,0.0004456442,0.00007081852,0.00014842229,0.000007646306],"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":[0.00013088822,0.0016634831,0.016484838,0.00018786779,0.0019306917,0.00024667117,0.03516547,0.5589286,0.0405616,0.083713144,0.003656003,0.25733075],"study_design_scores_gemma":[0.0064612627,0.0018355446,0.2601858,0.00091503566,0.00011138203,0.0013874947,0.013490319,0.67942536,0.0010708735,0.034011126,0.00053131074,0.0005745045],"about_ca_topic_score_codex":0.000004133635,"about_ca_topic_score_gemma":0.000008343418,"teacher_disagreement_score":0.32144114,"about_ca_system_score_codex":0.000059162325,"about_ca_system_score_gemma":0.00006544554,"threshold_uncertainty_score":0.28013358},"labels":[],"label_agreement":null},{"id":"W4382135499","doi":"10.3390/electronics12132794","title":"Physically Plausible Realistic Grip-Lift Interaction Based on Hand Kinematics in VR","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea; Hanyang University","keywords":"Kinematics; Virtual reality; Lift (data mining); Embodied cognition; Human–computer interaction; Computer science; Interaction design; Simulation; Artificial intelligence; Physics","score_opus":0.021061944879482285,"score_gpt":0.27930393913934354,"score_spread":0.2582419942598613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382135499","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":[0.17266558,0.0002774209,0.7908185,0.0069818143,0.0022596105,0.0014188413,0.000021089787,0.0014848439,0.02407231],"genre_scores_gemma":[0.9975823,0.000028310116,0.0013831956,0.0003985274,0.00013884428,0.000051493018,0.000031762138,0.00001658399,0.00036895912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986414,0.00010960257,0.0002522546,0.00029715375,0.0003235809,0.00037602164],"domain_scores_gemma":[0.9991669,0.00031007626,0.00009334515,0.00030937002,0.000061952,0.000058374488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038370478,0.0001313624,0.0001824224,0.00025381966,0.000075329284,0.00016263447,0.00031252622,0.00006591717,0.000009815759],"category_scores_gemma":[0.00011790602,0.00012196966,0.000049800685,0.0010774813,0.000014083543,0.00017300002,0.00004258437,0.0002746736,0.00043156772],"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":[0.00033557627,0.002353106,0.0016037478,0.0011260916,0.00019659517,0.00045101807,0.0077247075,0.2361036,0.050270505,0.25729442,0.057287812,0.38525283],"study_design_scores_gemma":[0.00071731233,0.0003823839,0.0011145504,0.00023519415,0.000006921924,0.000010948838,0.000026483382,0.95024234,0.005650997,0.005495887,0.03584246,0.00027449915],"about_ca_topic_score_codex":0.000012373017,"about_ca_topic_score_gemma":0.00024429918,"teacher_disagreement_score":0.8249168,"about_ca_system_score_codex":0.00015773135,"about_ca_system_score_gemma":0.00016818417,"threshold_uncertainty_score":0.55470735},"labels":[],"label_agreement":null},{"id":"W4382365491","doi":"10.54941/ahfe1003281","title":"Towards Kenyan Sign Language Hand Gesture Recognition Dataset","year":2023,"lang":"en","type":"article","venue":"AHFE international","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gesture; Computer science; Sign language; Artificial intelligence; Gesture recognition; Lexicon; Natural language processing; Sign (mathematics); Alphabet; MNIST database; Speech recognition; Deep learning; Linguistics","score_opus":0.034781387623053425,"score_gpt":0.30101140914616403,"score_spread":0.2662300215231106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382365491","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":[0.31313494,0.0005789522,0.4789849,0.052422725,0.026471484,0.0018416357,0.0148772,0.004156888,0.10753131],"genre_scores_gemma":[0.98303074,0.000040702893,0.0037777217,0.0013836253,0.0010379697,0.0000771701,0.008612872,0.000019557534,0.0020196573],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99868596,0.00005556894,0.00021933751,0.00034778484,0.0004918366,0.00019951675],"domain_scores_gemma":[0.9993054,0.000086452405,0.000084985324,0.0002990478,0.00014058698,0.00008350437],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036934915,0.00012151435,0.000110888875,0.00023593949,0.000091400994,0.00034015972,0.0007436309,0.00007793978,0.00026711746],"category_scores_gemma":[0.0001135856,0.0001116045,0.000053456748,0.00038479688,0.000030104238,0.0005267753,0.0002160183,0.00013510871,0.0040619983],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001883759,0.00011105268,0.0008000096,0.000026362617,0.00016915827,0.0003985061,0.0034389205,0.00002601584,0.0058345445,0.0030810595,0.47669736,0.50939816],"study_design_scores_gemma":[0.001885371,0.00013001417,0.025001382,0.00020723382,0.000021252588,0.00037592844,0.0005707932,0.013058137,0.018874852,0.009219407,0.9298241,0.00083152903],"about_ca_topic_score_codex":0.00007471669,"about_ca_topic_score_gemma":0.00004000821,"teacher_disagreement_score":0.6698958,"about_ca_system_score_codex":0.000041943273,"about_ca_system_score_gemma":0.000052493826,"threshold_uncertainty_score":0.99671346},"labels":[],"label_agreement":null},{"id":"W4382395111","doi":"10.18280/ts.400311","title":"EMG Signal Classification Using Deep Learning and Time Domain Descriptors-Based Feature Extraction for Hand Grip Movement Recognition","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Movement (music); Artificial intelligence; Computer science; Feature extraction; Pattern recognition (psychology); Time domain; SIGNAL (programming language); Speech recognition; Domain (mathematical analysis); Computer vision; Mathematics; Acoustics","score_opus":0.049405801160155756,"score_gpt":0.27837781407144335,"score_spread":0.2289720129112876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382395111","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":[0.14539623,0.00007089361,0.85234076,0.0006092467,0.00025587852,0.000893737,0.000019513514,0.00026870653,0.00014504588],"genre_scores_gemma":[0.9610245,0.000011453336,0.037213344,0.00028277695,0.00049652037,0.00026570717,0.0003677347,0.000036661895,0.0003012703],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980279,0.00023973282,0.00037226366,0.00054236635,0.00044614848,0.00037157015],"domain_scores_gemma":[0.9990387,0.00023441986,0.0002579891,0.00012539963,0.00020982284,0.00013369125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011418416,0.00022915898,0.00021733921,0.0003163681,0.0005072959,0.0004466999,0.00016041275,0.00014096414,0.00008748522],"category_scores_gemma":[0.000024607816,0.00022665749,0.00009593434,0.0004801927,0.000045058554,0.0005347264,0.000034175388,0.00017958607,0.00008154283],"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":[0.0001506613,0.00017239591,0.0007331487,0.00018712903,0.000093921335,0.000014296013,0.0013569236,0.0014574758,0.7821372,0.0003802495,0.0014275161,0.21188904],"study_design_scores_gemma":[0.0027135285,0.00051709625,0.0043171523,0.0002465065,0.00006589967,0.000022807688,0.0004961723,0.9506154,0.026960913,0.0028140484,0.010677697,0.00055279664],"about_ca_topic_score_codex":0.00000732361,"about_ca_topic_score_gemma":0.0000066834305,"teacher_disagreement_score":0.9491579,"about_ca_system_score_codex":0.00014765208,"about_ca_system_score_gemma":0.000059701033,"threshold_uncertainty_score":0.92428243},"labels":[],"label_agreement":null},{"id":"W4384158850","doi":"10.1109/i2mtc53148.2023.10176037","title":"Interactive and Markerless Visual Recognition of Brazilian Sign Language Alphabet","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Alphabet; Sign language; Computer science; Gesture; Classifier (UML); Gesture recognition; Artificial intelligence; Sign (mathematics); Speech recognition; Natural language processing; Computer vision; Human–computer interaction; Linguistics","score_opus":0.015686771051194995,"score_gpt":0.2888882108525147,"score_spread":0.2732014398013197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384158850","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":[0.8874648,0.00006587707,0.086275235,0.0005853478,0.0005121602,0.00035757883,0.000023526354,0.0004805581,0.024234913],"genre_scores_gemma":[0.9974222,0.000013011313,0.001642802,0.00010481277,0.000044513556,0.000015350923,0.000016741833,0.0000061360925,0.0007344059],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991855,0.00011009974,0.00017986636,0.00022889311,0.00015742387,0.00013818852],"domain_scores_gemma":[0.99938685,0.0002580111,0.0000739896,0.00013478727,0.00008478966,0.000061555744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033681566,0.0000816921,0.00014436718,0.00017524493,0.00003230895,0.00005865703,0.00014868652,0.000045854777,0.00006077582],"category_scores_gemma":[0.000053253672,0.00006999067,0.000032255404,0.00045077933,0.000025203582,0.0003628723,0.00011254852,0.00006300686,0.0002599105],"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":[0.000020063275,0.000059436712,0.0010764959,0.000077498116,0.00004804603,0.000037450092,0.005022697,7.994063e-7,0.010706516,0.00035147398,0.0031733213,0.9794262],"study_design_scores_gemma":[0.0086877495,0.0018375182,0.29466558,0.001998118,0.00011319519,0.0005903345,0.039130915,0.12871961,0.4904767,0.01663166,0.013872778,0.0032758466],"about_ca_topic_score_codex":0.000040584513,"about_ca_topic_score_gemma":0.000028525825,"teacher_disagreement_score":0.97615033,"about_ca_system_score_codex":0.00000947725,"about_ca_system_score_gemma":0.000018343451,"threshold_uncertainty_score":0.33407104},"labels":[],"label_agreement":null},{"id":"W4384281610","doi":"10.5281/zenodo.849328","title":"Computer Vision Method For Guitarist Fingering Retrieval","year":2006,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"McGill University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Information retrieval","score_opus":0.0308465615909553,"score_gpt":0.326620204063788,"score_spread":0.2957736424728327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384281610","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":[0.00016044278,0.0001441647,0.9877577,0.000908516,0.0038139448,0.0008375595,0.000023233808,0.0005005323,0.0058539393],"genre_scores_gemma":[0.0049024504,0.0000045284073,0.99154043,0.00032660054,0.0012952627,0.000050171933,0.00006888342,0.000027020054,0.0017846263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974275,0.00016450447,0.0006028587,0.0010308538,0.00040999983,0.0003642729],"domain_scores_gemma":[0.99785453,0.00045233173,0.00028298446,0.00096861535,0.00033651685,0.00010505037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013278036,0.0003340681,0.0005253671,0.00016252557,0.00013262457,0.0006968752,0.0012043966,0.0004206692,0.000014541999],"category_scores_gemma":[0.000039632192,0.00029417264,0.0003144466,0.00019287346,0.000015969668,0.00017381756,0.0016863345,0.00038393575,0.000050063034],"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":[0.000049371436,0.00023183474,0.00008373798,0.0013958032,0.00021486598,0.000050242765,0.0006117891,0.009649294,0.000807561,0.014022421,0.20439161,0.76849145],"study_design_scores_gemma":[0.0005256373,0.00014762176,0.00044004127,0.00037345197,0.000023553635,0.000046272133,0.0000031690147,0.8649923,0.0020331095,0.010782021,0.11992563,0.00070718175],"about_ca_topic_score_codex":0.00016374847,"about_ca_topic_score_gemma":0.00003143033,"teacher_disagreement_score":0.855343,"about_ca_system_score_codex":0.000101923804,"about_ca_system_score_gemma":0.00011841517,"threshold_uncertainty_score":0.99995106},"labels":[],"label_agreement":null},{"id":"W4385078258","doi":"10.18280/isi.280311","title":"Enhancing Virtual and Augmented Reality Interactions with a MediaPipe-Based Hand Gesture Recognition User Interface","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Sangmyung University","keywords":"Gesture; Computer science; Human–computer interaction; Augmented reality; Interaction technique; Fist; Virtual reality; Gesture recognition; Interface (matter); Heuristics; User interface; Process (computing); Artificial intelligence; Computer vision","score_opus":0.022261248315176212,"score_gpt":0.25466884696679515,"score_spread":0.23240759865161895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078258","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":[0.2856908,0.00001877223,0.71143407,0.00042213668,0.0004645749,0.0004270106,0.0000353228,0.0005426086,0.0009647288],"genre_scores_gemma":[0.99588364,0.000013729074,0.0033315723,0.00021178355,0.00006927814,0.00015929116,0.00021759048,0.000011897578,0.00010119021],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983885,0.0001538795,0.00054888526,0.00024441342,0.0003623849,0.00030190707],"domain_scores_gemma":[0.99856687,0.00024728614,0.0003269364,0.00029674388,0.00042744578,0.0001347323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069020124,0.00021493963,0.00023013022,0.0004741279,0.00039058778,0.00078120205,0.00021271312,0.0001076917,0.000015674963],"category_scores_gemma":[0.00023978265,0.00018246274,0.000042345036,0.0010060389,0.00012142297,0.004301545,0.00009714821,0.00021055047,0.0002656032],"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":[0.00048467773,0.00017181889,0.003234043,0.001850207,0.00039720593,0.00004751739,0.09468619,0.0025767835,0.013488204,0.0018877358,0.0066620815,0.8745135],"study_design_scores_gemma":[0.0147800315,0.0035291354,0.09349813,0.013808869,0.00031344258,0.0016837579,0.03318532,0.37030897,0.34648606,0.007937171,0.10944876,0.005020339],"about_ca_topic_score_codex":0.00008947179,"about_ca_topic_score_gemma":0.00027062907,"teacher_disagreement_score":0.8694932,"about_ca_system_score_codex":0.00017267007,"about_ca_system_score_gemma":0.00011887687,"threshold_uncertainty_score":0.7533149},"labels":[],"label_agreement":null},{"id":"W4385224492","doi":"10.1007/s12559-023-10177-w","title":"Dynamic Gesture Recognition Based on Deep 3D Natural Networks","year":2023,"lang":"en","type":"article","venue":"Cognitive Computation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Gesture; Convolution (computer science); Artificial intelligence; Gesture recognition; Kernel (algebra); Set (abstract data type); Computer vision; Process (computing); Memorization; Pattern recognition (psychology); Speech recognition; Artificial neural network","score_opus":0.018333311580842275,"score_gpt":0.27161903938896637,"score_spread":0.2532857278081241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385224492","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":[0.014278539,0.000102732076,0.9794032,0.0005200641,0.0018567189,0.0006080741,0.000016444132,0.0011091263,0.0021051322],"genre_scores_gemma":[0.9931412,0.000016197353,0.004047538,0.0013376165,0.00017132977,0.00011208881,0.0010563645,0.00003087063,0.00008683313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766856,0.00036886625,0.0003486091,0.0006875725,0.0004991605,0.00042721664],"domain_scores_gemma":[0.99780875,0.0010804088,0.00020394931,0.00013073662,0.0006509361,0.00012518911],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048615108,0.00028308935,0.0002510328,0.00052475603,0.00027276453,0.00025713723,0.00022116111,0.0001659849,0.000014615517],"category_scores_gemma":[0.0002474226,0.00027663232,0.00011943707,0.0017459944,0.00005040727,0.00043637582,0.000083566105,0.0003789251,0.0015503179],"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":[0.000038780752,0.00006751438,0.00012761322,0.000029970888,0.000028981054,0.000064032174,0.00020426836,0.013359377,0.00003685796,0.000025818248,0.00049290725,0.9855239],"study_design_scores_gemma":[0.0010513294,0.00015787808,0.01965184,0.00029041342,0.000019277964,0.000018189357,0.00008142021,0.9773491,0.00006777811,0.0008538393,0.00010646594,0.00035244037],"about_ca_topic_score_codex":0.0000028308555,"about_ca_topic_score_gemma":0.000013654287,"teacher_disagreement_score":0.98517144,"about_ca_system_score_codex":0.00009504775,"about_ca_system_score_gemma":0.00006071266,"threshold_uncertainty_score":0.9999686},"labels":[],"label_agreement":null},{"id":"W4385333940","doi":"10.1109/isscs58449.2023.10190966","title":"Pressure Sensor Positioning for Accurate Human Interaction with a Robotic Hand","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Windsor","funders":"","keywords":"Gesture; Computer science; Focus (optics); Artificial intelligence; Gesture recognition; Reliability (semiconductor); Robot; Pressure sensor; Computer vision; Robotic hand; Real-time computing; Engineering","score_opus":0.04293542832619241,"score_gpt":0.30722318581915975,"score_spread":0.26428775749296735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385333940","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":[0.015120197,0.00001015849,0.98028785,0.0009933234,0.00030991313,0.0004221877,0.0000026629923,0.00044577074,0.0024079634],"genre_scores_gemma":[0.98512435,6.475543e-7,0.008036119,0.00010501512,0.000112069894,0.00009209365,0.000017261225,0.0000092621585,0.006503198],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992811,0.000038958853,0.00013454218,0.00024455058,0.00012935311,0.00017146856],"domain_scores_gemma":[0.9994547,0.00011057423,0.00006677693,0.0001827854,0.00013663583,0.00004854132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015717463,0.000084315376,0.00010811774,0.000113210306,0.00025524502,0.00040215228,0.00014995241,0.00003515737,0.000012993255],"category_scores_gemma":[0.000014085785,0.000062617015,0.000033514414,0.00031918532,0.00001312117,0.00046894798,0.000038199873,0.000056204408,0.00012135125],"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":[0.00045623764,0.00077594066,0.0052082436,0.0026712618,0.0025657217,0.0004115502,0.0370449,0.19870016,0.36156493,0.18094625,0.11651125,0.09314354],"study_design_scores_gemma":[0.0042315014,0.0013680721,0.009498611,0.0009449591,0.00015683111,0.00058350136,0.0015265155,0.86742806,0.059698164,0.0033690645,0.0499158,0.0012789081],"about_ca_topic_score_codex":0.000017682665,"about_ca_topic_score_gemma":0.000036587255,"teacher_disagreement_score":0.9722517,"about_ca_system_score_codex":0.000010560468,"about_ca_system_score_gemma":0.000016690581,"threshold_uncertainty_score":0.38779637},"labels":[],"label_agreement":null},{"id":"W4385410998","doi":"10.3389/frvir.2023.1171230","title":"Hand interaction designs in mixed and augmented reality head mounted display: a scoping review and classification","year":2023,"lang":"en","type":"review","venue":"Frontiers in Virtual Reality","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs; VMware","keywords":"Mixed reality; Computer science; Human–computer interaction; Augmented reality; Metaphor; Modality (human–computer interaction); Artificial intelligence","score_opus":0.19489334975221786,"score_gpt":0.42599096600604464,"score_spread":0.23109761625382677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385410998","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":[0.00001117662,0.82262355,0.1732657,0.00025448677,0.00089210557,0.0026814484,0.00006771644,0.000098020464,0.00010580186],"genre_scores_gemma":[0.00015965401,0.9981646,0.00064182555,0.00006142287,0.000051928604,0.00057102914,0.00023143849,0.000030342,0.00008776147],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9943202,0.0021330165,0.0016434467,0.0011593342,0.0003521878,0.00039182365],"domain_scores_gemma":[0.99775547,0.00048900134,0.00078274455,0.00070829334,0.00009168579,0.0001728295],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033559385,0.00047251993,0.002238166,0.00056368776,0.00011641071,0.00027302877,0.00042699155,0.00039167464,0.0000014062971],"category_scores_gemma":[0.0007096364,0.0004250614,0.00013558805,0.0014410695,0.00014404919,0.00057101017,0.00026432355,0.0006280328,0.000011958277],"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":[0.000010893531,0.000059397054,0.0000857019,0.060444362,0.000067493194,0.000016702363,0.00020738624,0.0000016042264,7.519975e-7,0.00030554412,0.001493024,0.9373071],"study_design_scores_gemma":[0.0011791454,0.00017328422,0.00228376,0.81506366,0.0004900076,0.00011646374,0.00022238294,0.0148735745,0.0000017501542,0.0005981208,0.16366783,0.001330024],"about_ca_topic_score_codex":0.0003452133,"about_ca_topic_score_gemma":0.0012953092,"teacher_disagreement_score":0.9359771,"about_ca_system_score_codex":0.0005518922,"about_ca_system_score_gemma":0.0002802115,"threshold_uncertainty_score":0.9998201},"labels":[],"label_agreement":null},{"id":"W4385478006","doi":"10.1109/cai54212.2023.00072","title":"Exploration and Comparison of Locomotion Mode Recognition Models for Prosthetic Gait","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Computer science; Gait analysis; Mode (computer interface); Artificial intelligence; Computer vision; Physical medicine and rehabilitation; Human–computer interaction; Medicine","score_opus":0.13606845586021377,"score_gpt":0.33133146833698446,"score_spread":0.1952630124767707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385478006","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":[0.022883,0.000020642843,0.9746395,0.00082433654,0.00012758409,0.00061901094,0.000006676032,0.00020935966,0.0006699421],"genre_scores_gemma":[0.9847425,0.000019361723,0.014752369,0.00002430812,0.000029105504,0.00018793567,0.00005822585,0.000006818594,0.00017937271],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918675,0.000049395745,0.0002667149,0.00021873358,0.00015734983,0.00012102597],"domain_scores_gemma":[0.9994347,0.00008346798,0.000104441795,0.0001470143,0.00019010894,0.000040284216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029062474,0.000074535994,0.0001510499,0.00015225852,0.000059856902,0.00005672406,0.00010022807,0.000053981912,0.0000016981083],"category_scores_gemma":[0.000019288416,0.00006662814,0.000032680662,0.00031291245,0.00001560396,0.00075818173,0.0000368672,0.000032561536,0.000034006564],"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":[0.00003649845,0.0002210033,0.00022452477,0.0004495369,0.000031503732,7.6928205e-7,0.009562198,0.013392775,0.005684599,0.031393003,0.0049044266,0.93409914],"study_design_scores_gemma":[0.00032415657,0.000114771865,0.000045136523,0.000047887712,0.0000047187114,0.0000024681926,0.00021412659,0.8681599,0.010115897,0.12074483,0.00014208304,0.000084049],"about_ca_topic_score_codex":0.000011520088,"about_ca_topic_score_gemma":0.000019801802,"teacher_disagreement_score":0.9618595,"about_ca_system_score_codex":0.000010380493,"about_ca_system_score_gemma":0.000015499198,"threshold_uncertainty_score":0.27170166},"labels":[],"label_agreement":null},{"id":"W4385579223","doi":"10.1007/s11803-023-2204-x","title":"Erratum to: Graph-based structural joint pose estimation in non-line-of-sight conditions","year":2023,"lang":"en","type":"erratum","venue":"Earthquake Engineering and Engineering Vibration","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Joint (building); Pose; Sight; Graph; Computer science; Line (geometry); Line-of-sight; Estimation; Line graph; Artificial intelligence; Structural engineering; Computer vision; Mathematics; Engineering; Theoretical computer science; Geometry; Systems engineering; Optics; Physics; Aerospace engineering","score_opus":0.011838052717958064,"score_gpt":0.22460870404267821,"score_spread":0.21277065132472014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385579223","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":[0.019886622,0.00023588218,0.9585328,0.00039178022,0.019192219,0.000711073,0.00007954841,0.00080738805,0.00016268619],"genre_scores_gemma":[0.9672305,0.000030349944,0.02974925,0.000039083636,0.0006837721,0.00018757166,0.000802252,0.000114353,0.0011628988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979358,0.000031574393,0.0006981498,0.0005362582,0.00039691068,0.00040136307],"domain_scores_gemma":[0.9989719,0.00012373216,0.00015693992,0.00044366287,0.00011368302,0.00019009475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032182087,0.00044317436,0.0005575713,0.001487955,0.00006253707,0.0002033862,0.0002676134,0.00040067913,0.000004224189],"category_scores_gemma":[0.00022825942,0.0004842611,0.00011629829,0.0013191958,0.00001348002,0.00041088142,0.00007124285,0.000636444,0.00002541487],"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":[0.0000024456267,0.000009060652,0.00000953095,0.0006533193,0.00003411793,0.00001829659,0.00041538864,0.98625225,0.002767043,0.00049029797,0.008009857,0.0013383946],"study_design_scores_gemma":[0.00033137263,0.00010390348,0.022680122,0.0013377126,0.000014145224,0.000013909822,0.0000066954576,0.97297543,0.0007239162,0.00008666066,0.0012190065,0.0005071136],"about_ca_topic_score_codex":0.000034371908,"about_ca_topic_score_gemma":0.00002719122,"teacher_disagreement_score":0.9473438,"about_ca_system_score_codex":0.000062952146,"about_ca_system_score_gemma":0.00010614627,"threshold_uncertainty_score":0.9997609},"labels":[],"label_agreement":null},{"id":"W4385819714","doi":"10.1109/tim.2023.3304703","title":"Wearable Smart Rings for Multifinger Gesture Recognition Using Supervised Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","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":"Concordia University","funders":"Concordia University","keywords":"Artificial intelligence; Computer science; Gesture recognition; Support vector machine; Feature selection; Random forest; Pattern recognition (psychology); Gesture; Naive Bayes classifier; Feature extraction; Wearable computer; Accelerometer; Feature vector; k-nearest neighbors algorithm; Normalization (sociology); Feature (linguistics); Gyroscope; Computer vision; Speech recognition; Engineering","score_opus":0.11272883673900498,"score_gpt":0.28257949472829613,"score_spread":0.16985065798929116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385819714","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":[0.15534894,0.000020317832,0.842131,0.0003993915,0.0010218292,0.0006350073,0.000009864896,0.00031048438,0.00012313273],"genre_scores_gemma":[0.9881825,0.00007939042,0.011061325,0.0001809788,0.000044499855,0.00024920382,0.000008737582,0.000018683804,0.00017469826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985232,0.00010673756,0.00028042682,0.00037412162,0.0004630402,0.0002524922],"domain_scores_gemma":[0.99936104,0.00006053568,0.000086318,0.00013571775,0.00025212375,0.000104257924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007507639,0.00015823128,0.00014920268,0.0003020442,0.00051715487,0.00018159537,0.00009722274,0.000080478996,0.000016912361],"category_scores_gemma":[0.000013945641,0.0001603722,0.000078069184,0.00042995135,0.000019607452,0.00043459743,0.0000018905481,0.00015387083,0.000068719586],"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":[0.00006889929,0.00013918077,0.0001550544,0.00017059475,0.00011674449,0.000002290665,0.0031706824,0.00517139,0.07844576,0.000031095453,0.00013018165,0.9123981],"study_design_scores_gemma":[0.009148417,0.0008246616,0.0022880775,0.0011632701,0.00018894604,0.000069739704,0.0034475634,0.57137775,0.40008357,0.00065761403,0.009516379,0.0012339745],"about_ca_topic_score_codex":0.000045127188,"about_ca_topic_score_gemma":0.000037131256,"teacher_disagreement_score":0.91116416,"about_ca_system_score_codex":0.00013393321,"about_ca_system_score_gemma":0.000057031884,"threshold_uncertainty_score":0.6539789},"labels":[],"label_agreement":null},{"id":"W4386011151","doi":"10.1007/s00500-023-09077-w","title":"A study on hand gesture recognition algorithm realized with the aid of efficient feature extraction method and convolution neural networks: design and its application to VR environment","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea","keywords":"Gesture; Computer science; Gesture recognition; Artificial intelligence; Convolutional neural network; Feature extraction; Feature (linguistics); Computer vision; Motion (physics); Pattern recognition (psychology); Speech recognition","score_opus":0.029559578715376858,"score_gpt":0.2848866008191272,"score_spread":0.25532702210375036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386011151","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":[0.11477247,0.00012359436,0.88271016,0.0005652988,0.000102381615,0.0015843252,0.000002549149,0.00013149688,0.000007718903],"genre_scores_gemma":[0.97585064,0.000007226448,0.023822287,0.00008524088,0.000111627625,0.00008738234,0.000010246886,0.000013362182,0.000012012654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818194,0.00055878,0.00021403214,0.0005055112,0.0003261952,0.00021355734],"domain_scores_gemma":[0.9987225,0.0006644951,0.0002194432,0.00022681823,0.00008861501,0.00007814394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001511828,0.00017196039,0.00021102812,0.00012788284,0.00034517547,0.000116014024,0.00014344689,0.000076284356,2.446662e-7],"category_scores_gemma":[0.000030738847,0.0001208637,0.000022955564,0.000536504,0.000027360733,0.00008583611,0.00011606891,0.00019971107,0.000006018383],"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":[0.00008220136,0.000113369126,0.0001606446,0.000025402122,0.00004836763,0.0000087259305,0.005687697,0.28578836,0.0017006154,0.00003099856,0.00013072013,0.7062229],"study_design_scores_gemma":[0.0007954912,0.00048853067,0.010749963,0.00007799565,0.000031342344,0.0000638794,0.0004938107,0.9863424,0.0006254128,0.000035785553,0.00014178781,0.00015361788],"about_ca_topic_score_codex":0.000008932813,"about_ca_topic_score_gemma":0.0000024480798,"teacher_disagreement_score":0.86107814,"about_ca_system_score_codex":0.000036307723,"about_ca_system_score_gemma":0.000012029733,"threshold_uncertainty_score":0.49286786},"labels":[],"label_agreement":null},{"id":"W4386074556","doi":"10.11159/cist23.131","title":"Identification of Knee Prostheses from Lateral Radiographs Using Deep Learning Techniques","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Radiography; Computer science; Artificial intelligence; Orthodontics; Deep learning; Computer vision; Medicine; Radiology; Biology","score_opus":0.009304914829670847,"score_gpt":0.21988303807920848,"score_spread":0.21057812324953762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386074556","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":[0.981037,0.00038030435,0.016978512,0.000075671865,0.00087802863,0.00035097572,0.0000013915733,0.00025224552,0.00004586842],"genre_scores_gemma":[0.998551,0.000022665134,0.0012526043,0.000006668087,0.00007416345,0.000016081176,1.2595375e-7,0.000006478037,0.000070205184],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870765,0.000012967252,0.00033089973,0.0003434924,0.0003805438,0.00022442853],"domain_scores_gemma":[0.99927306,0.0001124252,0.00022370013,0.00011353292,0.0002080326,0.00006925347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006526436,0.00012418478,0.00023503655,0.0005533273,0.00016073535,0.00031366368,0.0004856654,0.000035895104,7.512058e-8],"category_scores_gemma":[0.000050274226,0.00008925872,0.00004100982,0.002278014,0.00010782415,0.00032451894,0.00018214661,0.00014327237,3.0707895e-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":[0.00003522136,0.00011747352,0.053870983,0.000976733,0.00015381777,0.000005458208,0.0024733082,0.009286946,0.69495004,0.10423201,0.0001494684,0.13374855],"study_design_scores_gemma":[0.000090586705,0.00007372208,0.0077541233,0.00039167755,0.000006438941,0.000017679002,0.00000970834,0.9528416,0.03842422,0.00009955154,0.00016929899,0.00012143416],"about_ca_topic_score_codex":0.000024589279,"about_ca_topic_score_gemma":3.001582e-7,"teacher_disagreement_score":0.94355464,"about_ca_system_score_codex":0.000018310579,"about_ca_system_score_gemma":0.000015980657,"threshold_uncertainty_score":0.3639865},"labels":[],"label_agreement":null},{"id":"W4386256486","doi":"10.32920/24050766","title":"Hand Gesture Recognition Using CNN &amp; Publication of World’s Largest ASL Database","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sign language; Computer science; Alphabet; RGB color model; American Sign Language; Gesture; Artificial intelligence; Sign (mathematics); Gesture recognition; Database; Natural language processing; Speech recognition; Pattern recognition (psychology); Mathematics; Linguistics","score_opus":0.19522234029705365,"score_gpt":0.3364131276047875,"score_spread":0.14119078730773382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386256486","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":[0.0072081415,0.00024177684,0.977684,0.002604265,0.0030936562,0.0010840409,0.00049542414,0.0007295587,0.006859094],"genre_scores_gemma":[0.34342164,0.0003060003,0.6155671,0.0012176352,0.0025180993,0.0006012138,0.013199603,0.00023653985,0.022932218],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99659646,0.00031490167,0.00089272636,0.0011015922,0.0007171212,0.00037722153],"domain_scores_gemma":[0.9957897,0.0002485916,0.0008155948,0.0017464899,0.0012159738,0.0001836457],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014408068,0.00037673014,0.0005470832,0.0010121141,0.00015510074,0.0007413415,0.0011696895,0.0003745033,0.00016095197],"category_scores_gemma":[0.00038926408,0.00035616028,0.0001911089,0.0013110714,0.000070433074,0.0007234769,0.0015759118,0.0006681578,0.0008533337],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001700315,0.0034568252,0.018827945,0.015036357,0.0026103964,0.00020134948,0.010070933,0.0024192065,0.04533725,0.025606263,0.4239045,0.45235893],"study_design_scores_gemma":[0.008907472,0.00034434747,0.03192832,0.023536848,0.00139836,0.00073454547,0.00061407377,0.23011412,0.0825129,0.106162585,0.4999806,0.013765832],"about_ca_topic_score_codex":0.0006815268,"about_ca_topic_score_gemma":0.0013877521,"teacher_disagreement_score":0.4385931,"about_ca_system_score_codex":0.00011023026,"about_ca_system_score_gemma":0.00033142246,"threshold_uncertainty_score":0.9999246},"labels":[],"label_agreement":null},{"id":"W4386256687","doi":"10.32920/24050766.v1","title":"Hand Gesture Recognition Using CNN &amp; Publication of World’s Largest ASL Database","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Alphabet; Sign language; Gesture; Computer science; American Sign Language; RGB color model; Artificial intelligence; Gesture recognition; Sign (mathematics); Database; Natural language processing; Pattern recognition (psychology); Speech recognition; Mathematics; Linguistics","score_opus":0.19522234029705365,"score_gpt":0.3364131276047875,"score_spread":0.14119078730773382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386256687","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":[0.0072081415,0.00024177684,0.977684,0.002604265,0.0030936562,0.0010840409,0.00049542414,0.0007295587,0.006859094],"genre_scores_gemma":[0.34342164,0.0003060003,0.6155671,0.0012176352,0.0025180993,0.0006012138,0.013199603,0.00023653985,0.022932218],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99659646,0.00031490167,0.00089272636,0.0011015922,0.0007171212,0.00037722153],"domain_scores_gemma":[0.9957897,0.0002485916,0.0008155948,0.0017464899,0.0012159738,0.0001836457],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014408068,0.00037673014,0.0005470832,0.0010121141,0.00015510074,0.0007413415,0.0011696895,0.0003745033,0.00016095197],"category_scores_gemma":[0.00038926408,0.00035616028,0.0001911089,0.0013110714,0.000070433074,0.0007234769,0.0015759118,0.0006681578,0.0008533337],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001700315,0.0034568252,0.018827945,0.015036357,0.0026103964,0.00020134948,0.010070933,0.0024192065,0.04533725,0.025606263,0.4239045,0.45235893],"study_design_scores_gemma":[0.008907472,0.00034434747,0.03192832,0.023536848,0.00139836,0.00073454547,0.00061407377,0.23011412,0.0825129,0.106162585,0.4999806,0.013765832],"about_ca_topic_score_codex":0.0006815268,"about_ca_topic_score_gemma":0.0013877521,"teacher_disagreement_score":0.4385931,"about_ca_system_score_codex":0.00011023026,"about_ca_system_score_gemma":0.00033142246,"threshold_uncertainty_score":0.9999246},"labels":[],"label_agreement":null},{"id":"W4386600982","doi":"10.3390/s23187790","title":"Saliency-Driven Hand Gesture Recognition Incorporating Histogram of Oriented Gradients (HOG) and Deep Learning","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Gesture; Histogram; Computer science; Gesture recognition; Computer vision; Histogram of oriented gradients; Pattern recognition (psychology); Noise (video); Range (aeronautics); Canny edge detector; Enhanced Data Rates for GSM Evolution; Image (mathematics); Edge detection; Image processing; Engineering","score_opus":0.01791551944211436,"score_gpt":0.23629699284759823,"score_spread":0.21838147340548386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386600982","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":[0.99084896,0.00007171479,0.007038493,0.00012866879,0.00046244607,0.00025614176,0.0000053090953,0.00030184365,0.0008864355],"genre_scores_gemma":[0.99476564,0.00003529125,0.0047889664,0.00002303957,0.00006661825,0.000015853817,0.000039935097,0.000014832403,0.00024980825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853784,0.00020464022,0.0003246323,0.000380912,0.00030397708,0.00024800887],"domain_scores_gemma":[0.99908245,0.00014372884,0.0002620477,0.0002015563,0.00020249392,0.00010771261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003625394,0.00014595974,0.00022855752,0.00028438366,0.0002237131,0.00007991574,0.00016631122,0.000101933634,0.0000036667604],"category_scores_gemma":[0.00021369879,0.00013762433,0.000053011107,0.0010192703,0.00009603007,0.00020098835,0.00011783179,0.00019258626,0.00008496564],"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":[0.000055410434,0.00021883132,0.12515445,0.00056004047,0.00019663505,0.0002663599,0.039431702,0.0038577316,0.022343779,0.0027424532,0.0005220726,0.80465055],"study_design_scores_gemma":[0.007408172,0.0025712973,0.26115108,0.0024960085,0.00020347754,0.00065012765,0.009402547,0.6327741,0.025079949,0.013488431,0.041473422,0.003301417],"about_ca_topic_score_codex":0.000054203694,"about_ca_topic_score_gemma":0.000055396573,"teacher_disagreement_score":0.8013491,"about_ca_system_score_codex":0.000028164564,"about_ca_system_score_gemma":0.000022206708,"threshold_uncertainty_score":0.56121576},"labels":[],"label_agreement":null},{"id":"W4386708043","doi":"10.1145/3604264","title":"PAWS: Personalized Arm and Wrist Movements With Sensitivity Mappings for Controller-Free Locomotion in Virtual Reality","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Teleportation; Personalization; Computer science; Human–computer interaction; Virtual reality; Controller (irrigation); Sensitivity (control systems); Quantum; Engineering","score_opus":0.05608731803806983,"score_gpt":0.30965177906552055,"score_spread":0.2535644610274507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386708043","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":[0.93884605,0.0000050231874,0.053358037,0.0049557774,0.0006879394,0.0014544817,0.000017432509,0.00022364256,0.0004516405],"genre_scores_gemma":[0.99721575,0.0000036455629,0.0019933486,0.0002653051,0.00020622679,0.00010409432,0.000005314207,0.000014263967,0.0001920711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984575,0.00004993266,0.000380031,0.0004987777,0.00037610275,0.00023767122],"domain_scores_gemma":[0.9985741,0.000282696,0.00042141182,0.0003434388,0.0003280604,0.00005032766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010926452,0.00019821156,0.00032378786,0.00026724595,0.00017428958,0.00020982258,0.00072311575,0.000073845404,0.0000013867005],"category_scores_gemma":[0.00021318874,0.00014566405,0.00009396629,0.00040370438,0.000065262844,0.00064393703,0.0005507083,0.00019888172,0.0000050231106],"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":[0.0066223345,0.004937463,0.028568823,0.004239481,0.0019156699,0.000041791332,0.06597767,0.004115265,0.25522637,0.16418484,0.11706177,0.3471085],"study_design_scores_gemma":[0.03177317,0.006208007,0.17534807,0.0063605267,0.00012493065,0.00031263896,0.0038377172,0.61005366,0.060326725,0.09339368,0.010180213,0.0020806862],"about_ca_topic_score_codex":0.00006179323,"about_ca_topic_score_gemma":0.000023812836,"teacher_disagreement_score":0.6059384,"about_ca_system_score_codex":0.00010993603,"about_ca_system_score_gemma":0.000012371642,"threshold_uncertainty_score":0.5940007},"labels":[],"label_agreement":null},{"id":"W4386810310","doi":"10.18280/ria.370414","title":"A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sign (mathematics); Architecture; Deep learning; Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Geography","score_opus":0.0870438607780836,"score_gpt":0.28750526773379653,"score_spread":0.20046140695571293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386810310","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":[0.003712746,0.00011563137,0.9936066,0.00028707113,0.00035495663,0.0007474581,0.000006261657,0.0004166358,0.0007526311],"genre_scores_gemma":[0.8959412,0.000013952344,0.10237974,0.00016401407,0.00031910875,0.00027558414,0.00012556321,0.000044634267,0.000736221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979209,0.00018705329,0.0004526362,0.0006815602,0.00024402984,0.0005138511],"domain_scores_gemma":[0.99848986,0.00049409136,0.0001808317,0.000445075,0.00025405447,0.0001361119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091924623,0.00022287053,0.00027303514,0.00036417975,0.00048263214,0.00031530455,0.00052257295,0.00014720575,0.000029119861],"category_scores_gemma":[0.00029986005,0.00021687734,0.00018891184,0.0014380097,0.000080594335,0.00019138274,0.00008114643,0.00025535628,0.00050109136],"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":[0.00001783037,0.0000709157,0.000028734457,0.00017415045,0.000016173528,0.000008099872,0.0016357,0.75652325,0.004063079,0.0001547671,0.00013768874,0.2371696],"study_design_scores_gemma":[0.0000937643,0.00013565375,0.0000058982823,0.000102594015,0.000012646888,0.000030265031,0.00041297416,0.9552588,0.03653112,0.00202447,0.005104857,0.00028699858],"about_ca_topic_score_codex":0.000013096787,"about_ca_topic_score_gemma":0.0000060175817,"teacher_disagreement_score":0.8922284,"about_ca_system_score_codex":0.000049540522,"about_ca_system_score_gemma":0.000067739355,"threshold_uncertainty_score":0.8844001},"labels":[],"label_agreement":null},{"id":"W4386834176","doi":"10.18280/ria.370407","title":"A Deep Learning Framework for Hand Gesture Recognition and Multimodal Interface Control","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Interface (matter); Human–computer interaction; Deep learning; Artificial intelligence; Operating system","score_opus":0.04840659122913834,"score_gpt":0.2933015395792339,"score_spread":0.24489494835009554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386834176","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":[0.018955525,0.00044688396,0.97769517,0.0011738478,0.00058134104,0.00062222814,0.000009155356,0.0003387237,0.00017710804],"genre_scores_gemma":[0.9792145,0.000116366355,0.01951567,0.00013126407,0.00021284653,0.00019431584,0.000015987347,0.000023322862,0.0005757539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831307,0.00012714874,0.00039594123,0.00058522663,0.0001593937,0.00041924597],"domain_scores_gemma":[0.99799377,0.0011832287,0.00014934599,0.00031902897,0.00021807302,0.0001365836],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00073319353,0.00019468022,0.0002713062,0.00018646385,0.0003694053,0.00035649928,0.00035064874,0.00018472066,0.000027543067],"category_scores_gemma":[0.0007499682,0.00018861896,0.00009798135,0.0006438764,0.000080442784,0.00029123938,0.00010312755,0.00031828423,0.0009663756],"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":[0.000068602865,0.000092088805,0.00051032356,0.0002750607,0.00007295321,0.000024843263,0.01405714,0.032454263,0.006800917,0.0048936824,0.00032339018,0.9404267],"study_design_scores_gemma":[0.00016510303,0.00020851062,0.00012169081,0.00029397995,0.000014942346,0.000048800775,0.001202205,0.9521934,0.016820338,0.021655437,0.006972794,0.00030279846],"about_ca_topic_score_codex":0.000010846686,"about_ca_topic_score_gemma":0.000010799063,"teacher_disagreement_score":0.96025896,"about_ca_system_score_codex":0.000026481755,"about_ca_system_score_gemma":0.000020051008,"threshold_uncertainty_score":0.9998115},"labels":[],"label_agreement":null},{"id":"W4387653383","doi":"10.3390/info14100569","title":"An AI-Based Framework for Translating American Sign Language to English and Vice Versa","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Hand Gesture Recognition Systems","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":"Athabasca University","funders":"Faculty Professional Development Council, Pennsylvania State System of Higher Education; Pennsylvania State System of Higher Education","keywords":"Versa; American Sign Language; Computer science; Construct (python library); Artificial intelligence; Natural language processing; Sign language; Sign (mathematics); Deep learning; Image (mathematics); American English; Linguistics; Programming language","score_opus":0.011870552818291233,"score_gpt":0.289849091096539,"score_spread":0.27797853827824776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387653383","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":[0.030885717,0.0000026169314,0.96670187,0.0012835017,0.00018472651,0.00029021798,0.00001552343,0.00032930265,0.00030652838],"genre_scores_gemma":[0.9428474,4.1599364e-7,0.054884978,0.00207627,0.000085703985,0.000061175095,0.00003803038,0.0000034480827,0.0000026260477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994663,0.000025168181,0.00015542914,0.00008594811,0.00013786578,0.00012928642],"domain_scores_gemma":[0.9994123,0.00015753777,0.000061198305,0.00014040036,0.00013917447,0.00008938969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029487276,0.000058784583,0.000073567215,0.00014874813,0.00007366822,0.00021367379,0.00014086122,0.000028680317,0.0000012811643],"category_scores_gemma":[0.0001825691,0.00005771622,0.000017947523,0.00052564556,0.000008191133,0.0011722008,0.00001034965,0.000051758136,0.000055498956],"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":[0.000027072514,0.000009593814,0.0004121991,0.00012973559,0.00000789615,0.0000011830896,0.12082838,0.0025184362,0.0001997425,0.007264839,0.0007861053,0.86781484],"study_design_scores_gemma":[0.0012669428,0.0006377124,0.00912706,0.00028357073,0.000014488804,0.000002389479,0.012969964,0.913879,0.004513309,0.0027767413,0.053848818,0.0006800267],"about_ca_topic_score_codex":0.000020682823,"about_ca_topic_score_gemma":0.000007933079,"teacher_disagreement_score":0.9119616,"about_ca_system_score_codex":0.000012950169,"about_ca_system_score_gemma":0.0000295969,"threshold_uncertainty_score":0.23535992},"labels":[],"label_agreement":null},{"id":"W4388200759","doi":"10.1007/978-3-031-23636-5_1","title":"Enhanced Functionality of Footing Machine through Deep Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.030823623194502516,"score_gpt":0.28259294684317876,"score_spread":0.25176932364867627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388200759","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":[0.0001153109,0.022915302,0.8515728,0.000016414346,0.0025960791,0.00040174113,0.0000048507663,0.00020145412,0.12217602],"genre_scores_gemma":[0.9456561,0.0065200133,0.004133125,0.000041997322,0.00076639734,0.00003053582,0.00004010543,0.000102065045,0.042709634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696755,0.00012830048,0.0012814797,0.0007729989,0.0005104359,0.00033924953],"domain_scores_gemma":[0.99748,0.0008567803,0.0010189236,0.00035268796,0.00022489078,0.00006673206],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009910292,0.0003896641,0.0008318973,0.00023895683,0.00016364401,0.000104539475,0.00043707734,0.00022903261,0.000007599558],"category_scores_gemma":[0.00009460155,0.0003715072,0.0001415382,0.00019443555,0.0000744852,0.00033951,0.00038569717,0.0005820759,0.000045531226],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011285055,0.00002234197,0.0004352502,0.0016299136,0.00011201906,0.00002659954,0.0022616796,0.03149885,0.000025726644,0.5327576,0.000009385397,0.4312094],"study_design_scores_gemma":[0.0016552457,0.00092464284,0.00036217168,0.03370378,0.00011980515,0.0004136577,0.002533284,0.42115054,0.0012734191,0.1148115,0.4189492,0.004102788],"about_ca_topic_score_codex":0.00007913707,"about_ca_topic_score_gemma":0.00006761062,"teacher_disagreement_score":0.9455408,"about_ca_system_score_codex":0.00008116792,"about_ca_system_score_gemma":0.000033114884,"threshold_uncertainty_score":0.9998737},"labels":[],"label_agreement":null},{"id":"W4388474035","doi":"10.18280/ria.370529","title":"Machine Learning and Vision Based Techniques for Detecting and Recognizing Indian Sign Language","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sign (mathematics); Sign language; Artificial intelligence; Computer science; Natural language processing; Computer vision; Linguistics; Mathematics; Philosophy","score_opus":0.03497680019877773,"score_gpt":0.31034708811218337,"score_spread":0.2753702879134056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388474035","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":[0.0378451,0.00053975225,0.95886236,0.00088742696,0.00014971256,0.0005300267,0.000005222169,0.00064862706,0.00053177663],"genre_scores_gemma":[0.9797881,0.000087509914,0.019488016,0.0000837598,0.000069648784,0.000066432505,0.000008407181,0.000017831588,0.00039024078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988425,0.000093763396,0.00027065334,0.0004219677,0.00010317898,0.000267969],"domain_scores_gemma":[0.9988809,0.00067579665,0.000103142134,0.00018293712,0.00006864908,0.000088548804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011474037,0.00012870993,0.00016792268,0.0002599179,0.00030418127,0.00020594364,0.0001722987,0.00008016494,0.0000074416653],"category_scores_gemma":[0.00037227452,0.00012362981,0.00004121257,0.0005690184,0.000034131604,0.00020277589,0.000114621165,0.00016212418,0.000044193464],"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":[0.0000055529736,0.000010782962,0.00041129743,0.00014874963,0.0000048053917,0.000016408947,0.0029433942,0.00016503697,0.017659308,0.00016201565,0.000032162596,0.97844046],"study_design_scores_gemma":[0.000054947606,0.00021860567,0.0000341264,0.00031432026,0.000004892843,0.000042031366,0.0012841348,0.7707405,0.2191863,0.00070067105,0.007197453,0.00022197563],"about_ca_topic_score_codex":0.00002574783,"about_ca_topic_score_gemma":0.000020855745,"teacher_disagreement_score":0.9782185,"about_ca_system_score_codex":0.000015414545,"about_ca_system_score_gemma":0.000016074344,"threshold_uncertainty_score":0.5041477},"labels":[],"label_agreement":null},{"id":"W4388623294","doi":"10.1109/ro-man57019.2023.10309649","title":"Recognizing diver hand gestures for human to robot communication underwater","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gesture; Computer science; Process (computing); Set (abstract data type); Task (project management); Salient; Robot; Gesture recognition; Artificial intelligence; Human–computer interaction; Underwater; Human–robot interaction; Engineering","score_opus":0.08331413092980007,"score_gpt":0.32650675635145765,"score_spread":0.2431926254216576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388623294","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":[0.015542244,0.000031698008,0.9684913,0.008174903,0.00031070708,0.00067574077,0.0000040263308,0.00057172414,0.006197669],"genre_scores_gemma":[0.94507205,0.0000061560277,0.04433237,0.0010550378,0.00009434404,0.00018401362,0.00003361658,0.0000141576265,0.009208236],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990867,0.00006997319,0.00019049148,0.00026880376,0.0001533955,0.00023065927],"domain_scores_gemma":[0.99907106,0.00017936963,0.00004154813,0.0004869585,0.00013531763,0.00008576434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047065408,0.000096015494,0.00012558741,0.00016355819,0.00039937132,0.00035316183,0.00058822817,0.000056280132,0.000016231445],"category_scores_gemma":[0.000033165736,0.00007776277,0.00005197818,0.00038299785,0.000017903925,0.00024187556,0.0003072665,0.00005734848,0.00065748714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031093336,0.00022248673,0.0032580881,0.00026621492,0.00025924467,0.000015871568,0.031636868,0.0012106154,0.11640859,0.08412527,0.42868647,0.3338792],"study_design_scores_gemma":[0.0036278698,0.000649774,0.038666327,0.00055798946,0.000053043335,0.000043696175,0.0016666652,0.014305514,0.14656995,0.118704915,0.67311627,0.0020379543],"about_ca_topic_score_codex":0.00006291746,"about_ca_topic_score_gemma":0.00022530192,"teacher_disagreement_score":0.92952985,"about_ca_system_score_codex":0.000022655962,"about_ca_system_score_gemma":0.0000125539245,"threshold_uncertainty_score":0.8450886},"labels":[],"label_agreement":null},{"id":"W4388951008","doi":"10.1109/icccnt56998.2023.10307372","title":"A Comparative Study of Deep Learning Models for American SL Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gesture; Computer science; Alphabet; Artificial intelligence; Transformer; Deep learning; Speech recognition; Machine learning; Natural language processing; Linguistics; Engineering","score_opus":0.13076495164913182,"score_gpt":0.33291962180267926,"score_spread":0.20215467015354743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388951008","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":[0.29052928,0.000005086418,0.70529413,0.000056914956,0.00008987068,0.00056349183,0.000001953527,0.0002504787,0.003208782],"genre_scores_gemma":[0.9914586,0.0000025548911,0.008049072,0.000024078125,0.00002953961,0.00018175418,0.000009311881,0.000005500123,0.000239576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990008,0.00014858141,0.00023767081,0.00025716197,0.00019499462,0.0001608092],"domain_scores_gemma":[0.99910575,0.0003070663,0.0001487398,0.00015566265,0.00023625376,0.000046505436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000351631,0.000083686005,0.00025620495,0.0001751206,0.00008359867,0.000042763797,0.00021059027,0.000017717979,0.0000046764976],"category_scores_gemma":[0.000025982503,0.000071930466,0.00004650474,0.0008647154,0.000020593023,0.00025341846,0.0000665529,0.00006238935,0.000089129666],"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":[0.00007511871,0.00091965497,0.0013956338,0.00008991778,0.0003063515,0.000009189594,0.10991385,0.030296842,0.001099182,0.0026328813,0.0012043662,0.85205704],"study_design_scores_gemma":[0.00091289985,0.0012876219,0.0011742892,0.000020297784,0.000012376114,0.0000034440923,0.024937464,0.96639454,0.0009568573,0.003826807,0.0002866457,0.00018675491],"about_ca_topic_score_codex":0.0000701775,"about_ca_topic_score_gemma":0.00012607504,"teacher_disagreement_score":0.9360977,"about_ca_system_score_codex":0.0000120986415,"about_ca_system_score_gemma":0.0000131425495,"threshold_uncertainty_score":0.2933239},"labels":[],"label_agreement":null},{"id":"W4389076604","doi":"10.1109/access.2023.3336591","title":"Two-Dimensional Parallel Spatio-Temporal Pyramid Pooling for Hand Gesture Recognition","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Computer science; Pooling; Gesture recognition; Gesture; Convolutional neural network; Artificial intelligence; Pyramid (geometry); Pattern recognition (psychology); American Sign Language; Sign language; Computer vision; Speech recognition","score_opus":0.07508261156965974,"score_gpt":0.3304886235081017,"score_spread":0.255406011938442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389076604","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":[0.4904384,0.00015645033,0.49659923,0.0031541707,0.0058598593,0.0015034942,0.00011173378,0.0011254174,0.0010512455],"genre_scores_gemma":[0.98833704,0.000006334051,0.009095734,0.00068417285,0.00089530495,0.00028286927,0.00027876542,0.000027789934,0.00039196445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998218,0.00008303494,0.00036137566,0.00053660135,0.0003947182,0.00040628028],"domain_scores_gemma":[0.9987071,0.00029089337,0.00018122247,0.00034563034,0.0003401269,0.00013503562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005648523,0.00019807523,0.00024670176,0.00025335257,0.00032676628,0.000686366,0.00071854744,0.00012307214,0.000019161282],"category_scores_gemma":[0.000073702,0.00018088247,0.0001175605,0.0007374921,0.00004204134,0.0010940745,0.0001374304,0.0001470474,0.00042502748],"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":[0.0006680106,0.0007736512,0.016600637,0.0011356629,0.0005896319,0.00057277473,0.0056743966,0.06737374,0.013467006,0.004927135,0.27748564,0.6107317],"study_design_scores_gemma":[0.015655225,0.000697422,0.02434998,0.0014328125,0.00016496584,0.00042660563,0.0002056571,0.5965357,0.079264365,0.21028814,0.066940114,0.0040390017],"about_ca_topic_score_codex":0.000087380206,"about_ca_topic_score_gemma":0.00014641983,"teacher_disagreement_score":0.60669273,"about_ca_system_score_codex":0.000035217534,"about_ca_system_score_gemma":0.00010121613,"threshold_uncertainty_score":0.7376173},"labels":[],"label_agreement":null},{"id":"W4390397819","doi":"10.18280/ts.400646","title":"Enhanced Recognition of Offline Marathi Handwriting via a Self-Additive Attention Mechanism","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mechanism (biology); Marathi; Computer science; Handwriting; Artificial intelligence; Speech recognition; Physics; Linguistics","score_opus":0.020521001212066202,"score_gpt":0.2388657367402327,"score_spread":0.2183447355281665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390397819","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":[0.16153722,0.000014385344,0.8353443,0.00023418407,0.00036037806,0.00051364,0.000049833838,0.00051498105,0.0014310656],"genre_scores_gemma":[0.9919335,0.000024542182,0.007263355,0.00010491399,0.00027023244,0.00014096213,0.00014307704,0.000014988694,0.00010441579],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979759,0.00019442245,0.00055874157,0.00041856736,0.0005335421,0.0003188326],"domain_scores_gemma":[0.9989517,0.00015576926,0.00029214084,0.00018482789,0.0003249854,0.000090595546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009326091,0.00018062533,0.00026172792,0.00028682596,0.00012986186,0.00007435716,0.0002823336,0.00008942076,0.00015506627],"category_scores_gemma":[0.00002254344,0.0001791975,0.00013088282,0.00077384635,0.000020101097,0.00043279133,0.000091327034,0.000114397604,0.00045835116],"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":[0.000041973442,0.00036464893,0.00003096367,0.00020986769,0.00020013953,0.000044012217,0.0030652646,0.000024587089,0.7143931,0.004537369,0.00088582124,0.27620226],"study_design_scores_gemma":[0.004764574,0.001121813,0.0038792908,0.0009448745,0.00011667967,0.00008116313,0.0009528119,0.12261967,0.8410177,0.022431046,0.0010588963,0.0010114753],"about_ca_topic_score_codex":0.00000916222,"about_ca_topic_score_gemma":0.0000065864956,"teacher_disagreement_score":0.8303963,"about_ca_system_score_codex":0.00004978474,"about_ca_system_score_gemma":0.000044281413,"threshold_uncertainty_score":0.7307462},"labels":[],"label_agreement":null},{"id":"W4390482023","doi":"10.1109/ssci52147.2023.10371944","title":"Enhancing Conducting Gesture Analysis: Integrating Laban Movement Analysis with Tree Ensembles and Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity Western University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Trinity Western University","keywords":"Gesture; Computer science; Adaptability; Benchmark (surveying); Artificial neural network; Set (abstract data type); Artificial intelligence; Movement (music); Tree (set theory); Gesture recognition; Invariant (physics); Machine learning; Mathematics","score_opus":0.02529497459189677,"score_gpt":0.25464419510242337,"score_spread":0.22934922051052659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390482023","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":[0.34250906,0.00011433975,0.65574795,0.0004267176,0.00008142536,0.00012491351,0.0000011508722,0.00035389359,0.0006405399],"genre_scores_gemma":[0.9907707,0.0000112825155,0.008338461,0.00032137212,0.00009689717,0.000025632513,0.000019925887,0.0000094739835,0.00040623517],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812275,0.00016356369,0.0003884284,0.00060169754,0.00034266408,0.0003808878],"domain_scores_gemma":[0.9988448,0.00028205107,0.00018524371,0.00040369944,0.00015103156,0.00013316002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007574443,0.00021946977,0.00047816648,0.0007810648,0.00023287111,0.000457502,0.0002863124,0.00007425746,0.000013130196],"category_scores_gemma":[0.000038026923,0.00014959072,0.00015351555,0.00738936,0.00002730176,0.0003858076,0.00016614227,0.00019685594,0.0000045428783],"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":[0.000028815602,0.00011723107,0.5131318,0.00013414728,0.022902055,0.00035593225,0.02053321,0.24767461,0.009933418,0.0062966305,0.0005330237,0.1783591],"study_design_scores_gemma":[0.0002092424,0.000057126603,0.015384618,0.00003012577,0.0008456534,0.000008153355,0.0034377698,0.97856426,0.0011333945,0.000039253697,0.000024877805,0.00026551],"about_ca_topic_score_codex":0.00028642712,"about_ca_topic_score_gemma":0.00875879,"teacher_disagreement_score":0.7308897,"about_ca_system_score_codex":0.000024903042,"about_ca_system_score_gemma":0.000018804363,"threshold_uncertainty_score":0.61001325},"labels":[],"label_agreement":null},{"id":"W4390485203","doi":"10.1109/ssci52147.2023.10371840","title":"Enhancing Gesture Recognition for Musical Conducting: A Study on Diverse Data Classification and Stacked Neural Network Architectures","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity Western University; Western University","funders":"","keywords":"Gesture; Computer science; Artificial neural network; Gesture recognition; Focus (optics); Key (lock); Node (physics); Architecture; Musical; Artificial intelligence; Speech recognition; Human–computer interaction; Engineering","score_opus":0.3022871073710787,"score_gpt":0.35888100282139207,"score_spread":0.05659389545031335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390485203","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":[0.9382723,0.000012405145,0.05710585,0.0014351973,0.00089355855,0.0014675726,0.000041404608,0.0005669193,0.00020481058],"genre_scores_gemma":[0.99433845,0.0000021581952,0.004342473,0.00031506058,0.00057896436,0.00010416367,0.00019612238,0.000013483959,0.00010912048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813855,0.00023051439,0.00027742228,0.00076815597,0.0002792372,0.00030613123],"domain_scores_gemma":[0.9982178,0.0007674735,0.00011652913,0.0006911496,0.00010397933,0.000103036204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010101252,0.00016325824,0.00020049947,0.00013555422,0.00028472766,0.00023900335,0.00049721287,0.000073623996,0.000005141593],"category_scores_gemma":[0.00029861188,0.00013052604,0.000031786065,0.0005219191,0.000028829605,0.0002573642,0.00036167057,0.00017718363,0.000050661518],"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":[0.0003490773,0.00096569373,0.012049962,0.0002801202,0.00039378757,0.00010080622,0.026107116,0.00084980804,0.01083832,0.0024898085,0.046415824,0.89915967],"study_design_scores_gemma":[0.009428716,0.003893681,0.34922707,0.00047411656,0.00026260834,0.00018264951,0.033164814,0.5746899,0.0022645702,0.011668688,0.012290987,0.002452203],"about_ca_topic_score_codex":0.000016223921,"about_ca_topic_score_gemma":0.00018880222,"teacher_disagreement_score":0.8967075,"about_ca_system_score_codex":0.000012606102,"about_ca_system_score_gemma":0.000027732014,"threshold_uncertainty_score":0.5322697},"labels":[],"label_agreement":null},{"id":"W4390551000","doi":"10.1109/rmkmate59243.2023.10369455","title":"American Sign Language Recognition System for Numerical and Alphabets","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Sign language; American Sign Language; Convolutional neural network; Natural language processing; Artificial intelligence; Classifier (UML); Identification (biology); Spoken language; Speech recognition; Linguistics","score_opus":0.023934460556756153,"score_gpt":0.2718731976096501,"score_spread":0.24793873705289396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390551000","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":[0.0762898,0.000051810435,0.9154854,0.00096828677,0.00048043748,0.00058036076,0.00002541372,0.0015889407,0.0045295814],"genre_scores_gemma":[0.9853942,0.0000046835985,0.013677372,0.0001845814,0.00013384159,0.0001419526,0.000021228305,0.000010680833,0.00043146522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990844,0.00007173948,0.00016741834,0.00030442703,0.00014748462,0.0002245005],"domain_scores_gemma":[0.99931216,0.00026973084,0.00006702204,0.00018142854,0.00006579235,0.00010384752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033145316,0.00009381012,0.00017484819,0.00012042371,0.00008055794,0.0001127058,0.00016447651,0.000032036423,0.0000040411537],"category_scores_gemma":[0.00004321406,0.000077699326,0.00004290248,0.00056849513,0.000022577447,0.00016922217,0.00006114459,0.000040983985,0.00037598505],"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":[0.0000062844033,0.000014955798,0.00015833105,0.00010284805,0.000022761216,0.000019827621,0.00096502475,0.000001895211,0.0013103816,0.0013118625,0.004321014,0.99176484],"study_design_scores_gemma":[0.012576204,0.0047129835,0.040869042,0.0015583638,0.00023303519,0.0018403507,0.067313895,0.5816668,0.15597725,0.0069081597,0.11997935,0.006364559],"about_ca_topic_score_codex":0.00005017642,"about_ca_topic_score_gemma":0.0000071740087,"teacher_disagreement_score":0.98540026,"about_ca_system_score_codex":0.000020514575,"about_ca_system_score_gemma":0.000016853686,"threshold_uncertainty_score":0.48326525},"labels":[],"label_agreement":null},{"id":"W4390707848","doi":"10.1007/978-3-031-23161-2_510","title":"Data Gloves for Hand and Finger Motion Interactions","year":2024,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computer Graphics and Games","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Motion (physics); Computer science; Finger print; Artificial intelligence; Computer vision","score_opus":0.032139757592212964,"score_gpt":0.26881701788118345,"score_spread":0.2366772602889705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390707848","genre_codex":"methods","genre_gemma":"other","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":[0.00019447261,0.014900638,0.88451713,0.0016136122,0.004619323,0.0008985499,0.0009701075,0.00015769569,0.09212845],"genre_scores_gemma":[0.04189235,0.10291766,0.22292365,0.0018796485,0.013383473,0.00025610463,0.0025792504,0.0005383401,0.6136295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984823,0.000015422253,0.00042778457,0.0007165417,0.00020446755,0.0001534964],"domain_scores_gemma":[0.99852455,0.00036271775,0.00021809366,0.00064143434,0.00015595599,0.00009726926],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002616111,0.00028788802,0.0004195564,0.0003716303,0.00009439696,0.00033346383,0.00047594108,0.00017892658,0.0000056691338],"category_scores_gemma":[0.000011986371,0.00025304366,0.0000990257,0.00006763351,0.000126235,0.0003763963,0.0006401199,0.00025064134,0.000005374424],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063462944,0.00003277387,0.000032569722,0.0007378544,0.00028219353,0.000010216394,0.00080747873,0.0000017130043,0.0000030492397,0.7484783,0.017099287,0.23250826],"study_design_scores_gemma":[0.00018245491,0.000113766335,0.00010247226,0.000595298,0.00008292174,0.000050772425,0.000004005983,0.018049242,0.000003852957,0.11248673,0.86805254,0.00027592992],"about_ca_topic_score_codex":0.0000065655936,"about_ca_topic_score_gemma":0.00005177474,"teacher_disagreement_score":0.8509533,"about_ca_system_score_codex":0.000005933704,"about_ca_system_score_gemma":0.000039503204,"threshold_uncertainty_score":0.9999922},"labels":[],"label_agreement":null},{"id":"W4390830448","doi":"10.1038/s42256-023-00780-9","title":"Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Coastal Health; Vancouver Coastal Health Research Institute; University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Robotics; Robustness (evolution); Human–computer interaction; Robot","score_opus":0.025722801369088645,"score_gpt":0.3078758334009204,"score_spread":0.28215303203183173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390830448","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":[0.060188256,0.031347573,0.89189667,0.0011670794,0.0047710766,0.00083743886,0.00013992267,0.0009810234,0.008670962],"genre_scores_gemma":[0.9917278,0.0001564087,0.006241018,0.00023634892,0.000181388,0.000013153713,0.000034057564,0.000032113745,0.0013777075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777573,0.0001560502,0.00044401028,0.00078582997,0.00042980848,0.00040855093],"domain_scores_gemma":[0.9988703,0.00031509934,0.000115712464,0.00038743461,0.00014054499,0.0001708942],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00040861825,0.00034949117,0.0003247361,0.00036634423,0.00036462155,0.0010537232,0.0005471509,0.00018740386,0.00022321226],"category_scores_gemma":[0.00014351646,0.00029927722,0.000112496025,0.00071935763,0.00008901254,0.0006872037,0.00039340823,0.0013880437,0.00010565597],"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":[0.00015259336,0.00069285766,0.026080137,0.0019331668,0.0014979655,0.0010606115,0.02985182,0.0057413895,0.11370268,0.027465956,0.0016206328,0.7902002],"study_design_scores_gemma":[0.00016311176,0.00011178627,0.00087546505,0.00056999165,0.000030803956,0.00051556574,0.00020668538,0.89627296,0.021234943,0.0027065338,0.07675702,0.0005551399],"about_ca_topic_score_codex":0.001061051,"about_ca_topic_score_gemma":0.0006063377,"teacher_disagreement_score":0.93153954,"about_ca_system_score_codex":0.0001082227,"about_ca_system_score_gemma":0.00006907217,"threshold_uncertainty_score":0.99998325},"labels":[],"label_agreement":null},{"id":"W4390874643","doi":"10.1109/gcaiot61060.2023.10385100","title":"Towards Smart Interaction: Hand Gesture Recognition Using Machine Learning in IoT Scenarios","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Artificial intelligence; Human–computer interaction; Sign language; Sketch recognition; Transfer of learning; Automation; Machine learning; Computer vision; Engineering","score_opus":0.06384051040534695,"score_gpt":0.29438622362383743,"score_spread":0.2305457132184905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390874643","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":[0.8099093,0.00014767192,0.17131048,0.0035913882,0.0026979654,0.0005619145,0.00000664114,0.0013637547,0.01041091],"genre_scores_gemma":[0.989822,0.00002473102,0.008541661,0.00027326556,0.00019651616,0.000023278553,0.000033133863,0.000017420522,0.0010679935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984912,0.00020394415,0.00032106877,0.00040203426,0.0002822557,0.00029947655],"domain_scores_gemma":[0.9993873,0.00010616576,0.00009746055,0.00020669555,0.00011650633,0.000085822605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063882774,0.00015778831,0.00020565614,0.00045086717,0.00018028455,0.00032025605,0.000244571,0.000115860064,0.000106380074],"category_scores_gemma":[0.00011659687,0.00014087085,0.00006404593,0.0013246086,0.000020298907,0.00043085555,0.00016283344,0.00038686453,0.0006969449],"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":[0.00006052416,0.00022270676,0.049159933,0.00021902104,0.000099516,0.0005033894,0.009167583,0.006393538,0.016689314,0.0002578179,0.0018111968,0.91541547],"study_design_scores_gemma":[0.0013909874,0.0001395424,0.016764011,0.0005318224,0.000012846662,0.00044161596,0.0006255451,0.93966097,0.00792846,0.0007774305,0.031072447,0.00065432687],"about_ca_topic_score_codex":0.00050729164,"about_ca_topic_score_gemma":0.00050012366,"teacher_disagreement_score":0.9332674,"about_ca_system_score_codex":0.00009076675,"about_ca_system_score_gemma":0.00007164906,"threshold_uncertainty_score":0.89580494},"labels":[],"label_agreement":null},{"id":"W4391054376","doi":"10.2316/j.2024.201-0376","title":"BIMODAL EMOTION DEPTH RECOGNITION METHOD OF FACIAL EXPRESSION AND POSTURE IN CYBER-PHYSICAL SYSTEMS, 1-10.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Facial expression; Face (sociological concept); Facial expression recognition; Emotion recognition; Feature (linguistics); Facial recognition system; Feature extraction; Computer science; Expression (computer science); Artificial intelligence; Psychology; Computer vision; Pattern recognition (psychology)","score_opus":0.013802799768569454,"score_gpt":0.2605516198239075,"score_spread":0.24674882005533805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391054376","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":[0.1630633,0.015559728,0.8166843,0.00024058345,0.0019654217,0.0015150046,0.00009218076,0.00022344285,0.0006560151],"genre_scores_gemma":[0.9987105,0.000055522614,0.0005480862,0.000011769737,0.00038014996,0.00014494995,0.000009190454,0.000016464382,0.00012338067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776787,0.00048577957,0.0005112661,0.0005793826,0.00035675298,0.0002989245],"domain_scores_gemma":[0.99905574,0.00032088478,0.00015903974,0.00024321208,0.000111672416,0.00010947602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010742242,0.00023437318,0.00057759613,0.00028132764,0.000070598,0.000319309,0.00016568744,0.00019575005,0.000002910204],"category_scores_gemma":[0.00003830158,0.00018332977,0.00008051095,0.00029297252,0.000024376119,0.00046548678,0.000063963984,0.00020798483,0.000018020322],"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":[0.0002595318,0.00034987624,0.0010700311,0.004793748,0.00044275395,0.00019202889,0.007973487,0.0006264913,0.17417404,0.057782847,0.0005092715,0.7518259],"study_design_scores_gemma":[0.0066216807,0.0009680383,0.0023681868,0.0055254055,0.00018366746,0.0013809532,0.0025649592,0.96471536,0.0032517174,0.004387449,0.006949836,0.0010827557],"about_ca_topic_score_codex":0.00033000956,"about_ca_topic_score_gemma":0.00002111274,"teacher_disagreement_score":0.96408886,"about_ca_system_score_codex":0.00006759144,"about_ca_system_score_gemma":0.00007982267,"threshold_uncertainty_score":0.7475971},"labels":[],"label_agreement":null},{"id":"W4391329583","doi":"10.18280/mmep.110129","title":"Ululate: A Non-Intrusive, Wearable Tongue Gesture Detection System for Human-Computer Interaction","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gesture; Wearable computer; Computer science; Tongue; Human–computer interaction; Communication; Computer vision; Psychology; Medicine; Embedded system; Pathology","score_opus":0.019155534268899495,"score_gpt":0.23027527050357427,"score_spread":0.21111973623467478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391329583","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":[0.009652762,0.00025738735,0.9878923,0.00006850544,0.0008088066,0.00042941867,0.0000015178961,0.00071642007,0.00017288033],"genre_scores_gemma":[0.9272933,0.000008571885,0.07206515,0.000005881575,0.00030765476,0.00017285126,0.0000018893826,0.000029453156,0.00011527107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886817,0.000013089404,0.00032735817,0.00038601045,0.00014684557,0.00025853072],"domain_scores_gemma":[0.9994568,0.00015862541,0.00003577524,0.00019488565,0.00006162771,0.0000922651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036335565,0.00019100572,0.00024474566,0.00015986178,0.00011179844,0.0005177101,0.00014009909,0.00012862026,0.0000013133396],"category_scores_gemma":[0.0000066049993,0.00016038243,0.00008308236,0.00018841024,0.000008056829,0.0003138673,0.000046173547,0.00020904782,0.000034220564],"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":[0.0000030952058,0.00003254362,5.097009e-7,0.0078397235,0.000087859386,0.000009987538,0.0023020166,0.94319415,0.008752178,0.025222518,0.000029991184,0.01252544],"study_design_scores_gemma":[0.00013385972,0.00006458264,7.263651e-7,0.0022548297,0.000018585764,0.00015354952,0.000018343022,0.98970574,0.0028510252,0.0038327228,0.00077411527,0.00019189522],"about_ca_topic_score_codex":0.000006698761,"about_ca_topic_score_gemma":4.9170103e-7,"teacher_disagreement_score":0.9176405,"about_ca_system_score_codex":0.000059297003,"about_ca_system_score_gemma":0.000009204479,"threshold_uncertainty_score":0.6540206},"labels":[],"label_agreement":null},{"id":"W4391392416","doi":"10.1007/s11042-024-18313-w","title":"A lightweight GRU-based gesture recognition model for skeleton dynamic graphs","year":2024,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Skeleton (computer programming); Gesture; Artificial intelligence; Gesture recognition; Computer vision; Human–computer interaction; Pattern recognition (psychology); Programming language","score_opus":0.03012795199719312,"score_gpt":0.27575220554334456,"score_spread":0.24562425354615144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391392416","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":[0.00083781034,0.0008376342,0.99300265,0.0026269876,0.00016348575,0.0013477186,0.00037787625,0.0004323774,0.00037344004],"genre_scores_gemma":[0.6737696,0.00024616325,0.31361133,0.00066918577,0.00033369745,0.009555026,0.00094612007,0.000053085885,0.0008157976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988095,0.000023822124,0.00025056355,0.0005378508,0.00015431781,0.00022394862],"domain_scores_gemma":[0.9990063,0.00036641076,0.00005307299,0.00031746348,0.00012472642,0.00013205236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019244927,0.00016940353,0.00016233679,0.00016192641,0.00019101695,0.0004009391,0.00024618744,0.00012996476,0.0000056570525],"category_scores_gemma":[0.000019872072,0.00014498706,0.000104188264,0.00040916898,0.000040595245,0.00034292054,0.00003435044,0.00013320007,0.0000988926],"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":[0.000003963459,0.00008281997,0.000011859013,0.0002148356,0.000029572671,0.0000016058423,0.00041870846,0.00012946883,0.002406487,0.009200682,0.0017574597,0.9857425],"study_design_scores_gemma":[0.00030704198,0.000024519535,0.00009941744,0.00008002288,0.000025862839,0.000008114094,0.000009919422,0.92956907,0.0005838042,0.019529995,0.049550552,0.00021170155],"about_ca_topic_score_codex":0.000003445349,"about_ca_topic_score_gemma":0.000017038186,"teacher_disagreement_score":0.98553085,"about_ca_system_score_codex":0.000029367726,"about_ca_system_score_gemma":0.00008722803,"threshold_uncertainty_score":0.59124005},"labels":[],"label_agreement":null},{"id":"W4392143308","doi":"10.48175/ijarsct-15504","title":"Music Player System for User Facial Recognition using CNN Algorithm","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Science Communication and Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Speech recognition; Artificial intelligence; Facial recognition system; Pattern recognition (psychology)","score_opus":0.1261090752238259,"score_gpt":0.4341322999042761,"score_spread":0.3080232246804502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392143308","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":[0.16403447,0.0043523544,0.81238484,0.014007237,0.0033450492,0.00074561965,0.000024044051,0.00017788807,0.00092849357],"genre_scores_gemma":[0.8495793,0.00034141066,0.14993022,0.000019564219,0.00007843848,0.00002803543,0.000001644881,0.0000056285794,0.000015798385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811316,0.0001376534,0.00046328752,0.00025368045,0.0007803335,0.00025189455],"domain_scores_gemma":[0.99665195,0.00044027288,0.00015264291,0.00029761624,0.0023859765,0.00007153892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040370156,0.00007783721,0.00014779146,0.0030138765,0.00021341378,0.00041234787,0.0020320464,0.000091333175,0.000002826096],"category_scores_gemma":[0.00042869628,0.00006722383,0.000036543806,0.0020239942,0.00052424456,0.0015203666,0.0004670113,0.0005097171,0.00000752447],"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":[0.000013108989,0.000033653454,0.00005041702,0.000021468075,0.00001570131,0.0000316834,0.0003465392,0.000047246296,0.013731489,0.017674902,0.000030952415,0.96800286],"study_design_scores_gemma":[0.0047571003,0.00091955136,0.0005311614,0.0073652063,0.000018144041,0.0065192776,0.011257791,0.55708003,0.07607495,0.21617329,0.11851908,0.00078439957],"about_ca_topic_score_codex":0.000008475844,"about_ca_topic_score_gemma":0.000008824883,"teacher_disagreement_score":0.96721846,"about_ca_system_score_codex":0.00040197483,"about_ca_system_score_gemma":0.00043708787,"threshold_uncertainty_score":0.397628},"labels":[],"label_agreement":null},{"id":"W4392147615","doi":"","title":"Real-Time Sign Language Recognition Based on Motion Gesture Identification Using Wireless Body Area Sensors","year":2024,"lang":"en","type":"book-chapter","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sign language; Gesture; Gesture recognition; Identification (biology); Computer science; Motion (physics); Speech recognition; Sign (mathematics); Wireless; Artificial intelligence; Computer vision; Telecommunications; Mathematics; Linguistics; Biology","score_opus":0.01826953972753517,"score_gpt":0.22560092636784154,"score_spread":0.20733138664030637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392147615","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065637543,0.00041851867,0.39123988,0.0042579523,0.00093432766,0.0015051407,0.00035132482,0.0016648052,0.5930643],"genre_scores_gemma":[0.36338398,0.00046469845,0.025188288,0.0002414186,0.000323723,0.00014277376,0.0040124967,0.00038362827,0.605859],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99420637,0.0019719447,0.0009146149,0.0014675774,0.0010121339,0.00042738678],"domain_scores_gemma":[0.99307984,0.0013016289,0.0008912856,0.0024211241,0.0020571747,0.00024894997],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004349149,0.0006405796,0.00057403184,0.0007612078,0.00040056926,0.0010306927,0.0011792831,0.0006640495,0.00024427427],"category_scores_gemma":[0.00031350544,0.00067379937,0.0003670051,0.00044340323,0.00014586866,0.0004189922,0.00028035988,0.00075649895,0.0014430331],"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":[0.00008481415,0.0013132611,0.000046092136,0.0016424832,0.00070473645,0.00040495108,0.020838384,0.00067582156,0.16148855,0.41295207,0.0061766747,0.39367217],"study_design_scores_gemma":[0.0014933867,0.000007234091,0.00027168714,0.018225208,0.00049355265,0.0001935835,0.0001391888,0.83017975,0.102887414,0.022755705,0.020396212,0.002957061],"about_ca_topic_score_codex":0.00019451953,"about_ca_topic_score_gemma":0.00009472535,"teacher_disagreement_score":0.82950395,"about_ca_system_score_codex":0.0003791448,"about_ca_system_score_gemma":0.00024130433,"threshold_uncertainty_score":0.9995713},"labels":[],"label_agreement":null},{"id":"W4392946387","doi":"10.1109/cac59555.2023.10450205","title":"Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene","year":2023,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China University of Geosciences; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Residual; Face (sociological concept); Computer science; Artificial intelligence; Facial recognition system; Expression (computer science); Computer vision; Pattern recognition (psychology); Speech recognition; Algorithm","score_opus":0.04149603736021808,"score_gpt":0.27834936958139156,"score_spread":0.2368533322211735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392946387","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":[0.018641802,0.000022071492,0.97547466,0.0016661334,0.00042563636,0.00073124585,0.000013072016,0.0011108891,0.0019144984],"genre_scores_gemma":[0.6435823,0.000007706342,0.35257468,0.000865213,0.00081446616,0.00043911082,0.00022362662,0.000044753877,0.0014481861],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980817,0.0002407659,0.0003017786,0.00051677134,0.00038536038,0.0004736326],"domain_scores_gemma":[0.99872,0.00046866917,0.0001352329,0.00039236687,0.0001484827,0.00013524985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011993762,0.00018235714,0.00021247672,0.00012804438,0.00037648928,0.0002179304,0.00037710587,0.00010168228,0.000013489298],"category_scores_gemma":[0.000097663215,0.00013588592,0.00006248471,0.0004945496,0.00002567595,0.00039659365,0.000080653364,0.00015387012,0.00021502536],"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":[0.0008108576,0.0004331247,0.0045760744,0.00055828324,0.00017437639,0.00018077032,0.003474677,0.030435825,0.028950317,0.0035019584,0.27081403,0.6560897],"study_design_scores_gemma":[0.01303187,0.0025969138,0.0063249026,0.004878032,0.000106134525,0.00012020007,0.0013024943,0.6325635,0.24430816,0.008526465,0.08306153,0.0031798189],"about_ca_topic_score_codex":0.000011719959,"about_ca_topic_score_gemma":0.00007814965,"teacher_disagreement_score":0.6529099,"about_ca_system_score_codex":0.000026239231,"about_ca_system_score_gemma":0.00010448327,"threshold_uncertainty_score":0.5541267},"labels":[],"label_agreement":null},{"id":"W4393033137","doi":"10.32920/25413103","title":"The Effect of Locomotive Horn Characteristics on Motorist Detection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Office of Research and Development","keywords":"French horn; Transport engineering; Automotive engineering; Business; Engineering; Physics; Acoustics","score_opus":0.008249470220169678,"score_gpt":0.24635383310287517,"score_spread":0.2381043628827055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393033137","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":[0.12064605,0.0005981942,0.797484,0.0011759796,0.037528485,0.003621705,0.000098995835,0.0010970705,0.037749533],"genre_scores_gemma":[0.9981734,0.000032054482,0.0001287624,0.000016215068,0.00044769974,0.0001459026,0.000004443151,0.000015174274,0.001036362],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983008,0.00032649265,0.00036741237,0.00046619138,0.00036964857,0.00016944602],"domain_scores_gemma":[0.9981085,0.0006983764,0.0002381383,0.00076841074,0.00013172616,0.000054814638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084854494,0.00024275327,0.0003404886,0.000117644864,0.000095050236,0.0003339391,0.00070141564,0.00021685753,0.0000033541141],"category_scores_gemma":[0.00011603427,0.00013661371,0.0001920873,0.00019315157,0.00004339032,0.000028393015,0.0008735883,0.0006826959,0.00031986058],"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":[0.000063494466,0.000035740362,0.00018548859,0.0011273719,0.00025426908,0.000027049788,0.00055221596,0.000029617786,0.0010190193,0.0029884304,0.001041148,0.99267614],"study_design_scores_gemma":[0.0016291719,0.007528387,0.012924633,0.00605597,0.00045048646,0.00017899527,0.00010708938,0.114449404,0.75180197,0.026614027,0.07559976,0.0026601288],"about_ca_topic_score_codex":0.00003567365,"about_ca_topic_score_gemma":0.000014630259,"teacher_disagreement_score":0.99001604,"about_ca_system_score_codex":0.000091081056,"about_ca_system_score_gemma":0.00004977545,"threshold_uncertainty_score":0.5570946},"labels":[],"label_agreement":null},{"id":"W4393033195","doi":"10.32920/25413103.v1","title":"The Effect of Locomotive Horn Characteristics on Motorist Detection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Office of Research and Development","keywords":"French horn; Transport engineering; Business; Automotive engineering; Engineering; Acoustics; Physics","score_opus":0.008249470220169678,"score_gpt":0.24635383310287517,"score_spread":0.2381043628827055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393033195","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":[0.12064605,0.0005981942,0.797484,0.0011759796,0.037528485,0.003621705,0.000098995835,0.0010970705,0.037749533],"genre_scores_gemma":[0.9981734,0.000032054482,0.0001287624,0.000016215068,0.00044769974,0.0001459026,0.000004443151,0.000015174274,0.001036362],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983008,0.00032649265,0.00036741237,0.00046619138,0.00036964857,0.00016944602],"domain_scores_gemma":[0.9981085,0.0006983764,0.0002381383,0.00076841074,0.00013172616,0.000054814638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084854494,0.00024275327,0.0003404886,0.000117644864,0.000095050236,0.0003339391,0.00070141564,0.00021685753,0.0000033541141],"category_scores_gemma":[0.00011603427,0.00013661371,0.0001920873,0.00019315157,0.00004339032,0.000028393015,0.0008735883,0.0006826959,0.00031986058],"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":[0.000063494466,0.000035740362,0.00018548859,0.0011273719,0.00025426908,0.000027049788,0.00055221596,0.000029617786,0.0010190193,0.0029884304,0.001041148,0.99267614],"study_design_scores_gemma":[0.0016291719,0.007528387,0.012924633,0.00605597,0.00045048646,0.00017899527,0.00010708938,0.114449404,0.75180197,0.026614027,0.07559976,0.0026601288],"about_ca_topic_score_codex":0.00003567365,"about_ca_topic_score_gemma":0.000014630259,"teacher_disagreement_score":0.99001604,"about_ca_system_score_codex":0.000091081056,"about_ca_system_score_gemma":0.00004977545,"threshold_uncertainty_score":0.5570946},"labels":[],"label_agreement":null},{"id":"W4393171264","doi":"10.1109/tim.2024.3381288","title":"Surface EMG-Based Intersession/Intersubject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Session (web analytics); Computer science; Gesture; Transfer of learning; Artificial intelligence; Gesture recognition; Speech recognition; Transfer (computing); Subject (documents); Human–computer interaction; Machine learning; Pattern recognition (psychology); World Wide Web","score_opus":0.047633451573789065,"score_gpt":0.26030395330185424,"score_spread":0.21267050172806518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393171264","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":[0.120068185,0.00037602696,0.8756191,0.0018016415,0.0012268436,0.000375518,0.000021727468,0.0002805623,0.00023037488],"genre_scores_gemma":[0.9977842,0.00020690131,0.0012050102,0.0005457337,0.00002663353,0.00006240597,0.000018838124,0.000020826406,0.0001294515],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981285,0.00023063163,0.00033060595,0.00055323786,0.00053104537,0.00022597895],"domain_scores_gemma":[0.9994964,0.000075118944,0.00003794619,0.00013039482,0.00010530165,0.00015482813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005440115,0.0002451619,0.00018119795,0.00021911971,0.00024809415,0.00046248667,0.00010251811,0.00009758688,0.00006781988],"category_scores_gemma":[0.0000032552778,0.0002195286,0.00007685875,0.00024907925,0.00003890607,0.000570811,0.0000018145989,0.00035339,0.000035144163],"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":[0.00012238607,0.00024548938,0.00006720804,0.00041284508,0.00038003983,0.000019054612,0.009247112,0.0011280093,0.14740899,0.000078696416,0.0012044642,0.83968574],"study_design_scores_gemma":[0.0050835456,0.001131548,0.00023026858,0.0030294165,0.0003078734,0.00017270642,0.0026178444,0.08328611,0.8585071,0.00019120175,0.044120304,0.0013220604],"about_ca_topic_score_codex":0.000040646897,"about_ca_topic_score_gemma":0.000043714514,"teacher_disagreement_score":0.877716,"about_ca_system_score_codex":0.00016447426,"about_ca_system_score_gemma":0.00006895451,"threshold_uncertainty_score":0.89521164},"labels":[],"label_agreement":null},{"id":"W4393719136","doi":"10.5281/zenodo.8099882","title":"Dataset of Human Hand Motion Planning","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Motion (physics); Human motion; Computer science; Artificial intelligence; Computer vision","score_opus":0.08052451806121413,"score_gpt":0.3026833140427036,"score_spread":0.22215879598148947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393719136","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028241879,0.000044432236,0.013767476,0.00009316798,0.00029303666,0.0003504587,0.9839591,0.00039904434,0.0010650377],"genre_scores_gemma":[0.00044633148,0.000031551226,0.000111566085,0.000043138407,0.00020551789,6.2172866e-8,0.9985143,0.0005034924,0.0001440448],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740297,0.00049072877,0.0005027639,0.00061973836,0.0006460026,0.00033780906],"domain_scores_gemma":[0.9977325,0.000045483604,0.0003896112,0.0011864157,0.00048974954,0.00015619924],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011658731,0.00021178811,0.00029970607,0.0006024149,0.0016132187,0.0012563525,0.002864985,0.00016982936,0.00090761646],"category_scores_gemma":[0.0003974387,0.00022160713,0.0000616498,0.0008656943,0.00013630016,0.00035584287,0.002516486,0.00040409632,0.022411225],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043039927,0.000056913508,1.1947785e-7,0.00021310599,0.00004023238,0.000031726322,0.0001724342,0.000014238406,0.0003136274,0.00011445592,0.9916501,0.0073887575],"study_design_scores_gemma":[0.00028236597,0.00016229728,0.000038873754,0.00018673172,0.000016576985,0.00007977538,0.00004281766,0.00009797617,0.0001532516,0.0000820804,0.9986461,0.00021117649],"about_ca_topic_score_codex":0.00007604229,"about_ca_topic_score_gemma":0.0000011117485,"teacher_disagreement_score":0.02150361,"about_ca_system_score_codex":0.00007963016,"about_ca_system_score_gemma":0.0000056925014,"threshold_uncertainty_score":0.9997804},"labels":[],"label_agreement":null},{"id":"W4393779305","doi":"10.5281/zenodo.8099762","title":"Dataset of Human Hand Motion Planning","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Human motion; Motion (physics); Computer science; Artificial intelligence; Computer vision","score_opus":0.08052451806121413,"score_gpt":0.3026833140427036,"score_spread":0.22215879598148947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393779305","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028241879,0.000044432236,0.013767476,0.00009316798,0.00029303666,0.0003504587,0.9839591,0.00039904434,0.0010650377],"genre_scores_gemma":[0.00044633148,0.000031551226,0.000111566085,0.000043138407,0.00020551789,6.2172866e-8,0.9985143,0.0005034924,0.0001440448],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740297,0.00049072877,0.0005027639,0.00061973836,0.0006460026,0.00033780906],"domain_scores_gemma":[0.9977325,0.000045483604,0.0003896112,0.0011864157,0.00048974954,0.00015619924],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011658731,0.00021178811,0.00029970607,0.0006024149,0.0016132187,0.0012563525,0.002864985,0.00016982936,0.00090761646],"category_scores_gemma":[0.0003974387,0.00022160713,0.0000616498,0.0008656943,0.00013630016,0.00035584287,0.002516486,0.00040409632,0.022411225],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043039927,0.000056913508,1.1947785e-7,0.00021310599,0.00004023238,0.000031726322,0.0001724342,0.000014238406,0.0003136274,0.00011445592,0.9916501,0.0073887575],"study_design_scores_gemma":[0.00028236597,0.00016229728,0.000038873754,0.00018673172,0.000016576985,0.00007977538,0.00004281766,0.00009797617,0.0001532516,0.0000820804,0.9986461,0.00021117649],"about_ca_topic_score_codex":0.00007604229,"about_ca_topic_score_gemma":0.0000011117485,"teacher_disagreement_score":0.02150361,"about_ca_system_score_codex":0.00007963016,"about_ca_system_score_gemma":0.0000056925014,"threshold_uncertainty_score":0.9997804},"labels":[],"label_agreement":null},{"id":"W4394667522","doi":"10.4230/lipics.fun.2022.3","title":"Pushing Blocks via Checkable Gadgets: PSPACE-Completeness of Push-1F and Block/Box Dude","year":2022,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.02073116981896781,"score_gpt":0.25911460801713004,"score_spread":0.23838343819816224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394667522","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":[0.25953254,0.0014431813,0.69863003,0.0017364046,0.010151464,0.007772011,0.0038077272,0.0011305963,0.015796015],"genre_scores_gemma":[0.9407107,0.00027438084,0.05412048,0.0009557733,0.0005092266,0.0011887774,0.0012066967,0.00016823503,0.0008656745],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9936506,0.00018979798,0.0026828423,0.00092136825,0.0013624724,0.0011929035],"domain_scores_gemma":[0.9941997,0.00038456032,0.002136065,0.00212837,0.0007226651,0.000428648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020283796,0.0010010834,0.0017093302,0.0008270226,0.00062078243,0.0009984428,0.0030521948,0.00067513937,0.00007670758],"category_scores_gemma":[0.00016631276,0.0009783121,0.00058802794,0.0005969927,0.00020785163,0.0013136936,0.0055160397,0.0016437758,0.000038909377],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016702147,0.008728667,0.09751151,0.11069492,0.014388107,0.0004690561,0.41594538,0.030165344,0.005227122,0.03332363,0.07343268,0.20844337],"study_design_scores_gemma":[0.01457565,0.0014193564,0.003282266,0.0051766173,0.00069805165,0.001780076,0.008378973,0.32038173,0.010594441,0.01129847,0.6151669,0.0072474545],"about_ca_topic_score_codex":0.00017597167,"about_ca_topic_score_gemma":0.00005308537,"teacher_disagreement_score":0.6811782,"about_ca_system_score_codex":0.00028626632,"about_ca_system_score_gemma":0.00031586387,"threshold_uncertainty_score":0.99926674},"labels":[],"label_agreement":null},{"id":"W4395450721","doi":"10.22214/ijraset.2024.60791","title":"Customizable Sign Language Gesture Prediction for Assistive Devices Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sign language; Gesture; Computer science; Gesture recognition; Human–computer interaction; Assistive technology; Sign (mathematics); Artificial intelligence; Natural language processing; Linguistics; Mathematics","score_opus":0.050193516312079925,"score_gpt":0.380345483358953,"score_spread":0.3301519670468731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395450721","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":[0.04170462,0.0010573982,0.95251185,0.0024990987,0.0013332746,0.00044699907,0.00001449669,0.00023582266,0.00019641597],"genre_scores_gemma":[0.9643957,0.000046986064,0.035187695,0.000012344557,0.00020474734,0.000099540506,0.000002028301,0.000009312862,0.00004165436],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985822,0.000013325663,0.00019004458,0.00030720595,0.00054883293,0.00035841417],"domain_scores_gemma":[0.9991439,0.0002777098,0.000032019794,0.00008134899,0.00039654318,0.00006846899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034323311,0.00008092498,0.00010292871,0.0020343729,0.00024323739,0.00061504997,0.0007479759,0.00008898477,0.0000010795584],"category_scores_gemma":[0.0003950064,0.000069613925,0.000023582967,0.0011625489,0.00010942456,0.00037487107,0.00018202853,0.0005616661,0.0000021329522],"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":[0.00006950918,0.00006621342,0.0008563728,0.00024772465,0.00012632665,0.00015570584,0.001450079,0.021103604,0.45961428,0.19188584,0.00034131977,0.32408303],"study_design_scores_gemma":[0.00030990734,0.00007804156,0.000068889145,0.00021019117,0.0000022554907,0.0003458984,0.00025088148,0.9636397,0.0059681926,0.0035760966,0.02546007,0.000089908994],"about_ca_topic_score_codex":0.000009471969,"about_ca_topic_score_gemma":0.0000048097118,"teacher_disagreement_score":0.94253606,"about_ca_system_score_codex":0.00030162814,"about_ca_system_score_gemma":0.00020105344,"threshold_uncertainty_score":0.5930941},"labels":[],"label_agreement":null},{"id":"W4396515568","doi":"10.22215/etd/2024-15869","title":"Enhancing American Sign Language Communication with Virtual Reality: A Gesture Recognition Application on Oculus Quest 2","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Gesture; Sign language; American Sign Language; Headset; Virtual reality; Computer science; Human–computer interaction; Gesture recognition; Interface (matter); Augmentative and alternative communication; Sign (mathematics); Multimedia; Psychology; Linguistics; Artificial intelligence","score_opus":0.011088157379432308,"score_gpt":0.27835203569846323,"score_spread":0.2672638783190309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396515568","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":[0.08057755,0.002039781,0.684178,0.0039514685,0.0019526667,0.005468165,0.00017152802,0.005069,0.21659184],"genre_scores_gemma":[0.9727409,0.00030043264,0.008540189,0.0007032284,0.00045613703,0.0011547895,0.0069254064,0.00012735656,0.009051556],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970007,0.00038056556,0.0005719031,0.0010029911,0.0007206437,0.0003232313],"domain_scores_gemma":[0.99730784,0.00028022833,0.0005755594,0.0013184975,0.0003731961,0.00014466359],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006418172,0.00048221878,0.00051379943,0.00042796016,0.00019533609,0.0005067119,0.000815649,0.00031061986,0.000017872679],"category_scores_gemma":[0.000048359798,0.000392248,0.00011877021,0.0010964104,0.00005143473,0.0003685957,0.00006282232,0.0007921175,0.0010401201],"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":[0.00010167577,0.00015891643,0.000004685754,0.00030900913,0.00014960555,0.000027092086,0.012583474,0.000013629541,0.0027594836,0.0073104505,0.001775717,0.97480625],"study_design_scores_gemma":[0.008747764,0.020830078,0.008611083,0.04849126,0.0030883432,0.0012371336,0.22721355,0.046130724,0.4255286,0.025301466,0.16166778,0.023152228],"about_ca_topic_score_codex":0.0012730493,"about_ca_topic_score_gemma":0.012216331,"teacher_disagreement_score":0.951654,"about_ca_system_score_codex":0.00021762811,"about_ca_system_score_gemma":0.00021669391,"threshold_uncertainty_score":0.99985296},"labels":[],"label_agreement":null},{"id":"W4396620075","doi":"10.1016/j.patcog.2024.110536","title":"Decoupled and boosted learning for skeleton-based dynamic hand gesture recognition","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; National Natural Science Foundation of China","keywords":"Skeleton (computer programming); Computer science; Gesture; Artificial intelligence; Gesture recognition; Computer vision; Pattern recognition (psychology)","score_opus":0.021897252489085473,"score_gpt":0.2644574912809476,"score_spread":0.24256023879186212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396620075","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":[0.15497829,0.0008577429,0.8402455,0.001151103,0.0009930335,0.0008491039,0.000087982204,0.00062781497,0.00020943888],"genre_scores_gemma":[0.99225855,0.00009154782,0.0053491932,0.0004913328,0.0002629344,0.00046170613,0.0008934928,0.000053481177,0.0001377837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793375,0.00018417538,0.00041095068,0.0007938412,0.00028001776,0.00039729787],"domain_scores_gemma":[0.9985473,0.0006781466,0.00012972395,0.0002093789,0.00028423278,0.00015118078],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062113884,0.0002955953,0.00028075706,0.00039156564,0.00030290877,0.0009555554,0.00018892028,0.00022479257,0.000058726673],"category_scores_gemma":[0.00013896472,0.00028539012,0.00014211163,0.00039907903,0.00004991511,0.0006060279,0.00004947375,0.0003310004,0.00036090065],"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":[0.000015956255,0.000034245353,0.00026018012,0.00052521424,0.000045858076,0.000022351964,0.00037174416,0.000011884635,0.0027166766,0.0000037762602,0.00022243426,0.9957697],"study_design_scores_gemma":[0.0031099536,0.0007540757,0.0039473954,0.0030955072,0.00018874081,0.00031554533,0.00017304286,0.9520146,0.012528485,0.007470175,0.015054339,0.0013481434],"about_ca_topic_score_codex":0.000025004494,"about_ca_topic_score_gemma":0.00010348656,"teacher_disagreement_score":0.99442154,"about_ca_system_score_codex":0.000084022155,"about_ca_system_score_gemma":0.00008339116,"threshold_uncertainty_score":0.9999598},"labels":[],"label_agreement":null},{"id":"W4396628303","doi":"10.1145/3645259.3645263","title":"Gesture Detection Using an Infrared Camera","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Windsor","funders":"","keywords":"Gesture; Computer science; Convolutional neural network; Gesture recognition; Artificial intelligence; Computer vision; Speech recognition; Human–computer interaction","score_opus":0.03259011981946676,"score_gpt":0.2787577608750803,"score_spread":0.24616764105561356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396628303","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":[0.054676138,0.00021146965,0.93412936,0.00014041156,0.0013424048,0.00009653789,8.4883334e-7,0.00073168566,0.008671129],"genre_scores_gemma":[0.98724324,0.0000028612942,0.011271212,0.00018850192,0.00027451856,0.000006190935,0.0000010659425,0.00000780325,0.0010045964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926716,0.000058930895,0.00011936997,0.0002689565,0.0001550851,0.00013051667],"domain_scores_gemma":[0.9995908,0.00002897608,0.000015540123,0.00024186642,0.000050094084,0.0000727117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001661739,0.000079047095,0.00007364706,0.00012460272,0.00006888117,0.00045968825,0.00019251675,0.00006691864,0.000029397106],"category_scores_gemma":[0.000008371463,0.0000626512,0.000035378234,0.0004430563,0.000008572923,0.0008239175,0.00004309809,0.00010617151,0.00016416577],"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":[0.0000035245193,0.00007958382,0.00023696608,0.00013168661,0.00007263684,0.0001683824,0.004780354,0.00037157332,0.07168358,0.014650491,0.0010850566,0.90673614],"study_design_scores_gemma":[0.00019897064,0.00013987909,0.0011765723,0.00013408353,0.000015125604,0.0007075994,0.00020678679,0.8773692,0.03654473,0.0065471535,0.07649675,0.00046310885],"about_ca_topic_score_codex":0.000055615346,"about_ca_topic_score_gemma":0.00003616983,"teacher_disagreement_score":0.9325671,"about_ca_system_score_codex":0.00003624052,"about_ca_system_score_gemma":0.00004722794,"threshold_uncertainty_score":0.44327843},"labels":[],"label_agreement":null},{"id":"W4396833613","doi":"10.1145/3613904.3642935","title":"Exploring Visualizations for Precisely Guiding Bare Hand Gestures in Virtual Reality","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"University of Waterloo","funders":"","keywords":"Gesture; Computer science; Human–computer interaction; Visualization; Virtual reality; Formative assessment; Interaction technique; Reliability (semiconductor); Workload; Gesture recognition; Eye tracking; Multimedia; Artificial intelligence; Psychology","score_opus":0.2928236689899491,"score_gpt":0.35563368861936623,"score_spread":0.06281001962941712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396833613","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":[0.0067627463,0.0002375378,0.9868156,0.0009172418,0.0010314509,0.00036171396,0.000009726758,0.00033861515,0.0035253917],"genre_scores_gemma":[0.99431753,0.000037842314,0.004158616,0.00008885087,0.00022814718,0.0003623696,0.000011028358,0.000013506702,0.0007821236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998836,0.000071470815,0.0002983286,0.00039522926,0.00018632499,0.00021263932],"domain_scores_gemma":[0.9992349,0.00036255203,0.00001603692,0.00022871552,0.00008945714,0.000068328525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005683573,0.000110467816,0.00014238895,0.00022688918,0.00011889492,0.00062264904,0.00028158101,0.00004734712,0.000010485511],"category_scores_gemma":[0.00014233921,0.00009321223,0.000060387356,0.0005862708,0.000015259084,0.0009963992,0.000089864916,0.000053090906,0.00003083448],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014428495,0.0001288222,0.0005222776,0.00043630096,0.00008183204,0.000044055556,0.021729873,0.0021868194,0.0054222927,0.76086557,0.025778983,0.18278874],"study_design_scores_gemma":[0.0013069534,0.0003541255,0.003861134,0.001812825,0.000031237683,0.0000785105,0.0021126815,0.44148204,0.049714796,0.0133423535,0.4847151,0.0011882571],"about_ca_topic_score_codex":0.000043982203,"about_ca_topic_score_gemma":0.00016104554,"teacher_disagreement_score":0.9875548,"about_ca_system_score_codex":0.000051513103,"about_ca_system_score_gemma":0.000072053816,"threshold_uncertainty_score":0.6004219},"labels":[],"label_agreement":null},{"id":"W4398757486","doi":"10.2139/ssrn.4823761","title":"Sign Language Recognition Using LSTM","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Sign language; Computer science; Natural language processing; Sign (mathematics); Speech recognition; Artificial intelligence; Linguistics; Mathematics; Philosophy","score_opus":0.018852599006849192,"score_gpt":0.27008226158250975,"score_spread":0.25122966257566054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398757486","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":[0.05371312,0.010247444,0.9319911,0.00057666085,0.0011730456,0.00010729909,0.0000023323757,0.0002469539,0.0019420652],"genre_scores_gemma":[0.9950722,0.00058409135,0.002603094,0.00010590477,0.0009132474,0.0000040677046,0.0000030618023,0.000019974465,0.0006943932],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99780834,0.00014900435,0.00024280603,0.00025013092,0.00028768496,0.001262035],"domain_scores_gemma":[0.99954844,0.000068027795,0.00006612845,0.00015988489,0.00007540898,0.00008213183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017292015,0.00012693067,0.00012703601,0.00023166572,0.00014559779,0.00046631333,0.00035066716,0.00007109376,0.00002733736],"category_scores_gemma":[0.000031164644,0.00010679726,0.0001142303,0.0004657069,0.000013803097,0.00069240667,0.00004112926,0.0011485559,0.0004054549],"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":[0.000004744442,0.000026845872,0.000021247439,0.000024837283,0.00014076271,0.00010867605,0.0011712856,0.000017018032,0.0049531036,0.034161326,0.00020432175,0.9591658],"study_design_scores_gemma":[0.0012162972,0.0007158087,0.00007358196,0.0009498893,0.00013524435,0.036814164,0.0046736063,0.06288612,0.005678961,0.86706513,0.018523818,0.0012673717],"about_ca_topic_score_codex":0.00002731721,"about_ca_topic_score_gemma":0.00006315706,"teacher_disagreement_score":0.95789844,"about_ca_system_score_codex":0.0005603942,"about_ca_system_score_gemma":0.0014039599,"threshold_uncertainty_score":0.52114373},"labels":[],"label_agreement":null},{"id":"W4399461117","doi":"10.1061/jcemd4.coeng-14458","title":"Crane Signalman Hand-Signal Classification Framework Using Sensor-Based Smart Construction Glove and Machine-Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; SIGNAL (programming language); Machine learning; Wired glove; Algorithm; Programming language","score_opus":0.0174413775954931,"score_gpt":0.2301200144249506,"score_spread":0.21267863682945748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399461117","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":[0.021486456,0.0010074144,0.9755802,0.0002911848,0.0013510914,0.00010817079,0.0000015549986,0.0000873121,0.00008660475],"genre_scores_gemma":[0.6846423,0.0002837338,0.31479156,0.000023279026,0.00022131603,0.0000028991499,0.0000012313914,0.000012542908,0.000021115009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892616,0.000060379807,0.00038508748,0.00021196608,0.00027346527,0.00014295858],"domain_scores_gemma":[0.9994352,0.00009815231,0.00016902841,0.00008883519,0.00010492991,0.00010387117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045520443,0.00014753426,0.00019359923,0.0004861375,0.00011974187,0.00044133223,0.00008245362,0.00007609057,0.000007470204],"category_scores_gemma":[0.000017288337,0.00013601151,0.000060662147,0.0003190489,0.000058455407,0.00037005416,0.000031729025,0.00030776398,0.000001774032],"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":[0.000037906,0.000039704093,0.0015412546,0.0014090295,0.0006519472,0.00029948418,0.0007559582,0.05549927,0.0105801085,0.060796987,0.000046281955,0.86834204],"study_design_scores_gemma":[0.0004342849,0.00009020928,0.00064952613,0.0009194949,0.000097227894,0.0028024644,0.0002860759,0.9813259,0.0008255388,0.00055058044,0.011814969,0.00020368019],"about_ca_topic_score_codex":0.000003962515,"about_ca_topic_score_gemma":1.6364237e-7,"teacher_disagreement_score":0.92582667,"about_ca_system_score_codex":0.000061054314,"about_ca_system_score_gemma":0.000025411411,"threshold_uncertainty_score":0.55463886},"labels":[],"label_agreement":null},{"id":"W4399562445","doi":"10.2196/55427","title":"Sign Language Recognition System for Deaf Patients: Protocol for a Systematic Review","year":2024,"lang":"en","type":"review","venue":"JMIR Research Protocols","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Sign language; Protocol (science); Computer science; Sign (mathematics); Deaf culture; Medicine; Natural language processing; World Wide Web; Linguistics; Alternative medicine","score_opus":0.44082891458601814,"score_gpt":0.6036708466572284,"score_spread":0.16284193207121023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399562445","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.38644e-10,0.19401592,0.006783327,0.00004285221,0.00004041755,0.7977583,0.00044624068,0.0005243514,0.00038860872],"genre_scores_gemma":[2.4069393e-9,0.01926058,0.0017783395,0.000049460854,0.000525381,0.97683346,0.0002767439,0.00020239268,0.0010736655],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9882558,0.0038146793,0.0029251587,0.0018099803,0.0018744521,0.001319906],"domain_scores_gemma":[0.99124086,0.002347221,0.0012647419,0.0021309804,0.0026242177,0.00039197732],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010284254,0.0009367875,0.0040965816,0.0010203721,0.00038255475,0.0014645894,0.0029513207,0.0006536383,0.000013620518],"category_scores_gemma":[0.0018056823,0.0006097724,0.0015465902,0.0021506178,0.00006920989,0.00054780475,0.0005914838,0.0010345068,0.0018246623],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014747474,0.00017411985,1.1369459e-8,0.7569735,0.00011236461,0.000010566211,0.00006540029,3.096806e-9,8.880642e-8,0.00028715003,0.0360575,0.20630457],"study_design_scores_gemma":[0.0006539031,0.00060368807,2.819177e-9,0.5114129,0.00009830461,0.000029567787,0.000012803256,0.000016421272,0.0000031215022,0.00008619542,0.48673165,0.0003514118],"about_ca_topic_score_codex":0.00000264905,"about_ca_topic_score_gemma":0.000002075298,"teacher_disagreement_score":0.45067415,"about_ca_system_score_codex":0.00097082986,"about_ca_system_score_gemma":0.0013348423,"threshold_uncertainty_score":0.99963534},"labels":[],"label_agreement":null},{"id":"W4399619764","doi":"10.1109/tnsm.2024.3414267","title":"A Graph Learning-Based Approach for Lateral Movement Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Movement (music); Computer science; Artificial intelligence; Graph; Computer vision; Theoretical computer science; Physics; Acoustics","score_opus":0.013196508519219476,"score_gpt":0.2179085273981057,"score_spread":0.20471201887888624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399619764","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":[0.00036711065,0.000105121966,0.9955008,0.00070038566,0.0009442509,0.00070497073,0.0000030290244,0.0003937694,0.001280581],"genre_scores_gemma":[0.9827121,0.000096618016,0.013942691,0.0017148199,0.00014142206,0.00073733553,0.0000068527065,0.000021154987,0.0006270161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988445,0.00006696175,0.00019172467,0.00044924,0.00018792649,0.00025966665],"domain_scores_gemma":[0.99959636,0.000059181526,0.00002936149,0.00020117754,0.000041810978,0.00007210275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031213867,0.00016339561,0.00012874533,0.00018835523,0.0002933041,0.00031156908,0.00016281156,0.00005784885,0.000005510511],"category_scores_gemma":[1.4902031e-7,0.00014793569,0.000094103896,0.00066020107,0.000008648034,0.00015223169,0.000003405157,0.00015132473,0.000016164851],"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":[0.000035260244,0.00009423233,0.0000020819286,0.00067261,0.00018070887,0.00000621038,0.00029038882,0.69966775,0.000028026076,0.0008216778,0.00017808056,0.29802296],"study_design_scores_gemma":[0.0004910653,0.0001813467,0.00003376671,0.00011148228,0.000063508836,0.0000033733686,0.000059994076,0.9740717,0.0003317325,0.00065983,0.023790285,0.00020194046],"about_ca_topic_score_codex":0.000017651757,"about_ca_topic_score_gemma":0.00005424875,"teacher_disagreement_score":0.982345,"about_ca_system_score_codex":0.00003617876,"about_ca_system_score_gemma":0.000010052493,"threshold_uncertainty_score":0.6032642},"labels":[],"label_agreement":null},{"id":"W4399897860","doi":"10.18280/ria.380319","title":"Alphabet Recognition in Sign Language Using Deep Learning Algorithm with Bayesian Optimization","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Alphabet; Computer science; Sign (mathematics); Sign language; Bayesian optimization; Artificial intelligence; Bayesian probability; Algorithm; Natural language processing; Pattern recognition (psychology); Machine learning; Mathematics; Linguistics","score_opus":0.02867816036678559,"score_gpt":0.2684387988702281,"score_spread":0.2397606385034425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399897860","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":[0.0024489192,0.00086941465,0.994154,0.00014883916,0.00032603292,0.00027792054,0.0000028757515,0.00028142615,0.0014906107],"genre_scores_gemma":[0.79060715,0.000081316706,0.20857115,0.000055125216,0.00017515816,0.000040476458,0.00003421172,0.000034449164,0.00040095733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983593,0.000174804,0.0003899609,0.00054240576,0.00021147577,0.00032200138],"domain_scores_gemma":[0.9992986,0.0001789926,0.00007856653,0.00026435187,0.000096377364,0.00008311999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005878783,0.00017547901,0.00019088277,0.00035890707,0.00011124472,0.00036947304,0.00027492922,0.00009475513,0.00012074014],"category_scores_gemma":[0.000048975897,0.00016175174,0.000054333603,0.0014165429,0.000034566587,0.0006595928,0.00005474334,0.00029225624,0.00026875973],"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":[0.0000031111815,0.00003514113,0.000041485247,0.000064258835,0.00001100474,0.00015613387,0.0046876017,0.3815432,0.00055609056,0.00015713467,0.0000078766725,0.612737],"study_design_scores_gemma":[0.000037934857,0.00008780348,0.0000044383946,0.0005677035,0.000008434758,0.00016497023,0.0015191862,0.9871632,0.009543044,0.00024546077,0.000422205,0.00023561787],"about_ca_topic_score_codex":0.00006242496,"about_ca_topic_score_gemma":0.000029718933,"teacher_disagreement_score":0.78815824,"about_ca_system_score_codex":0.00009850159,"about_ca_system_score_gemma":0.000055260418,"threshold_uncertainty_score":0.65960443},"labels":[],"label_agreement":null},{"id":"W4399990340","doi":"10.1109/iotdi61053.2024.00020","title":"SUPER: Seated Upper Body Pose Estimation using mmWave Radars","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Torso; Computer science; Point cloud; Computer vision; Radar; Artificial intelligence; Pose; Fuse (electrical); Motion (physics); Task (project management); Telecommunications; Engineering","score_opus":0.03042228845481458,"score_gpt":0.2934427060186901,"score_spread":0.2630204175638755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399990340","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":[0.049184043,0.0007663311,0.92593163,0.0013749838,0.0065451856,0.0008383038,0.00002965038,0.0013112725,0.014018606],"genre_scores_gemma":[0.87040865,0.000017518993,0.12678061,0.00027374557,0.00042149986,0.00003492416,0.00007168386,0.000044873763,0.001946474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975508,0.0001694699,0.0005050221,0.00091564516,0.00053272763,0.00032632353],"domain_scores_gemma":[0.99854314,0.00006990244,0.00011752675,0.00088271085,0.00023829169,0.00014843098],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048409015,0.0003578694,0.0003949453,0.0003890752,0.000090914444,0.0010159893,0.00071708753,0.00039092486,0.00007869293],"category_scores_gemma":[0.000048274327,0.00030484426,0.00020351257,0.00041569266,0.00002799718,0.00024416522,0.0017401961,0.00063511805,0.0009193824],"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":[0.00007862286,0.0011974304,0.0022556575,0.012378344,0.0039843535,0.0026880775,0.032129165,0.063804306,0.036190774,0.18771312,0.07323878,0.58434135],"study_design_scores_gemma":[0.00016902696,0.000020358479,0.00011218774,0.0008463284,0.00005399362,0.00019446912,0.000044187214,0.978165,0.0047543324,0.0135587845,0.0015575809,0.00052378874],"about_ca_topic_score_codex":0.0002773054,"about_ca_topic_score_gemma":0.000010134958,"teacher_disagreement_score":0.91436064,"about_ca_system_score_codex":0.00019700793,"about_ca_system_score_gemma":0.00040644387,"threshold_uncertainty_score":0.9999404},"labels":[],"label_agreement":null},{"id":"W4400417570","doi":"10.22214/ijraset.2024.62035","title":"Bridging the Gap: Deep Learning Techniques for American Sign Language Recognition","year":2024,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bridging (networking); Sign language; American Sign Language; Computer science; Sign (mathematics); Artificial intelligence; Natural language processing; Linguistics; Mathematics; Philosophy; Computer security","score_opus":0.050058695478636596,"score_gpt":0.3846730359683223,"score_spread":0.3346143404896857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400417570","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":[0.01428661,0.0003202352,0.9757392,0.008149738,0.00051214267,0.00042492742,0.0000025470788,0.00027400904,0.0002905836],"genre_scores_gemma":[0.9649523,0.000114014016,0.03427424,0.00003619322,0.00026469375,0.00032914977,0.0000012436549,0.000009660824,0.000018526647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859077,0.00001813801,0.00018956796,0.0002820278,0.0005340803,0.0003853986],"domain_scores_gemma":[0.99898684,0.00042759188,0.000037216214,0.00010490955,0.00038888407,0.000054572873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004796591,0.00007628789,0.000093902345,0.0017588536,0.00025884935,0.00074224675,0.0010349847,0.00004639936,7.595165e-7],"category_scores_gemma":[0.0005957642,0.000057732876,0.000027451695,0.0014630189,0.00026855743,0.0002570702,0.00019166473,0.0005870166,0.0000042514203],"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":[0.000005723136,0.000005844515,0.000008228304,0.000015294874,0.000009834695,0.000014930964,0.00033019567,0.00006920719,0.040541187,0.024678146,0.000060321814,0.9342611],"study_design_scores_gemma":[0.00044566867,0.00038043552,0.0000833271,0.00055029505,0.0000046918376,0.0012428504,0.0021365245,0.7753466,0.07317562,0.062352546,0.08392559,0.00035584794],"about_ca_topic_score_codex":0.0000070994947,"about_ca_topic_score_gemma":0.0000038896724,"teacher_disagreement_score":0.95066565,"about_ca_system_score_codex":0.00019693447,"about_ca_system_score_gemma":0.00010944379,"threshold_uncertainty_score":0.7157502},"labels":[],"label_agreement":null},{"id":"W4400727865","doi":"10.1109/iwcmc61514.2024.10592402","title":"A Transformer-Based Approach for Better Hand Gesture Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"New Brunswick Innovation Foundation","keywords":"Gesture recognition; Computer science; Transformer; Gesture; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Engineering; Electrical engineering","score_opus":0.03496242949026351,"score_gpt":0.2550509367843194,"score_spread":0.22008850729405588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400727865","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":[0.00068974064,0.00021830366,0.9820076,0.0028708766,0.000450824,0.00060489814,0.00002654608,0.0004202858,0.012710924],"genre_scores_gemma":[0.6856125,0.00000496792,0.30951804,0.0022274991,0.00050748524,0.0006632864,0.00014747087,0.000029112305,0.0012896484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889946,0.000039426646,0.00020039608,0.000433784,0.00019218057,0.00023477606],"domain_scores_gemma":[0.9994754,0.00014084364,0.000018799466,0.00019759398,0.000089108915,0.000078233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031777422,0.00014205425,0.00014368417,0.00015573896,0.00010228581,0.00058129936,0.0002330064,0.00011460344,0.00003474584],"category_scores_gemma":[0.000009886282,0.00010540498,0.00014638009,0.00034190272,0.00002453268,0.0003929594,0.000007561133,0.00011329341,0.00013066365],"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":[0.000017138398,0.00012125524,0.000023300849,0.00075642054,0.000078100406,0.000011934713,0.0010066065,0.000031733518,0.004588583,0.003114867,0.019764304,0.97048575],"study_design_scores_gemma":[0.0021189684,0.00045329155,0.000104485676,0.00037590694,0.00008282927,0.0001457487,0.00011126729,0.4718103,0.08802746,0.011100203,0.42465684,0.0010126778],"about_ca_topic_score_codex":0.0000051778593,"about_ca_topic_score_gemma":0.000005614694,"teacher_disagreement_score":0.96947306,"about_ca_system_score_codex":0.000023245133,"about_ca_system_score_gemma":0.00007482154,"threshold_uncertainty_score":0.56054837},"labels":[],"label_agreement":null},{"id":"W4400910803","doi":"10.1109/ic_aset61847.2024.10596214","title":"Breaking Barriers: Real-Time Sign Language Recognition Using LSTM Networks for Enhanced Communication Accessibility","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Sign language; Sign (mathematics); Speech recognition; Artificial intelligence","score_opus":0.03159823170764631,"score_gpt":0.3171672600989566,"score_spread":0.2855690283913103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400910803","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":[0.04018225,0.00035725365,0.9503443,0.00015510232,0.0003803801,0.00058703125,0.00001175214,0.0007242723,0.007257623],"genre_scores_gemma":[0.9289537,0.00003248214,0.07027585,0.00012335603,0.00021499465,0.00008888409,0.0000629185,0.00001952797,0.00022827812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842674,0.00026274475,0.0003772163,0.00048455145,0.00017747693,0.000271268],"domain_scores_gemma":[0.9983617,0.00060653914,0.000104470164,0.00063456385,0.00018070007,0.00011201573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012135375,0.00015644757,0.00020031225,0.00011958231,0.0002180297,0.00067583396,0.00052628195,0.00013408161,0.00012175825],"category_scores_gemma":[0.00010555643,0.00013935093,0.00010919497,0.00051239994,0.00003085897,0.0011397928,0.00014203535,0.00013736392,0.000071788],"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":[0.000033353055,0.00005396602,0.000034349727,0.00030738735,0.00011584753,0.000009182686,0.006032126,0.0004823105,0.08343892,0.0014723542,0.0013801929,0.90664],"study_design_scores_gemma":[0.00032030026,0.000048052752,0.00008226983,0.0005825626,0.000032939777,0.000024862533,0.00037837992,0.9709404,0.023479637,0.0032773537,0.00046660576,0.00036665468],"about_ca_topic_score_codex":0.00012822966,"about_ca_topic_score_gemma":0.00003447197,"teacher_disagreement_score":0.9704581,"about_ca_system_score_codex":0.00011210673,"about_ca_system_score_gemma":0.00010207404,"threshold_uncertainty_score":0.65170825},"labels":[],"label_agreement":null},{"id":"W4400971942","doi":"10.2316/j.2024.206-1052","title":"MULTI-OBJECT GRASPING DETECTION BASED ON THE IMPROVED SHUFFLENET NETWORK","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Object (grammar); Object detection; Artificial intelligence; Computer vision; Pattern recognition (psychology)","score_opus":0.01762200947271747,"score_gpt":0.26329679833485026,"score_spread":0.2456747888621328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400971942","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":[0.003940657,0.00019853517,0.98797077,0.004619096,0.0030704585,0.00007587607,7.957464e-7,0.000047820355,0.000075993405],"genre_scores_gemma":[0.98079425,0.000028211129,0.018296203,0.00032328247,0.00052667694,0.0000027130977,0.0000011431905,0.0000056347417,0.000021873693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991168,0.00007921468,0.00029146572,0.00010559712,0.00032506627,0.00008189447],"domain_scores_gemma":[0.9992027,0.00025461643,0.00017859387,0.00007826724,0.00025183588,0.000033959277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067539624,0.00007603329,0.00008202993,0.00016265399,0.00007046145,0.00053098606,0.00026166873,0.000039104638,0.000003389773],"category_scores_gemma":[0.00007817928,0.000049246162,0.000069083246,0.0001581173,0.000013037243,0.0003043427,0.000029362196,0.00015463747,0.0000072837074],"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":[0.00003273338,0.00010535702,0.0002843933,0.000054109645,0.00030299305,0.000079941696,0.0007190037,0.50137335,0.013457255,0.015679695,0.0007679441,0.4671432],"study_design_scores_gemma":[0.00018650935,0.000079356876,0.0026618487,0.00028044995,0.000008293912,0.000101336474,0.000011598357,0.99423814,0.00072501035,0.0009768375,0.0006737303,0.00005689046],"about_ca_topic_score_codex":0.0000035449525,"about_ca_topic_score_gemma":0.0000050147855,"teacher_disagreement_score":0.9768536,"about_ca_system_score_codex":0.00005671788,"about_ca_system_score_gemma":0.000057083453,"threshold_uncertainty_score":0.5120311},"labels":[],"label_agreement":null},{"id":"W4402307001","doi":"10.18280/ts.410415","title":"Optimized Hybrid Convolution Neural Network with Machine Learning for Arabic Sign Language Recognition","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Arabic; Convolution (computer science); Convolutional neural network; Artificial intelligence; Sign (mathematics); Artificial neural network; Sign language; Speech recognition; Natural language processing; Pattern recognition (psychology); Linguistics; Mathematics","score_opus":0.019598054312705008,"score_gpt":0.2354079803135141,"score_spread":0.2158099260008091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402307001","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":[0.023217943,0.0010277943,0.9729417,0.0005037943,0.0004798848,0.00081673573,0.000025741403,0.0005904513,0.00039594877],"genre_scores_gemma":[0.9760781,0.000012930354,0.022206038,0.00020970378,0.0007009623,0.0002590799,0.0002443674,0.000027336291,0.00026151002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998344,0.00019371069,0.000316064,0.0004646959,0.00030702126,0.00037450145],"domain_scores_gemma":[0.99928117,0.00028666566,0.000095153475,0.00013375435,0.000103367,0.000099910074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007185185,0.00020874961,0.00022754523,0.00011946978,0.0001851405,0.00035002705,0.0002167019,0.000044033182,0.00012628382],"category_scores_gemma":[0.000015054799,0.00016585173,0.00011012252,0.00030734268,0.000028345752,0.00047551413,0.000033756096,0.00020433964,0.00007826644],"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":[0.00075250067,0.00025452097,0.0003106338,0.00068897964,0.00057283643,0.00040240245,0.0037795287,0.14863196,0.0057445224,0.0023441338,0.008122659,0.8283953],"study_design_scores_gemma":[0.0017314896,0.000628328,0.000117955686,0.00027804673,0.00006062551,0.00016003898,0.00006479809,0.98898846,0.0012409886,0.000443374,0.0059477845,0.00033811675],"about_ca_topic_score_codex":0.000016538675,"about_ca_topic_score_gemma":0.0000069057546,"teacher_disagreement_score":0.9528601,"about_ca_system_score_codex":0.000058885824,"about_ca_system_score_gemma":0.000052967145,"threshold_uncertainty_score":0.6763238},"labels":[],"label_agreement":null},{"id":"W4402435460","doi":"10.1016/j.compbiomed.2024.109125","title":"Finite element and experimental modeling of jaw movement-induced deformations in the human earcanal","year":2024,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite element method; Condyle; Temporomandibular joint; Cantilever; Computer science; Motion (physics); Geometry; Structural engineering; Anatomy; Orthodontics; Mathematics; Engineering; Computer vision; Medicine","score_opus":0.04561430339527649,"score_gpt":0.344647942830104,"score_spread":0.2990336394348275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402435460","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":[0.6698451,0.002172865,0.3240698,0.002402098,0.00045303552,0.00024134568,0.0000015776241,0.000020247777,0.00079393573],"genre_scores_gemma":[0.99862254,0.000037331803,0.00074806437,0.0005238409,0.0000437525,0.0000145354625,0.0000061144824,0.000001390607,0.000002413816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993512,0.00009292589,0.00023973428,0.00015018528,0.00006199226,0.00010395896],"domain_scores_gemma":[0.9997173,0.00012828415,0.000024625968,0.000094317045,0.000011308509,0.000024173441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053571013,0.00006397206,0.00012819293,0.00018373034,0.00004162814,0.000012346182,0.00014270857,0.00003612148,0.0000027690185],"category_scores_gemma":[0.0000067506157,0.000039372706,0.000009681928,0.0001636113,0.00006084269,0.00007085338,0.000073773364,0.000100295954,5.943542e-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":[0.00003072857,0.0003670762,0.02025807,0.0005184365,0.00019546338,0.00012993987,0.16143723,0.0031274538,0.03185777,0.60346943,0.0007965323,0.17781185],"study_design_scores_gemma":[0.0013682694,0.0005174929,0.0016008337,0.0005919988,0.000006311077,0.000037744645,0.0023195907,0.98003554,0.0005474235,0.012450744,0.00039930147,0.0001247258],"about_ca_topic_score_codex":0.00011290388,"about_ca_topic_score_gemma":0.000045504414,"teacher_disagreement_score":0.9769081,"about_ca_system_score_codex":0.000014209929,"about_ca_system_score_gemma":0.000012690115,"threshold_uncertainty_score":0.16055724},"labels":[],"label_agreement":null},{"id":"W4402452629","doi":"10.11159/mvml24.106","title":"[Case Study] Transfer Learning with Inflated 3D CNN for Word-Level Recognition for Azerbaijani Sign Language Dataset","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Word (group theory); Sign (mathematics); Transfer of learning; Speech recognition; Linguistics; Mathematics","score_opus":0.020874584114486137,"score_gpt":0.24092099011494897,"score_spread":0.22004640600046282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402452629","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":[0.6260436,0.00063849834,0.36878178,0.0001797682,0.0014963598,0.0024217467,0.000077207274,0.0003183894,0.000042649663],"genre_scores_gemma":[0.996962,0.0000040126924,0.0025684047,0.000021637952,0.00014369295,0.00016195286,0.0000026376122,0.000013830106,0.00012179385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858093,0.000011963668,0.00027089842,0.0005365037,0.00029565362,0.00030401835],"domain_scores_gemma":[0.9991793,0.00033016058,0.000055846685,0.000106888496,0.00021997213,0.00010784265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087794726,0.00018709063,0.00025784667,0.0003383625,0.0002476937,0.0007703097,0.0003696524,0.000037083435,1.3848387e-7],"category_scores_gemma":[0.000042671676,0.000120801174,0.000035323985,0.0011661479,0.00005900425,0.0004320131,0.000071279304,0.00019600955,3.7695077e-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":[0.0003599673,0.0005399008,0.0011948855,0.0060241385,0.0006851717,0.0002772651,0.011922232,0.013166387,0.018736651,0.038502514,0.0032517198,0.9053392],"study_design_scores_gemma":[0.0005898072,0.00066216604,0.00010057007,0.0006802824,0.00003385038,0.00066225807,0.00010910589,0.9937806,0.0013060386,0.000014922886,0.0018179682,0.00024243776],"about_ca_topic_score_codex":0.000017495004,"about_ca_topic_score_gemma":0.0000029286061,"teacher_disagreement_score":0.9806142,"about_ca_system_score_codex":0.000022281363,"about_ca_system_score_gemma":0.000040720915,"threshold_uncertainty_score":0.7428114},"labels":[],"label_agreement":null},{"id":"W4402464113","doi":"10.11159/mvml24.105","title":"EPT-MoE: Toward Efficient Parallel Transformers with Mixture-of-Experts for 3D Hand Gesture Recognition","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Transformer; Gesture; Artificial intelligence; Deep learning; Pattern recognition (psychology); Graph; Gesture recognition; Machine learning; Artificial neural network; Engineering; Theoretical computer science","score_opus":0.009965244031006012,"score_gpt":0.2124973304517198,"score_spread":0.2025320864207138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402464113","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":[0.1970178,0.0043554017,0.79111856,0.0011813635,0.003914978,0.0017276068,0.000013931639,0.00025733636,0.00041299782],"genre_scores_gemma":[0.99321336,0.000027980228,0.006423757,0.000028116148,0.00012442551,0.00006813719,2.8447533e-7,0.000010623917,0.00010329465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847823,0.0000068936556,0.0002881643,0.0004838615,0.00043423427,0.00030860413],"domain_scores_gemma":[0.99925643,0.00016378566,0.00009504755,0.000090295085,0.00027365374,0.00012079868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049625995,0.00019448782,0.0003017175,0.00034311274,0.00015431568,0.00048492357,0.00040812668,0.000052003972,1.5047718e-7],"category_scores_gemma":[0.000022290376,0.00011644845,0.00005832794,0.0012138721,0.00016256931,0.00023812437,0.000058487585,0.00014959364,2.2342746e-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":[0.00039859567,0.0004889122,0.00094231975,0.01118279,0.00055370893,0.0000190664,0.009704958,0.02204344,0.045750704,0.24510667,0.003269258,0.66053957],"study_design_scores_gemma":[0.00036601175,0.0003796784,0.00024184673,0.0015545244,0.000020070089,0.00011279729,0.000017765626,0.98848134,0.0056682257,0.00004981204,0.0028869526,0.00022099876],"about_ca_topic_score_codex":0.000006651401,"about_ca_topic_score_gemma":6.997771e-7,"teacher_disagreement_score":0.9664379,"about_ca_system_score_codex":0.000029715284,"about_ca_system_score_gemma":0.00005699489,"threshold_uncertainty_score":0.47486302},"labels":[],"label_agreement":null},{"id":"W4402473646","doi":"10.1109/ccece59415.2024.10667296","title":"American Sign Language Recognition Using a Multimodal Transformer Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Computer science; Sign language; Transformer; American Sign Language; Speech recognition; Natural language processing; Artificial intelligence; Linguistics; Engineering; Electrical engineering; Voltage","score_opus":0.024627488078773983,"score_gpt":0.283534010929111,"score_spread":0.25890652285033705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402473646","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":[0.026303085,0.00029804924,0.9600298,0.00032714492,0.0006682143,0.0001965733,0.000005537638,0.0006035461,0.011568072],"genre_scores_gemma":[0.9085179,0.000010955103,0.09022278,0.00046201606,0.0004999817,0.000018545597,0.000007946736,0.000014834947,0.00024503464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989239,0.00008680027,0.0001937627,0.00032527745,0.00019195696,0.00027829202],"domain_scores_gemma":[0.99956226,0.00011169751,0.000027653254,0.00017136807,0.000039701852,0.00008734197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027954823,0.00012147141,0.00014683147,0.00009974791,0.00006934426,0.00027362534,0.00018789979,0.000040402094,0.00009143161],"category_scores_gemma":[0.000007403972,0.0000979855,0.00008842727,0.0007713249,0.000030562023,0.00043122633,0.000017990604,0.000119386335,0.00047620473],"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":[0.000003108677,0.000017017048,0.000029869034,0.00003150432,0.00003512821,0.00005937321,0.0018138965,0.000119052886,0.002553681,0.00050329644,0.00080135674,0.99403274],"study_design_scores_gemma":[0.00058341963,0.0002496805,0.0004212029,0.00058300624,0.000058444723,0.0004830636,0.001206341,0.95777655,0.008477155,0.0023522156,0.026781488,0.0010274632],"about_ca_topic_score_codex":0.0002498598,"about_ca_topic_score_gemma":0.000058941958,"teacher_disagreement_score":0.9930053,"about_ca_system_score_codex":0.00003688018,"about_ca_system_score_gemma":0.000055660807,"threshold_uncertainty_score":0.61208075},"labels":[],"label_agreement":null},{"id":"W4402521086","doi":"10.1145/3677386.3688890","title":"Exploring Gesture Interaction in Underwater Virtual Reality","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Universitas Brawijaya","keywords":"Gesture; Virtual reality; Computer science; Human–computer interaction; Underwater; Mixed reality; Computer graphics (images); Computer vision; Geology","score_opus":0.1602850363712312,"score_gpt":0.3119636252656166,"score_spread":0.1516785888943854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402521086","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":[0.0579399,0.00007766063,0.90533805,0.005657403,0.0034848321,0.00016206258,0.0000010512828,0.0007086072,0.026630463],"genre_scores_gemma":[0.9981144,0.000021066124,0.0006383028,0.00012887361,0.00015608603,0.000036352627,0.0000019204927,0.0000054434036,0.00089754397],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991301,0.00007538943,0.00021237253,0.0002902467,0.00014907673,0.000142819],"domain_scores_gemma":[0.9996378,0.000089691275,0.000014853908,0.00019232793,0.000023137536,0.00004219387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035328604,0.00008046682,0.00009529051,0.00017283426,0.0000221096,0.0002806396,0.00017597219,0.000038571798,0.000030497622],"category_scores_gemma":[0.000011813433,0.000060090642,0.000036655478,0.00036700987,0.000006807932,0.0014243803,0.00007775698,0.00017302496,0.00045302493],"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":[0.0000124312455,0.000114481554,0.00070954853,0.00016560486,0.000056510555,0.00035206502,0.014194377,0.0006279565,0.0036443998,0.1759347,0.0064552426,0.79773265],"study_design_scores_gemma":[0.0011030877,0.00038409323,0.021361364,0.0019641842,0.000017490038,0.00068226585,0.004319946,0.21645196,0.04178333,0.018373122,0.6919713,0.0015878384],"about_ca_topic_score_codex":0.00011306773,"about_ca_topic_score_gemma":0.0001887031,"teacher_disagreement_score":0.9401745,"about_ca_system_score_codex":0.000074556534,"about_ca_system_score_gemma":0.000025055808,"threshold_uncertainty_score":0.582287},"labels":[],"label_agreement":null},{"id":"W4402722070","doi":"10.1145/3670947.3670961","title":"Above-Screen Fingertip Tracking and Hand Representation for Precise Touch Input with a Phone in Virtual Reality","year":2024,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Waterloo","funders":"","keywords":"Computer science; Virtual reality; Phone; Representation (politics); Tracking (education); Human–computer interaction; Computer graphics (images); Computer vision; Artificial intelligence; Psychology","score_opus":0.050894189808655656,"score_gpt":0.32102466726900636,"score_spread":0.2701304774603507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402722070","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":[0.16206588,0.0005629034,0.835406,0.0009309079,0.0002648281,0.00041952194,0.000020098933,0.00010446619,0.00022541333],"genre_scores_gemma":[0.99759316,0.00004944362,0.0019863334,0.00005658016,0.000063363295,0.000075664764,0.0000061805163,0.0000133799285,0.0001558753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866486,0.00008916744,0.00028972424,0.0005339697,0.00021712336,0.00020513721],"domain_scores_gemma":[0.99907535,0.00039263122,0.000063065,0.00029029907,0.00011284237,0.00006582476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005406709,0.00014168232,0.00019342857,0.00025212328,0.00007817445,0.00055297726,0.00023062441,0.000089389905,0.0000021001185],"category_scores_gemma":[0.00008669759,0.000118404656,0.00004295004,0.00070053333,0.00007227229,0.0005660892,0.00007987906,0.00020203664,0.0000043100335],"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":[0.0012232934,0.00049442967,0.010852299,0.0015108971,0.0005755645,0.00022942573,0.09821263,0.0030974008,0.031777814,0.075119436,0.007775458,0.76913136],"study_design_scores_gemma":[0.0062922337,0.0022827454,0.01906986,0.0066753225,0.00011997456,0.00057372206,0.0019254591,0.66374785,0.2322946,0.038731243,0.026210185,0.0020767958],"about_ca_topic_score_codex":0.00015566005,"about_ca_topic_score_gemma":0.00073328917,"teacher_disagreement_score":0.8355273,"about_ca_system_score_codex":0.000028107821,"about_ca_system_score_gemma":0.00004593139,"threshold_uncertainty_score":0.5332373},"labels":[],"label_agreement":null},{"id":"W4402722073","doi":"10.1145/3675094.3678370","title":"Understanding Novice Users' Mental Models of Gesture Discoverability and Designing Effective Onboarding","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Discoverability; Onboarding; Mental model; Computer science; Human–computer interaction; Gesture; Multimedia; Psychology; Cognitive science; Artificial intelligence","score_opus":0.06371536847514297,"score_gpt":0.2723798766189904,"score_spread":0.2086645081438474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402722073","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":[0.042346474,0.0003134317,0.9512335,0.00024105691,0.00032980685,0.00035157593,0.000004775544,0.00015585625,0.0050234883],"genre_scores_gemma":[0.99069524,0.000011648386,0.009155999,0.00003925575,0.000034997363,0.000013849443,0.0000010678034,0.000007721883,0.000040248262],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890196,0.00012227443,0.0001867691,0.0003949691,0.00023156275,0.00016247289],"domain_scores_gemma":[0.9991893,0.0005015459,0.000035973422,0.00017707031,0.000030846375,0.0000652234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005874426,0.00012531865,0.00018964247,0.0001117953,0.00009070696,0.00025414967,0.0001561064,0.00006086185,0.000003678035],"category_scores_gemma":[0.000017860535,0.000097046366,0.000058992522,0.0003209728,0.00004332725,0.001067066,0.0001389304,0.00012667586,0.000004073284],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053890733,0.00015301417,0.0049211015,0.0018574505,0.00060445257,0.00006626835,0.054043293,0.0028643757,0.079089485,0.8180652,0.00081625534,0.03746519],"study_design_scores_gemma":[0.0010243308,0.00030345542,0.00199719,0.0014967321,0.00006597812,0.00016524541,0.0045687896,0.85457665,0.036843237,0.09797195,0.00025598597,0.00073045737],"about_ca_topic_score_codex":0.000040468036,"about_ca_topic_score_gemma":0.000015877256,"teacher_disagreement_score":0.94834876,"about_ca_system_score_codex":0.00015774544,"about_ca_system_score_gemma":0.00003677229,"threshold_uncertainty_score":0.39574358},"labels":[],"label_agreement":null},{"id":"W4402722152","doi":"10.1145/3670947.3670964","title":"Interactive Mediation Techniques for Error-Aware Gesture Input Systems","year":2024,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Gesture; Human–computer interaction; Mediation; Multimedia; Artificial intelligence; Computer vision","score_opus":0.026296434313131077,"score_gpt":0.3152598848590773,"score_spread":0.28896345054594624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402722152","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":[0.001209824,0.001956045,0.98788065,0.0016729174,0.004254116,0.00086394016,0.00005537411,0.0011564243,0.00095073896],"genre_scores_gemma":[0.99569815,0.000067485744,0.0027496829,0.00014837625,0.00040650187,0.0004076561,0.000019227089,0.000029563025,0.00047333588],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983372,0.000121892495,0.0003785128,0.000574139,0.00031599205,0.0002722407],"domain_scores_gemma":[0.9985311,0.00047309214,0.00011680744,0.0004304683,0.0003577119,0.00009083541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062300864,0.00022952542,0.00024860978,0.00043085794,0.00010059291,0.00065887056,0.0006477808,0.00022276351,0.0000042721153],"category_scores_gemma":[0.00008720236,0.00019389292,0.0001483293,0.0006680801,0.00004157931,0.0007682612,0.00014088972,0.00038694404,0.000068249676],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013499302,0.0003730484,0.00068264984,0.004809345,0.0013929129,0.00015313069,0.03748916,0.0004288179,0.011721409,0.60469854,0.18539898,0.152717],"study_design_scores_gemma":[0.00047167527,0.00070145953,0.00014002815,0.0032290467,0.00006744963,0.0003691564,0.001202114,0.29428768,0.047359392,0.0177461,0.6332339,0.0011919651],"about_ca_topic_score_codex":0.000022351905,"about_ca_topic_score_gemma":0.00002667012,"teacher_disagreement_score":0.99448836,"about_ca_system_score_codex":0.00009774327,"about_ca_system_score_gemma":0.0000803831,"threshold_uncertainty_score":0.79067236},"labels":[],"label_agreement":null},{"id":"W4402842892","doi":"10.5267/j.dsl.2024.7.002","title":"Hand gesture recognition based on CNN and YOLO techniques","year":2024,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gesture; Computer science; Artificial intelligence; Computer vision; Gesture recognition; Pattern recognition (psychology)","score_opus":0.02142571655456492,"score_gpt":0.28176380215809743,"score_spread":0.2603380856035325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402842892","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":[0.09695846,0.000118897915,0.8852291,0.0141617535,0.0012464322,0.00030225888,0.0000066947687,0.00046803028,0.0015083697],"genre_scores_gemma":[0.9341614,0.000012544322,0.055377096,0.010208937,0.00015771233,0.000039986102,0.000002347109,0.000011123024,0.000028822313],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99745256,0.00006829347,0.00025753293,0.00088662666,0.001012974,0.00032200877],"domain_scores_gemma":[0.99860466,0.00056988985,0.000051154613,0.00049525197,0.000109629655,0.00016944524],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017646372,0.00017371196,0.00015608085,0.00086338125,0.00035055974,0.0020473094,0.0007155078,0.00007850197,0.000016964002],"category_scores_gemma":[0.00023492692,0.00012967369,0.000057089946,0.0017949258,0.0003553573,0.0011060485,0.00013502841,0.00022801227,0.0003042781],"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":[0.0000070384945,0.000016170927,0.000080097,0.000014063432,0.0000021620717,0.00008384618,0.00028288926,0.0000108650465,0.04681556,0.00033067676,0.0058649625,0.94649166],"study_design_scores_gemma":[0.0013804107,0.0010182916,0.014664046,0.004376782,0.000038976763,0.000815833,0.0001466776,0.27282414,0.36750874,0.02144592,0.31340617,0.0023739934],"about_ca_topic_score_codex":0.0000048594475,"about_ca_topic_score_gemma":0.0000016407879,"teacher_disagreement_score":0.94411767,"about_ca_system_score_codex":0.00007156153,"about_ca_system_score_gemma":0.00007903829,"threshold_uncertainty_score":0.9989887},"labels":[],"label_agreement":null},{"id":"W4403001090","doi":"10.4028/p-ssw44m","title":"Development of a Web-Based Diagnostic Tool Using Acoustic Testing and Computer Vision","year":2024,"lang":"en","type":"article","venue":"Engineering headway","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Algonquin College","funders":"","keywords":"Computer science; Human–computer interaction; World Wide Web; Multimedia","score_opus":0.019896044485304458,"score_gpt":0.24265058949420523,"score_spread":0.22275454500890077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403001090","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":[0.23989333,0.0002522142,0.75904566,0.000017508977,0.00044982292,0.000096555064,0.0000010581523,0.00023105762,0.000012804216],"genre_scores_gemma":[0.64203465,5.9008175e-7,0.3578704,0.00000999411,0.00006496738,0.000006360955,5.1292005e-7,0.000009422331,0.000003127196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991281,0.00001600698,0.000262643,0.00024205587,0.00017644142,0.0001747276],"domain_scores_gemma":[0.99868727,0.0010379249,0.000026793256,0.00014248898,0.000047055008,0.00005846097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030222512,0.00012314526,0.00015061931,0.00018222547,0.000043970984,0.00013010249,0.00012306368,0.000043881835,0.0000019036856],"category_scores_gemma":[0.00016411734,0.000112755595,0.000023139994,0.0003900925,0.000009099645,0.00015393543,0.00007609507,0.00009568591,0.000011377003],"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":[0.0000030848053,0.000084577485,0.0007179041,0.0035214527,0.000084428924,0.0002455192,0.0020705783,0.36616954,0.27614725,0.0008465283,0.0001689313,0.3499402],"study_design_scores_gemma":[0.00012437558,0.000030495114,0.0017184457,0.0014087035,0.0000056673944,0.00004609775,0.0000018370072,0.99282575,0.0025135574,0.0000055783953,0.0011780183,0.00014146573],"about_ca_topic_score_codex":0.000002265509,"about_ca_topic_score_gemma":7.3490725e-7,"teacher_disagreement_score":0.62665623,"about_ca_system_score_codex":0.00004531341,"about_ca_system_score_gemma":0.00013479177,"threshold_uncertainty_score":0.45980397},"labels":[],"label_agreement":null},{"id":"W4403677272","doi":"10.1109/issi63632.2024.10720498","title":"Postural Sway Detection using Kolmogorov-Arnold Network as Siamese Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence","score_opus":0.025419328817304295,"score_gpt":0.27568834914057583,"score_spread":0.25026902032327153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403677272","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":[0.051377825,0.0005674456,0.93323785,0.0005890515,0.0018233609,0.00017622371,8.7258877e-7,0.0007647594,0.011462621],"genre_scores_gemma":[0.97933555,0.0000088402585,0.017634796,0.0004023289,0.0005064968,0.000010677759,0.0000010695621,0.000015481623,0.0020847914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986831,0.00006636153,0.00022935173,0.00042066074,0.00028069667,0.00031985858],"domain_scores_gemma":[0.99940336,0.00007107727,0.000034589517,0.00030913527,0.000076358476,0.0001054797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031021718,0.00015268575,0.0001432653,0.00011253959,0.00013517609,0.00053533126,0.0003002986,0.000099498066,0.00001949862],"category_scores_gemma":[0.000014784432,0.0001208609,0.00009677974,0.000645387,0.000015797306,0.00071053405,0.00012782073,0.00017507096,0.00050795905],"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":[0.000037223825,0.00012411676,0.00032816394,0.0003112993,0.00035314512,0.00039604845,0.0048383153,0.20949651,0.066907905,0.14105655,0.0067003905,0.5694503],"study_design_scores_gemma":[0.00007712832,0.00003418553,0.000051107287,0.000071161325,0.0000082045535,0.00023573013,0.00002274973,0.98670864,0.0014954109,0.008768245,0.0023462337,0.00018123227],"about_ca_topic_score_codex":0.00011303393,"about_ca_topic_score_gemma":0.00004336785,"teacher_disagreement_score":0.9279577,"about_ca_system_score_codex":0.00007650746,"about_ca_system_score_gemma":0.00009371986,"threshold_uncertainty_score":0.65289557},"labels":[],"label_agreement":null},{"id":"W4403985208","doi":"10.23977/acss.2024.080613","title":"A dual-branch network architecture for sEMG-based gesture recognition","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Architecture; Gesture recognition; Gesture; Dual (grammatical number); Speech recognition; Computer architecture; Artificial intelligence; History; Linguistics","score_opus":0.020146350096948533,"score_gpt":0.26892640571899656,"score_spread":0.24878005562204802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403985208","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":[0.0013570744,0.045410853,0.94683564,0.00058170233,0.0043272744,0.00095971103,0.000031188036,0.00029497308,0.00020160138],"genre_scores_gemma":[0.9389675,0.00043354137,0.055264786,0.0007290599,0.0037658615,0.0006624574,0.000051857794,0.0000463057,0.00007863409],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977015,0.0002559101,0.00053783663,0.0007738928,0.0002804254,0.00045043774],"domain_scores_gemma":[0.9983309,0.001013821,0.00011275041,0.00030575827,0.00011858302,0.00011820842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009160802,0.00028753432,0.00044786444,0.0002252765,0.0001252873,0.00069485884,0.0002825591,0.0001575012,0.000002388736],"category_scores_gemma":[0.00001338647,0.00023337209,0.00011457923,0.0006158758,0.00003600262,0.0005967236,0.00006992805,0.00023572239,0.000017302535],"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":[0.000026348616,0.00004946318,0.00024420506,0.0019034487,0.00005492387,0.000088321816,0.0008671496,0.117425986,0.00012431407,0.00324926,0.003137317,0.87282926],"study_design_scores_gemma":[0.00079083594,0.000326734,0.00009155558,0.0032850027,0.000014536913,0.00023901807,0.000016091204,0.7589674,0.00009896168,0.018418035,0.21721771,0.0005341459],"about_ca_topic_score_codex":0.000009402933,"about_ca_topic_score_gemma":0.000035945846,"teacher_disagreement_score":0.93761045,"about_ca_system_score_codex":0.000033490014,"about_ca_system_score_gemma":0.000065249485,"threshold_uncertainty_score":0.9516638},"labels":[],"label_agreement":null},{"id":"W4404851736","doi":"10.1016/j.dsp.2024.104922","title":"A low-resolution infrared gesture recognition method combining weak information reconstruction and joint training strategy","year":2024,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Key Research and Development Program of China; Fujian Provincial Department of Science and Technology; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Joint (building); Computer science; Artificial intelligence; Gesture; Training (meteorology); Gesture recognition; Computer vision; Infrared; Low resolution; Pattern recognition (psychology); High resolution; Speech recognition; Remote sensing; Optics; Geography; Engineering; Physics","score_opus":0.0369937759991173,"score_gpt":0.2580425635378314,"score_spread":0.2210487875387141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404851736","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":[0.007037387,0.00048967113,0.9737357,0.0001504527,0.00027258665,0.00022847574,0.00001948344,0.0005636139,0.01750267],"genre_scores_gemma":[0.99052286,0.000009225879,0.00906184,0.00007300613,0.0001523943,0.00003143872,0.00008042908,0.000013412382,0.00005536724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844414,0.00007339174,0.0005247414,0.00035939255,0.0003181658,0.000280145],"domain_scores_gemma":[0.9992948,0.00010052427,0.00018576805,0.00009581868,0.00020189479,0.00012118987],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005602398,0.0002059423,0.00022200383,0.0003460834,0.00023132427,0.0039043357,0.00011466199,0.00015228757,0.000008685653],"category_scores_gemma":[0.00006255769,0.00019248691,0.00006369156,0.0006237459,0.00004608119,0.010587976,0.00006571789,0.00030093978,0.000054012526],"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":[0.0000060402917,0.0000089951845,0.000008835449,0.00027017252,0.000015097519,0.0000051067077,0.0026897471,0.00009727844,0.0004976424,0.00040377417,0.00004588742,0.9959514],"study_design_scores_gemma":[0.00058461307,0.00020034473,0.0003074354,0.0032635753,0.000025284977,0.0016761422,0.0034970387,0.951339,0.001398053,0.034466855,0.002638684,0.0006029923],"about_ca_topic_score_codex":0.0000039615725,"about_ca_topic_score_gemma":8.984571e-7,"teacher_disagreement_score":0.99534845,"about_ca_system_score_codex":0.000073023504,"about_ca_system_score_gemma":0.00020163869,"threshold_uncertainty_score":0.9971297},"labels":[],"label_agreement":null},{"id":"W4404912850","doi":"10.2196/64590","title":"Video Remote Sign Language Interpreting in Health Communication for Deaf People: Protocol for a Randomized Controlled Trial","year":2024,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Interpreter; Sign language; Language interpretation; Protocol (science); Randomized controlled trial; Health care; Medicine; American Sign Language; Interpretation (philosophy); Applied psychology; Medical education; Psychology; Nursing; Computer science; Linguistics; Alternative medicine","score_opus":0.14488481603556347,"score_gpt":0.5601788316733174,"score_spread":0.4152940156377539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404912850","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000055820024,0.000011924897,0.24998172,0.00306729,0.000020231655,0.74617696,0.0000057294806,0.00018848674,0.0005420786],"genre_scores_gemma":[0.0008900227,3.8493937e-7,0.018001113,0.00008982862,0.00020773026,0.98003405,0.000008041749,0.00003576541,0.0007330625],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9905181,0.0058768094,0.0014514348,0.0006549313,0.000738193,0.0007605087],"domain_scores_gemma":[0.9861681,0.012031452,0.00029284752,0.00084957195,0.0004929897,0.00016505334],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.034881014,0.0002201597,0.0012120003,0.0006316509,0.0002758721,0.001079314,0.0012171245,0.00015090713,0.000019198225],"category_scores_gemma":[0.0043173735,0.00015963284,0.0004080592,0.0009152603,0.00008458463,0.00044490062,0.00028872656,0.0005847026,0.000026790509],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.89430475,0.0003179162,0.0000017022082,0.0044697556,0.000057524878,0.0000042877373,0.0080302125,0.0000055225123,0.00021067556,0.0077252295,0.0059566484,0.07891579],"study_design_scores_gemma":[0.8050296,0.0012628123,7.2988547e-7,0.0094488915,0.0000011171469,0.0000046344294,0.0001439665,0.1051615,0.0001654824,0.005566225,0.0730524,0.00016267881],"about_ca_topic_score_codex":0.00010621343,"about_ca_topic_score_gemma":0.00019470119,"teacher_disagreement_score":0.2338571,"about_ca_system_score_codex":0.00027905218,"about_ca_system_score_gemma":0.0010754223,"threshold_uncertainty_score":0.9999577},"labels":[],"label_agreement":null},{"id":"W4405248237","doi":"10.1007/978-981-97-8422-6_29","title":"Real-Time Sign Language Recognition System for Physically Challenged Community Using Deep Learning Technique","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sign (mathematics); Sign language; Computer science; Artificial intelligence; Speech recognition; Linguistics; Mathematics; Philosophy","score_opus":0.01623244873495059,"score_gpt":0.23586905977816763,"score_spread":0.21963661104321705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405248237","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":[0.00016521172,0.0011311998,0.9878485,0.000043495587,0.00027797758,0.0012024207,0.000012567324,0.0013904386,0.007928195],"genre_scores_gemma":[0.84873664,0.00022086485,0.14476359,0.00006088259,0.002339981,0.0008259305,0.00030878972,0.000666174,0.0020771371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977272,0.00013515174,0.0005860275,0.0006223987,0.0003586139,0.00057058164],"domain_scores_gemma":[0.9973581,0.0016838523,0.00019195613,0.0004885854,0.0001544463,0.00012310439],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007997005,0.00059087743,0.0008171454,0.00069497817,0.00014652393,0.00017148272,0.00060438516,0.0008350491,0.0000057286425],"category_scores_gemma":[0.00031956506,0.00059359346,0.00028860968,0.00041300387,0.000019365772,0.00014409809,0.0001502422,0.0027071475,0.00005997679],"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":[0.000075660224,0.0001405833,8.155559e-7,0.006321643,0.00059001404,0.00044736077,0.0042504696,0.08493443,0.31101233,0.02235932,0.000013290514,0.5698541],"study_design_scores_gemma":[0.00046834632,0.00043519263,0.0000016597587,0.0043423353,0.00012956122,0.00030250344,0.000008673157,0.95960504,0.02216611,0.010068094,0.0010547253,0.0014177891],"about_ca_topic_score_codex":0.000042175725,"about_ca_topic_score_gemma":0.000010737615,"teacher_disagreement_score":0.87467057,"about_ca_system_score_codex":0.0007310592,"about_ca_system_score_gemma":0.00007363893,"threshold_uncertainty_score":0.99965155},"labels":[],"label_agreement":null},{"id":"W4405489710","doi":"10.1109/embc53108.2024.10782354","title":"Visual Scene Understanding for Enhanced EMG Gesture Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Gesture; Gesture recognition; Speech recognition; Artificial intelligence; Computer vision; Human–computer interaction","score_opus":0.07811220468161076,"score_gpt":0.3145098955355796,"score_spread":0.23639769085396886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405489710","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":[0.0015831525,0.0001921787,0.9811647,0.0012549558,0.0016447859,0.00037597815,0.0000072548105,0.0006985997,0.013078383],"genre_scores_gemma":[0.9740331,0.000020924741,0.02369397,0.00029810175,0.00043336893,0.00009300192,0.000021971628,0.000016608104,0.0013889075],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887407,0.00004101781,0.00021454434,0.00042316577,0.00019992105,0.00024727732],"domain_scores_gemma":[0.99939054,0.00026256705,0.00003134276,0.00015083986,0.00008233058,0.00008235154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034577583,0.00012958133,0.00013718999,0.00016513308,0.00011430027,0.0005049211,0.00019634962,0.00009986079,0.00005353939],"category_scores_gemma":[0.000036303394,0.00010609981,0.00009713455,0.0004233198,0.000015158761,0.00052804424,0.00004509974,0.00010015431,0.0003345549],"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":[0.000029586785,0.000120541205,0.000016926153,0.00067195884,0.00019038834,0.000037772108,0.0024593768,0.0000107725755,0.0459985,0.12702946,0.022435626,0.8009991],"study_design_scores_gemma":[0.0026630603,0.00109781,0.00015788402,0.0022398834,0.00010927742,0.00033932607,0.0022543522,0.20117807,0.3766733,0.32766506,0.08345169,0.002170261],"about_ca_topic_score_codex":0.0000033529993,"about_ca_topic_score_gemma":0.000014322983,"teacher_disagreement_score":0.97245,"about_ca_system_score_codex":0.00011811387,"about_ca_system_score_gemma":0.00006758368,"threshold_uncertainty_score":0.48689654},"labels":[],"label_agreement":null},{"id":"W4405603230","doi":"10.2316/j.2025.206-1052","title":"MULTI-OBJECT GRASPING DETECTION BASED ON THE IMPROVED SHUFFLENET NETWORK, 33-42.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Object detection; Computer vision; Artificial intelligence; Object (grammar); Remote sensing; Pattern recognition (psychology); Geography","score_opus":0.018769216977374176,"score_gpt":0.2639024319139,"score_spread":0.24513321493652582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405603230","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":[0.004446227,0.00020673683,0.98747396,0.0044354815,0.0031975785,0.00008804157,0.0000014685362,0.00005348301,0.000097028555],"genre_scores_gemma":[0.9823284,0.000032640608,0.016691687,0.00034274952,0.0005608777,0.0000032832265,0.0000016321766,0.000006770504,0.000031961372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899185,0.000090937996,0.00033185523,0.00012215678,0.00036795577,0.00009522635],"domain_scores_gemma":[0.9991003,0.00027181001,0.00020672042,0.00008868102,0.0002925564,0.000039892373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007383739,0.000088549656,0.00009479453,0.00018914131,0.000074465606,0.0005966204,0.00029018364,0.000045467415,0.000003890933],"category_scores_gemma":[0.00008562201,0.000058262252,0.00007905759,0.00016976394,0.000015070559,0.00035643528,0.000034533026,0.00017911586,0.0000089082205],"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":[0.000040837433,0.00012870466,0.00031134667,0.00006628369,0.00035055826,0.00009493064,0.00083563896,0.49506363,0.016322171,0.015191291,0.0009287626,0.47066584],"study_design_scores_gemma":[0.00023024196,0.00008903518,0.0026358273,0.00030964008,0.0000100410525,0.000118574964,0.000013563994,0.99405086,0.0006306806,0.0009607357,0.000883779,0.00006702309],"about_ca_topic_score_codex":0.000004756426,"about_ca_topic_score_gemma":0.00000703567,"teacher_disagreement_score":0.97788215,"about_ca_system_score_codex":0.00006674748,"about_ca_system_score_gemma":0.00006603516,"threshold_uncertainty_score":0.5753224},"labels":[],"label_agreement":null},{"id":"W4405899614","doi":"10.69845/innovare.v13i2.426","title":"Convolutional neural network for dactylological alphabet recognition of Honduran Sign Language (LESHO)","year":2024,"lang":"en","type":"article","venue":"Innovare Revista de ciencia y tecnología","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algonquin College","funders":"","keywords":"Sign language; Alphabet; Convolutional neural network; Computer science; Sign (mathematics); Artificial intelligence; Speech recognition; Pattern recognition (psychology); Linguistics; Mathematics","score_opus":0.03371527437243288,"score_gpt":0.2852244645304108,"score_spread":0.2515091901579779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405899614","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":[0.19682977,0.0077938293,0.788416,0.0017918312,0.0010657357,0.0011477702,0.00036444404,0.0009816275,0.0016089481],"genre_scores_gemma":[0.9772085,0.000026404416,0.021533469,0.00041004253,0.0004538508,0.00013127505,0.0001243571,0.000017772656,0.00009433976],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99764365,0.00020801582,0.0006679573,0.00060476875,0.00031916288,0.00055645255],"domain_scores_gemma":[0.9983942,0.00056904944,0.000196448,0.00039788472,0.0003458983,0.00009655065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013978158,0.00022987058,0.00038821666,0.00024239802,0.00013322568,0.00024686355,0.00073710794,0.00024138228,0.00006371588],"category_scores_gemma":[0.0004193488,0.00019136416,0.00018980878,0.0015382181,0.00016429898,0.00036244479,0.0001409517,0.00030001177,0.00006071824],"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":[0.00006440154,0.00021298906,0.002011713,0.00095452747,0.00020758552,0.00029344033,0.0011744823,0.000113695714,0.05055791,0.133017,0.039478824,0.7719134],"study_design_scores_gemma":[0.005559305,0.004322965,0.037685875,0.0056706206,0.00045939605,0.0053264904,0.0015848619,0.20617132,0.036114577,0.071246155,0.62057,0.0052884594],"about_ca_topic_score_codex":0.000009461654,"about_ca_topic_score_gemma":0.0000044976514,"teacher_disagreement_score":0.7803787,"about_ca_system_score_codex":0.00012063448,"about_ca_system_score_gemma":0.0002531514,"threshold_uncertainty_score":0.7803604},"labels":[],"label_agreement":null},{"id":"W4405909168","doi":"10.1109/csrswtc64338.2024.10811495","title":"Radar-Based Joint Gesture and Identity Recognition via Multi-Feature Two-Stream Neural Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Gesture; Artificial neural network; Feature (linguistics); Gesture recognition; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Identity (music); Feature extraction; Radar; Telecommunications; Engineering","score_opus":0.03094633899771365,"score_gpt":0.2743082583029509,"score_spread":0.24336191930523726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405909168","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":[0.010601728,0.0025467693,0.97699124,0.0037151552,0.0027603738,0.0005072631,0.000017904049,0.0010944542,0.0017651389],"genre_scores_gemma":[0.8651825,0.000040652558,0.13058193,0.001964508,0.0010784761,0.000055634082,0.00006692615,0.000033446253,0.0009959389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981889,0.00018590387,0.00027054435,0.00065213966,0.00034186296,0.00036064675],"domain_scores_gemma":[0.9991432,0.0001329869,0.0000644456,0.0003514685,0.0001229,0.00018503594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049073517,0.00025101608,0.00024962256,0.00014070103,0.00016531692,0.00089155976,0.0002733855,0.00016114542,0.000048418868],"category_scores_gemma":[0.000025864862,0.00019818792,0.00012202594,0.00068826665,0.000043405824,0.0010689495,0.00013171314,0.00038716767,0.00021535513],"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":[0.00002549955,0.00022264385,0.001585433,0.0005698965,0.00018564699,0.00063224585,0.0007556534,0.00047724554,0.00453498,0.003487184,0.04276889,0.94475466],"study_design_scores_gemma":[0.0025607774,0.00029410335,0.013349011,0.00096955,0.00011352162,0.0010261522,0.000076849035,0.9407419,0.0039787437,0.009044562,0.026454622,0.0013902097],"about_ca_topic_score_codex":0.00007606851,"about_ca_topic_score_gemma":0.0002952993,"teacher_disagreement_score":0.9433645,"about_ca_system_score_codex":0.000042406973,"about_ca_system_score_gemma":0.000059428985,"threshold_uncertainty_score":0.85973316},"labels":[],"label_agreement":null},{"id":"W4406049506","doi":"10.2139/ssrn.5080990","title":"A Fully Embedded High-Density Electromyography Gesture Recognition Platform with a Few-Bit Quantized Siamese Convolution Network","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Bit (key); Convolution (computer science); Computer science; Electromyography; Gesture; Gesture recognition; Speech recognition; Artificial intelligence; Computer network; Physical medicine and rehabilitation; Medicine; Artificial neural network","score_opus":0.011263026421568519,"score_gpt":0.22650362897909213,"score_spread":0.2152406025575236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406049506","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":[0.09772001,0.005882579,0.8883456,0.0021092028,0.0024561917,0.0016826919,0.000036032176,0.00068681553,0.0010808707],"genre_scores_gemma":[0.971397,0.0068148235,0.016727407,0.00075148686,0.002492722,0.000251793,0.00030567942,0.000090706715,0.0011684264],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99117976,0.0007038132,0.0011485579,0.0014100372,0.001207326,0.004350529],"domain_scores_gemma":[0.99561906,0.0002368112,0.0015155401,0.0011084139,0.0012004464,0.00031971274],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004038622,0.00096934405,0.0013055763,0.00086110906,0.0007512149,0.00087526086,0.0017680394,0.001009108,0.000016147438],"category_scores_gemma":[0.00009273825,0.00082575163,0.0006516602,0.0016819745,0.00011188496,0.0007083899,0.00054717093,0.009732049,0.00005546234],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005518823,0.0017753154,0.003144479,0.0014123971,0.01611522,0.0005917495,0.0039606234,0.0042840163,0.0010816808,0.43369552,0.009834317,0.51858586],"study_design_scores_gemma":[0.0052129123,0.0018134251,0.0015904952,0.0024057897,0.0006542623,0.004480266,0.00048931537,0.0031780715,0.00036273,0.9761611,0.0015986625,0.002052924],"about_ca_topic_score_codex":0.00029762616,"about_ca_topic_score_gemma":0.003526828,"teacher_disagreement_score":0.87367696,"about_ca_system_score_codex":0.001809878,"about_ca_system_score_gemma":0.008990527,"threshold_uncertainty_score":0.99941933},"labels":[],"label_agreement":null},{"id":"W4406137625","doi":"10.1109/jsen.2024.3524319","title":"Enhancing ML Model Generalizability for Locomotion Mode Recognition in Prosthetic Gait","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Holland Bloorview Kids Rehabilitation Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Generalizability theory; Gait; Computer science; Gait analysis; Mode (computer interface); Physical medicine and rehabilitation; Artificial intelligence; Computer vision; Biomedical engineering; Engineering; Psychology; Medicine; Human–computer interaction","score_opus":0.030486448588479553,"score_gpt":0.2977567423106318,"score_spread":0.2672702937221523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406137625","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":[0.33193576,0.000036350193,0.6657972,0.0007645852,0.00071852305,0.00037327167,0.0000043008476,0.000044765115,0.00032526886],"genre_scores_gemma":[0.9574223,0.000027677252,0.041550808,0.0003034084,0.00018679001,0.00005603711,0.000003742554,0.00001163352,0.00043760988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981948,0.0002670805,0.0006062072,0.00034930406,0.00024141339,0.00034118065],"domain_scores_gemma":[0.99898505,0.00010829555,0.00018556022,0.0002529007,0.00037269268,0.0000955024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001270201,0.00015793554,0.0002478938,0.00036113354,0.00018316356,0.0002188468,0.00028438517,0.00012312572,0.0000042594297],"category_scores_gemma":[0.00011307099,0.00014716524,0.00013463799,0.00039387462,0.000023327886,0.00046186618,0.000025302365,0.0002846321,0.000018363173],"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":[0.00028067187,0.001175134,0.0013507439,0.00084203976,0.00016132458,0.00008390795,0.008756339,0.4591748,0.2261478,0.0025944212,0.007904132,0.29152867],"study_design_scores_gemma":[0.0011633048,0.00006228511,0.00016289558,0.0003455862,0.000013227667,0.00018846928,0.000048217604,0.8700895,0.0683911,0.059039924,0.00026936017,0.00022616002],"about_ca_topic_score_codex":0.000014029969,"about_ca_topic_score_gemma":0.00007739731,"teacher_disagreement_score":0.62548655,"about_ca_system_score_codex":0.0002130113,"about_ca_system_score_gemma":0.00018599952,"threshold_uncertainty_score":0.60012245},"labels":[],"label_agreement":null},{"id":"W4406520995","doi":"10.1109/jsen.2025.3528308","title":"ASTRA Glove: A Wearable Tracking Device for “Accurate Sensing and Tracking of Realtime Articulations”","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Canadian Institute for Theoretical Astrophysics","funders":"","keywords":"ASTRA; Wearable computer; Tracking (education); Computer science; Glovebox; Computer vision; Artificial intelligence; Embedded system; Engineering; Mechanical engineering; Physics","score_opus":0.03723822131873341,"score_gpt":0.3104890630643556,"score_spread":0.2732508417456222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406520995","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":[0.385227,0.0002917394,0.612128,0.00084604486,0.00065569906,0.00020061285,0.0000031685836,0.0000373427,0.0006103806],"genre_scores_gemma":[0.9643928,0.000029454706,0.035114694,0.000107057574,0.00016257106,0.0000011840522,3.2180907e-7,0.000008268007,0.00018368824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985912,0.00015412734,0.00054282695,0.00022440929,0.00021104958,0.00027639203],"domain_scores_gemma":[0.9984986,0.00041394625,0.0003193966,0.00017376021,0.0004945952,0.000099696495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091162755,0.00012868637,0.0002639639,0.00023320214,0.00028305553,0.0003685328,0.000151885,0.000076291966,0.0000026837536],"category_scores_gemma":[0.0001731202,0.0001177519,0.00009577027,0.00040493297,0.000032219945,0.0005186183,0.00001875832,0.00020870888,0.0000035711726],"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":[0.0001101133,0.00014134329,0.0018904085,0.00051691796,0.00040512424,0.00012216646,0.0056516672,0.019474493,0.19018456,0.0015775788,0.0017726321,0.778153],"study_design_scores_gemma":[0.004157267,0.00023936717,0.01749849,0.0041444674,0.00020602618,0.0042651338,0.0012131641,0.75268376,0.19690943,0.010657172,0.007209484,0.0008162225],"about_ca_topic_score_codex":0.0000139617,"about_ca_topic_score_gemma":0.0000142650115,"teacher_disagreement_score":0.7773368,"about_ca_system_score_codex":0.00003870248,"about_ca_system_score_gemma":0.00012128565,"threshold_uncertainty_score":0.48017833},"labels":[],"label_agreement":null},{"id":"W4406714402","doi":"10.1016/j.rcim.2025.102957","title":"MuViH: Multi-View Hand gesture dataset and recognition pipeline for human–robot interaction in a collaborative robotic finishing platform","year":2025,"lang":"en","type":"article","venue":"Robotics and Computer-Integrated Manufacturing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Polytechnique Montréal","funders":"National Research Council Canada","keywords":"Pipeline (software); Gesture; Computer science; Gesture recognition; Human–computer interaction; Robot; Artificial intelligence; Human–robot interaction; Computer vision; Operating system","score_opus":0.0390156555869773,"score_gpt":0.2931929338244153,"score_spread":0.254177278237438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406714402","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":[0.014597393,0.00048127523,0.9824239,0.000538958,0.0007622815,0.00092170516,0.00013141504,0.000103257116,0.000039797957],"genre_scores_gemma":[0.8376046,0.0003289942,0.15955773,0.0006721049,0.00017765495,0.00012650949,0.0013636029,0.00003237595,0.00013646031],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796325,0.00011284222,0.00063045626,0.0007587385,0.00014917011,0.0003855571],"domain_scores_gemma":[0.9987679,0.0003780088,0.0002269969,0.00031510653,0.00019179887,0.000120181176],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00044571894,0.0003798096,0.00054854935,0.0004360278,0.00037462622,0.0010522645,0.0003117369,0.00020644612,0.000002349274],"category_scores_gemma":[0.000043233715,0.00034171616,0.000052496216,0.00038570876,0.00005972687,0.00079064135,0.00026129556,0.0004381748,0.000004007422],"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":[0.00015625019,0.0006024214,0.00039112577,0.0015746286,0.00032366219,0.000112298716,0.0035712179,0.054349557,0.0011634135,0.0034638348,0.0034332,0.9308584],"study_design_scores_gemma":[0.003958315,0.00025236883,0.0023070488,0.0027039768,0.00008333149,0.00009582523,0.00047553785,0.9712322,0.008812171,0.0034327866,0.0058775265,0.00076892495],"about_ca_topic_score_codex":0.0001803617,"about_ca_topic_score_gemma":0.0008944405,"teacher_disagreement_score":0.9300895,"about_ca_system_score_codex":0.00010584309,"about_ca_system_score_gemma":0.00007435609,"threshold_uncertainty_score":0.99998474},"labels":[],"label_agreement":null},{"id":"W4406757985","doi":"10.1016/j.jbiomech.2025.112534","title":"Two cameras can be as good as four for markerless hand tracking during simple finger movements","year":2025,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Simple (philosophy); Computer vision; Tracking (education); Artificial intelligence; Computer science; Movement (music); Psychology; Art; Aesthetics; Philosophy","score_opus":0.024260916746543366,"score_gpt":0.29622904215770435,"score_spread":0.271968125411161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406757985","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":[0.40334374,0.00045726734,0.5888372,0.0029915557,0.0029782972,0.00049535854,0.000030401796,0.0000451403,0.0008210552],"genre_scores_gemma":[0.9925056,0.00004089991,0.0051545384,0.000986996,0.0002442884,0.00001256737,0.0000020730547,0.00001636284,0.0010366496],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982319,0.000083471146,0.0006437037,0.00024901037,0.00044786753,0.00034405236],"domain_scores_gemma":[0.9984021,0.00016051828,0.00054236926,0.00026519856,0.00048188886,0.0001479078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086124166,0.00017896105,0.0003631421,0.00045445448,0.0002767556,0.0004067258,0.000640455,0.000093624396,0.000014412033],"category_scores_gemma":[0.00014356872,0.00016008956,0.00020554049,0.0005441444,0.000013336717,0.000359619,0.00014035727,0.00019359587,0.0000057428465],"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":[0.00025248245,0.00041836526,0.00032750098,0.0005732715,0.0010546453,0.0004526291,0.0027156763,0.000057377063,0.47133383,0.009716819,0.0028733474,0.51022404],"study_design_scores_gemma":[0.010659175,0.00088439404,0.0003505605,0.0015329885,0.00017965336,0.0013871922,0.0013111663,0.026911864,0.79442674,0.070957385,0.09041614,0.000982754],"about_ca_topic_score_codex":0.00005086035,"about_ca_topic_score_gemma":0.00005699864,"teacher_disagreement_score":0.5891619,"about_ca_system_score_codex":0.00015292896,"about_ca_system_score_gemma":0.00028724733,"threshold_uncertainty_score":0.65282625},"labels":[],"label_agreement":null},{"id":"W4406857612","doi":"10.1109/tmm.2025.3535363","title":"Hand Gesture Recognition From an Open-Set Perspective","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Perspective (graphical); Gesture; Gesture recognition; Set (abstract data type); Artificial intelligence; Human–computer interaction; Speech recognition; Computer vision; Programming language","score_opus":0.03744854021147527,"score_gpt":0.31677089472382225,"score_spread":0.279322354512347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406857612","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":[0.008204585,0.00007547361,0.9824819,0.0018118105,0.0026170136,0.0006780383,0.00026899297,0.0002710108,0.0035911894],"genre_scores_gemma":[0.96949124,0.000032021617,0.027996242,0.0011015411,0.0001488907,0.00024161773,0.000042858792,0.000019740357,0.00092585094],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981696,0.00025924892,0.00028889687,0.0007504984,0.00026986646,0.0002619166],"domain_scores_gemma":[0.99841815,0.00033764463,0.000081709084,0.0006634215,0.00032720077,0.000171876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023321976,0.00023785616,0.0002779721,0.00030031323,0.00039612898,0.0006044083,0.0009598605,0.00021064121,0.00016246384],"category_scores_gemma":[0.000020369418,0.00022560863,0.000084797204,0.00064199505,0.00007869647,0.0010611492,0.000007850772,0.0004094612,0.0005613449],"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":[0.0001820325,0.0012895992,0.000036358913,0.000020323936,0.0003404529,0.00004307077,0.014227827,0.00047343984,0.011739867,0.00030112136,0.0025734634,0.9687725],"study_design_scores_gemma":[0.020515958,0.002079168,0.009633732,0.001592048,0.000589016,0.00009727064,0.010035029,0.21162038,0.66029143,0.04773079,0.031929158,0.003886017],"about_ca_topic_score_codex":0.0011218014,"about_ca_topic_score_gemma":0.0010776527,"teacher_disagreement_score":0.9648864,"about_ca_system_score_codex":0.00015436245,"about_ca_system_score_gemma":0.00014654327,"threshold_uncertainty_score":0.9200053},"labels":[],"label_agreement":null},{"id":"W4407426458","doi":"10.3390/s25041126","title":"A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Lakehead University","funders":"Universidade de Macau","keywords":"Gesture; Computer science; Feature selection; Identification (biology); Selection (genetic algorithm); Feature (linguistics); Gesture recognition; Artificial intelligence; Machine learning; Human–computer interaction; Pattern recognition (psychology)","score_opus":0.01029037630390018,"score_gpt":0.25989645963501795,"score_spread":0.24960608333111778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407426458","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":[0.09695247,0.000049125545,0.8993719,0.001812781,0.000546597,0.0007537054,0.000014947791,0.00010465385,0.00039379334],"genre_scores_gemma":[0.99043244,0.000002172675,0.004839034,0.00010899182,0.000026001719,0.000056040568,0.000032678217,0.000007753584,0.0044949153],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991306,0.000029477624,0.00020245023,0.00031339872,0.00018133197,0.00014273619],"domain_scores_gemma":[0.9992886,0.000094552015,0.00016213283,0.00021038568,0.0002166141,0.000027720527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031373938,0.000112110276,0.0001480502,0.00021927609,0.00015550647,0.000099119585,0.00016789362,0.000097023694,0.0000016342691],"category_scores_gemma":[0.000048390262,0.00009962569,0.00007371765,0.0004616641,0.000015655265,0.00007238058,0.00002049668,0.00014985091,0.00000421496],"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":[0.00028096547,0.0002962636,0.0017036304,0.000547216,0.000110164365,7.8687196e-7,0.0007376392,0.052457567,0.8779224,0.0033442138,0.00063424534,0.061964907],"study_design_scores_gemma":[0.0016735005,0.00016460655,0.003190645,0.00012037696,0.000019094483,0.0000026177206,0.000034745583,0.87527704,0.115286514,0.000106090934,0.004003325,0.00012141741],"about_ca_topic_score_codex":0.000038509213,"about_ca_topic_score_gemma":0.00010127259,"teacher_disagreement_score":0.8945329,"about_ca_system_score_codex":0.000071369366,"about_ca_system_score_gemma":0.00005626826,"threshold_uncertainty_score":0.4062618},"labels":[],"label_agreement":null},{"id":"W4407898168","doi":"10.2139/ssrn.5115825","title":"Sign language recognition using LSTM","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Sign language; Sign (mathematics); Speech recognition; Natural language processing; Artificial intelligence; Linguistics; Mathematics; Philosophy","score_opus":0.013879326657060563,"score_gpt":0.2684516152907209,"score_spread":0.2545722886336604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407898168","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":[0.06000906,0.0025199484,0.9311348,0.00064106815,0.00066512654,0.000118773896,0.000001086287,0.00009774534,0.0048123915],"genre_scores_gemma":[0.99416715,0.00034279676,0.0035232448,0.00033487505,0.00030010007,0.0000047799113,0.0000023128275,0.000009427159,0.0013153105],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979106,0.00018120598,0.00026503965,0.00021491708,0.00021391286,0.0012143012],"domain_scores_gemma":[0.9994241,0.00006194025,0.0001259879,0.0002016141,0.00012936624,0.00005697496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015368965,0.000119538476,0.00015035392,0.00025726846,0.00021493305,0.00019305803,0.00044657136,0.000075607786,0.000012244502],"category_scores_gemma":[0.00006117577,0.00010709329,0.00009481531,0.0005276243,0.00001542953,0.00041795804,0.000057488098,0.00092561013,0.000090498484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000138608775,0.00006851118,0.0002406515,0.000016922957,0.00017741726,0.000022885712,0.00058676815,0.00002713098,0.005005335,0.059147727,0.0002564555,0.9344363],"study_design_scores_gemma":[0.0026778057,0.00038125485,0.00040332737,0.0005121566,0.00010827985,0.00442799,0.004740088,0.011685626,0.008015118,0.95888263,0.0073144175,0.0008513085],"about_ca_topic_score_codex":0.000037095622,"about_ca_topic_score_gemma":0.00011061662,"teacher_disagreement_score":0.9341581,"about_ca_system_score_codex":0.0005621037,"about_ca_system_score_gemma":0.0016192225,"threshold_uncertainty_score":0.43671376},"labels":[],"label_agreement":null},{"id":"W4407950258","doi":"10.1109/aixvr63409.2025.00036","title":"Towards Skeleton Based Keystroke Recognition in Virtual Reality","year":2025,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Virtual reality; Skeleton (computer programming); Keystroke logging; Human–computer interaction; Artificial intelligence; Keystroke dynamics; Computer vision; Computer security; Password; Programming language","score_opus":0.028127216929858114,"score_gpt":0.28148403682489803,"score_spread":0.25335681989503994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407950258","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":[0.008590994,0.000015795224,0.8558524,0.0037762118,0.000561547,0.00022795415,0.000006571202,0.00022326635,0.13074526],"genre_scores_gemma":[0.99102086,0.000004150412,0.0059880824,0.00144273,0.00004885718,0.000054698652,0.000016865832,0.0000038905023,0.0014198542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865055,0.00021720011,0.00032539893,0.0003717666,0.00021925589,0.00021581315],"domain_scores_gemma":[0.99929327,0.00012166455,0.000051550276,0.0003583158,0.000117097305,0.000058091035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007148755,0.0001152179,0.00017984348,0.00029394275,0.000044950648,0.00011129864,0.00035938606,0.00010260341,0.000058767884],"category_scores_gemma":[0.00010303305,0.0001035461,0.00006258233,0.0007896458,0.000019412659,0.00027338846,0.000078143086,0.00013343699,0.00015894244],"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":[0.000019995481,0.00019077754,0.0012514247,0.00005858927,0.000015454578,0.000020281866,0.00017627586,0.000103648636,0.0006269339,0.019698406,0.0057432647,0.97209495],"study_design_scores_gemma":[0.013072877,0.00093432935,0.1201346,0.0019765194,0.000049508104,0.000045590998,0.0006526151,0.44520336,0.14110985,0.07850688,0.1956719,0.002641968],"about_ca_topic_score_codex":0.00031984167,"about_ca_topic_score_gemma":0.00028105718,"teacher_disagreement_score":0.98242986,"about_ca_system_score_codex":0.00009113562,"about_ca_system_score_gemma":0.00019924925,"threshold_uncertainty_score":0.42224875},"labels":[],"label_agreement":null},{"id":"W4408037262","doi":"10.18280/ts.420146","title":"Performance Analysis of Neural Network Based Human Palmprint and Hand Gesture Recognition Techniques","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Gesture; Pattern recognition (psychology); Gesture recognition; Speech recognition","score_opus":0.01836294639920607,"score_gpt":0.2490614975615271,"score_spread":0.23069855116232102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408037262","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":[0.76927245,0.000110305955,0.22827065,0.0003379787,0.00009495266,0.00037553385,0.000010417549,0.00012612922,0.0014015883],"genre_scores_gemma":[0.99447507,0.000010050178,0.005018399,0.00030677795,0.00006303169,0.000057778492,0.000031621177,0.0000041772514,0.000033122302],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99875593,0.00011765237,0.00038750516,0.00031621798,0.0002285605,0.00019415472],"domain_scores_gemma":[0.9993454,0.00008392065,0.00015138162,0.00021655664,0.00015034736,0.00005234957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052828155,0.0001458346,0.0002994681,0.0003742214,0.00018104719,0.00011315222,0.00024372729,0.00007265552,0.000044561068],"category_scores_gemma":[0.0000054389375,0.0001289902,0.00010101225,0.0010603766,0.00006415115,0.00018038583,0.00006902522,0.00010654556,0.0000014382892],"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":[0.0000852324,0.00040812194,0.17712955,0.0005202692,0.0014226179,0.000014086607,0.0010492562,0.005535469,0.030981828,0.0019796921,0.0012846608,0.77958924],"study_design_scores_gemma":[0.0012299757,0.00050581835,0.53178936,0.0005265453,0.0007738681,0.0000050150243,0.00004246572,0.41413334,0.047467694,0.0006665708,0.0023539024,0.0005054622],"about_ca_topic_score_codex":0.00001648994,"about_ca_topic_score_gemma":0.00003101238,"teacher_disagreement_score":0.7790837,"about_ca_system_score_codex":0.000023347686,"about_ca_system_score_gemma":0.000028626971,"threshold_uncertainty_score":0.52600676},"labels":[],"label_agreement":null},{"id":"W4408142095","doi":"10.5220/0013318400003912","title":"Diffusion Transformer Framework for Speech-Driven Stylized Gesture Generation","year":2025,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Stylized fact; Computer science; Transformer; Gesture; Diffusion; Speech recognition; Artificial intelligence; Electrical engineering; Engineering; Physics; Voltage","score_opus":0.024837664905985983,"score_gpt":0.2946500798171319,"score_spread":0.26981241491114594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408142095","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":[0.0051293205,0.000088304,0.98070383,0.004886935,0.0010519627,0.00067958166,0.000004229276,0.00020484049,0.0072509865],"genre_scores_gemma":[0.4405566,0.000046883262,0.5499652,0.0026233802,0.00042160618,0.00020302585,0.000026721422,0.000011798104,0.0061448123],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989859,0.00005172165,0.00024122332,0.000342084,0.0001790151,0.0002000947],"domain_scores_gemma":[0.99928397,0.00015467778,0.00004463822,0.0003228762,0.00013847508,0.000055350447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018742408,0.00012907646,0.00018040916,0.000121550365,0.000156555,0.00017041285,0.0003402239,0.00018295618,0.000038198083],"category_scores_gemma":[0.000051860417,0.000099322526,0.00010707612,0.00035777644,0.000012670465,0.00023743033,0.000026742122,0.000112999274,0.00003991485],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002015549,0.00013013816,0.00036226297,0.0000839666,0.000060796192,0.0000030432125,0.0008345275,0.000036788944,0.030312225,0.6139832,0.015135804,0.3390371],"study_design_scores_gemma":[0.0050268206,0.0003244157,0.0028797318,0.00048776536,0.00008464891,0.0000310959,0.00022662533,0.19978727,0.15140873,0.12464066,0.51400363,0.0010985666],"about_ca_topic_score_codex":0.000008901312,"about_ca_topic_score_gemma":0.000044592856,"teacher_disagreement_score":0.49886787,"about_ca_system_score_codex":0.000031104355,"about_ca_system_score_gemma":0.000060762195,"threshold_uncertainty_score":0.40502548},"labels":[],"label_agreement":null},{"id":"W4408345998","doi":"10.1109/icassp49660.2025.10888240","title":"L2G: Head Gesture Animation Using an Emotion Guided Language Model","year":2025,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Gesture; Animation; Head (geology); Human–computer interaction; Computer facial animation; Computer graphics (images); Computer animation; Artificial intelligence; Geology","score_opus":0.0628528315647968,"score_gpt":0.34579430970201464,"score_spread":0.2829414781372178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408345998","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":[0.1473299,0.000041232026,0.8383342,0.00053962483,0.00020613673,0.00015140114,0.0000012799883,0.00025771244,0.013138532],"genre_scores_gemma":[0.9053976,0.0000014367023,0.092120506,0.00068635127,0.000059426242,0.000005327237,0.000008686563,0.0000048494535,0.0017158346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991063,0.00008662644,0.00020850575,0.00027633412,0.00017435246,0.00014787698],"domain_scores_gemma":[0.9993803,0.000018068893,0.000054392178,0.0003744568,0.00012198237,0.00005082436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002832156,0.000097754906,0.00011762125,0.0001494458,0.00009974541,0.00017856043,0.00027786318,0.00008564547,0.0000091156435],"category_scores_gemma":[0.000022687307,0.00008453075,0.00003635542,0.00038749183,0.000008471927,0.00071088533,0.00006740938,0.00007282323,0.0000288731],"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":[0.000017547443,0.000725831,0.0017963663,0.000369887,0.00011518904,0.000040299496,0.02032772,0.09126712,0.28052348,0.29764146,0.013012658,0.29416245],"study_design_scores_gemma":[0.00021699941,0.000014292689,0.00060257484,0.000051300027,0.00000516516,0.000015239797,0.00015228135,0.9923294,0.004359886,0.0018936301,0.0002526712,0.00010654114],"about_ca_topic_score_codex":0.00009631806,"about_ca_topic_score_gemma":0.0000690873,"teacher_disagreement_score":0.9010623,"about_ca_system_score_codex":0.000057115107,"about_ca_system_score_gemma":0.000072923365,"threshold_uncertainty_score":0.3447064},"labels":[],"label_agreement":null},{"id":"W4408358385","doi":"10.1109/icses63760.2024.10910797","title":"A Real-Time Robotic Movement Control System Based on Hand Gesture Recognition Using Human Computer Interaction (HCI) Principles","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Gesture recognition; Computer science; Gesture; Movement (music); Robotic hand; Human–computer interaction; Movement control; Control (management); Artificial intelligence; Computer vision; Robot; Physical medicine and rehabilitation","score_opus":0.04168527492378776,"score_gpt":0.27393596026411177,"score_spread":0.232250685340324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408358385","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":[0.02243777,0.000024819428,0.96739286,0.00042346056,0.0018061762,0.0008183103,0.000010969484,0.000986691,0.006098942],"genre_scores_gemma":[0.97719055,0.0000014906476,0.021230062,0.0004292498,0.00063844764,0.00006709635,0.00003705574,0.000032084175,0.00037397514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763644,0.00031936265,0.000538369,0.0007043956,0.00048163685,0.00031980406],"domain_scores_gemma":[0.99881995,0.0002670907,0.00014794484,0.00044262374,0.00018579123,0.00013661759],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006061875,0.00029137114,0.00034942912,0.00040005523,0.00026379834,0.001186295,0.0002864849,0.00013945757,0.00006382468],"category_scores_gemma":[0.00000958037,0.00023570599,0.0001574884,0.00037414525,0.00002699849,0.000529135,0.00006398614,0.00022367471,0.0006163012],"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":[0.00031341676,0.0019040519,0.00073797524,0.006627654,0.0016490003,0.0017679739,0.004582084,0.46656734,0.27251127,0.043243535,0.009704314,0.19039139],"study_design_scores_gemma":[0.00068593456,0.0002445067,0.00028935075,0.0019922897,0.000044150656,0.00006311973,0.0000370694,0.9934414,0.002268255,0.00007930058,0.00055424846,0.0003003491],"about_ca_topic_score_codex":0.00011574731,"about_ca_topic_score_gemma":0.000019284536,"teacher_disagreement_score":0.95475274,"about_ca_system_score_codex":0.00041873328,"about_ca_system_score_gemma":0.00008639715,"threshold_uncertainty_score":0.9998506},"labels":[],"label_agreement":null},{"id":"W4408565385","doi":"10.1109/iccirt59484.2024.10921810","title":"Sign Language Detection in Real-Time Applications","year":2024,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Sign (mathematics); Artificial intelligence","score_opus":0.010549463633100834,"score_gpt":0.26226072163926833,"score_spread":0.2517112580061675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408565385","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":[0.003010474,0.000120927514,0.92960775,0.00026981055,0.000119843,0.00022108553,0.0000010210831,0.0006358617,0.066013224],"genre_scores_gemma":[0.9916613,0.000009634582,0.003873198,0.00004260507,0.00009801721,0.0001409884,0.00000184427,0.0000049189744,0.004167493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949133,0.000034395736,0.00010613647,0.00019274258,0.00008652666,0.00008884312],"domain_scores_gemma":[0.99969363,0.000071900635,0.000009374873,0.00018066877,0.000014717562,0.000029714738],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018494768,0.00004562555,0.000052929878,0.00013277736,0.000020190697,0.00012160801,0.00014524932,0.000033934662,0.0000477076],"category_scores_gemma":[0.000004137082,0.000038165286,0.000021883357,0.0005547646,0.0000051486936,0.0001844394,0.000027516546,0.000053278352,0.0022166865],"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":[5.6976273e-7,0.000026914437,0.000024642643,0.000040373725,0.000007988762,0.000026858368,0.001394465,0.000010792445,0.08381251,0.015424994,0.00078748254,0.8984424],"study_design_scores_gemma":[0.0009794221,0.0002361167,0.005993244,0.0004092692,0.000023781222,0.00048397225,0.0010420337,0.3900393,0.2667391,0.016086834,0.31645015,0.0015167878],"about_ca_topic_score_codex":0.00009392903,"about_ca_topic_score_gemma":0.000076510565,"teacher_disagreement_score":0.9886508,"about_ca_system_score_codex":0.000029375802,"about_ca_system_score_gemma":0.000020770423,"threshold_uncertainty_score":0.9985602},"labels":[],"label_agreement":null},{"id":"W4408738012","doi":"10.1016/j.dib.2025.111502","title":"Kenyan sign language word-based pose dataset","year":2025,"lang":"en","type":"article","venue":"Data in Brief","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung; International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Kenya; Word (group theory); Sign (mathematics); Computer science; Sign language; Natural language processing; Research article; Artificial intelligence; Linguistics; Library science; Mathematics; Biology; Ecology","score_opus":0.028318776472697628,"score_gpt":0.3111571357547778,"score_spread":0.2828383592820802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408738012","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":[0.009769191,0.0018503189,0.9181858,0.011115889,0.0031257216,0.0013334809,0.04096253,0.0006703391,0.012986757],"genre_scores_gemma":[0.8654207,0.000024056746,0.040382855,0.019713163,0.00038360202,0.00008352594,0.07306374,0.000028440942,0.00089990033],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987417,0.0001361282,0.00022820462,0.0005006458,0.00018455507,0.0002087984],"domain_scores_gemma":[0.99738246,0.00019400106,0.000048331458,0.002300411,0.000021525113,0.000053272986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006330185,0.000103424856,0.00015055756,0.00017552344,0.00004309522,0.00018893422,0.0021442606,0.00005451724,0.000039264716],"category_scores_gemma":[0.00017008117,0.00009835108,0.000014301271,0.00066716503,0.000026011901,0.0005125919,0.0006642138,0.00013047563,0.00018840728],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001398482,0.00019816105,0.0019350938,0.000078221354,0.00002189337,0.00025297957,0.00025557476,0.000014062658,0.0005085829,0.003088716,0.81366014,0.17997262],"study_design_scores_gemma":[0.001161097,0.000019525682,0.0058338963,0.00020373554,0.000007931796,0.0000069455073,0.00006415772,0.012997091,0.0011895432,0.0003878005,0.97785735,0.00027090524],"about_ca_topic_score_codex":0.00041632558,"about_ca_topic_score_gemma":0.00036728062,"teacher_disagreement_score":0.8778029,"about_ca_system_score_codex":0.00002375312,"about_ca_system_score_gemma":0.00012203266,"threshold_uncertainty_score":0.40106407},"labels":[],"label_agreement":null},{"id":"W4409760500","doi":"10.5038/2074-1235.45.2.1230","title":"Geographical Variation in Incubation Shift Length of Ancient Murrelets Synthliborapmphus Antiquus Determined from Geolocator Devices","year":2017,"lang":"en","type":"article","venue":"Marine ornithology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"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":"Environment and Climate Change Canada","keywords":"Seabird; Ornithology; Incubation; Variation (astronomy); Zoology; Geography; Biology; Ecology; Southern Hemisphere; Physics; Astronomy","score_opus":0.013947646471284825,"score_gpt":0.2555773451053344,"score_spread":0.2416296986340496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409760500","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":[0.95327497,0.00004585423,0.04400199,0.0007500564,0.00067447836,0.00022645012,0.000010474897,0.000063566105,0.0009521877],"genre_scores_gemma":[0.99443465,0.000011627784,0.0052281646,0.00015498055,0.000094013114,0.000036928606,0.000021066153,0.000008791261,0.000009781702],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982104,0.0002885612,0.00049340393,0.0004963977,0.00023100496,0.0002802477],"domain_scores_gemma":[0.9981768,0.00024328376,0.0005086468,0.0008599181,0.00013622221,0.0000751122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006696591,0.00016666681,0.00037345302,0.00035663522,0.00013723718,0.00011338397,0.0008757606,0.00020331975,0.00003473076],"category_scores_gemma":[0.0003619922,0.00015537179,0.000059777813,0.00024267721,0.00012242419,0.00044518674,0.00041706424,0.00017884429,0.000040254246],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027819704,0.00014128402,0.9481375,0.000026312822,0.000023485987,0.00006179183,0.00043344175,0.000011110355,0.0013276294,0.0014079286,0.000013884309,0.048387766],"study_design_scores_gemma":[0.0007206523,0.00010121673,0.99211556,0.00003010199,0.000011095067,0.000023119692,0.0000066905563,0.003165614,0.00077984965,0.002530933,0.0003569348,0.00015822037],"about_ca_topic_score_codex":0.0009157772,"about_ca_topic_score_gemma":0.002230904,"teacher_disagreement_score":0.048229545,"about_ca_system_score_codex":0.000026137104,"about_ca_system_score_gemma":0.000059402148,"threshold_uncertainty_score":0.6335878},"labels":[],"label_agreement":null},{"id":"W4411100376","doi":"10.1007/978-3-031-91578-9_1","title":"Electron microscopy image showing a detailed view of a surface with a textured, uneven pattern. The image highlights the fine structural details and variations in the surface morphology, typical of high-resolution electron microscopy. The grayscale tones emphasize the contrast between different areas, revealing intricate features and potential material composition differences. AirLetters A simple black and white symbol depicting a hand with three horizontal lines extending from the palm, representing a gesture or motion. The design is minimalistic and stylized. : An Open Vide A blue and white road sign with a red border, featuring a symbol of a bridge. The bridge icon is depicted in light blue against a white background, enclosed within a circular red border. The sign indicates the presence of a bridge or overpass. Dataset of Characters Drawn in the Air","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Surface (topology); Resolution (logic); Morphology (biology); Computer science; Image (mathematics); Mathematical morphology; Electron microscope; High resolution; Computer vision; Artificial intelligence; Microscopy; Image formation; Computer graphics (images); Optics; Image processing; Materials science; Physics; Geometry; Geology; Mathematics; Remote sensing","score_opus":0.015309186268521199,"score_gpt":0.2712221496279156,"score_spread":0.2559129633593944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411100376","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":[0.83696675,0.0007287624,0.14608204,0.011481275,0.000035632132,0.0042814924,0.00041441427,0.000009319357,2.8617134e-7],"genre_scores_gemma":[0.9975662,0.00013100989,0.0016474602,0.0003714219,0.00006153232,0.00012467835,0.00006955251,0.00002693444,0.0000011677957],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9940623,0.0024864832,0.0010707354,0.001058068,0.0007645514,0.00055783644],"domain_scores_gemma":[0.991902,0.005120648,0.0015175121,0.0011748058,0.00023615359,0.000048893264],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0028889247,0.0006584624,0.0010021554,0.00016080518,0.0011347156,0.0013410604,0.0025242919,0.00018747455,9.875835e-7],"category_scores_gemma":[0.00020445113,0.00021024984,0.00006526733,0.0012095772,0.002746139,0.00046179423,0.0008009138,0.0010592549,1.6593498e-8],"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":[0.013561874,0.0005603906,0.18571346,0.0014540711,0.0021002176,0.00020496875,0.3762966,0.008008577,0.39991054,0.00024604663,0.00010198808,0.01184124],"study_design_scores_gemma":[0.0024626956,0.0007227474,0.9388412,0.0025353464,0.00049948465,0.00041420915,0.00073742185,0.040976457,0.011995261,0.00040215027,0.0000017578913,0.00041123168],"about_ca_topic_score_codex":0.002488234,"about_ca_topic_score_gemma":0.008774984,"teacher_disagreement_score":0.75312775,"about_ca_system_score_codex":0.000062246116,"about_ca_system_score_gemma":0.00030651226,"threshold_uncertainty_score":0.9999678},"labels":[],"label_agreement":null},{"id":"W4411712364","doi":"10.1145/3733052","title":"VibRing: A Wearable Vibroacoustic Sensor for Single-Handed Gesture Recognition","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wearable computer; Gesture; Gesture recognition; Computer science; Speech recognition; Artificial intelligence; Embedded system","score_opus":0.07046285692990022,"score_gpt":0.31309323018541424,"score_spread":0.242630373255514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411712364","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":[0.688633,0.00010237136,0.26266488,0.0088042645,0.010189113,0.00368476,0.000027702867,0.0010094477,0.024884414],"genre_scores_gemma":[0.9697026,0.000004654264,0.028145218,0.0006644671,0.0005147919,0.00013787311,0.0000052439227,0.000022479977,0.000802681],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982652,0.000028213684,0.0005127372,0.0005843683,0.0003142739,0.0002952167],"domain_scores_gemma":[0.9979195,0.00030762353,0.00047308102,0.00052748405,0.00071964343,0.000052655436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003897016,0.00025484248,0.00032514532,0.00036487644,0.00030716317,0.00040703523,0.0015566489,0.0001512296,0.0000062510167],"category_scores_gemma":[0.0003708175,0.00020119634,0.00024140302,0.00051573466,0.00003842278,0.00074104546,0.00051714736,0.00029716728,0.00002455117],"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":[0.00037773052,0.0010756595,0.0003304864,0.0017059837,0.00043502948,0.0000019172958,0.0025727602,0.00040274573,0.76714075,0.0071021505,0.035998087,0.1828567],"study_design_scores_gemma":[0.0030228137,0.0012565996,0.0020931617,0.0064743483,0.00018484077,0.00017668171,0.00040212777,0.030457174,0.88521606,0.056502122,0.01336524,0.0008488559],"about_ca_topic_score_codex":0.000008378968,"about_ca_topic_score_gemma":0.0000019915183,"teacher_disagreement_score":0.28106958,"about_ca_system_score_codex":0.00015842212,"about_ca_system_score_gemma":0.000027370937,"threshold_uncertainty_score":0.82045484},"labels":[],"label_agreement":null},{"id":"W4412399046","doi":"10.1109/iccsai64074.2025.11064088","title":"Assistive Vision: Intelligent Blind Stick","year":2025,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Computer vision; Human–computer interaction; Artificial intelligence; Machine vision","score_opus":0.021360393600259178,"score_gpt":0.3111228724819096,"score_spread":0.28976247888165046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412399046","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":[0.0003519118,0.00008671437,0.8274595,0.0014408493,0.0007655817,0.00011511374,5.336915e-7,0.00012267866,0.16965713],"genre_scores_gemma":[0.9494831,0.000010988768,0.02297522,0.0013754572,0.00007844855,0.000022921411,0.0000015656793,0.0000033108602,0.026048997],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922687,0.000053496293,0.00017844808,0.000255789,0.00015755069,0.00012786002],"domain_scores_gemma":[0.9993225,0.00018957438,0.000030383615,0.00030770074,0.00009440287,0.000055470027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021017551,0.00007638427,0.000107927815,0.00012380395,0.00006271545,0.00016077729,0.0003824167,0.000045677552,0.00011035278],"category_scores_gemma":[0.000052511517,0.000057662666,0.000044035194,0.00047247426,0.00001803561,0.00014799467,0.000169508,0.00007206217,0.00069115276],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001263874,0.00015821264,0.0007109935,0.000032660046,0.00007133429,0.000022651135,0.00039034084,0.000022292988,0.00017186017,0.2845203,0.057137024,0.65674967],"study_design_scores_gemma":[0.0014703035,0.00023647124,0.012705351,0.00039009348,0.00002688748,0.000035726385,0.0004388536,0.030602682,0.023907747,0.018980894,0.9105912,0.00061378727],"about_ca_topic_score_codex":0.000008939036,"about_ca_topic_score_gemma":0.000011387055,"teacher_disagreement_score":0.9491312,"about_ca_system_score_codex":0.000029891962,"about_ca_system_score_gemma":0.00007026438,"threshold_uncertainty_score":0.8883601},"labels":[],"label_agreement":null},{"id":"W4412446280","doi":"10.1109/icici65870.2025.11069459","title":"Implementation and Evaluation of a Real Time Sign Language Recognition System","year":2025,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Sign language; Sign (mathematics); Speech recognition; Artificial intelligence; Natural language processing; Linguistics","score_opus":0.027239746059637884,"score_gpt":0.33144065013103285,"score_spread":0.304200904071395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412446280","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":[0.7364273,0.00013194677,0.14673145,0.00023545275,0.00025517758,0.0010058356,0.0000132655105,0.00024229135,0.11495725],"genre_scores_gemma":[0.99658954,0.000003903749,0.0031186999,0.00002342296,0.000018969784,0.000045414337,0.00001953764,0.0000020574178,0.00017846466],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903286,0.00023997534,0.00022435984,0.00016893663,0.00025973064,0.00007416483],"domain_scores_gemma":[0.9994046,0.000073738935,0.00008779732,0.00014273827,0.00026953832,0.000021605303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012228152,0.00005589819,0.00010253625,0.00013999165,0.000034465353,0.000043912958,0.000084013525,0.0000342978,0.00004160192],"category_scores_gemma":[0.000015101259,0.000049482187,0.000019534444,0.0002562588,0.000008427802,0.00020840505,0.000035785575,0.000021497643,0.000040785086],"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":[0.0000043141054,0.000017838074,0.00031732456,0.00012161042,0.00003326827,7.838146e-7,0.001206281,0.0000026683335,0.018086148,0.005225301,0.0006697529,0.9743147],"study_design_scores_gemma":[0.012372645,0.0006002636,0.06461595,0.001722123,0.0006525412,0.00011813032,0.032634016,0.33363703,0.5416862,0.010205287,0.0006161345,0.001139735],"about_ca_topic_score_codex":0.00018782358,"about_ca_topic_score_gemma":0.00004111044,"teacher_disagreement_score":0.973175,"about_ca_system_score_codex":0.000052786865,"about_ca_system_score_gemma":0.00007276279,"threshold_uncertainty_score":0.20178251},"labels":[],"label_agreement":null},{"id":"W4412494709","doi":"10.14419/mwb8ve64","title":"Gesture Language Recognition Through Computer Vision and a Spatial-Temporal Mathematical Model","year":2025,"lang":"en","type":"article","venue":"International Journal of Basic and Applied Sciences","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Artificial intelligence; Human–computer interaction; Computer vision; Speech recognition; Computer graphics (images)","score_opus":0.0206910363272568,"score_gpt":0.2986150721000214,"score_spread":0.2779240357727646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412494709","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":[0.1276215,0.00016087432,0.8640198,0.0026377202,0.0003048406,0.00007065612,0.0000027667165,0.000014456479,0.0051673823],"genre_scores_gemma":[0.9357983,0.000035799145,0.06324978,0.00071538694,0.00014784532,0.0000023141804,9.587745e-7,0.0000018700057,0.000047742906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989442,0.000030476562,0.00030830255,0.00018969407,0.00043003296,0.000097253454],"domain_scores_gemma":[0.99942493,0.0001335895,0.00018808142,0.000059667313,0.00014685877,0.00004687163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005721184,0.00009163005,0.00016634296,0.00015767029,0.00009446418,0.0003116384,0.00039143048,0.000047343026,0.000006336689],"category_scores_gemma":[0.000019523255,0.00006295149,0.000037057052,0.00014303537,0.00014242316,0.0004240981,0.00014025226,0.00011692225,0.0000046063547],"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":[0.00006606563,0.00020274585,0.0005808716,0.00005954747,0.00011273677,0.000055030436,0.005349959,0.00051153713,0.0035845411,0.109663,0.0021242304,0.8776897],"study_design_scores_gemma":[0.0024148067,0.00038967046,0.0027859819,0.0010717723,0.000041768082,0.0010564424,0.0012599714,0.52924514,0.0058024875,0.45425406,0.0012430538,0.00043482942],"about_ca_topic_score_codex":0.000009334104,"about_ca_topic_score_gemma":0.000006240091,"teacher_disagreement_score":0.8772549,"about_ca_system_score_codex":0.000015214091,"about_ca_system_score_gemma":0.00007534592,"threshold_uncertainty_score":0.30051363},"labels":[],"label_agreement":null},{"id":"W4412934359","doi":"10.2139/ssrn.5354741","title":"Uncertainty Reduction in Automated Rula Ergonomic Assessment Using Ensemble of Deep Learning Based Human Pose Estimation Models","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Reduction (mathematics); Computer science; Pose; Estimation; Deep learning; Machine learning; Engineering; Mathematics; Systems engineering","score_opus":0.02123404434624816,"score_gpt":0.3138276196816027,"score_spread":0.29259357533535457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412934359","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":[0.20132805,0.0004189949,0.7968716,0.00008984766,0.00055807526,0.00031901774,0.0000017165812,0.00015837819,0.00025429795],"genre_scores_gemma":[0.98073334,0.00012705056,0.018889157,0.00000803751,0.000104635845,0.00001956819,0.0000363208,0.00001667865,0.000065193264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99619055,0.00077949313,0.0009679009,0.0005141684,0.00041390478,0.0011340068],"domain_scores_gemma":[0.99812347,0.00006848231,0.0010484227,0.00038421384,0.00030889778,0.00006651874],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003462988,0.00030099408,0.0005343357,0.0008900371,0.00023000748,0.00020695823,0.00066042336,0.00030116533,0.0000031171091],"category_scores_gemma":[0.00003603863,0.0003182246,0.00019321001,0.00043409827,0.000026849419,0.0004031965,0.00023062434,0.0032649636,0.0000013808176],"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":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00001288746,0.00007002326,0.000059955735,0.000082960105,0.00008447344,0.000002181332,0.00025172232,0.9701411,0.0017566514,0.0089792395,0.000002025269,0.018556774],"study_design_scores_gemma":[0.0006297602,0.000091169844,0.00013944926,0.0005993881,0.000037353722,0.000116957475,0.00019131723,0.89683336,0.00025606042,0.10087122,0.0000032241765,0.00023074595],"about_ca_topic_score_codex":0.00050400884,"about_ca_topic_score_gemma":0.0003321008,"teacher_disagreement_score":0.7794053,"about_ca_system_score_codex":0.0047116796,"about_ca_system_score_gemma":0.0063397516,"threshold_uncertainty_score":0.999927},"labels":[],"label_agreement":null},{"id":"W4413159666","doi":"10.3390/app15168921","title":"Hand Kinematic Model Construction Based on Tracking Landmarks","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Computer science; Geology; Artificial intelligence; Physics; Classical mechanics","score_opus":0.023368791585145474,"score_gpt":0.26494756464645325,"score_spread":0.24157877306130776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413159666","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":[0.0076940395,0.000013545117,0.88876396,0.0007015353,0.0003026015,0.00018133143,8.184678e-7,0.00009281856,0.10224933],"genre_scores_gemma":[0.9509531,6.3048543e-7,0.048181314,0.00074188877,0.000021827747,0.00003337992,6.8011775e-7,0.0000017643998,0.00006537849],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988918,0.000031942487,0.00018748599,0.0003657474,0.0003323687,0.00019071011],"domain_scores_gemma":[0.999428,0.00018964241,0.00007247122,0.00022696833,0.000041751402,0.0000411926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063029944,0.000098755096,0.00013650315,0.0002350525,0.00039570508,0.00039609557,0.00050090236,0.000047976984,0.000005314292],"category_scores_gemma":[0.000030068939,0.00007582584,0.00003219241,0.000799714,0.00020393636,0.00017190722,0.00004040782,0.00008117635,0.000026469188],"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":[0.00001127444,0.000097171585,0.0011067996,0.00010719313,0.000013891242,0.0000030006736,0.00062882464,0.12971067,0.006486267,0.7111192,0.0009820288,0.14973369],"study_design_scores_gemma":[0.000315012,0.000022898847,0.00023486203,0.00008570893,0.0000042943634,0.00000205523,0.000097710195,0.9758942,0.0062087253,0.016776172,0.00023608851,0.00012226516],"about_ca_topic_score_codex":0.0000041156095,"about_ca_topic_score_gemma":0.00000962592,"teacher_disagreement_score":0.9432591,"about_ca_system_score_codex":0.00001980747,"about_ca_system_score_gemma":0.000138674,"threshold_uncertainty_score":0.38195586},"labels":[],"label_agreement":null},{"id":"W4413176273","doi":"10.18280/ts.420445","title":"YOLO-OSAM: Reassembly Spatial Attention Mechanisms for Facial Expression Recognition","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Higher Education, Malaysia","keywords":"Facial expression recognition; Artificial intelligence; Expression (computer science); Computer science; Pattern recognition (psychology); Computer vision; Facial recognition system","score_opus":0.023439806629601226,"score_gpt":0.2578781471418401,"score_spread":0.23443834051223889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413176273","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":[0.011851496,0.0000307436,0.9830689,0.0007436183,0.0012315062,0.0009785949,0.000046244575,0.00023034385,0.0018185904],"genre_scores_gemma":[0.9708076,0.000005378731,0.027529454,0.00035403704,0.0003407607,0.0004963161,0.00013255207,0.000010003745,0.00032390028],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99817187,0.00015269604,0.0004612935,0.0005207477,0.0003759468,0.00031746965],"domain_scores_gemma":[0.9991706,0.0001201931,0.0001692187,0.00022317433,0.00023096427,0.000085867774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062516954,0.00020081931,0.00022680408,0.00022921246,0.00026165947,0.0002009787,0.00037669967,0.00013489644,0.00007067991],"category_scores_gemma":[0.000033864977,0.000187332,0.00016160445,0.00027945839,0.000017286251,0.00042524707,0.00006578657,0.00009630391,0.00006539492],"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":[0.00012462327,0.00024154907,0.00007206806,0.00011311481,0.000055952823,0.0000052839937,0.00025540998,0.000012339682,0.25832272,0.007345711,0.0036138985,0.72983736],"study_design_scores_gemma":[0.013660478,0.001681357,0.013987967,0.0017948907,0.00023410274,0.00004513225,0.00044920234,0.0562525,0.72320825,0.14623874,0.04060961,0.0018377518],"about_ca_topic_score_codex":0.000023650784,"about_ca_topic_score_gemma":0.00001969995,"teacher_disagreement_score":0.9589561,"about_ca_system_score_codex":0.0000734744,"about_ca_system_score_gemma":0.00007716552,"threshold_uncertainty_score":0.76391774},"labels":[],"label_agreement":null},{"id":"W4413459118","doi":"10.1109/icoct64433.2025.11118370","title":"A Web-Based Framework for Real-Time Sign Language Translation: Integrating Gesture and Speech for Enhanced Accessibility","year":2025,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Gesture; Sign language; Speech translation; Translation (biology); Sign (mathematics); Natural language processing; Speech recognition; Machine translation; Artificial intelligence; Human–computer interaction; Linguistics","score_opus":0.018536438017686573,"score_gpt":0.31894625194825016,"score_spread":0.3004098139305636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413459118","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":[0.014570767,0.00010402322,0.9766154,0.0017860143,0.00019517606,0.0012692801,0.000019380528,0.00027406352,0.005165886],"genre_scores_gemma":[0.47095636,0.0000019603192,0.52787906,0.00036419925,0.00007389148,0.00022339018,0.000011287991,0.0000061394467,0.0004837391],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988122,0.00007825974,0.00030058483,0.00047875225,0.00012215531,0.00020807005],"domain_scores_gemma":[0.99760735,0.0017236351,0.000085985106,0.00034980572,0.00017573849,0.000057512865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006402026,0.0001521283,0.00025024265,0.00009443256,0.000141036,0.00023508785,0.00031271856,0.00017378408,0.000020889654],"category_scores_gemma":[0.00027383273,0.0001193011,0.00009326911,0.00036343595,0.000024311863,0.00022906184,0.00002692749,0.000107421365,0.000003786119],"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":[0.000114823604,0.0001137818,0.00023279483,0.0006527917,0.000059033504,0.0000020675197,0.002682907,0.000012813108,0.095476046,0.02835777,0.0013024654,0.8709927],"study_design_scores_gemma":[0.0071388497,0.00055031845,0.0020001754,0.0015507875,0.000100675956,0.0000070036176,0.0012123983,0.34394348,0.53871226,0.09723214,0.0064001344,0.0011517956],"about_ca_topic_score_codex":0.0000138625865,"about_ca_topic_score_gemma":0.000061496416,"teacher_disagreement_score":0.8698409,"about_ca_system_score_codex":0.00002592061,"about_ca_system_score_gemma":0.00014790564,"threshold_uncertainty_score":0.48649576},"labels":[],"label_agreement":null},{"id":"W4413474339","doi":"10.1049/csy2.70024","title":"Lightweight Hand Acupoint Recognition Based on Middle Finger Cun Measurement","year":2025,"lang":"en","type":"article","venue":"IET Cyber-Systems and Robotics","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"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":"Petroleum Technology Research Centre; Guizhou Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Middle finger; Artificial intelligence; Pattern recognition (psychology); Medicine; Anatomy; Thumb","score_opus":0.045983224771907485,"score_gpt":0.23619238091451863,"score_spread":0.19020915614261114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413474339","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":[0.0032603214,0.0023688003,0.95603687,0.0056559555,0.0069830753,0.0013522084,0.000029022789,0.00034003,0.023973703],"genre_scores_gemma":[0.9903777,0.000054523625,0.006385989,0.0008991289,0.00036015263,0.00010961316,0.00001633667,0.000023923947,0.001772619],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976001,0.00024092416,0.0005441263,0.000593268,0.00065206207,0.0003695346],"domain_scores_gemma":[0.9984269,0.00017522255,0.00018348795,0.00057363434,0.00047717278,0.00016358301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009750798,0.0002977547,0.00041308493,0.00028121495,0.00036365347,0.0006368938,0.00031789174,0.00020442785,0.0000054845746],"category_scores_gemma":[0.00008599148,0.00023358833,0.000106414875,0.00046178183,0.000050546303,0.00020663768,0.00009027339,0.0002170402,0.00012037608],"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":[0.00057899544,0.0062546874,0.008920934,0.010204918,0.0024982507,0.00074373756,0.010448433,0.120636925,0.024693578,0.1304528,0.22390154,0.4606652],"study_design_scores_gemma":[0.007102017,0.0011042539,0.004173381,0.012506195,0.00029119244,0.00013176432,0.00037656463,0.71765393,0.014201572,0.0038912103,0.23613141,0.0024365173],"about_ca_topic_score_codex":0.0000666896,"about_ca_topic_score_gemma":0.000049173977,"teacher_disagreement_score":0.9871174,"about_ca_system_score_codex":0.0001308096,"about_ca_system_score_gemma":0.00015409634,"threshold_uncertainty_score":0.9525456},"labels":[],"label_agreement":null},{"id":"W4414000437","doi":"10.1101/2025.09.02.673854","title":"Tool and hand adaptation and localization in immersive virtual reality","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Western University","funders":"","keywords":"Adaptation (eye); Virtual reality; Human–computer interaction; Computer science; Psychology; Cognitive psychology; Neuroscience","score_opus":0.020482273182787617,"score_gpt":0.23042530857474722,"score_spread":0.2099430353919596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414000437","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":[0.2509793,0.0010304244,0.7455427,0.00041384817,0.00089855574,0.0008747086,0.00010264433,0.00013840171,0.000019446727],"genre_scores_gemma":[0.9968374,0.00022887376,0.002512629,0.0002050505,0.00009358151,0.00010176367,4.0607443e-7,0.000014952319,0.0000053663734],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978238,0.0002709693,0.00046433293,0.0009151996,0.00026508293,0.00026061205],"domain_scores_gemma":[0.9985358,0.00012275547,0.0002624066,0.00060021965,0.0003609424,0.00011784416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00074221834,0.00030698619,0.00039754063,0.00036469416,0.0001350872,0.00045340642,0.00030491984,0.0003819725,0.0000017969082],"category_scores_gemma":[0.00019213687,0.00033804865,0.000036994003,0.00055832736,0.000095552154,0.0002680597,0.0005329512,0.00034689772,0.0000049860687],"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":[0.00062811415,0.0021248003,0.2222901,0.015910579,0.0019951246,0.0010140839,0.010688729,0.030250646,0.44574186,0.26011243,0.004300811,0.0049427096],"study_design_scores_gemma":[0.0044759926,0.00027822328,0.35155815,0.005262872,0.00019108554,2.4741306e-7,0.0001241041,0.5169308,0.10723843,0.00019718794,0.0104057025,0.003337171],"about_ca_topic_score_codex":0.00016470968,"about_ca_topic_score_gemma":0.000024725301,"teacher_disagreement_score":0.7458581,"about_ca_system_score_codex":0.00013755784,"about_ca_system_score_gemma":0.0004035902,"threshold_uncertainty_score":0.99990714},"labels":[],"label_agreement":null},{"id":"W4414114173","doi":"10.1145/3743708","title":"ThumbSwype: Thumb-to-Finger Gesture Based Text-Entry for Head Mounted Displays MHCI031","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"L'Alliance Boviteq; Okanagan University College; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"SwIPe; Gesture; Text entry; Head (geology); Mobile device; Accelerometer","score_opus":0.040070105246205814,"score_gpt":0.35609379327004914,"score_spread":0.31602368802384334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414114173","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":[0.6330204,0.00006485368,0.3052249,0.034321904,0.0096748425,0.0040465924,0.00003400611,0.0007823026,0.012830176],"genre_scores_gemma":[0.9818808,0.0000015323092,0.01306843,0.0030956522,0.00049627514,0.00021313429,0.0000052412893,0.000027056663,0.0012119171],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978917,0.000033013326,0.0005718043,0.0007154516,0.00042021147,0.00036779913],"domain_scores_gemma":[0.9976372,0.00036294863,0.00043102607,0.00083167345,0.0006523724,0.00008479502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049793936,0.00033072667,0.00039220726,0.00044416086,0.0003646452,0.0004457204,0.0029882859,0.00015790208,0.000010381255],"category_scores_gemma":[0.000298322,0.00024514378,0.0003004389,0.00068961305,0.000039615858,0.0005961761,0.00079872616,0.00037591354,0.000039412476],"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":[0.0013306673,0.0027413638,0.008271952,0.0027721133,0.00093348045,0.0000039128427,0.008016194,0.002682449,0.1634496,0.1190583,0.49815437,0.19258559],"study_design_scores_gemma":[0.007751437,0.00270715,0.06640968,0.012581529,0.00032157323,0.00007704693,0.0006554549,0.16713254,0.51749605,0.045089565,0.17733634,0.002441626],"about_ca_topic_score_codex":0.000018603081,"about_ca_topic_score_gemma":0.000011579888,"teacher_disagreement_score":0.35404646,"about_ca_system_score_codex":0.00018867357,"about_ca_system_score_gemma":0.00004533113,"threshold_uncertainty_score":0.99966735},"labels":[],"label_agreement":null},{"id":"W4414638154","doi":"10.1007/978-3-031-97713-8_31","title":"Realtime Sign Language to Speech Conversion","year":2025,"lang":"en","type":"book-chapter","venue":"Information systems engineering and management","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sign language; Gesture; Bridge (graph theory); Language interpretation; Process (computing); Sign (mathematics); American Sign Language; Cued speech","score_opus":0.00566094838630861,"score_gpt":0.18980504776121448,"score_spread":0.18414409937490586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414638154","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028268596,0.00027480922,0.5070146,0.00015266225,0.001364966,0.0013208631,0.000020385225,0.000384804,0.4894641],"genre_scores_gemma":[0.008432744,0.000539712,0.012646413,0.00090677827,0.0003185561,0.0003476303,0.00018674356,0.000040054878,0.9765814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988222,0.000008438989,0.00044324284,0.0002035519,0.00035634296,0.00016623104],"domain_scores_gemma":[0.99917513,0.00003111372,0.00013368444,0.00043261296,0.00008811442,0.00013934814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034962632,0.00023439911,0.00025581158,0.0006862857,0.000048432412,0.00031584295,0.0002852699,0.00011356782,0.0000046658165],"category_scores_gemma":[0.0000080967475,0.00023363243,0.000047080262,0.00008849812,0.0000040471477,0.00043065823,0.00025553157,0.00011046056,0.0005021194],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008051642,0.0000046559544,8.788744e-7,0.0076572523,0.00023335993,0.000065481785,0.001627879,0.0049982085,0.00000478978,0.7989427,0.05771156,0.12874518],"study_design_scores_gemma":[0.00017692192,0.000017982738,0.000009354029,0.0015733005,0.00001737637,0.000012568723,0.000051836167,0.015206378,0.000007875282,0.0000174415,0.98265034,0.0002586412],"about_ca_topic_score_codex":0.000016954256,"about_ca_topic_score_gemma":3.3877924e-7,"teacher_disagreement_score":0.92493874,"about_ca_system_score_codex":0.00009540784,"about_ca_system_score_gemma":0.000018076753,"threshold_uncertainty_score":0.9527254},"labels":[],"label_agreement":null},{"id":"W4414721615","doi":"10.1007/978-981-96-7511-1_4","title":"Sign Language Recognition Using CNN Model","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Gesture; Sign language; Convolutional neural network; Gesture recognition; American Sign Language; Normalization (sociology); Feature extraction; Identity (music)","score_opus":0.03235763748418669,"score_gpt":0.25133493959565995,"score_spread":0.21897730211147326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414721615","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":[0.000040034047,0.015983209,0.93816584,0.000057752797,0.0014317004,0.0006791499,0.000025243018,0.00012989358,0.0434872],"genre_scores_gemma":[0.9367739,0.0017703184,0.016926844,0.0013336786,0.004338625,0.00014762682,0.0003014063,0.00019996056,0.03820764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977827,0.00012224549,0.000644439,0.0007654526,0.00030806375,0.00037710046],"domain_scores_gemma":[0.9984728,0.0004307818,0.00032030218,0.0005380242,0.00013439143,0.00010370596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005480143,0.0004720955,0.00075538584,0.00036186716,0.00011788764,0.0003624855,0.00037330596,0.00093354477,0.0000077674895],"category_scores_gemma":[0.000042084375,0.0004217656,0.0001269184,0.0001645525,0.000040436298,0.0001587996,0.00016033242,0.0007075579,0.000010628738],"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":[0.000024018917,0.000025594145,0.00003644924,0.00085061276,0.00018054357,0.00020541818,0.0012507105,0.53141826,0.0000527061,0.011087439,0.0008120887,0.45405617],"study_design_scores_gemma":[0.0002720416,0.000026979043,0.0000012126894,0.002838262,0.000036029796,0.00008328139,0.0000057585635,0.9868632,0.0000064841483,0.0074947365,0.001880812,0.0004912116],"about_ca_topic_score_codex":0.0000985866,"about_ca_topic_score_gemma":0.00010192573,"teacher_disagreement_score":0.93673384,"about_ca_system_score_codex":0.00012008234,"about_ca_system_score_gemma":0.00010832485,"threshold_uncertainty_score":0.9998234},"labels":[],"label_agreement":null},{"id":"W4415360260","doi":"10.59934/jaiea.v5i1.1589","title":"Convolutional Implementation Neural Network (CNN) on the System and Introduction Sign Language to Support Communication with People with Disabilities","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Convolutional neural network; Process (computing); Sign language; Intersection (aeronautics); Deep learning; Artificial neural network; Sign (mathematics); Range (aeronautics)","score_opus":0.020780496279526512,"score_gpt":0.27619847938416386,"score_spread":0.25541798310463737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415360260","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":[0.10952146,0.00060318696,0.8787645,0.009892367,0.0002565587,0.00086638454,0.00001096295,0.000034763278,0.000049805203],"genre_scores_gemma":[0.9939163,0.000101788755,0.0050896225,0.00010092736,0.00056060887,0.000170851,0.000008337583,0.000011786007,0.000039789153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834615,0.00011482514,0.0006961027,0.00028661068,0.00031849748,0.00023784341],"domain_scores_gemma":[0.99825037,0.0004959277,0.000297381,0.00041164676,0.00041174184,0.0001329467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009934596,0.00020675061,0.00026974056,0.00021330529,0.00045773204,0.0003863567,0.00032629262,0.0000523251,0.000014404028],"category_scores_gemma":[0.000041134306,0.00014657495,0.00004341966,0.0009079502,0.00011241994,0.00031998855,0.00007264625,0.00030010502,0.000009387595],"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":[0.00035394,0.00016550325,0.0014958234,0.00044406348,0.0002951883,0.0000030544456,0.011193715,0.25047386,0.0008071022,0.46627295,0.0012254912,0.26726928],"study_design_scores_gemma":[0.0007550001,0.005210653,0.021268109,0.0037124057,0.00095512974,0.0010881715,0.15923204,0.76860374,0.009677933,0.0041119745,0.023466753,0.0019181056],"about_ca_topic_score_codex":0.000040941915,"about_ca_topic_score_gemma":0.00010727877,"teacher_disagreement_score":0.8843948,"about_ca_system_score_codex":0.00012509758,"about_ca_system_score_gemma":0.00013305449,"threshold_uncertainty_score":0.59771526},"labels":[],"label_agreement":null},{"id":"W4415434080","doi":"10.1016/j.engappai.2025.112739","title":"Temporal diffuser: Timing scale-aware modulation for sign language production","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Education; Sejong University","keywords":"Sign language; Pooling; Noise reduction; Pipeline (software); Fidelity; Autoregressive model; Flexibility (engineering); Modulation (music); Production (economics); Motion (physics)","score_opus":0.023300583433461496,"score_gpt":0.28868010733616367,"score_spread":0.2653795239027022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415434080","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":[0.0073416065,0.00006984698,0.99087495,0.0004060653,0.00027935044,0.0007464611,0.0000065341474,0.00020876632,0.00006641667],"genre_scores_gemma":[0.9255536,0.0000032160608,0.07368008,0.000010129675,0.00011856575,0.00049187447,0.000014367091,0.000007763083,0.000120397024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905956,0.000013132205,0.00036963038,0.00029862855,0.000119728196,0.00013933383],"domain_scores_gemma":[0.99914634,0.000113951035,0.00009282408,0.0004169647,0.00019687187,0.000033041593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026553482,0.00010161014,0.00013436582,0.00023064068,0.000089387366,0.00005215303,0.00034387346,0.000058573027,0.0000019433458],"category_scores_gemma":[0.00007837109,0.000109815344,0.000056912213,0.00072200474,0.000020617144,0.00017839458,0.000042769476,0.00006979875,0.000013736545],"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":[0.000008379566,0.00015418274,0.00011443298,0.00027723936,0.000029489798,1.9106366e-7,0.0012164033,0.08978549,0.059775755,0.06851834,0.00014634259,0.77997375],"study_design_scores_gemma":[0.000022656668,0.000021072043,0.00018005267,0.000098474295,0.000009911876,0.0000017244417,0.0002259601,0.70959365,0.28390005,0.0041303914,0.0016572763,0.00015874374],"about_ca_topic_score_codex":0.000019697956,"about_ca_topic_score_gemma":0.0000091288175,"teacher_disagreement_score":0.918212,"about_ca_system_score_codex":0.000036365876,"about_ca_system_score_gemma":0.00003437498,"threshold_uncertainty_score":0.447814},"labels":[],"label_agreement":null},{"id":"W4415744297","doi":"10.1109/ihtc65087.2025.11216322","title":"Automated Real-Time Recognition and Translation of Indigenous Sign Language Using Deep Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Sign language; Gesture; Viewpoints; Deep learning; Process (computing); Gesture recognition; Sign (mathematics); Preprocessor; Set (abstract data type)","score_opus":0.027053771453485864,"score_gpt":0.2774194423502662,"score_spread":0.25036567089678036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415744297","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":[0.6268879,0.0012420673,0.36208203,0.00004922887,0.00027099185,0.0007054399,0.0000106265925,0.000536607,0.008215066],"genre_scores_gemma":[0.98021704,0.00021487357,0.018952088,0.000028409971,0.000049101167,0.000007766484,0.000036268026,0.00001604843,0.0004784194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741703,0.00065576955,0.00076352176,0.0005222687,0.00029682717,0.00034456444],"domain_scores_gemma":[0.9986739,0.000345704,0.00035326197,0.00023751857,0.00029481624,0.00009479994],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009216315,0.00025086416,0.00045671372,0.0005386268,0.0002753209,0.00021352035,0.00019425947,0.00027477695,0.00012006539],"category_scores_gemma":[0.00006456821,0.00026000492,0.00008789495,0.0011996807,0.00007623184,0.0005238878,0.00006622498,0.00020881485,0.00006806387],"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":[0.000029783674,0.00008984865,0.00042350183,0.00040301177,0.000116455594,0.000014519649,0.028148487,0.00044938052,0.11578157,0.000021803204,0.0000069785438,0.85451466],"study_design_scores_gemma":[0.0011396903,0.00021625578,0.0024684127,0.0009905545,0.00014658824,0.000057998168,0.0015279728,0.9700428,0.0227337,0.00024078703,0.000044688688,0.00039050033],"about_ca_topic_score_codex":0.00073277735,"about_ca_topic_score_gemma":0.000050998122,"teacher_disagreement_score":0.96959347,"about_ca_system_score_codex":0.00006472963,"about_ca_system_score_gemma":0.00018844349,"threshold_uncertainty_score":0.9999852},"labels":[],"label_agreement":null},{"id":"W4415837344","doi":"10.1017/s1759078725102535","title":"Interpretable hand gesture recognition: a rule-based framework for radar-based gesture onset detection and classification","year":2025,"lang":"en","type":"article","venue":"International Journal of Microwave and Wireless Technologies","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Interpretability; Gesture; Gesture recognition; Adaptability; Personalization; Robustness (evolution)","score_opus":0.018568338615482606,"score_gpt":0.2732891308290815,"score_spread":0.2547207922135989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415837344","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":[0.18129201,0.0010670715,0.80571455,0.010839147,0.0007115216,0.00020035848,0.00002342306,0.00010654857,0.00004536739],"genre_scores_gemma":[0.942927,0.00023316116,0.056274366,0.0004232453,0.00006771377,0.000037178044,0.000007824592,0.000007636656,0.00002187542],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988369,0.000053356172,0.00044220441,0.0002818419,0.00023107075,0.00015463974],"domain_scores_gemma":[0.9980622,0.0005146138,0.00043058614,0.00018428211,0.0007737824,0.000034544748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003810901,0.00017519896,0.00026621248,0.0006142074,0.00014586735,0.0004353134,0.00054955937,0.00032867838,0.0000013791343],"category_scores_gemma":[0.0003597324,0.00014774958,0.00009836272,0.00026262976,0.00017442732,0.00032082124,0.000092091796,0.0003844938,0.0000012406342],"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":[0.00028296022,0.00011620993,0.0013015959,0.00009823505,0.00020224837,0.000022663611,0.00015846905,0.000007634109,0.10298344,0.0029349078,0.00063876057,0.8912529],"study_design_scores_gemma":[0.004028555,0.0006092327,0.007802904,0.0036638356,0.00012904048,0.0005858512,0.0012172892,0.014457902,0.788194,0.16178524,0.016924774,0.00060138095],"about_ca_topic_score_codex":0.000003561664,"about_ca_topic_score_gemma":0.000015066921,"teacher_disagreement_score":0.8906515,"about_ca_system_score_codex":0.000084050436,"about_ca_system_score_gemma":0.00014186205,"threshold_uncertainty_score":0.6025053},"labels":[],"label_agreement":null},{"id":"W4415969735","doi":"10.1109/cist65886.2025.11224091","title":"Event-based motion analysis of Moroccan sign language video streams","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Optical flow; Motion (physics); Gesture; Sign language; Categorization; Cluster analysis; Motion analysis; Motion estimation","score_opus":0.010226628120504677,"score_gpt":0.2785572456171293,"score_spread":0.2683306174966246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415969735","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":[0.056527674,0.00051167427,0.9319402,0.0006827371,0.0005660756,0.00036964586,0.000043844342,0.00009106183,0.009267103],"genre_scores_gemma":[0.99364763,0.000009794174,0.0016704965,0.00039178377,0.000045548626,0.000021586238,0.000041986823,0.0000070831175,0.0041640704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693197,0.00048776096,0.0009528584,0.00071518234,0.00056036277,0.00035184334],"domain_scores_gemma":[0.9976878,0.00032842788,0.00038593932,0.0010294579,0.00042772057,0.00014061337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008575484,0.0002816742,0.0007493197,0.0015376356,0.00010247346,0.00018139185,0.00069305446,0.00018596323,0.00075951545],"category_scores_gemma":[0.00010610578,0.00025711846,0.00057374366,0.0069167395,0.00007082325,0.00026683963,0.00012699125,0.00014825327,0.00007044902],"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":[0.00003799297,0.0008519515,0.013727303,0.00037129773,0.0037868973,0.000025439502,0.002305733,0.043367494,0.0064819795,0.0046389005,0.0011930668,0.92321193],"study_design_scores_gemma":[0.0007855397,0.00011251567,0.01086212,0.00024032123,0.0015209698,6.0278677e-7,0.0008061956,0.94335157,0.041654084,0.000075708915,0.00030324774,0.0002871412],"about_ca_topic_score_codex":0.0009947937,"about_ca_topic_score_gemma":0.00062367896,"teacher_disagreement_score":0.93711996,"about_ca_system_score_codex":0.00010686625,"about_ca_system_score_gemma":0.00032536846,"threshold_uncertainty_score":0.9999881},"labels":[],"label_agreement":null},{"id":"W4416265698","doi":"10.1016/j.asoc.2025.114222","title":"A benchmark dataset and lightweight model for multi-distance gesture recognition in UAV control","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Department of Industrial and Systems Engineering, Hong Kong Polytechnic University; State Key Laboratory of Robotics; China Scholarship Council; Hong Kong Polytechnic University; Shenzhen Technology University","keywords":"Benchmark (surveying); Gesture; Gesture recognition; Pyramid (geometry); Modular design; Feature (linguistics); Feature extraction; Background subtraction","score_opus":0.026552838979220814,"score_gpt":0.27602716449782544,"score_spread":0.24947432551860463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416265698","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":[0.0022500101,0.00031400332,0.99506336,0.00039699933,0.00024425492,0.00092334347,0.00021448803,0.00012337606,0.0004701815],"genre_scores_gemma":[0.8347482,0.0000068558033,0.1635066,0.0012262266,0.00006340081,0.000119874676,0.00026743623,0.0000106320085,0.00005076181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983759,0.000052812793,0.00042781455,0.0006469657,0.00014155806,0.00035493163],"domain_scores_gemma":[0.99886334,0.0005045822,0.00014654065,0.0003312879,0.000084051346,0.000070223374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000678124,0.00020755905,0.00033201577,0.00018108757,0.00020468465,0.0001754883,0.00037845754,0.0001358716,7.5564253e-7],"category_scores_gemma":[0.00006424442,0.00020431333,0.000038559723,0.000379069,0.00003375652,0.0001720196,0.00015719037,0.0002029928,0.000007989592],"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":[0.00025068823,0.000746604,0.002840683,0.0012928031,0.00024393169,0.000035118974,0.0059979702,0.014035459,0.0030145315,0.07452549,0.024185594,0.8728311],"study_design_scores_gemma":[0.0025452364,0.0000125712795,0.00040444077,0.00017701107,0.000013300604,0.0000054067214,0.000039885388,0.98730236,0.00014108207,0.005355454,0.0037565602,0.00024670584],"about_ca_topic_score_codex":0.000006415138,"about_ca_topic_score_gemma":0.000058777638,"teacher_disagreement_score":0.9732669,"about_ca_system_score_codex":0.00004710926,"about_ca_system_score_gemma":0.00006875638,"threshold_uncertainty_score":0.8331656},"labels":[],"label_agreement":null},{"id":"W4416401209","doi":"10.1109/ismar-adjunct68609.2025.00214","title":"Task-Oriented Hand Gesture Mapping with Gaze-Hand Interactions in VR","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation","keywords":"Gesture; Task (project management); Object (grammar); Gesture recognition; Virtual reality; Virtual image; Task analysis","score_opus":0.0175511399160016,"score_gpt":0.264528211659859,"score_spread":0.2469770717438574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416401209","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":[0.021640953,0.0014432713,0.91192657,0.0039163237,0.0032980454,0.0010920287,0.000013974772,0.00012939532,0.05653946],"genre_scores_gemma":[0.96930605,0.00009379249,0.005121084,0.00094009435,0.00019301602,0.00011542652,0.000011387742,0.000019005838,0.024200136],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99635696,0.00036011566,0.000909561,0.0011609284,0.0004858014,0.0007266063],"domain_scores_gemma":[0.9977394,0.00035190198,0.00025713173,0.0008697733,0.0005590603,0.00022274733],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000570744,0.0004956403,0.0006279413,0.001126944,0.0006001481,0.0013994129,0.0006589153,0.00019101682,0.00018270263],"category_scores_gemma":[0.00011062134,0.00040710662,0.00012722386,0.003923209,0.00025793794,0.0010163982,0.00035810375,0.00083314755,0.0002351517],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006379728,0.0036242623,0.060214452,0.0025324824,0.0017936532,0.0017676305,0.08202763,0.0014836113,0.019185508,0.04967753,0.031251878,0.7458034],"study_design_scores_gemma":[0.006684635,0.00038545256,0.049955413,0.011041717,0.00012794026,0.0006019237,0.0058080875,0.067758694,0.009007166,0.0005864365,0.84633195,0.0017105693],"about_ca_topic_score_codex":0.0004835096,"about_ca_topic_score_gemma":0.005690418,"teacher_disagreement_score":0.9476651,"about_ca_system_score_codex":0.00023290908,"about_ca_system_score_gemma":0.00055586814,"threshold_uncertainty_score":0.99983805},"labels":[],"label_agreement":null},{"id":"W4417438664","doi":"10.1109/jiot.2025.3644894","title":"mmWave Radar-Based Continuous Sign Language Recognition: Lightweight Modeling, Contextual Optimization, and Embedded Implementation","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sign language; Vocabulary; Wearable computer; Semantics (computer science); Natural language; Gesture recognition; Feature extraction; Sign (mathematics)","score_opus":0.019089797733373697,"score_gpt":0.2865302968152802,"score_spread":0.2674404990819065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417438664","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":[0.05983794,0.0019168743,0.93135446,0.0010479232,0.0036157668,0.00058656314,0.000031392512,0.00006469429,0.0015443843],"genre_scores_gemma":[0.9506454,0.00017517245,0.046780817,0.0011798956,0.00034152492,0.000016677275,0.000028575185,0.000031003605,0.00080096925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99531496,0.0006742869,0.0020687706,0.00065727776,0.00076477614,0.00051993463],"domain_scores_gemma":[0.99585307,0.00029759936,0.0014792811,0.0003552004,0.0017313091,0.00028352416],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019061052,0.00048235344,0.0008254104,0.0007834805,0.00026774567,0.0011848953,0.00080291263,0.00029589282,0.0005149284],"category_scores_gemma":[0.00016912854,0.0004640006,0.00029820454,0.0005074744,0.00015345351,0.0014969715,0.0001350599,0.00076192955,0.000021482714],"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":[0.0013938089,0.0013534949,0.0006399979,0.0014054402,0.003277402,0.0007330784,0.12073206,0.02532636,0.012170329,0.0017979019,0.029986136,0.801184],"study_design_scores_gemma":[0.005760082,0.0006910034,0.000008502035,0.0029430427,0.00028334704,0.00060977,0.0053962963,0.9286076,0.053414363,0.0012860439,0.00040228706,0.00059768074],"about_ca_topic_score_codex":0.00016233692,"about_ca_topic_score_gemma":0.0000142654635,"teacher_disagreement_score":0.9032812,"about_ca_system_score_codex":0.0001757988,"about_ca_system_score_gemma":0.00057070836,"threshold_uncertainty_score":0.99985194},"labels":[],"label_agreement":null},{"id":"W52610099","doi":"10.5281/zenodo.1176849","title":"Visual Methods For The Retrieval Of Guitarist Fingering","year":2006,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","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":"McGill University","funders":"","keywords":"Computer science; Gesture; Artificial intelligence; Computer vision; Focus (optics); Segmentation","score_opus":0.02620403619244506,"score_gpt":0.36798890082764313,"score_spread":0.3417848646351981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W52610099","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":[0.00074653456,0.00020323461,0.9923705,0.00045171817,0.00036202362,0.00017359953,7.941514e-7,0.000052897096,0.005638712],"genre_scores_gemma":[0.4381628,0.0000020555874,0.5600423,0.00008624223,0.00020171948,0.00001304899,9.2342424e-7,0.000005301064,0.001485575],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938333,0.0000609785,0.00020243993,0.00013323371,0.00010425442,0.000115788476],"domain_scores_gemma":[0.9988187,0.00078969117,0.000068763606,0.00020087801,0.00010587135,0.00001610112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096714974,0.00005404804,0.00010362212,0.000033569126,0.00007122336,0.000052864318,0.00032014202,0.000031660686,0.000009016937],"category_scores_gemma":[0.0000886318,0.00003284012,0.00006599139,0.00020584867,0.00001978371,0.00008876169,0.000068238,0.000035227975,0.0000058668675],"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":[0.00003091417,0.00013398055,0.0005073561,0.00011795066,0.00008328677,0.0000017000481,0.0007036461,0.00024265808,0.054717433,0.1656015,0.009610121,0.76824945],"study_design_scores_gemma":[0.00097195973,0.00024479124,0.0050471635,0.000050661005,0.00002992634,0.00003525519,0.00015277008,0.2943164,0.42325583,0.016137868,0.2593784,0.00037893705],"about_ca_topic_score_codex":0.00007426929,"about_ca_topic_score_gemma":0.000010933412,"teacher_disagreement_score":0.7678705,"about_ca_system_score_codex":0.000009352948,"about_ca_system_score_gemma":0.000023378865,"threshold_uncertainty_score":0.13391812},"labels":[],"label_agreement":null},{"id":"W53323940","doi":"","title":"Hand-Eye: A Vision-Based Approach to Data Glove Calibration","year":2000,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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":"University of British Columbia","funders":"","keywords":"Wired glove; Computer vision; Computer science; Artificial intelligence; Feature (linguistics); Calibration; Filter (signal processing); Personalization; Mathematics","score_opus":0.03743637405613563,"score_gpt":0.28583820760515277,"score_spread":0.24840183354901713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W53323940","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":[0.0006118415,0.000017465221,0.9288361,0.0016697422,0.00011059075,0.00027236977,0.000015344387,0.00017369913,0.06829282],"genre_scores_gemma":[0.8311139,0.0000011357005,0.15969318,0.0036433,0.000187136,0.000032274304,0.00013707227,0.000011556553,0.0051804893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986785,0.00009104137,0.00020180056,0.0005306023,0.0003164768,0.0001815875],"domain_scores_gemma":[0.99854404,0.000052769792,0.00002692231,0.0011805204,0.000045824185,0.0001498967],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033657067,0.000104367835,0.0001231155,0.000073438474,0.00009345497,0.00046360277,0.0010778314,0.000055655935,0.00029950548],"category_scores_gemma":[0.000021387983,0.00008118588,0.000024878542,0.0004922666,0.000016029664,0.0006730266,0.0001275604,0.00005610619,0.0009020152],"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":[0.000036675945,0.0006397592,0.0002853502,0.000056091383,0.000033136264,0.000011690572,0.0013483275,0.0033953087,0.001411239,0.004684317,0.19503985,0.7930582],"study_design_scores_gemma":[0.00031405097,0.0000483891,0.0002414627,0.000016759548,0.0000026267767,0.0000059651566,0.00001242393,0.898297,0.0009936963,0.00009938025,0.099797875,0.00017036605],"about_ca_topic_score_codex":0.000058404272,"about_ca_topic_score_gemma":0.000015860902,"teacher_disagreement_score":0.8949017,"about_ca_system_score_codex":0.000015756243,"about_ca_system_score_gemma":0.00006868152,"threshold_uncertainty_score":0.9998759},"labels":[],"label_agreement":null},{"id":"W574313897","doi":"","title":"The Use of Microsoft Kinect for Human Movement Analysis","year":2015,"lang":"en","type":"article","venue":"International Journal of Sports Science","topic":"Hand Gesture Recognition Systems","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":"Lakehead University","funders":"","keywords":"Kinematics; Computer science; Motion analysis; Reliability (semiconductor); Motion capture; Artificial intelligence; Movement (music); Computer vision; Usability; Simulation; Software; Measure (data warehouse); Displacement (psychology); Motion (physics); Human–computer interaction; Data mining; Psychology","score_opus":0.08165247973763075,"score_gpt":0.33499548626618564,"score_spread":0.2533430065285549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W574313897","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":[0.36125815,0.00011502952,0.6337951,0.0019458493,0.0024697788,0.00014319534,0.000008382143,0.000010449715,0.00025406553],"genre_scores_gemma":[0.9872105,0.000010156467,0.012326511,0.000178262,0.00012201508,0.0000019864467,5.9670606e-7,0.0000020267382,0.00014792384],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976879,0.000016633476,0.0005475837,0.00014066172,0.0014741049,0.00013314116],"domain_scores_gemma":[0.9956323,0.0001253107,0.00076427154,0.00020240148,0.0031589,0.00011683871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022014535,0.00006322799,0.00014765211,0.00044377943,0.00009704663,0.00034378353,0.0015879092,0.000015680927,0.0000027940418],"category_scores_gemma":[0.0002774556,0.00003976956,0.00015455895,0.00069578626,0.00016954314,0.00071032695,0.00012209541,0.000055221408,8.146369e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003174009,0.0009382643,0.36241648,0.00002655059,0.0026567378,0.00033203524,0.007964715,0.045014597,0.10602797,0.16431627,0.01445496,0.29553404],"study_design_scores_gemma":[0.0054712254,0.0013873919,0.49447155,0.00063737476,0.00051251554,0.00086445664,0.0009787914,0.058924716,0.15210946,0.07765781,0.20585875,0.0011259643],"about_ca_topic_score_codex":0.000020948584,"about_ca_topic_score_gemma":0.000018615865,"teacher_disagreement_score":0.62595236,"about_ca_system_score_codex":0.00009418581,"about_ca_system_score_gemma":0.00028905296,"threshold_uncertainty_score":0.33151126},"labels":[],"label_agreement":null},{"id":"W600097623","doi":"","title":"Proceedings : IEEE ICCV workshop on recognition, analysis, and tracking of faces and gestures in real-time systems, 13 July 2001, Vancouver, B.C., Canada","year":2001,"lang":"en","type":"book","venue":"IEEE Computer Society Press eBooks","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gesture; Facial recognition system; Face (sociological concept); Facial expression; Gesture recognition; Computer science; Artificial intelligence; Computer vision; Speech recognition; Pattern recognition (psychology); Sociology","score_opus":0.031231949588878913,"score_gpt":0.23618326953968002,"score_spread":0.20495131995080113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W600097623","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03414397,0.014076536,0.2265946,0.0003509422,0.028117038,0.016594969,0.0018531119,0.0026100709,0.67565876],"genre_scores_gemma":[0.036643874,0.0144838765,0.033726014,0.002195288,0.007219801,0.0010374907,0.00031453793,0.00062480546,0.9037543],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99561423,0.00015721873,0.0011764843,0.0014118126,0.00097403285,0.00066621596],"domain_scores_gemma":[0.99687946,0.0006109471,0.0010408836,0.0005377012,0.00063173607,0.00029927833],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007240483,0.0007857915,0.0016057021,0.00033372856,0.00018379663,0.00052537484,0.0008479902,0.0007147769,0.0000014003981],"category_scores_gemma":[0.000012014669,0.0007618106,0.00029640144,0.00024839182,0.00015858948,0.00029466118,0.00021130344,0.0007832619,0.0000015637096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003584305,0.000104423554,0.00016377961,0.0021496925,0.002451383,0.00010309668,0.007256712,0.0010908113,0.0000977366,0.00009009558,0.93416065,0.052295756],"study_design_scores_gemma":[0.007790408,0.0008957061,0.0020885817,0.025428569,0.0033831326,0.0004719157,0.0006699264,0.21410617,0.0018672985,0.0019876559,0.7321755,0.009135089],"about_ca_topic_score_codex":0.044776086,"about_ca_topic_score_gemma":0.070992745,"teacher_disagreement_score":0.22809555,"about_ca_system_score_codex":0.0003296578,"about_ca_system_score_gemma":0.0006023964,"threshold_uncertainty_score":0.9994833},"labels":[],"label_agreement":null},{"id":"W6887191217","doi":"10.15468/dl.qz6ag8","title":"Occurrence Download","year":2024,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geodetic datum; Coordinate system; Matching (statistics); Download; Range (aeronautics); Geographic coordinate conversion","score_opus":0.016369018579265877,"score_gpt":0.2241587896972178,"score_spread":0.20778977111795194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6887191217","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010815507,0.00006412426,0.00045196465,0.00038517703,0.002449791,0.00031424503,0.99567425,0.00033060586,0.0003190051],"genre_scores_gemma":[0.000007831702,0.00004661772,0.0000089395535,0.0005200644,0.0000035471728,0.0000066514112,0.99940616,1.01381845e-8,2.0563839e-7],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99808043,0.00008943074,0.0004628149,0.00037131232,0.0006846958,0.00031133357],"domain_scores_gemma":[0.9984167,0.000026856642,0.00023584953,0.0008136227,0.00030835744,0.00019859262],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000399097,0.00031240872,0.00029952038,0.0001664818,0.0001923173,0.00078213366,0.0013583694,0.0003832737,0.0003563096],"category_scores_gemma":[0.00008264585,0.0002941047,0.00019355614,0.00070881774,0.000081721526,0.0018879196,0.0008061407,0.0003719553,0.8129448],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003980402,0.00001551749,0.00004508366,0.0002729247,0.000030599505,0.00000750107,0.000064543914,0.0000017058293,1.2879926e-8,7.461827e-7,0.99516755,0.004389841],"study_design_scores_gemma":[0.000150781,0.000025075979,0.000008950711,0.000010216304,0.000027939295,0.000027044462,0.000038587743,0.0000012818023,0.0000020786351,0.0000063250504,0.99939394,0.00030779082],"about_ca_topic_score_codex":0.00012748364,"about_ca_topic_score_gemma":0.0000097902075,"teacher_disagreement_score":0.8125885,"about_ca_system_score_codex":0.00028469224,"about_ca_system_score_gemma":0.00023950667,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W6889449205","doi":"10.25549/wpacards-c8-144182","title":"WPA household census for 2237 AVON STREET, Los Angeles","year":2014,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Population; Context (archaeology); Work (physics); Quarter (Canadian coin)","score_opus":0.01440753126586765,"score_gpt":0.17410650606114753,"score_spread":0.15969897479527986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889449205","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011897409,0.00008026847,0.006418622,0.00008441382,0.000116849005,0.0003216731,0.9921196,0.00021586278,0.0005237287],"genre_scores_gemma":[0.0003753366,0.000016883641,0.0008814737,0.000110556626,0.00022635996,8.454451e-7,0.99804103,0.000032286578,0.00031520144],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844706,0.00006345171,0.00027849883,0.0005434961,0.00034351417,0.00032394892],"domain_scores_gemma":[0.99841756,0.00021086448,0.0003986021,0.0006969325,0.00006059486,0.0002154483],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008678746,0.00033313682,0.00048925955,0.00026347337,0.00013122622,0.00023873741,0.00154848,0.00034158895,0.00004009835],"category_scores_gemma":[0.000024406585,0.0003337467,0.00031740632,0.00022779244,0.00012884669,0.0004797244,0.0005398902,0.00021365406,0.0004371154],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029720046,0.00007674302,0.00013767369,0.00028939257,0.000051108145,0.000028680315,0.000006678927,0.000004003768,6.647926e-7,0.00001505908,0.997293,0.0020673028],"study_design_scores_gemma":[0.00046334963,0.00005095142,0.0000030424203,0.00022399469,0.00003845232,0.0000075925673,0.00016238296,0.00007706695,0.000017498525,0.00028070092,0.99830824,0.0003667049],"about_ca_topic_score_codex":0.00003124801,"about_ca_topic_score_gemma":0.000015831694,"teacher_disagreement_score":0.005921448,"about_ca_system_score_codex":0.000027341228,"about_ca_system_score_gemma":0.00013167823,"threshold_uncertainty_score":0.9999114},"labels":[],"label_agreement":null},{"id":"W6902299261","doi":"10.6084/m9.figshare.18482512","title":"Additional file 1 of Evaluation of a breastmilk hand expression toolkit: the M.I.L.K survey study","year":2022,"lang":"en","type":"article","venue":"Open MIND","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; Ottawa Hospital; University of Ottawa","funders":"","keywords":"Survey research; Expression (computer science); MEDLINE; Population; Data collection","score_opus":0.10414953656444348,"score_gpt":0.3319732826140343,"score_spread":0.22782374604959083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902299261","genre_codex":"dataset","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":[0.18697232,0.00007198612,0.0017499698,0.0001751129,0.0005326891,0.0047255815,0.77851784,0.000004819904,0.027249688],"genre_scores_gemma":[0.9877045,9.786546e-8,0.002100059,0.000013618676,0.000028926548,0.0010726022,0.007885889,0.000004878465,0.0011893956],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969094,0.0015568117,0.00026852026,0.00022865331,0.0009553806,0.00008125802],"domain_scores_gemma":[0.997552,0.0013953747,0.0002675045,0.00039059704,0.00036954228,0.000025025984],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028987203,0.000063306245,0.00014028349,0.00005210703,0.00020710292,0.00009053093,0.000796867,0.000015218366,0.6375242],"category_scores_gemma":[0.00044597284,0.000045767123,0.00003180305,0.00037320482,0.000030193318,0.00021493937,0.0007176003,0.00007645152,0.00007047478],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028902168,0.0005028822,0.0001818895,0.0000022619065,0.00003079824,0.0000017856701,0.0031931256,0.00016450186,0.00025464915,0.0000024828466,0.8365871,0.15904963],"study_design_scores_gemma":[0.0033595,0.001090831,0.2755287,0.00027068335,0.00005954993,0.00010724414,0.0054583107,0.01623957,0.0045332327,0.00033893454,0.69256806,0.00044536564],"about_ca_topic_score_codex":0.00007831499,"about_ca_topic_score_gemma":0.00013999168,"teacher_disagreement_score":0.8007322,"about_ca_system_score_codex":0.000026241541,"about_ca_system_score_gemma":0.00032083504,"threshold_uncertainty_score":0.36280712},"labels":[],"label_agreement":null},{"id":"W6962478980","doi":"10.1594/pangaea.964754","title":"Soil temperature and water content data from a forest, lowshrub and lichen vegetation zone in the low-Arctic Tasiapik valley in the forest-tundra ecotone in Northern Quebec","year":2024,"lang":"en","type":"dataset","venue":"Publishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Horizon 2020","keywords":"Ecotone; Tundra; Snow; Shrub; Vegetation (pathology); Lichen; Soil water","score_opus":0.06397534760175339,"score_gpt":0.27100487249273586,"score_spread":0.20702952489098247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6962478980","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049758546,0.0059768897,0.0007575029,0.0022950186,0.0007018137,0.0022239916,0.9382623,0.000014561851,0.000009373244],"genre_scores_gemma":[0.039361186,0.0018528597,0.0005432243,0.00022893643,0.0004586496,0.00032438655,0.9570398,0.000030446052,0.00016055623],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99541324,0.000513324,0.00064666825,0.0017118105,0.00081174745,0.00090322876],"domain_scores_gemma":[0.99703646,0.0006118014,0.00012733508,0.002000722,0.00003850169,0.00018521241],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0068915985,0.00044067347,0.00049020216,0.00043277608,0.0006449106,0.0050509316,0.0029480697,0.0003524494,0.000004811053],"category_scores_gemma":[0.00026900612,0.00027540635,0.000052478525,0.0004634901,0.0006338188,0.0034946594,0.004447952,0.0011397444,0.000009029217],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006943408,0.00017280057,0.0052987183,0.00061252696,0.000076111195,0.000049200484,0.0011068304,0.00008486662,0.000023485647,0.00016115668,0.98558205,0.0067628263],"study_design_scores_gemma":[0.0014609266,0.00010788226,0.017015396,0.00053211604,0.00008778787,0.000078954705,0.0009389946,0.011628148,0.000002630512,0.0018789081,0.9658113,0.00045695223],"about_ca_topic_score_codex":0.6661309,"about_ca_topic_score_gemma":0.9845589,"teacher_disagreement_score":0.31842798,"about_ca_system_score_codex":0.0001251116,"about_ca_system_score_gemma":0.000115286886,"threshold_uncertainty_score":0.99996984},"labels":[],"label_agreement":null},{"id":"W6976802092","doi":"10.60692/3ypgh-9yp84","title":"Dynamic Gesture Classification for Vietnamese Sign Language Recognition","year":2017,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gesture; Hidden Markov model; Gesture recognition; Feature extraction; Pattern recognition (psychology); Vietnamese; Sign language; Support vector machine","score_opus":0.050825169675192856,"score_gpt":0.2574798230367186,"score_spread":0.20665465336152578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976802092","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":[0.07414636,0.000006881831,0.91572493,0.0005318413,0.0013981798,0.001307335,0.00020902994,0.00058046525,0.0060949675],"genre_scores_gemma":[0.991936,2.4142406e-7,0.0070087085,0.00014194693,0.000119730925,0.00042749124,0.00009456954,0.000012361226,0.00025889368],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838483,0.00008420485,0.00060886523,0.00028711781,0.0003432095,0.0002917948],"domain_scores_gemma":[0.99734235,0.000019558474,0.0008939055,0.0011760913,0.00044965587,0.000118415825],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007404596,0.00021632688,0.00025960238,0.0002462935,0.00067241146,0.0014397778,0.0008483277,0.000199156,0.0000056346566],"category_scores_gemma":[0.00010753044,0.00018365693,0.0001284285,0.00011989123,0.00003123401,0.0029321911,0.00009651903,0.0001111221,0.0013611455],"study_design_candidate":"qualitative","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":[0.00024076435,0.00003304195,0.022643281,0.004051087,0.00034447137,0.000024466655,0.5002618,0.00006674754,0.0003542355,0.005972806,0.0029289918,0.46307835],"study_design_scores_gemma":[0.012103563,0.0005273989,0.3911115,0.0024623636,0.00019998905,0.0006631515,0.07836315,0.49658844,0.003909382,0.00023053554,0.010697754,0.0031427818],"about_ca_topic_score_codex":0.0000050262593,"about_ca_topic_score_gemma":0.0000011377381,"teacher_disagreement_score":0.9177897,"about_ca_system_score_codex":0.00014511078,"about_ca_system_score_gemma":0.00005647712,"threshold_uncertainty_score":0.99959683},"labels":[],"label_agreement":null},{"id":"W6991940779","doi":"","title":"Investor Ideas Potcasts, Cannabis News and Stocks on the Move: Happy 1 Year Anniversary Canada!","year":2019,"lang":"en","type":"other","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cannabis; Government (linguistics); Action (physics); Product (mathematics)","score_opus":0.011182597343279572,"score_gpt":0.20001347307345393,"score_spread":0.18883087573017437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991940779","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000033257922,0.0004493515,0.0023563616,0.0075704884,0.0012320626,0.0007017912,0.00008822071,0.00020956129,0.9873589],"genre_scores_gemma":[0.0013347631,0.00003449565,0.0005093089,0.0050296024,0.00030661514,0.000019099905,0.0000059989197,0.00010386225,0.99265623],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855477,0.00011842345,0.00015344322,0.0004903671,0.00042807808,0.00025494208],"domain_scores_gemma":[0.998807,0.00007445148,0.00015087615,0.00076857954,0.000040031446,0.00015906178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012503378,0.0002681855,0.0002844252,0.00013980453,0.000051181043,0.00012123962,0.0006628968,0.00018818249,0.00041321368],"category_scores_gemma":[0.000029310324,0.00017353783,0.000045495664,0.00017988992,0.00004128719,0.0000644718,0.00016212241,0.00023905865,0.00035026466],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011869896,0.0000046728396,0.00007019687,0.000018748855,0.000033524462,0.00001132841,0.00007926097,5.2729735e-7,0.0000010261283,0.006181885,0.9913074,0.002290275],"study_design_scores_gemma":[0.00014131548,0.000027943464,0.00022453364,0.00013454775,0.000005988078,0.000012063386,0.00011223174,0.0000945812,0.00000639195,0.00008967121,0.9989011,0.00024959235],"about_ca_topic_score_codex":0.5616909,"about_ca_topic_score_gemma":0.5237345,"teacher_disagreement_score":0.037956383,"about_ca_system_score_codex":0.00010404918,"about_ca_system_score_gemma":0.00074957474,"threshold_uncertainty_score":0.70766675},"labels":[],"label_agreement":null},{"id":"W6992955870","doi":"","title":"Neural based machine translation from Catalan Sign Language glosses to written catalan","year":2024,"lang":"en","type":"dissertation","venue":"Repositori digital de la UPF (Universitat Pompeu Fabra)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Machine translation; Catalan; Rule-based machine translation; Sign (mathematics); Term (time); Translation (biology)","score_opus":0.005949260506316845,"score_gpt":0.23455076258074187,"score_spread":0.22860150207442503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992955870","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":[0.6758626,0.009311387,0.11928278,0.0037453717,0.014512624,0.0029049832,0.004543542,0.003973379,0.16586334],"genre_scores_gemma":[0.98421305,0.0000058548467,0.0021560278,0.000110734276,0.0005856697,0.000018610168,0.007337493,0.00007915951,0.0054933694],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99719316,0.00017060044,0.00039145866,0.001048381,0.0007351955,0.00046121047],"domain_scores_gemma":[0.9980711,0.00046174528,0.00020506905,0.00071759115,0.00015630203,0.00038824166],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00015176348,0.0005374028,0.0005096901,0.0005726346,0.00019879537,0.0019735033,0.0010615414,0.00045826047,0.000014278358],"category_scores_gemma":[0.000038708986,0.0005764754,0.00033207992,0.00090316136,0.000032993037,0.001410251,0.00009813017,0.0005004255,0.00028869027],"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":[0.0010735798,0.0012007824,0.0048009385,0.0030463932,0.0026398557,0.025291797,0.20048538,0.0010700658,0.060690768,0.0039075958,0.039735276,0.6560576],"study_design_scores_gemma":[0.014425442,0.004135405,0.07170895,0.013537994,0.0042325654,0.003140693,0.07778777,0.27494732,0.0866872,0.0055200937,0.42159572,0.022280838],"about_ca_topic_score_codex":0.0011440305,"about_ca_topic_score_gemma":0.00026361312,"teacher_disagreement_score":0.6337767,"about_ca_system_score_codex":0.00029247245,"about_ca_system_score_gemma":0.000278153,"threshold_uncertainty_score":0.99966866},"labels":[],"label_agreement":null},{"id":"W6999593154","doi":"","title":"À découvrir… Comptines\\n . Canada [Québec], 1975, 9 minutes","year":2000,"lang":"fr","type":"article","venue":"Érudit (Université de Montréal)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Work (physics); Identification (biology); Product (mathematics)","score_opus":0.008127946508287312,"score_gpt":0.1554825292951789,"score_spread":0.14735458278689156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999593154","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0787482,0.36156604,0.005488087,0.47089309,0.0043548704,0.00080991467,0.00036338388,0.00043442665,0.07734202],"genre_scores_gemma":[0.04023216,0.003668149,0.0018968713,0.0019699396,0.0005792664,0.0000074742966,0.000029886054,0.000040808725,0.95157546],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99718434,0.000246656,0.00035664393,0.00069760444,0.0006137594,0.00090098334],"domain_scores_gemma":[0.9979416,0.00027364292,0.00018827962,0.00075475115,0.00023189158,0.0006098289],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00023207799,0.0004226891,0.0004885684,0.00012690459,0.0007555276,0.000098396595,0.0010178282,0.00023648833,0.0023524207],"category_scores_gemma":[0.000023688324,0.0004972089,0.00018997538,0.00076222915,0.00012716593,0.00050958915,0.0002624268,0.00032264384,0.0015544533],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003109629,0.00007266468,0.0004940639,0.000060696882,0.00011997729,0.0043110163,0.00058000727,0.00046770254,0.00004607693,0.0022671951,0.73795176,0.25359774],"study_design_scores_gemma":[0.0009020314,0.00007084076,0.0013438935,0.00016961667,0.000082899154,0.0021699197,0.00016976509,0.0076081357,0.00011970289,0.00012034899,0.9866978,0.00054507295],"about_ca_topic_score_codex":0.9843779,"about_ca_topic_score_gemma":0.99901175,"teacher_disagreement_score":0.8742334,"about_ca_system_score_codex":0.008216868,"about_ca_system_score_gemma":0.0013236334,"threshold_uncertainty_score":0.99974793},"labels":[],"label_agreement":null},{"id":"W7033484725","doi":"","title":"The Progress &amp; Freedom Foundation 051807 - PFF Congressional Seminar on The Complexities of Regulating TV Violence","year":2007,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Commission; Agency (philosophy); Government (linguistics); Foundation (evidence); Wright; Royal Commission","score_opus":0.014565011310786254,"score_gpt":0.22380125936353973,"score_spread":0.20923624805275348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033484725","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000047947673,0.00041474908,0.0001314548,0.0005049216,0.00066722924,0.0006883441,0.00004646809,0.00013434906,0.9973645],"genre_scores_gemma":[0.003926126,0.00011500646,0.0035592592,0.0001458808,0.0003042163,0.000038976264,0.00008283873,0.00005444546,0.99177325],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973136,0.00023250924,0.0008970143,0.0002388034,0.0010044684,0.00031359808],"domain_scores_gemma":[0.99621254,0.0010698574,0.0015097348,0.00072479754,0.00040966834,0.00007338525],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011019072,0.0003181723,0.0003675952,0.000111081514,0.00028189932,0.00017942212,0.0010570463,0.00027061076,0.021503365],"category_scores_gemma":[0.00022756393,0.00019993188,0.00014970079,0.000031676856,0.0005348167,8.9786977e-7,0.00019118586,0.00030662268,0.0026096452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003542387,0.000025576019,0.000004102982,0.00019262343,0.000044812437,0.0000020027708,0.00023739225,0.000088238885,4.4903175e-7,0.0048450776,0.978491,0.01603327],"study_design_scores_gemma":[0.00023786094,0.00009063653,0.00001870068,0.0017693642,0.000013352641,0.00005708609,0.00013813638,0.00028854862,0.0000342071,0.0002064336,0.99692357,0.00022209644],"about_ca_topic_score_codex":0.00057585473,"about_ca_topic_score_gemma":0.0003258694,"teacher_disagreement_score":0.018893719,"about_ca_system_score_codex":0.000043918662,"about_ca_system_score_gemma":0.000057805417,"threshold_uncertainty_score":0.9981669},"labels":[],"label_agreement":null},{"id":"W7034355475","doi":"","title":"A Survey of Patient Experience in CML: American and Canadian Perspectives","year":2023,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Icon; Patient experience; Quality of life (healthcare); Trustworthiness; Patient satisfaction; Myeloid leukemia","score_opus":0.2690219249938531,"score_gpt":0.5329075547595666,"score_spread":0.26388562976571356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034355475","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":[0.9945637,0.003271993,0.00033322812,0.00016247325,0.00026531474,0.00029336583,0.00003795597,0.00002566264,0.0010462981],"genre_scores_gemma":[0.9978197,0.0018328801,0.00016406734,0.00008037211,0.000018946223,0.000038290123,0.0000029999978,0.000013733643,0.000029017605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975871,0.00052673876,0.0005983877,0.00043957707,0.00048710412,0.00036111014],"domain_scores_gemma":[0.9979645,0.00041109725,0.00050752645,0.00039571588,0.00037619282,0.00034496252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013525602,0.00017577253,0.0005550234,0.0014424584,0.00011025164,0.0006073784,0.0019135675,0.00004481788,0.00021443625],"category_scores_gemma":[0.00049825234,0.00016257388,0.000053645705,0.0036159905,0.00018950629,0.0014094667,0.0006158,0.00018146375,0.000008520307],"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":[0.000014662827,0.000066185676,0.9399182,0.000020715723,0.000032722914,0.00005134654,0.010492395,0.000043134914,0.0018313174,0.000072399846,0.0016367572,0.045820113],"study_design_scores_gemma":[0.00015833315,0.0000147745,0.99461925,0.0001692121,0.0000025482198,0.000009106144,0.001955442,0.0003637176,0.001455932,0.0002764163,0.0007772381,0.00019800774],"about_ca_topic_score_codex":0.30620348,"about_ca_topic_score_gemma":0.10445186,"teacher_disagreement_score":0.20175163,"about_ca_system_score_codex":0.00010528585,"about_ca_system_score_gemma":0.0003078606,"threshold_uncertainty_score":0.91188955},"labels":[],"label_agreement":null},{"id":"W7036922157","doi":"","title":"De la motivation à l'idéal professionnel : une voie éthique d'ordre autorégulatoire d'accomplissement d'un projet de vie professionnelle en massothérapie","year":2009,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Occupational training; Continuing education; Work environment; Professional qualification; Social environment","score_opus":0.006735059629286668,"score_gpt":0.18243148481185567,"score_spread":0.175696425182569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036922157","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022693688,0.0022218484,0.029121438,0.0813544,0.002002683,0.0029921634,0.00042942128,0.00016231317,0.859022],"genre_scores_gemma":[0.7246525,0.00040215594,0.019616857,0.0061755013,0.00060810224,0.0001537822,0.00005587807,0.0001927356,0.24814248],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99352443,0.0014797655,0.00084984855,0.00084804586,0.002401178,0.00089673157],"domain_scores_gemma":[0.99696296,0.00096165086,0.0008111653,0.0005891071,0.0000056954163,0.00066942745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017798548,0.0007041068,0.00075998856,0.00011481117,0.000561753,0.00017610463,0.0009411771,0.0003303861,0.00045734338],"category_scores_gemma":[0.00003360876,0.00064882473,0.00010396016,0.0003549451,0.00016519423,0.0005995536,0.00047483796,0.000783729,8.329305e-8],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006275772,0.001058117,0.08380605,0.0037564626,0.0010119401,0.0012563727,0.0075652846,0.00053073553,0.03129042,0.36727774,0.093897656,0.40792164],"study_design_scores_gemma":[0.0020174289,0.00031754118,0.3320673,0.008109906,0.00011856647,0.00018640303,0.006155998,0.015417105,0.03278807,0.015140336,0.5859438,0.0017374983],"about_ca_topic_score_codex":0.002825586,"about_ca_topic_score_gemma":0.009263187,"teacher_disagreement_score":0.70195884,"about_ca_system_score_codex":0.00012563639,"about_ca_system_score_gemma":0.008119154,"threshold_uncertainty_score":0.9995963},"labels":[],"label_agreement":null},{"id":"W7039316311","doi":"","title":"L'influence du contexte, du sexe et de l'âge sur la régulation des expressions de la joie et de la colère chez les enfants de 4 a 12 ans","year":2001,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identity (music); Perspective (graphical); Qualitative research; Ethnic group; Subject (documents); Narrative; Perception; Gender identity","score_opus":0.005917208186357769,"score_gpt":0.19242375094479425,"score_spread":0.18650654275843648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039316311","genre_codex":"other","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":[0.20758614,0.00084463827,0.027449787,0.0020582506,0.00015013234,0.0004154511,0.00054913404,0.0000509977,0.7608955],"genre_scores_gemma":[0.94261146,0.0011802883,0.006980227,0.0024983864,0.000180273,0.000051722385,0.000013998013,0.000089528665,0.046394095],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99454266,0.0026433575,0.00044276466,0.00049466506,0.0012122202,0.00066432764],"domain_scores_gemma":[0.9948631,0.0037472928,0.00039738425,0.00032892905,0.0000043269656,0.0006589727],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030562666,0.00044599344,0.00048418407,0.000070777554,0.00037034022,0.0002620615,0.0006197812,0.00024305687,0.00025623557],"category_scores_gemma":[0.00009681445,0.00045133327,0.00007742004,0.00015447197,0.00047219722,0.0007179693,0.000253633,0.0004673754,3.5018513e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020765248,0.00023509306,0.80319595,0.00083798444,0.00030049527,0.0015164796,0.00806912,0.00352496,0.028119113,0.11657836,0.0095596295,0.027855154],"study_design_scores_gemma":[0.0009120431,0.000077097866,0.5769189,0.002513244,0.00005317743,0.00045611055,0.0015124214,0.005300888,0.008093453,0.002331265,0.40120575,0.0006256196],"about_ca_topic_score_codex":0.04740529,"about_ca_topic_score_gemma":0.13496396,"teacher_disagreement_score":0.73502535,"about_ca_system_score_codex":0.000099067904,"about_ca_system_score_gemma":0.009232179,"threshold_uncertainty_score":0.9997938},"labels":[],"label_agreement":null},{"id":"W7043019194","doi":"","title":"Replacing a Mouse with Hand Gesture in a Plane-Based Augmented Reality System","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Augmented reality; Gesture; Silhouette; Representation (politics); Perspective (graphical); Virtual reality; Gesture recognition; Simple (philosophy); Virtual image","score_opus":0.017303390556741134,"score_gpt":0.2247284463770666,"score_spread":0.20742505582032547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043019194","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":[0.40265694,0.00006748283,0.53927827,0.00094998657,0.00035256823,0.0010763999,0.000021031863,0.00058350724,0.055013817],"genre_scores_gemma":[0.9906529,5.3892325e-7,0.008865548,0.00013617489,0.00002302279,0.000061131774,0.000005701431,0.000010436862,0.00024453562],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99832976,0.0003365433,0.0002722996,0.0004352527,0.00033858907,0.0002875524],"domain_scores_gemma":[0.99902433,0.00009996191,0.000116118186,0.0005680891,0.0000802992,0.00011117905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060085003,0.00015463609,0.0002437724,0.00013694401,0.00009549059,0.00016273135,0.0002515385,0.00008857287,0.000009754269],"category_scores_gemma":[0.000048177022,0.00011929771,0.000030602958,0.0004734592,0.000029526866,0.00015580242,0.000021062755,0.00017125536,0.000029854808],"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":[0.0019667118,0.0035089985,0.16354114,0.007863663,0.00076671754,0.0088448,0.03359904,0.008084511,0.41256115,0.2868002,0.031691756,0.04077132],"study_design_scores_gemma":[0.04169566,0.0024865584,0.031268626,0.009630273,0.00013704144,0.0043642554,0.0040541673,0.11035796,0.6922125,0.002635973,0.095765114,0.005391858],"about_ca_topic_score_codex":0.00005992261,"about_ca_topic_score_gemma":0.00022322214,"teacher_disagreement_score":0.58799595,"about_ca_system_score_codex":0.00012585171,"about_ca_system_score_gemma":0.00014640436,"threshold_uncertainty_score":0.48648193},"labels":[],"label_agreement":null},{"id":"W7084180406","doi":"","title":"NAVIGATING NEW HORIZONS: EXAMINING THE TRANSITION EXPERIENCES AND SUPPORT SYSTEMS FOR AFRICAN INTERNATIONAL STUDENTS AT THE UNIVERSITY OF SASKATCHEWAN","year":2025,"lang":"en","type":"article","venue":"University Library (University of Saskatchewan)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Internationalization; Context (archaeology); Higher education; Focus group; International education; Individualism; Cultural diversity; Social constructivism","score_opus":0.012454477371238849,"score_gpt":0.2105995741281925,"score_spread":0.19814509675695366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084180406","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":[0.846922,0.000183101,0.14559755,0.0043385443,0.00049943954,0.0006716475,0.000114428054,0.00011578023,0.0015575403],"genre_scores_gemma":[0.98648196,0.00007098651,0.002573676,0.000052455965,0.00003224968,1.4608939e-7,0.000028122677,0.000005244529,0.01075514],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9987002,0.0002006139,0.00015233045,0.00039517696,0.00035086143,0.00020080093],"domain_scores_gemma":[0.99880797,0.00036007067,0.00027507942,0.00034845687,0.00010602028,0.00010241035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002964972,0.00015495255,0.00027122884,0.000116265444,0.00073895435,0.00007129942,0.0017727218,0.00010709525,0.000067787434],"category_scores_gemma":[0.0000069975154,0.00015043313,0.00012579304,0.0005746497,0.00040312868,0.0014269174,0.00077348825,0.00017085706,0.0000035721353],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003509602,0.00011471754,0.0089382995,0.00016441352,0.0004407665,0.000055391236,0.9639807,0.00015982118,0.00038091367,0.0007029488,0.003789029,0.020922061],"study_design_scores_gemma":[0.0012361746,0.00012851572,0.0027398267,0.00022119615,0.0000764188,0.000009569883,0.97893083,0.0012156124,0.00024723672,0.000093782895,0.01493531,0.00016551021],"about_ca_topic_score_codex":0.0020043724,"about_ca_topic_score_gemma":0.00086975464,"teacher_disagreement_score":0.14302388,"about_ca_system_score_codex":0.00008428442,"about_ca_system_score_gemma":0.000377935,"threshold_uncertainty_score":0.6134485},"labels":[],"label_agreement":null},{"id":"W7117143315","doi":"10.55452/1998-6688-2025-22-4-155-167","title":"DEVELOPMENT OF A MODEL FOR REAL-TIME RECOGNITION OF KAZAKH SIGN LANGUAGE USING MEDIAPIPE AND DEEP LEARNING METHODS","year":2025,"lang":"","type":"article","venue":"Herald of Kazakh-British technical university","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Scientists in School","funders":"","keywords":"Sign language; Gloss (optics); Sentence; Natural language; Kazakh; Language identification; Sign (mathematics)","score_opus":0.0368170479937287,"score_gpt":0.30358544798336234,"score_spread":0.26676839998963364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117143315","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":[0.15606645,0.00065642776,0.8408799,0.00007699702,0.00009730868,0.0007959431,0.00007030883,0.00009267435,0.0012639922],"genre_scores_gemma":[0.3455877,0.0004939534,0.6534867,0.00002151419,0.000016973676,0.0000032894707,0.000019440595,0.000015663096,0.0003547509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968098,0.0005999781,0.0010471931,0.0007311319,0.0003959059,0.00041602508],"domain_scores_gemma":[0.99706227,0.00077389117,0.0008033183,0.0003526484,0.00083182787,0.00017603161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020570576,0.00028575197,0.0010462344,0.0004524036,0.00028455415,0.000047011312,0.0006071179,0.00047702718,0.000036075093],"category_scores_gemma":[0.0004259794,0.00040013468,0.00026711854,0.0010195152,0.00034120047,0.00044735047,0.0005974348,0.0003539097,0.0000010695195],"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":[0.00018637757,0.0003209176,0.00008030789,0.001324243,0.00019695521,0.000027016398,0.002624628,0.00032668034,0.18079026,0.00042691012,0.00006031784,0.8136354],"study_design_scores_gemma":[0.006140145,0.00058511505,0.002463565,0.009635785,0.0007456027,0.00014173149,0.0024377508,0.8805078,0.09167479,0.0030727137,0.0012247766,0.0013701873],"about_ca_topic_score_codex":0.00047549952,"about_ca_topic_score_gemma":0.0003075475,"teacher_disagreement_score":0.88018113,"about_ca_system_score_codex":0.00021543914,"about_ca_system_score_gemma":0.0005593112,"threshold_uncertainty_score":0.999845},"labels":[],"label_agreement":null},{"id":"W7117359192","doi":"10.1016/j.compeleceng.2025.110916","title":"Mizo hand sign language detection using a multi-scale transformer-based hybrid feature extractor and fusion network","year":2025,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sign language; Feature extraction; Classifier (UML); Pattern recognition (psychology); Residual; Extractor; Gesture; Support vector machine","score_opus":0.007362389995936414,"score_gpt":0.2197955266944544,"score_spread":0.212433136698518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117359192","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":[0.08472928,0.001413528,0.9125477,0.00007732923,0.0006184071,0.00033066474,0.0000012590867,0.00026932816,0.000012490853],"genre_scores_gemma":[0.8952945,0.000008855413,0.10437446,0.000116571886,0.00014932486,0.000020873442,0.000003228314,0.0000152194,0.00001696615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987128,0.00006780379,0.00021772485,0.0004151426,0.00017070898,0.00041581749],"domain_scores_gemma":[0.9993031,0.00026368408,0.000044660803,0.00020616075,0.00005078014,0.00013160813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022405991,0.00022535269,0.00028841922,0.0002502368,0.00017178922,0.00023912528,0.00024109936,0.00011908334,7.2913633e-7],"category_scores_gemma":[0.000023947652,0.00021864628,0.00009008007,0.0008724968,0.000016329665,0.00019612623,0.00003644228,0.00033014492,0.0000018311015],"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":[0.000049153703,0.00011359037,0.00019952335,0.00026032745,0.00007705645,0.000079787955,0.00032617053,0.08842555,0.4221253,0.00009581242,0.0001793489,0.48806837],"study_design_scores_gemma":[0.0007939018,0.00006235218,0.0013463222,0.00021510913,0.000016667254,0.000050917442,0.0000022367212,0.95789117,0.03796967,0.000009781937,0.0014151463,0.00022669985],"about_ca_topic_score_codex":0.000015148867,"about_ca_topic_score_gemma":0.0000057762545,"teacher_disagreement_score":0.86946565,"about_ca_system_score_codex":0.00010343659,"about_ca_system_score_gemma":0.000052380918,"threshold_uncertainty_score":0.8916136},"labels":[],"label_agreement":null},{"id":"W7117465268","doi":"10.3390/s26010172","title":"Real-Time Radar-Based Hand Motion Recognition on FPGA Using a Hybrid Deep Learning Model","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Robustness (evolution); Field-programmable gate array; Deep learning; Convolutional neural network; Normalization (sociology); Pipeline (software); Support vector machine; Discriminative model","score_opus":0.02639725690308513,"score_gpt":0.2569198000255178,"score_spread":0.23052254312243264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117465268","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":[0.42609438,0.000013246221,0.56590235,0.00023194097,0.00025454178,0.00022819883,0.0000056016497,0.00026187208,0.007007826],"genre_scores_gemma":[0.9702425,0.00000857574,0.02839723,0.00019691342,0.000081031765,0.0000111706795,0.000035525343,0.000018193172,0.0010088398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983746,0.0002926504,0.00028692477,0.00047303148,0.0002917222,0.00028111687],"domain_scores_gemma":[0.9991282,0.00016598629,0.00013675961,0.00030967963,0.0001809197,0.00007845429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041210165,0.00018212106,0.00022015533,0.00034763463,0.000314091,0.0002145555,0.00019193317,0.000086211636,0.000013131551],"category_scores_gemma":[0.000117131705,0.00018311047,0.000096008174,0.00044062902,0.00003611527,0.00018909053,0.000040440922,0.00020525043,0.0002314717],"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":[0.000106705585,0.00021655274,0.0001055649,0.0001482687,0.000078089884,0.00007894322,0.0008653222,0.5259467,0.06731019,0.0004333162,0.000396431,0.40431395],"study_design_scores_gemma":[0.0005497961,0.000048321257,0.0000635792,0.00023075982,0.00001644551,0.000013779454,0.00002069373,0.95434237,0.04284721,0.001501109,0.00017370054,0.00019226978],"about_ca_topic_score_codex":0.000045920126,"about_ca_topic_score_gemma":0.000004296713,"teacher_disagreement_score":0.54414815,"about_ca_system_score_codex":0.00013001807,"about_ca_system_score_gemma":0.00008778421,"threshold_uncertainty_score":0.74670285},"labels":[],"label_agreement":null},{"id":"W7117568876","doi":"10.18280/isi.301113","title":"VOGUE-Based Approach for Segmenting Movement Epenthesis in Continuous Sign Language Recognition","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Movement (music); Sign language; Feature (linguistics); Sign (mathematics); Segmentation","score_opus":0.018753134864485897,"score_gpt":0.24202104538667726,"score_spread":0.22326791052219136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117568876","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":[0.022942614,0.00048695228,0.9515449,0.00011959332,0.0010366454,0.0036800974,0.0001548027,0.00020554113,0.019828884],"genre_scores_gemma":[0.9572281,0.000024196439,0.039044566,0.0011959416,0.00012215621,0.001490801,0.0006500922,0.000021512791,0.00022262683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955888,0.00041523375,0.0020996996,0.0005257485,0.0005475751,0.0008229363],"domain_scores_gemma":[0.99705523,0.00045556735,0.0010346362,0.0005553187,0.0007737489,0.00012550307],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0029712408,0.000496551,0.00070523174,0.0013708865,0.0004642946,0.0013995635,0.00062790303,0.0003690337,0.00003008604],"category_scores_gemma":[0.0007355001,0.00053768564,0.00026948578,0.0016592228,0.000115437484,0.0036765134,0.00015412581,0.0002750149,0.00008817705],"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":[0.00014630827,0.0002625116,0.0007852117,0.0040669753,0.00012426467,0.0000037620368,0.012350118,0.0021960165,0.0003560609,0.0011246725,0.0003977137,0.97818637],"study_design_scores_gemma":[0.007969411,0.0004772605,0.0028319946,0.0064169103,0.00019642625,0.000015266693,0.019162457,0.917167,0.036214158,0.0055925543,0.0023911395,0.0015653932],"about_ca_topic_score_codex":0.00020131994,"about_ca_topic_score_gemma":0.00002518299,"teacher_disagreement_score":0.976621,"about_ca_system_score_codex":0.00093162863,"about_ca_system_score_gemma":0.0004493798,"threshold_uncertainty_score":0.99970746},"labels":[],"label_agreement":null},{"id":"W7124137613","doi":"10.1109/icacrs67045.2025.11324138","title":"Dynamic ASL Gesture Recognition with Real-Time Customization for Alphabets and Actions","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Gesture; Personalization; Gesture recognition; Flexibility (engineering); Sign language; Adaptability; Scalability; Bridging (networking); Landmark","score_opus":0.014813328429812207,"score_gpt":0.2681719155955724,"score_spread":0.2533585871657602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124137613","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":[0.012944354,0.00027671093,0.95022786,0.004481412,0.0010166719,0.0025236318,0.00009687758,0.000358921,0.028073572],"genre_scores_gemma":[0.80045515,0.0016461818,0.13892585,0.0011579558,0.00027312106,0.0009044472,0.0004119372,0.00008462629,0.056140717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973864,0.00022054142,0.00058759685,0.0010233116,0.0003001248,0.00048203772],"domain_scores_gemma":[0.9976527,0.0004844771,0.00029462663,0.0005142672,0.00086618145,0.00018774858],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056388415,0.00041122478,0.00048896775,0.0005042702,0.00062507344,0.0006589963,0.00029178307,0.00037381254,0.00009556319],"category_scores_gemma":[0.00009571275,0.00035507962,0.0001040973,0.0012309669,0.000117978496,0.0010092723,0.00011019475,0.00022407496,0.00013369077],"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":[0.0003109718,0.00040702638,0.00033470237,0.00078269804,0.00052324834,0.000008605044,0.001202529,0.0001327048,0.004000839,0.002765478,0.014212652,0.97531855],"study_design_scores_gemma":[0.01191452,0.0018492166,0.013143818,0.004082257,0.0012732086,0.00038758302,0.0011870126,0.90253466,0.0045949873,0.008940765,0.04757399,0.0025179822],"about_ca_topic_score_codex":0.00006114946,"about_ca_topic_score_gemma":0.0003129056,"teacher_disagreement_score":0.97280055,"about_ca_system_score_codex":0.00015779345,"about_ca_system_score_gemma":0.0003863614,"threshold_uncertainty_score":0.9998901},"labels":[],"label_agreement":null},{"id":"W7125596744","doi":"10.1109/icdds67737.2025.11344685","title":"Real-Time ISL Alphabet Recognition and Static Gesture Generation Using Supervised Machine Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Alphabet; Pattern recognition (psychology); Supervised learning; Support vector machine; Training set; Feature (linguistics)","score_opus":0.04782315310700055,"score_gpt":0.27125893507703014,"score_spread":0.22343578197002958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125596744","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":[0.5364026,0.0016235962,0.4501338,0.0019988434,0.0015714568,0.0013648844,0.00006850012,0.00039950255,0.0064368574],"genre_scores_gemma":[0.92088056,0.0022389344,0.06408377,0.0010188427,0.0007221179,0.000054936452,0.00048414082,0.000068197696,0.010448519],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955132,0.0012312506,0.0009859548,0.0011593711,0.00050619984,0.0006040363],"domain_scores_gemma":[0.9981395,0.0003361702,0.0003135133,0.0004745618,0.0005016296,0.00023464071],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013687853,0.0005258702,0.0006557254,0.00057065603,0.0007786449,0.0011869597,0.00032686268,0.00036944463,0.00051109865],"category_scores_gemma":[0.00018828282,0.000513497,0.00012440162,0.0012350965,0.00009859345,0.0012885618,0.00029643718,0.0005256583,0.00025999692],"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":[0.00008324844,0.00038936545,0.0033188227,0.0010385213,0.0005363184,0.00007736637,0.0073135495,0.0015627879,0.32991007,0.0005766697,0.0029119952,0.6522813],"study_design_scores_gemma":[0.0015640497,0.00020065183,0.00055548386,0.0006268246,0.00017792278,0.00008081337,0.00018895087,0.9881676,0.005920776,0.00066049764,0.0012552545,0.00060121244],"about_ca_topic_score_codex":0.0007279338,"about_ca_topic_score_gemma":0.000100047895,"teacher_disagreement_score":0.98660475,"about_ca_system_score_codex":0.00016438776,"about_ca_system_score_gemma":0.0003348605,"threshold_uncertainty_score":0.9998499},"labels":[],"label_agreement":null},{"id":"W7126195995","doi":"10.18280/isi.301222","title":"Hybrid CNN-Transformer for Dynamic Indian Sign Language Recognition with Non-Manual Gesture Analysis","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gesture; Gesture recognition; Sign language; Feature (linguistics); Sign (mathematics)","score_opus":0.007135161689478239,"score_gpt":0.24370396167207786,"score_spread":0.2365687999825996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126195995","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":[0.14826448,0.00031682538,0.84003806,0.00031458167,0.0009108928,0.002567189,0.00063850783,0.00022992988,0.0067195524],"genre_scores_gemma":[0.98867095,0.00008544731,0.0071903868,0.0007523934,0.00012133374,0.00066659314,0.0019801103,0.00002818538,0.00050458417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957312,0.00022626249,0.0017388888,0.0006441507,0.0007241437,0.0009353879],"domain_scores_gemma":[0.99623847,0.00033588713,0.0009804734,0.000776517,0.0014202676,0.00024839336],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013937994,0.0007055626,0.0009944403,0.002578894,0.0008840886,0.0017627248,0.00078047917,0.00043155975,0.00006817689],"category_scores_gemma":[0.00020861473,0.00064710976,0.00050866394,0.0038452798,0.00024404527,0.006836589,0.000074611555,0.00046133404,0.0002532876],"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":[0.0003267651,0.00012308446,0.0005900561,0.003098067,0.0025029287,0.00002336218,0.046630237,0.0012496687,0.000152601,0.00023641642,0.00070583244,0.944361],"study_design_scores_gemma":[0.020875825,0.004879221,0.05436888,0.015320286,0.012972615,0.0008476922,0.056214344,0.7607613,0.029553637,0.013060467,0.022547629,0.00859806],"about_ca_topic_score_codex":0.00022468349,"about_ca_topic_score_gemma":0.00038117368,"teacher_disagreement_score":0.93576294,"about_ca_system_score_codex":0.00072377926,"about_ca_system_score_gemma":0.00069819944,"threshold_uncertainty_score":0.999598},"labels":[],"label_agreement":null},{"id":"W7126239519","doi":"10.18280/isi.301221","title":"Adaptive Graph Convolution Deep Learning Model for Multilingual Hand Sign Recognition with Texture and Frequency-Based Features","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Texture (cosmology); Pattern recognition (psychology); Graph; Convolution (computer science); Convolutional neural network","score_opus":0.018616219972147744,"score_gpt":0.2389252162834299,"score_spread":0.22030899631128215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126239519","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":[0.017571684,0.0015899157,0.9758541,0.00012682348,0.0005453888,0.0023652513,0.00016162048,0.00023925111,0.0015459375],"genre_scores_gemma":[0.9500868,0.00006411101,0.048316993,0.0003667092,0.0000915177,0.00048043352,0.0004328948,0.00002441194,0.00013614763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967635,0.00032567448,0.0011292553,0.0005989028,0.0005094611,0.00067318085],"domain_scores_gemma":[0.9957198,0.00047548037,0.0009797816,0.00034676856,0.002285449,0.00019272466],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011151986,0.0006110557,0.00061638304,0.0009888337,0.0016596282,0.0016198502,0.00033588996,0.0005965764,0.0000055719142],"category_scores_gemma":[0.0005862483,0.00056884444,0.00016939436,0.0011405408,0.0005517559,0.0046507698,0.00008162677,0.00058464025,0.000016254442],"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":[0.0007904679,0.000077717836,0.00042178595,0.0021345008,0.00026674764,0.0000036040663,0.027509768,0.048491526,0.00027984084,0.0023846007,0.00013225913,0.9175072],"study_design_scores_gemma":[0.0033616547,0.00077117037,0.001124945,0.0027260263,0.00019168662,0.000052166364,0.0029203119,0.97257614,0.002092798,0.013246077,0.00022940912,0.00070763985],"about_ca_topic_score_codex":0.00012291683,"about_ca_topic_score_gemma":0.00033661624,"teacher_disagreement_score":0.9325151,"about_ca_system_score_codex":0.0003961095,"about_ca_system_score_gemma":0.00076920004,"threshold_uncertainty_score":0.9996763},"labels":[],"label_agreement":null},{"id":"W7130689430","doi":"10.1109/computingcon64838.2025.11377930","title":"Edge-AI Powered Real-Time Gesture Recognition System for Accessible Human-Computer Interaction","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Gesture; Gesture recognition; Embedding; Pipeline (software); Enhanced Data Rates for GSM Evolution; Software deployment; Feature extraction; Landmark","score_opus":0.029533518127682245,"score_gpt":0.3168358308720794,"score_spread":0.28730231274439716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130689430","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":[0.0062330524,0.00027778116,0.8528076,0.0030928256,0.018451864,0.0040281327,0.00011100396,0.0013509133,0.11364682],"genre_scores_gemma":[0.9351365,0.00012145931,0.021551844,0.0017748373,0.003853731,0.00094434887,0.00042607842,0.00011400746,0.036077186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935554,0.0007094889,0.0019973249,0.002049709,0.00063674065,0.0010513283],"domain_scores_gemma":[0.9947722,0.00065753935,0.0008624136,0.0013456962,0.0020342409,0.00032792467],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001579863,0.00087136374,0.0012136609,0.0011577283,0.001060923,0.0027103312,0.0014651446,0.00087588944,0.00043515177],"category_scores_gemma":[0.000070482616,0.00083166495,0.00064983586,0.0017737857,0.00009105198,0.0026427244,0.0005566884,0.00063438696,0.001740869],"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":[0.00041006843,0.0011039792,0.00021353526,0.006029478,0.0012316637,0.000058495156,0.002012151,0.00013955063,0.018362438,0.012609939,0.19618183,0.76164687],"study_design_scores_gemma":[0.02454637,0.004385121,0.0057499595,0.042514816,0.0021283627,0.0010018433,0.002965242,0.5056109,0.16513442,0.013453831,0.22392143,0.0085877245],"about_ca_topic_score_codex":0.00020620433,"about_ca_topic_score_gemma":0.000060207673,"teacher_disagreement_score":0.92890346,"about_ca_system_score_codex":0.0007087992,"about_ca_system_score_gemma":0.00047941454,"threshold_uncertainty_score":0.99941343},"labels":[],"label_agreement":null},{"id":"W7131065779","doi":"10.1109/iccvw69036.2025.00518","title":"A Signer-Invariant Conformer and Multi-Scale Fusion Transformer for Continuous Sign Language Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Waterloo","funders":"","keywords":"Transformer; Encoder; Sign language; Sentence; Language model; Generalization; Fusion; Word error rate","score_opus":0.02227737966417603,"score_gpt":0.27330991322647596,"score_spread":0.25103253356229993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131065779","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":[0.009963988,0.001274185,0.960754,0.0014640028,0.0014326461,0.003405323,0.0001923899,0.00019018544,0.021323286],"genre_scores_gemma":[0.90584683,0.00039620738,0.06643912,0.0027722132,0.0002791759,0.0005686797,0.000104856445,0.000043467375,0.023549417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966295,0.00023252868,0.0010309909,0.0010318226,0.00032972184,0.0007454878],"domain_scores_gemma":[0.99791735,0.0005752278,0.00023125109,0.0004412054,0.00055796886,0.00027701442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012278894,0.0005075283,0.0007571819,0.0004030923,0.00046311808,0.0006228671,0.00039160554,0.0004587408,0.0002795185],"category_scores_gemma":[0.00011294184,0.00043997524,0.00027148868,0.0006242919,0.00015301099,0.00076402846,0.00008245396,0.0002656788,0.00015116665],"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":[0.00016699187,0.00032213933,0.00007655374,0.00059413793,0.00014222841,0.000012688522,0.006445643,0.0000010295004,0.029444613,0.0012145629,0.0013729763,0.96020645],"study_design_scores_gemma":[0.050484713,0.004033602,0.0031540985,0.0062850793,0.0014752247,0.00047011283,0.026753236,0.47130695,0.33403847,0.005512199,0.09108596,0.005400374],"about_ca_topic_score_codex":0.00025739748,"about_ca_topic_score_gemma":0.00036729436,"teacher_disagreement_score":0.9548061,"about_ca_system_score_codex":0.00006349626,"about_ca_system_score_gemma":0.00028007413,"threshold_uncertainty_score":0.9998052},"labels":[],"label_agreement":null},{"id":"W7131082612","doi":"10.1109/csrswtc67757.2025.11384596","title":"A Multi-Task Framework for Joint Gesture and Identity Recognition Using mmWave Radar","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Gesture; Gesture recognition; Identity (music); Joint (building); Minimum bounding box; Object detection; Convolution (computer science); Object (grammar)","score_opus":0.08353488801594426,"score_gpt":0.33855277260525596,"score_spread":0.2550178845893117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131082612","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":[0.010134663,0.0026634731,0.9779993,0.0018885959,0.0043390277,0.0021989446,0.000096334305,0.00016830495,0.00051132694],"genre_scores_gemma":[0.23941189,0.0002924294,0.757218,0.0017548711,0.00047134687,0.00009548227,0.000015977374,0.000029186238,0.00071079546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960836,0.00037294463,0.0010389817,0.0013512156,0.0004498027,0.0007034248],"domain_scores_gemma":[0.9971863,0.0005857265,0.00042463045,0.0007118347,0.00081655046,0.0002749646],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014773515,0.00051813625,0.0007586015,0.0004930909,0.0007592361,0.0014912841,0.0004803876,0.0007311668,0.000059242906],"category_scores_gemma":[0.0008459744,0.0005052167,0.00030594209,0.0012294597,0.0001613619,0.0015640059,0.000498925,0.0005882362,0.00006518199],"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":[0.00015614243,0.0010171175,0.0010747142,0.0033787077,0.00095442386,0.000056646888,0.0058094384,0.00002639569,0.012021147,0.032150317,0.0021213228,0.94123363],"study_design_scores_gemma":[0.00832338,0.00057324866,0.0086318,0.011883574,0.0010108923,0.00037739778,0.0025353173,0.6274734,0.013707023,0.3107701,0.011773088,0.002940769],"about_ca_topic_score_codex":0.00016513653,"about_ca_topic_score_gemma":0.00016188079,"teacher_disagreement_score":0.93829286,"about_ca_system_score_codex":0.00018160498,"about_ca_system_score_gemma":0.00036301362,"threshold_uncertainty_score":0.99973994},"labels":[],"label_agreement":null},{"id":"W7131124802","doi":"10.1109/iccvw69036.2025.00523","title":"Can a Lightweight Transformer Deliver a Robust Multimodal Sign Language Word Recognition?","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transformer; Leverage (statistics); Sign language; Language model; Architecture; Word (group theory); Robustness (evolution)","score_opus":0.0189378059705654,"score_gpt":0.24824497453723093,"score_spread":0.22930716856666553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131124802","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":[0.026265567,0.0018916512,0.8164087,0.009201083,0.0038137722,0.0023025926,0.00021353799,0.00060500205,0.13929813],"genre_scores_gemma":[0.9334947,0.00024651768,0.031685434,0.003322853,0.0004792943,0.00023545706,0.000058516605,0.00004400987,0.03043324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99507403,0.00047151517,0.0012067123,0.0014436677,0.00071400317,0.0010900409],"domain_scores_gemma":[0.99744445,0.00035147462,0.00020943108,0.0009186145,0.0006263852,0.00044966632],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007690011,0.00071225065,0.0007819585,0.00077036023,0.00051122415,0.0007996008,0.001136259,0.00051488134,0.002620955],"category_scores_gemma":[0.00007093091,0.0006325979,0.00047427966,0.0023267423,0.00015552557,0.000811584,0.00014034208,0.00064118806,0.0017950695],"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":[0.00009419408,0.00049854664,0.000078896504,0.00026881395,0.00039189612,0.00016507065,0.012889559,0.0000353459,0.001488636,0.0014562152,0.0060229246,0.9766099],"study_design_scores_gemma":[0.040222727,0.0017688768,0.009177588,0.014409604,0.0027210624,0.0010423195,0.029871123,0.36278972,0.24547783,0.008148395,0.2699032,0.01446754],"about_ca_topic_score_codex":0.0010476437,"about_ca_topic_score_gemma":0.002663848,"teacher_disagreement_score":0.96214235,"about_ca_system_score_codex":0.00022833474,"about_ca_system_score_gemma":0.00061749795,"threshold_uncertainty_score":0.9996125},"labels":[],"label_agreement":null},{"id":"W7131188877","doi":"10.1109/iccvw69036.2025.00517","title":"FusionEnsembleNet: An Attention-Based Ensemble of Spatiotemporal Networks for Multimodal Sign Language Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","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 Waterloo","funders":"","keywords":"Sign language; Gesture; Modalities; RGB color model; Gesture recognition; Pattern recognition (psychology); Ensemble learning; Code (set theory)","score_opus":0.02393928254234479,"score_gpt":0.28637218091980354,"score_spread":0.26243289837745876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131188877","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":[0.075157575,0.0004684933,0.9167944,0.00062208413,0.0021829412,0.0021005697,0.00010041455,0.00017187186,0.0024016295],"genre_scores_gemma":[0.94895357,0.000022624696,0.04784149,0.0005916342,0.00035006774,0.0001935707,0.00039589114,0.000032163258,0.00161898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958205,0.0006052819,0.0013712784,0.001079261,0.0004800334,0.0006436143],"domain_scores_gemma":[0.99612063,0.0007608236,0.00064793194,0.000938402,0.0012959219,0.00023629706],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015022563,0.00046908477,0.00072728336,0.0005760888,0.00035460913,0.0002743164,0.0006707536,0.0005128051,0.00017545476],"category_scores_gemma":[0.0001570177,0.00047009278,0.00042495667,0.0011359357,0.00010794877,0.00061653147,0.000120030934,0.0002452233,0.00004375209],"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":[0.00035922966,0.0010737024,0.0013752655,0.000705991,0.00015732602,0.000009628156,0.0007465532,0.0042693657,0.014130711,0.0008329177,0.0014334371,0.97490585],"study_design_scores_gemma":[0.0037753596,0.0007375001,0.0027699117,0.0009832772,0.0001282334,0.0000042256856,0.00050063303,0.9630889,0.026326012,0.0006864472,0.0004175468,0.0005819498],"about_ca_topic_score_codex":0.00064059685,"about_ca_topic_score_gemma":0.0006011872,"teacher_disagreement_score":0.9743239,"about_ca_system_score_codex":0.0000914893,"about_ca_system_score_gemma":0.0005388385,"threshold_uncertainty_score":0.99977505},"labels":[],"label_agreement":null},{"id":"W7133103123","doi":"","title":"Word-Level American Sign Language (ASL) Translation Using Deep Learning, Leveraging Hand, Face, and Body Key Points","year":2025,"lang":"","type":"dissertation","venue":"TSpace","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Key (lock); Context (archaeology); Translation (biology); Word (group theory); Sign language; Deep learning; American Sign Language; Face (sociological concept)","score_opus":0.0428242937396236,"score_gpt":0.3316892742472615,"score_spread":0.2888649805076379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133103123","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":[0.41141894,0.0033904738,0.5801418,0.00030942436,0.0010117253,0.00085500954,0.000011448118,0.00015744369,0.0027037524],"genre_scores_gemma":[0.9720412,0.0002953941,0.014438274,0.00012576765,0.00024135361,0.000041684623,0.00025959648,0.00007473304,0.012481981],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950498,0.0009346435,0.0008461154,0.0014995302,0.0008541944,0.000815731],"domain_scores_gemma":[0.9971818,0.00051035546,0.0010339922,0.0005714297,0.000388847,0.000313613],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008998472,0.0008441858,0.001073741,0.0008457782,0.00097374385,0.0012080795,0.00059324957,0.0004001705,0.00008737762],"category_scores_gemma":[0.0002048824,0.0009441102,0.00023543599,0.0016564706,0.00015513334,0.00058047066,0.00010945395,0.0010825888,0.00007073758],"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":[0.00010615786,0.00008012629,0.001078272,0.00063920463,0.00023312037,0.000060218943,0.32940477,0.0012053049,0.014301777,0.000058105914,0.00002616199,0.65280676],"study_design_scores_gemma":[0.004880758,0.00057193154,0.028210867,0.007051206,0.0008880215,0.00021491667,0.265926,0.65729564,0.019676976,0.00015153344,0.0106707085,0.0044614547],"about_ca_topic_score_codex":0.0022019008,"about_ca_topic_score_gemma":0.0009433479,"teacher_disagreement_score":0.6560903,"about_ca_system_score_codex":0.00017565928,"about_ca_system_score_gemma":0.00039008108,"threshold_uncertainty_score":0.99982876},"labels":[],"label_agreement":null},{"id":"W7133311423","doi":"10.1109/ipmml68499.2025.11407031","title":"3D Hand Gesture Sequence Recognition Using Sinusoidal Positional Encoding Transformer","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformer; Gesture; Encoding (memory); Gesture recognition; Pattern recognition (psychology)","score_opus":0.06189473630878567,"score_gpt":0.30422097697749056,"score_spread":0.2423262406687049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133311423","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":[0.035881925,0.00088707183,0.90615195,0.0028487516,0.0032054214,0.0010050716,0.00014671004,0.00019534267,0.049677767],"genre_scores_gemma":[0.95736057,0.00011380412,0.037255503,0.002201854,0.000611137,0.0000458573,0.00006800473,0.000028795934,0.0023144553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951407,0.00046373502,0.0011932693,0.001355543,0.0008966595,0.00095014914],"domain_scores_gemma":[0.99756587,0.00034713713,0.00028104807,0.0005535223,0.00094804866,0.00030435453],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00097599655,0.0006522992,0.0006496482,0.0007175535,0.0013365275,0.0016954575,0.00077023526,0.00061276974,0.00074212666],"category_scores_gemma":[0.000086082284,0.0006446017,0.00034614242,0.0020179823,0.0003415187,0.002062483,0.000120500925,0.00073439785,0.00038933847],"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":[0.0001841908,0.00070746365,0.0008454683,0.00094480644,0.0006535864,0.00028441698,0.0057609333,0.00093180785,0.24035983,0.00673666,0.0017096485,0.7408812],"study_design_scores_gemma":[0.009304076,0.000906145,0.0035388588,0.019665249,0.0011098845,0.0041999444,0.0016748918,0.68451196,0.22228818,0.02108728,0.025901498,0.0058120308],"about_ca_topic_score_codex":0.00019708296,"about_ca_topic_score_gemma":0.00010153435,"teacher_disagreement_score":0.9214787,"about_ca_system_score_codex":0.00044620465,"about_ca_system_score_gemma":0.0012435424,"threshold_uncertainty_score":0.9999636},"labels":[],"label_agreement":null},{"id":"W7133398356","doi":"","title":"Construction and Training of a Neural Network for Gesture Recognition*","year":2021,"lang":"es","type":"article","venue":"Universidad Industrial de Santander","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"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":"China Scholarship Council; University of Toronto","keywords":"Artificial neural network; Rhythm; Limiting; Training (meteorology); Staring","score_opus":0.07425148002412038,"score_gpt":0.26217094896908255,"score_spread":0.18791946894496217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133398356","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":[0.81583387,0.0024096475,0.15498704,0.008046327,0.007856969,0.0018058904,0.00076042005,0.00019425288,0.008105622],"genre_scores_gemma":[0.97593474,0.00031191218,0.019752288,0.00048624634,0.0031378127,0.000008636633,0.00009208582,0.00002853691,0.00024770864],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982162,0.0003407178,0.00037259716,0.0004503716,0.00021572578,0.00040437415],"domain_scores_gemma":[0.99837965,0.00051283726,0.00030162642,0.00020178746,0.00042963942,0.00017443333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004334579,0.00021369898,0.0004316147,0.0001271713,0.00023595383,0.0002009273,0.00016293173,0.00049678725,0.000068350615],"category_scores_gemma":[0.00020942981,0.0002355015,0.0001476952,0.00062219397,0.00016804249,0.0003648897,0.00009481263,0.0003519856,0.0000023314185],"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":[0.0004997154,0.000116016956,0.011742849,0.0002593171,0.0006659538,0.00021713154,0.00539365,0.0005441131,0.0019803836,0.01359936,0.022339443,0.9426421],"study_design_scores_gemma":[0.07978634,0.0041663507,0.017636115,0.012495698,0.0038627824,0.011193484,0.082313225,0.052710705,0.029392386,0.028460003,0.67186475,0.0061181807],"about_ca_topic_score_codex":0.000021703221,"about_ca_topic_score_gemma":0.000013743406,"teacher_disagreement_score":0.9365239,"about_ca_system_score_codex":0.00007845381,"about_ca_system_score_gemma":0.00051731133,"threshold_uncertainty_score":0.96034724},"labels":[],"label_agreement":null},{"id":"W7140465697","doi":"10.1109/etcom66606.2025.11437006","title":"Sign Language Translation and Hand Gesture Identification using Deep Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Deep learning; Gesture; Identification (biology); Sign language; Translation (biology); Sign (mathematics)","score_opus":0.025001807695506542,"score_gpt":0.28357209079826584,"score_spread":0.2585702831027593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7140465697","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":[0.058058698,0.010678186,0.92627126,0.00068769907,0.0007686507,0.00045309842,0.0000014397432,0.00009639388,0.0029845664],"genre_scores_gemma":[0.99141204,0.00012396355,0.005001272,0.00010057273,0.00012279431,0.000009228663,0.000007961947,0.000010688686,0.0032114757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785024,0.00041590992,0.00053476647,0.0006371761,0.0002844735,0.00027741026],"domain_scores_gemma":[0.99901545,0.00020754545,0.00019437475,0.0003007495,0.00019059988,0.00009127803],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00083135325,0.00022554799,0.00026395547,0.00032272743,0.00061035587,0.0013605063,0.00023522966,0.00023549207,0.000047738773],"category_scores_gemma":[0.00007520098,0.00022308115,0.000069266236,0.00086399633,0.00008954793,0.0007336068,0.000071653216,0.00030332798,0.00003622969],"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":[0.0000098986675,0.00003745937,0.0011548996,0.00021704216,0.000058381564,0.000007733818,0.014762713,0.00066980766,0.054952413,0.0010851977,0.000022416743,0.92702204],"study_design_scores_gemma":[0.0010037585,0.000052845247,0.011810928,0.00048436612,0.00013077489,0.000048469126,0.0027675447,0.9642836,0.0155356405,0.00037687406,0.0030718367,0.000433398],"about_ca_topic_score_codex":0.00009484476,"about_ca_topic_score_gemma":0.00009866997,"teacher_disagreement_score":0.96361375,"about_ca_system_score_codex":0.00004998441,"about_ca_system_score_gemma":0.00008782344,"threshold_uncertainty_score":0.99967617},"labels":[],"label_agreement":null},{"id":"W7161140305","doi":"10.1109/acit68900.2025.11510569","title":"A Deep Learning Framework for Real-Time Word-Level Translation of American and Turkish Sign Languages","year":2025,"lang":"","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Dalhousie University","funders":"","keywords":"Turkish; Deep learning; Translation (biology); Sign (mathematics); Machine translation","score_opus":0.02350345815749141,"score_gpt":0.30040857827368345,"score_spread":0.27690512011619206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161140305","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":[0.0093904445,0.0010062609,0.98045725,0.0010649719,0.00021085434,0.0006895273,0.000010247978,0.0001068366,0.007063633],"genre_scores_gemma":[0.74375683,0.00023856774,0.25358793,0.00012207484,0.0000967619,0.000042776017,0.0000064263922,0.000013540625,0.0021350882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792814,0.00030604214,0.0005890182,0.00058116135,0.00025101737,0.0003446166],"domain_scores_gemma":[0.9970104,0.0019909216,0.00033527816,0.0003037691,0.00025376515,0.00010587709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006832412,0.00024163656,0.0005639308,0.00027483012,0.0001774974,0.00020250349,0.00030124848,0.00017805857,0.000058127996],"category_scores_gemma":[0.00027336983,0.00023128353,0.00013700395,0.0010772223,0.0001669578,0.00023864074,0.000066963235,0.0002373607,0.000012644574],"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":[0.00004293138,0.000058871836,0.00071782316,0.00022820897,0.00010050432,0.0000015466384,0.0050699716,0.00011568095,0.0015080912,0.0056299474,0.00007170312,0.9864547],"study_design_scores_gemma":[0.0047316607,0.002285986,0.05373095,0.0049910867,0.00057310873,0.000039856917,0.01590941,0.8642374,0.015948651,0.02429421,0.0110172145,0.0022404285],"about_ca_topic_score_codex":0.00021765823,"about_ca_topic_score_gemma":0.00005825045,"teacher_disagreement_score":0.9842143,"about_ca_system_score_codex":0.000024704243,"about_ca_system_score_gemma":0.00010198874,"threshold_uncertainty_score":0.9431469},"labels":[],"label_agreement":null},{"id":"W785699106","doi":"10.1007/s10489-015-0680-z","title":"Boosting real-time recognition of hand posture and gesture for virtual mouse operations with segmentation","year":2015,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Gesture; Gesture recognition; Frame (networking); Segmentation; Response time; Boosting (machine learning); Artificial intelligence; Frame rate; Image processing; Computer vision; Real-time computing; Image (mathematics); Computer graphics (images)","score_opus":0.03558158295449636,"score_gpt":0.2621570801212595,"score_spread":0.22657549716676312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W785699106","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":[0.13199952,0.000041447824,0.8651001,0.00015066697,0.00007508061,0.000807147,0.000028187787,0.0000791736,0.0017187019],"genre_scores_gemma":[0.8891706,0.000026950825,0.11018263,0.00010490733,0.00007296725,0.00016083987,0.00007136256,0.000014137263,0.0001956448],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998993,0.00003476355,0.00027359053,0.00032357837,0.00022090819,0.00015418185],"domain_scores_gemma":[0.99904364,0.00015235809,0.00011325551,0.00019925523,0.0003863803,0.00010512893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034289315,0.00013715809,0.00017701984,0.000083376304,0.000121355886,0.00014358014,0.00018555768,0.000081449,0.0000037428927],"category_scores_gemma":[0.0000569349,0.00011251709,0.000018914523,0.00023585412,0.00007243136,0.0003126042,0.000049554983,0.00007730723,0.000028276525],"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":[0.0003089651,0.00023491868,0.00019599077,0.00015673193,0.00013146634,0.000004917373,0.025249733,0.0029842732,0.56214243,0.03925647,0.0011583354,0.36817577],"study_design_scores_gemma":[0.0010385774,0.0012041694,0.00016025954,0.00017302057,0.000052801697,0.00008789172,0.0045838025,0.018985001,0.969233,0.0033475084,0.000560395,0.00057360565],"about_ca_topic_score_codex":0.000033128996,"about_ca_topic_score_gemma":0.000037706483,"teacher_disagreement_score":0.75717103,"about_ca_system_score_codex":0.000026681111,"about_ca_system_score_gemma":0.00008256235,"threshold_uncertainty_score":0.45883137},"labels":[],"label_agreement":null},{"id":"W86969342","doi":"10.1007/978-3-642-39094-4_17","title":"Sign Language Recognition Using Hilbert Curve Features","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Hilbert curve; Computer science; Gesture; Gesture recognition; Hilbert space; Artificial intelligence; Support vector machine; Feature vector; Invariant (physics); Pixel; Pattern recognition (psychology); Reproducing kernel Hilbert space; Classifier (UML); Representation (politics); Computer vision; Translation (biology); Sign language; Algorithm; Mathematics; Mathematical analysis","score_opus":0.028768233048463827,"score_gpt":0.2599323129668825,"score_spread":0.2311640799184187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W86969342","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":[0.00030386384,0.0009836354,0.9835889,0.00036912636,0.0026852922,0.0007229434,0.000014882242,0.00026183488,0.011069519],"genre_scores_gemma":[0.17385063,0.00008431538,0.81696475,0.003498746,0.0030316466,0.000050676186,0.00006004443,0.00014064719,0.0023185532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99560666,0.000108257154,0.0006294992,0.0017062742,0.0011727556,0.00077656034],"domain_scores_gemma":[0.99698484,0.0005550048,0.00044453214,0.0012931047,0.00046460514,0.000257925],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000939561,0.0006716519,0.0006717652,0.0010631807,0.00028087152,0.001044967,0.0023950406,0.0005584757,0.00012312827],"category_scores_gemma":[0.00013209884,0.0005936616,0.0001926917,0.0007241933,0.00039577464,0.0010871515,0.0008163785,0.0009935288,0.0005324381],"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":[0.0000029334326,0.000023411421,0.000012642189,0.00005528595,0.000018130511,0.00010893532,0.0014202929,0.0012534615,0.00058899785,0.00088669464,0.00012695174,0.99550223],"study_design_scores_gemma":[0.0022212842,0.00081051636,0.0006707013,0.006856029,0.00010683832,0.0032445742,0.000006760397,0.603034,0.022069903,0.34448346,0.009272064,0.007223842],"about_ca_topic_score_codex":0.00016581996,"about_ca_topic_score_gemma":0.00010999363,"teacher_disagreement_score":0.98827845,"about_ca_system_score_codex":0.000361689,"about_ca_system_score_gemma":0.00046229438,"threshold_uncertainty_score":0.9999921},"labels":[],"label_agreement":null},{"id":"W971354173","doi":"10.1007/978-3-642-35230-0_13","title":"Trajectory Analysis on Spherical Self-Organizing Maps with Application to Gesture Recognition","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Trajectory; Artificial intelligence; Gesture; Pattern recognition (psychology); Gesture recognition; Computer vision; Human–computer interaction; Physics","score_opus":0.01493122690632617,"score_gpt":0.24413178369909946,"score_spread":0.2292005567927733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W971354173","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":[0.00042856953,0.009740075,0.95283186,0.000042397496,0.00072655897,0.0011368452,0.0000129768205,0.0002860083,0.034794696],"genre_scores_gemma":[0.949358,0.002272008,0.040834013,0.00045000794,0.0020535607,0.00018201329,0.00014981431,0.00016858855,0.0045320224],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99724334,0.0001022484,0.00076423434,0.00096927286,0.00051936635,0.0004015422],"domain_scores_gemma":[0.9982203,0.00030692335,0.0005306671,0.0005286958,0.0001959082,0.00021750154],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058155885,0.0004832118,0.0008070774,0.0005450153,0.00014659585,0.00019052697,0.00040798017,0.00027782182,0.0000067937226],"category_scores_gemma":[0.000012467747,0.00041185383,0.000113749105,0.00055077026,0.000028091923,0.00029162897,0.00013027225,0.00046930287,0.00015316826],"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":[0.00005814422,0.00016396084,0.0021173842,0.000817216,0.00080088206,0.000048960483,0.00409376,0.019552566,0.000028274215,0.03812733,0.00009885498,0.93409264],"study_design_scores_gemma":[0.0010245985,0.0013802925,0.0011963138,0.009523616,0.0011320758,0.000513219,0.00117883,0.040760804,0.00045688875,0.0034561085,0.93448377,0.004893501],"about_ca_topic_score_codex":0.00003275315,"about_ca_topic_score_gemma":0.00008865035,"teacher_disagreement_score":0.9489294,"about_ca_system_score_codex":0.0002314506,"about_ca_system_score_gemma":0.00003473788,"threshold_uncertainty_score":0.99983335},"labels":[],"label_agreement":null},{"id":"W987906134","doi":"10.1007/978-3-319-19387-8_279","title":"Motor Control Assessment using Leap Motion: Filtering Methods and Performance in Indoor and Outdoor Environments","year":2015,"lang":"en","type":"book-chapter","venue":"World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Kinematics; Computer vision; Artificial intelligence; Motion (physics); Tracking (education); Motion capture; Match moving; Motion control","score_opus":0.026880030862223268,"score_gpt":0.2925834770607092,"score_spread":0.2657034461984859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W987906134","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":[0.017576413,0.014661186,0.8831179,0.0025208483,0.010754379,0.0048886808,0.00051849766,0.00077700015,0.065185085],"genre_scores_gemma":[0.843078,0.0042694663,0.049304415,0.0028110512,0.0044986513,0.00036510278,0.00027008008,0.0005289673,0.09487423],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99623793,0.00013661134,0.0008324992,0.0009116426,0.0013171852,0.00056415476],"domain_scores_gemma":[0.9978112,0.00030654154,0.00030394804,0.00055526424,0.000049804697,0.0009732589],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001349755,0.0006837375,0.0010440521,0.0003957828,0.000105090636,0.00016577175,0.00053589966,0.0005131536,0.00009456125],"category_scores_gemma":[0.00004390958,0.00060413586,0.00009960833,0.00014130946,0.00038260312,0.00029523566,0.00057040463,0.0012942846,0.000016782376],"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":[0.00014634091,0.0007388396,0.0013245826,0.0023993307,0.0012191787,0.0010554565,0.00063945673,0.00035897965,0.0014944819,0.024898227,0.0031911274,0.962534],"study_design_scores_gemma":[0.003984811,0.00034355186,0.0021027348,0.0027547162,0.00010415782,0.00015463278,0.0000067627584,0.4511569,0.000043427342,0.000577032,0.5375212,0.0012500645],"about_ca_topic_score_codex":0.000011275511,"about_ca_topic_score_gemma":0.000008702043,"teacher_disagreement_score":0.9612839,"about_ca_system_score_codex":0.00015092472,"about_ca_system_score_gemma":0.00012508658,"threshold_uncertainty_score":0.999641},"labels":[],"label_agreement":null}]}