{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"ce4f93c59673","filters":{"venue":"Systems and Computers in Japan"}},"results":[{"id":"W2087905578","doi":"10.1002/scj.1166","title":"Composition and decomposition learning of reaching movements under altered environments: An examination of the multiplicity of internal models","year":2002,"lang":"en","type":"article","venue":"Systems and Computers in Japan","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Kinematics; Transformation (genetics); Coordinate system; Computer science; Artificial intelligence; Physics; Classical mechanics","authors":[{"name":"Eri Nakano","is_ca":false},{"name":"J. Randall Flanagan","is_ca":true},{"name":"Hiroshi Imamizu","is_ca":false},{"name":"Rieko Osu","is_ca":false},{"name":"Toshinori Yoshioka","is_ca":false},{"name":"Mitsuo Kawato","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03068912266222302,"gpt":0.2355015982099236,"spread":0.2048124755477006,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000158986,0.00005956283,0.0001258503,0.00005870326,0.00004544945,0.00001268689,0.00006536489,0.00002502918,5.342193e-7],"category_scores_gemma":[0.000004490022,0.00004922319,0.00001671259,0.00004235241,0.00004942065,0.0001910659,0.00003648687,0.00006158188,4.105465e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000209408,"about_ca_system_score_gemma":0.000001009617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001905972,"about_ca_topic_score_gemma":0.000003436859,"domain_scores_codex":[0.9991387,0.000260982,0.0002611155,0.0001379378,0.0001430865,0.00005824896],"domain_scores_gemma":[0.9995966,0.00005881586,0.000243923,0.0000747123,0.000008294972,0.00001764461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001941244,0.0001384279,0.003689795,0.00007886589,0.000006560852,1.640912e-7,0.003656522,0.06842674,0.9064476,0.002240961,1.718225e-7,0.01529478],"study_design_scores_gemma":[0.0005636562,0.0000997021,0.1023226,0.0002083916,0.000003685205,0.000003468464,0.0002600889,0.8898201,0.006567316,0.000110497,7.071622e-7,0.00003979794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358801,0.00003182419,0.06373999,0.000006080138,0.00008297792,0.0001782109,0.00000305302,0.000003349205,0.00007439918],"genre_scores_gemma":[0.9997762,0.000011819,0.0001672044,0.00001790123,0.00001141728,0.000003476429,0.000001317895,0.000003732226,0.000006862857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8998803,"threshold_uncertainty_score":0.2007263,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4256419437","doi":"10.1002/scj.20320","title":"Real‐time depth‐mapping three‐dimension TV camera (Axi‐Vision camera)","year":2006,"lang":"en","type":"article","venue":"Systems and Computers in Japan","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer vision; Camera auto-calibration; Rolling shutter; Computer science; Shutter; Pixel; Stereo camera; Camera resectioning; Camera interface; Pinhole camera model; Three-CCD camera; Image resolution; Frame rate; Computer graphics (images); Optics; Image processing; Physics; Image (mathematics); Digital image processing","authors":[{"name":"Masahiro Kawakita","is_ca":false},{"name":"Keigo Iizuka","is_ca":true},{"name":"Yoshiki Iino","is_ca":false},{"name":"Hiroshi Kikuchi","is_ca":false},{"name":"Hideo Fujikake","is_ca":false},{"name":"Tahito Aida","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007639609924136721,"gpt":0.2240819506848572,"spread":0.2164423407607204,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001067574,0.0002019937,0.0003331572,0.0001075616,0.0001287532,0.00008728738,0.0001282391,0.00007882039,0.000003754039],"category_scores_gemma":[0.000002498457,0.0001807567,0.00004877533,0.0001954968,0.0001032313,0.0001149755,0.0001508259,0.0001793658,0.00001793431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004627639,"about_ca_system_score_gemma":0.00001062157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004628853,"about_ca_topic_score_gemma":0.00003122399,"domain_scores_codex":[0.998794,0.00003543981,0.0003262454,0.0003698231,0.000141234,0.000333327],"domain_scores_gemma":[0.9994296,0.00008829799,0.0001149425,0.0002857847,0.00003427301,0.00004710734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004500322,0.0003628518,0.2801488,0.0001389469,0.0001236303,0.00004861207,0.0005832958,0.01627132,0.03843579,0.2640763,0.005024952,0.3947406],"study_design_scores_gemma":[0.004397187,0.0007116481,0.2357589,0.003097437,0.00006397174,0.00005918234,0.003115109,0.6547664,0.001931342,0.08252712,0.01075076,0.00282096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9590865,0.0001376387,0.03604859,0.0001350714,0.0003078236,0.0002709488,0.00000356658,0.0001957188,0.003814142],"genre_scores_gemma":[0.9857544,0.000003392835,0.01389512,0.00001044113,0.0001914501,0.00000777549,0.0000152701,0.00001971727,0.0001024493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6384951,"threshold_uncertainty_score":0.7371042,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}