{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":4,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":4,"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"},"query_hash":"494b607c6909","filters":{"venue":"High Performance Graphics"}},"results":[{"id":"W2089848273","doi":"10.5555/2383795.2383812","title":"Adaptive scalable texture compression","year":2012,"lang":"en","type":"article","venue":"High Performance Graphics","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Texel; Color depth; Computer science; Computer vision; Lossy compression; Artificial intelligence; Pixel; Texture compression; Bilinear interpolation; Color space; Color image; Image texture; Image segmentation; Segmentation; Image processing; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01964150090634639,"gpt":0.2500547728112572,"spread":0.2304132719049108,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002915109,0.0002337751,0.0002141955,0.0002105743,0.0003569752,0.00006446029,0.001183874,0.0001476957,0.00003288515],"category_scores_gemma":[0.00001446653,0.0001911969,0.00005266861,0.0007502355,0.0001086496,0.003336922,0.0006892891,0.0004236721,0.0001023163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003298412,"about_ca_system_score_gemma":0.00002659682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001170097,"about_ca_topic_score_gemma":6.440211e-7,"domain_scores_codex":[0.9982874,0.00005908342,0.0002530407,0.0003481751,0.0004519669,0.0006003817],"domain_scores_gemma":[0.9984236,0.0000680779,0.0001450063,0.001041067,0.0001152538,0.0002070144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007398825,0.0006138153,0.03747052,0.0001203617,0.00004096837,0.000008697235,0.0008167598,0.0003055362,0.005446171,0.7249197,0.04896519,0.1812183],"study_design_scores_gemma":[0.001527115,0.0007017807,0.2056493,0.0007685005,0.00003413009,0.0001510034,0.00006350943,0.3157329,0.1929098,0.02183909,0.2582885,0.002334505],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06554343,0.0008215077,0.9294404,0.0001883433,0.0007756469,0.0002706705,0.00001389016,0.001090216,0.00185584],"genre_scores_gemma":[0.871793,0.0003686573,0.1270583,0.0004350723,0.0001367574,0.00004113771,0.00001362487,0.00001656693,0.0001368937],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8062496,"threshold_uncertainty_score":0.7796785,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077201829","doi":"10.5555/1921479.1921499","title":"A work-efficient GPU algorithm for level set segmentation","year":2010,"lang":"en","type":"article","venue":"High Performance Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Algorithm; Computational complexity theory; Granularity; Field (mathematics); Domain (mathematical analysis); Set (abstract data type); Segmentation; Image segmentation; Reduction (mathematics); Logarithm; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03171319170204532,"gpt":0.2871629754316046,"spread":0.2554497837295593,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000583373,0.0001638471,0.0001369963,0.000225505,0.0002438634,0.0001357442,0.0006397148,0.0001158776,0.00002508039],"category_scores_gemma":[0.00003399332,0.0001530669,0.00006291766,0.0006892461,0.0001258191,0.0003502527,0.0001168859,0.0002904462,0.00002247089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002158362,"about_ca_system_score_gemma":0.0000681686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009398434,"about_ca_topic_score_gemma":0.000003580062,"domain_scores_codex":[0.9985145,0.00002425094,0.0002966455,0.0003678916,0.0004629599,0.0003337296],"domain_scores_gemma":[0.9989762,0.0000760376,0.0001301394,0.0004816352,0.0002032286,0.0001327539],"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":[0.000009113432,0.0001355968,0.0008115338,0.00005024236,0.00001868892,0.00000242441,0.0006505503,0.0000469152,0.006614889,0.007339992,0.005316146,0.9790039],"study_design_scores_gemma":[0.00176081,0.00044867,0.02025655,0.00007621769,0.00002504288,0.00002920146,0.00005219848,0.6621204,0.3097568,0.001388554,0.003321344,0.000764235],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1042806,0.00001100643,0.8937885,0.00026215,0.0008367199,0.0004762569,0.0000198571,0.0002862407,0.00003864858],"genre_scores_gemma":[0.2109202,0.00004408104,0.7876523,0.0008082527,0.0001285492,0.0002431004,0.00005476176,0.00001511075,0.0001335734],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9782397,"threshold_uncertainty_score":0.6241887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2509022565","doi":"10.5555/2977336.2977348","title":"Deep g-buffers for stable global illumination approximation","year":2016,"lang":"en","type":"article","venue":"High Performance Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Memory footprint; Sorting; Representation (politics); Footprint; Bounded function; Reflection (computer programming); Computer graphics (images); Computer vision; Artificial intelligence; Pixel; Code (set theory); Computer hardware; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01280716732235093,"gpt":0.2504379452236803,"spread":0.2376307779013294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002201798,0.0001161592,0.0001037707,0.00009394713,0.0002243357,0.00006424733,0.0003696087,0.00004810468,0.000006035538],"category_scores_gemma":[0.00004144323,0.00008407058,0.0000433714,0.0004575956,0.00006179427,0.001815867,0.00007049163,0.00004387087,0.00002234996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000732294,"about_ca_system_score_gemma":0.00002936104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002591084,"about_ca_topic_score_gemma":0.000002553136,"domain_scores_codex":[0.9989941,0.0000148221,0.0001838253,0.0002815655,0.0002341501,0.000291523],"domain_scores_gemma":[0.9992915,0.00004955763,0.00009619672,0.0003199704,0.0001803105,0.00006248432],"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":[0.0000179596,0.00003907573,0.003384811,0.00004982409,0.000007190268,4.247058e-7,0.0001135128,0.0001180165,0.000871544,0.3541295,0.0003637511,0.6409044],"study_design_scores_gemma":[0.001121437,0.0001562978,0.02107615,0.00007610414,0.000005986473,0.000007775367,0.00002069088,0.934823,0.005717407,0.02722596,0.00946442,0.0003048109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04654954,0.00004273541,0.9514106,0.0009279372,0.0003943182,0.0001997159,0.000004587226,0.0001670284,0.0003035246],"genre_scores_gemma":[0.8709044,0.0001595483,0.128433,0.0002997385,0.00004920966,0.00004256201,0.000005630377,0.000006909968,0.00009892857],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.934705,"threshold_uncertainty_score":0.3428299,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2509044657","doi":"10.5555/2977336.2977356","title":"Filtering distributions of normals for shading antialiasing","year":2016,"lang":"en","type":"article","venue":"High Performance Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ubisoft (Canada)","funders":"","keywords":"Computer science; Rendering (computer graphics); Pixel; Shading; Computer vision; Artificial intelligence; Computer graphics (images); Outlier; Normal; Filter (signal processing); Panorama; Photometric stereo; Computer graphics; Image (mathematics); Mathematics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.02661572336442825,"gpt":0.2786813029633852,"spread":0.252065579598957,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003302778,0.0001197782,0.0001663545,0.0002618008,0.0002152142,0.00006040614,0.0005356661,0.00006093726,0.000004526599],"category_scores_gemma":[0.00002369939,0.00009255633,0.00008961974,0.000600994,0.00007561178,0.0006798225,0.0001711164,0.00004563435,9.541543e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001474827,"about_ca_system_score_gemma":0.00003131432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007327502,"about_ca_topic_score_gemma":0.000001395602,"domain_scores_codex":[0.9989975,0.00001729624,0.0003250655,0.0002377369,0.0001705835,0.0002518096],"domain_scores_gemma":[0.9990709,0.00009952544,0.0001591414,0.0003864187,0.0002322775,0.00005175034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003702283,0.00002492496,0.004375171,0.00005925322,0.00001034247,2.483335e-7,0.00006185294,0.000002122286,0.004464728,0.9799103,0.0001702575,0.0109171],"study_design_scores_gemma":[0.001177839,0.000652415,0.04688093,0.000875385,0.00002500655,0.0000206634,0.000005711752,0.2716834,0.6190852,0.04742186,0.01140535,0.0007662325],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2216172,0.00002165607,0.7776691,0.0001226047,0.0002196273,0.0001268524,0.00003252704,0.0001644399,0.00002601581],"genre_scores_gemma":[0.9746953,0.0002962533,0.02484964,0.00005011707,0.00005190526,0.0000272627,0.000007475684,0.000008950923,0.0000130628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9324884,"threshold_uncertainty_score":0.3774338,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}