{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":8,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":8,"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":"7fd41e74fdb0","filters":{"venue":"Real-Time Imaging"}},"results":[{"id":"W1973646532","doi":"10.1016/j.rti.2003.11.001","title":"Fast color correction using principal regions mapping in different color spaces","year":2003,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Gamut; Artificial intelligence; Computer vision; Computer science; Object (grammar); Color space; Pixel; Color correction; Principal component analysis; Principal (computer security); Color image; Pattern recognition (psychology); Color balance; Degree (music); Color histogram; Mathematics; Image (mathematics); Image processing","retraction":null,"screen_n_in":null,"score":{"opus":0.01730394804696294,"gpt":0.2631550714945737,"spread":0.2458511234476108,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004130731,0.0002387419,0.0002702897,0.0003685481,0.0001995864,0.0002818294,0.0003994109,0.00004688332,0.00002928605],"category_scores_gemma":[0.00009386981,0.0002362248,0.0000651575,0.0006084405,0.00006727393,0.0007740094,0.0001955494,0.0001944704,0.00003252595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004618715,"about_ca_system_score_gemma":0.00008991923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001596822,"about_ca_topic_score_gemma":0.00001406728,"domain_scores_codex":[0.9981614,0.000176423,0.0003383165,0.0005172626,0.0002757297,0.0005308443],"domain_scores_gemma":[0.9991017,0.0001010023,0.0001793093,0.0004605963,0.00007649387,0.00008087442],"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":[0.0000148717,0.0004276004,0.0250988,0.00007741831,0.00003592355,0.0002263028,0.003256708,0.0007804753,0.9389377,0.008872379,0.003011059,0.01926077],"study_design_scores_gemma":[0.0005401088,0.00004739892,0.004934537,0.0004039283,0.00001170881,0.0001581025,0.0002851104,0.8299786,0.1592866,0.0006631185,0.003110187,0.0005804995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3322826,0.00003510117,0.6550705,0.000320983,0.0005331844,0.0004504854,7.702545e-7,0.0006035665,0.01070276],"genre_scores_gemma":[0.8745012,0.00002973179,0.1232126,0.0001228779,0.00007333669,0.00006322467,0.000002926392,0.00003246624,0.001961647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8291982,"threshold_uncertainty_score":0.9632967,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2071096105","doi":"10.1006/rtim.2001.0285","title":"Multiresolution-Based Segmentation of Calcifications for the Early Detection of Breast Cancer","year":2002,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of South Florida; BC Cancer Agency; University of Florida","keywords":"Segmentation; Artificial intelligence; Mammography; Pixel; Computer science; Computer vision; Wavelet; Pattern recognition (psychology); Multiresolution analysis; Contrast (vision); Digital mammography; Fuzzy logic; CAD; Breast cancer; Wavelet transform; Discrete wavelet transform; Cancer; Medicine; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01760683202522484,"gpt":0.2664991107505257,"spread":0.2488922787253009,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001844132,0.00008222715,0.00009889422,0.0001062845,0.0001694739,0.00003050675,0.0002649003,0.00002334914,0.0000287719],"category_scores_gemma":[0.00001884003,0.00007163077,0.00007356978,0.0003914852,0.00007455808,0.0003063454,0.0000230297,0.00005033743,0.000006204463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514503,"about_ca_system_score_gemma":0.00003638219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001951916,"about_ca_topic_score_gemma":0.00002673867,"domain_scores_codex":[0.999162,0.00004121426,0.0002522462,0.0002021549,0.0001956261,0.0001467755],"domain_scores_gemma":[0.9988692,0.0002524178,0.0002543344,0.0003503117,0.0002472035,0.00002652724],"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":[0.00001623669,0.00005017365,0.00144693,0.00003930831,0.00002354074,1.19425e-7,0.0005997928,0.005903791,0.6537061,0.00009987226,0.0001730909,0.3379411],"study_design_scores_gemma":[0.0002923433,0.00001893655,0.02364472,0.00002944001,0.00002208907,0.000003068187,0.00002462793,0.8361474,0.1396496,0.00004575148,0.00005533143,0.00006674665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01717932,0.0001712935,0.9805286,0.001206934,0.0002225434,0.0004220048,0.00003208237,0.00009087074,0.0001463599],"genre_scores_gemma":[0.9891786,0.00004127358,0.01046875,0.00002318069,0.00005775912,0.000153655,0.000001358137,0.00001069807,0.00006475341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9719992,"threshold_uncertainty_score":0.2950726,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002397322","doi":"10.1006/rtim.2001.0268","title":"A Pyramid Approach to Motion Tracking","year":2001,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"BC Innovation Council","funders":"","keywords":"Pyramid (geometry); Computer vision; Artificial intelligence; Tracking (education); Motion (physics); Computer science; Mathematics; Geometry; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.01579254392065892,"gpt":0.2738908913484274,"spread":0.2580983474277684,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003011077,0.0001781725,0.0001800699,0.0002299715,0.00019621,0.0003387546,0.000622078,0.00002098973,0.00002672306],"category_scores_gemma":[0.00006076108,0.0001773993,0.00007086546,0.0007088385,0.0000288361,0.001365669,0.0002423433,0.0001345662,0.000427485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000944514,"about_ca_system_score_gemma":0.00002432015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003533201,"about_ca_topic_score_gemma":2.034688e-7,"domain_scores_codex":[0.9983619,0.000046579,0.0002399275,0.0005651761,0.000299875,0.0004865667],"domain_scores_gemma":[0.9990321,0.00004183493,0.00006952856,0.0005718169,0.00009040155,0.0001943258],"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":[0.000003983611,0.00007738499,0.0006242919,0.000007556774,0.000004190495,0.00003055861,0.0006823909,0.0008880455,0.03438938,0.001972682,0.0008823473,0.9604372],"study_design_scores_gemma":[0.0002990475,0.00001232664,0.003099892,0.00005758975,0.000004421248,0.0002244637,0.00008304446,0.9835765,0.002993487,0.001472212,0.007836073,0.000340969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002014608,0.00004251257,0.9021377,0.001263819,0.000121573,0.0001597411,4.450097e-7,0.0005599809,0.09369961],"genre_scores_gemma":[0.5554695,0.00003189039,0.4419417,0.001059924,0.0001489079,0.00001554405,0.000004517126,0.00003316095,0.001294914],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9826884,"threshold_uncertainty_score":0.7234133,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2092331430","doi":"10.1006/rtim.1999.0187","title":"Control Mechanisms for Error-Resilient MPEG-2 Video Communications over ATM Networks","year":2000,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer network; Asynchronous Transfer Mode; Protocol stack; Real-time computing; MPEG-4; Transmission (telecommunications); Provisioning; MPEG-2; Telecommunications; Coding (social sciences)","retraction":null,"screen_n_in":null,"score":{"opus":0.01253743580976985,"gpt":0.2669502462911668,"spread":0.2544128104813969,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003641691,0.0002100065,0.000269072,0.0001107344,0.0004941352,0.0002575191,0.002273415,0.00007573878,0.0001066781],"category_scores_gemma":[0.00004956068,0.0001943398,0.0001464032,0.0002945896,0.0001017823,0.000400228,0.0003958971,0.0001984804,0.0001069559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000629671,"about_ca_system_score_gemma":0.00004153656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008093521,"about_ca_topic_score_gemma":0.000002571984,"domain_scores_codex":[0.9983966,0.0000940622,0.0003290302,0.0004623883,0.0002210759,0.0004968267],"domain_scores_gemma":[0.9972783,0.0004019733,0.0001215725,0.00201774,0.00008798928,0.00009236665],"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":[0.00005225099,0.0002379932,0.0002646653,0.00002177826,0.00008295778,0.00001591803,0.0002853173,0.009244761,0.02110196,0.0994694,0.03947479,0.8297482],"study_design_scores_gemma":[0.0006989826,0.00003665803,0.0002778519,0.00008332085,0.00001832391,0.00001542683,0.00001890275,0.9681963,0.001433542,0.01682509,0.01213475,0.0002608003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002200275,0.0004826446,0.9840789,0.005852947,0.0001548658,0.0004570894,0.000008960447,0.001645133,0.005119208],"genre_scores_gemma":[0.9071741,0.0001915258,0.09014475,0.0007430845,0.00004760416,0.0001787112,0.000008732602,0.00002705024,0.001484406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9589516,"threshold_uncertainty_score":0.7924945,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2133423188","doi":"10.1006/rtim.2001.0228","title":"An Integrated Framework for Efficient Transport of Real-Time MPEG Video over ATM Best Effort Service","year":2001,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Computer science; Network packet; Computer network; Payload (computing); Real-time computing; Packet loss; Quality of service; Throughput; Frame (networking); Asynchronous Transfer Mode; Transmission (telecommunications); Forward error correction; Decoding methods; Algorithm; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01338231506411343,"gpt":0.2760919799748446,"spread":0.2627096649107312,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005231173,0.0003767625,0.0005456488,0.0003075448,0.0002387651,0.000130395,0.001778315,0.0001804252,0.00008495947],"category_scores_gemma":[0.00006485824,0.0003355194,0.0001896475,0.00110437,0.0001067462,0.00045434,0.0001846924,0.0002780341,0.00008617135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000089194,"about_ca_system_score_gemma":0.0001345368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007960736,"about_ca_topic_score_gemma":0.000006802155,"domain_scores_codex":[0.9973542,0.00005607394,0.0006198385,0.0008261327,0.0004681723,0.0006755453],"domain_scores_gemma":[0.9974222,0.0002853489,0.0002988534,0.00149562,0.0003276459,0.0001703173],"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":[0.0005172358,0.002323269,0.02678972,0.000522244,0.0002617514,0.0003106296,0.00413461,0.0185213,0.5428501,0.02227723,0.003898233,0.3775937],"study_design_scores_gemma":[0.0009898858,0.0002855517,0.006385074,0.001194032,0.00007939553,0.00007883464,0.0002652835,0.9385604,0.04228636,0.005957937,0.003063633,0.0008536716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5904072,0.00007584084,0.4046123,0.0008165694,0.0001946167,0.000437458,0.00001940956,0.001529545,0.001907074],"genre_scores_gemma":[0.864305,0.0001341528,0.1347925,0.0001785545,0.00007139606,0.00006698647,0.00003310668,0.0000554574,0.000362866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9200391,"threshold_uncertainty_score":0.9999097,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017960002","doi":"10.1016/j.rti.2005.03.004","title":"Detection of cyclic human activities based on the morphological analysis of the inter-frame similarity matrix","year":2005,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Similarity (geometry); Frame (networking); Segmentation; Matrix (chemical analysis); Artificial intelligence; Pattern recognition (psychology); Computer science; Ground truth; Motion (physics); Activity detection; Computer vision; Mathematics; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01751611560282881,"gpt":0.303968077489593,"spread":0.2864519618867643,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001227915,0.0001181289,0.0002720278,0.0001938419,0.0001539765,0.00005718728,0.000672858,0.00003815272,0.00004009102],"category_scores_gemma":[0.0001363876,0.00006982962,0.0002750964,0.0008807044,0.0001454737,0.0001637723,0.000142637,0.0001943074,0.000003135877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004582431,"about_ca_system_score_gemma":0.00001959595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001721554,"about_ca_topic_score_gemma":0.00003095604,"domain_scores_codex":[0.9985029,0.000525999,0.0002690923,0.0002483724,0.0002712972,0.000182312],"domain_scores_gemma":[0.9982901,0.0006055277,0.0002413316,0.0007875647,0.00005200984,0.00002348451],"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":[0.00002677341,0.0002937987,0.05801616,0.00002800854,0.0002625254,0.000007402714,0.0007036122,0.01340382,0.8409882,0.00119046,0.0001222192,0.084957],"study_design_scores_gemma":[0.0001506942,0.00003929146,0.1653143,0.00003238756,0.0001130801,0.000004652061,0.00003209319,0.6625174,0.1710396,0.0005143597,0.0001110667,0.0001310661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7845079,0.0000174479,0.2117644,0.001736627,0.00008167731,0.0001143186,0.000005847153,0.00008530939,0.001686467],"genre_scores_gemma":[0.9936858,0.000002695638,0.006037571,0.0001845533,0.00002925487,0.000004914228,9.00161e-7,0.000005940689,0.00004833698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6699487,"threshold_uncertainty_score":0.2847569,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089694135","doi":"10.1006/rtim.2001.0271","title":"Fast Trellis-Coded Color Quantization of Images","year":2002,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Trellis quantization; Dither; Color quantization; RGB color model; Quantization (signal processing); Computer vision; Computer science; Vector quantization; Artificial intelligence; High color; Color depth; Color space; Color image; Mathematics; Algorithm; Image processing; Image (mathematics); Noise shaping","retraction":null,"screen_n_in":null,"score":{"opus":0.01235361107548406,"gpt":0.257179868601643,"spread":0.2448262575261589,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001521569,0.0001486867,0.0002196825,0.0001764455,0.00008605167,0.00008316349,0.0007793963,0.00002958922,0.0001242875],"category_scores_gemma":[0.00006539233,0.0001423704,0.00005433497,0.000415634,0.00007794202,0.001196695,0.0003100966,0.00008474849,0.0001202538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003278141,"about_ca_system_score_gemma":0.00001215624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003393113,"about_ca_topic_score_gemma":2.832051e-7,"domain_scores_codex":[0.9987207,0.00006793431,0.0003214519,0.0003737683,0.0002652739,0.0002508763],"domain_scores_gemma":[0.9987217,0.0001208443,0.0002209033,0.0007450831,0.0001265866,0.00006491665],"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":[0.000005703548,0.0001440292,0.0005086276,0.00003614945,0.000009949378,0.00002710675,0.0003921433,0.0002803754,0.7726155,0.005529812,0.02294813,0.1975025],"study_design_scores_gemma":[0.0002317035,0.00002693786,0.0003413905,0.00009785743,0.000006270313,0.0000214123,0.00001384331,0.5865191,0.4094146,0.0013844,0.001724342,0.0002181212],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001996761,0.0001651667,0.9847726,0.0004219474,0.00006920507,0.0001761122,0.00001667649,0.0007446315,0.01163687],"genre_scores_gemma":[0.3320201,0.0004038525,0.6658197,0.0001238996,0.00005086094,0.00002218276,0.00002094652,0.00003549175,0.001502903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5862387,"threshold_uncertainty_score":0.5805698,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084029989","doi":"10.1016/j.rti.2004.08.006","title":"An embedded wavelet image coder with parallel encoding and sequential decoding of bit-planes","year":2004,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Decoding methods; Encoding (memory); Bit (key); Computer science; Wavelet; Image (mathematics); Algorithm; Arithmetic; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.008937142560488717,"gpt":0.2749912512121489,"spread":0.2660541086516602,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002446066,0.0002204958,0.0002875725,0.0001912365,0.000155184,0.0001899456,0.0006323484,0.00003533813,0.00001767054],"category_scores_gemma":[0.00002272197,0.0001901214,0.00003032853,0.000226527,0.0001538523,0.002078805,0.0002929026,0.0001333389,0.000007188268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004639433,"about_ca_system_score_gemma":0.00007531473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001255763,"about_ca_topic_score_gemma":0.000003693272,"domain_scores_codex":[0.9984511,0.00005981813,0.0003009488,0.0005391842,0.000287939,0.0003609854],"domain_scores_gemma":[0.9987735,0.00007721742,0.0002060766,0.0007029689,0.000101682,0.0001386092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003173979,0.00007330789,0.0004979979,0.00005505673,0.00001802028,0.0001889821,0.0008955888,0.0005349983,0.9729357,0.005377187,0.0001466448,0.01924475],"study_design_scores_gemma":[0.002116693,0.0002123318,0.0009606805,0.0007524421,0.00003662215,0.0008725139,0.0002145918,0.2173794,0.7618352,0.01443373,0.0001920644,0.0009937949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05416179,0.00004933322,0.9435674,0.0001473979,0.00003198556,0.0001862709,0.00001426184,0.0005140696,0.001327515],"genre_scores_gemma":[0.364719,0.00004876042,0.6351032,0.00004711108,0.00002513453,0.000008182877,0.00001562356,0.00001858688,0.00001443365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3105572,"threshold_uncertainty_score":0.7752926,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}