{"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":"fe7f3bda94a9","filters":{"venue":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture"}},"results":[{"id":"W4247646242","doi":"10.1145/3495018.3501199","title":"Handwritten character recognition based on CNN","year":2021,"lang":"en","type":"article","venue":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Character recognition; Computer science; Character (mathematics); Artificial intelligence; Speech recognition; Pattern recognition (psychology); Feature extraction; Intelligent word recognition; Natural language processing; Intelligent character recognition; Mathematics; Image (mathematics)","authors":[{"name":"Shengxuan Ji","is_ca":false},{"name":"Yihe Zeng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06041791743990403,"gpt":0.3071205096829152,"spread":0.2467025922430112,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001847221,0.0003058191,0.0002532809,0.0002546174,0.0001741684,0.0005592623,0.0005518089,0.0001598945,0.002785737],"category_scores_gemma":[0.0001861007,0.0002903632,0.0001156558,0.0002371745,0.00008428484,0.0006388138,0.0001329699,0.0004567889,0.0006226587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006855266,"about_ca_system_score_gemma":0.0001161903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005479847,"about_ca_topic_score_gemma":0.00002265134,"domain_scores_codex":[0.9977956,0.00009998653,0.0004247059,0.0008431945,0.000525472,0.0003109911],"domain_scores_gemma":[0.998509,0.0001578471,0.0001634051,0.0004454388,0.0005727933,0.0001514776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007979281,0.0002206457,0.000006842576,0.000009711527,0.00001996105,0.00009140643,0.0001089345,0.00007659608,0.01333479,0.07521787,0.0001755257,0.9106579],"study_design_scores_gemma":[0.0001181067,0.0002829277,0.0001291905,0.000309164,0.000008204922,0.00001928799,0.0002092286,0.0489005,0.7703776,0.1717088,0.007466742,0.0004702008],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01220415,0.00004317834,0.9291279,0.0284566,0.001657894,0.0004170878,0.0001182942,0.0003387426,0.02763614],"genre_scores_gemma":[0.9694515,0.0004065228,0.02219854,0.006417563,0.0002817949,0.00009096026,0.0002513037,0.00002114594,0.0008806694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9572474,"threshold_uncertainty_score":0.9999549,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4243918616","doi":"10.1145/3495018.3501158","title":"Prediction of virtual currency prices based on improved SVR algorithm","year":2021,"lang":"en","type":"article","venue":"2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","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":"Computer science; Mean squared error; Algorithm; Support vector machine; Currency; k-nearest neighbors algorithm; Data mining; Machine learning; Artificial intelligence; Mathematics; Statistics","authors":[{"name":"Boyi Fang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1642665161747351,"gpt":0.4037856354960598,"spread":0.2395191193213247,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001083146,0.0003018192,0.0004012226,0.0003880821,0.0001552208,0.0002726495,0.0006500192,0.0001578514,0.004050792],"category_scores_gemma":[0.004268576,0.0002469238,0.0001627772,0.0004967116,0.000195952,0.0003580338,0.0001478122,0.0004421818,0.00007104421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005908746,"about_ca_system_score_gemma":0.0002172812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009674712,"about_ca_topic_score_gemma":0.00002592867,"domain_scores_codex":[0.9962258,0.0002373766,0.0009556721,0.0009386092,0.001361821,0.0002807302],"domain_scores_gemma":[0.9961613,0.001469234,0.0004972551,0.0005342136,0.001192723,0.0001452273],"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":[0.0002308726,0.000199923,0.00006522375,0.00000471086,0.00002168271,0.00001175167,0.0001642529,0.002554137,0.01110566,0.02682587,0.00009843005,0.9587175],"study_design_scores_gemma":[0.0001780302,0.0007709482,0.001258275,0.0002142242,0.00001838326,0.000008618935,0.002172192,0.5469561,0.2946831,0.1478285,0.005577703,0.0003340142],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03899147,0.00007283753,0.9351828,0.003298448,0.00449627,0.0003857048,0.0005358753,0.00006061705,0.01697595],"genre_scores_gemma":[0.9694749,0.0001226275,0.02826494,0.0004224232,0.0002950168,0.00003106667,0.00008391647,0.00001882162,0.001286268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9583834,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}