{"meta":{"query_hash":"5b3ccaded874","filters":{"venue":"Innovative applications of AI."},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"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/5b3ccaded874","api":"https://metacan.xera.ac/api/v1/cohort?venue=Innovative+applications+of+AI."},"results":[{"id":"W4405949463","doi":"10.70695/mtedss57","title":"Research on Financial Information Management Based on Convolutional Neural Network Algorithm","year":2024,"lang":"en","type":"article","venue":"Innovative applications of AI.","topic":"Advanced Technologies and Applied Computing","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":"Future Earth","funders":"","keywords":"Computer science; Convolutional neural network; Autoencoder; Artificial neural network; Data mining; Algorithm; Finance; Artificial intelligence","score_opus":0.02446433298620433,"score_gpt":0.3324707201833459,"score_spread":0.3080063871971416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405949463","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":[0.00022689729,0.000024072326,0.98970044,0.0030430392,0.00009971933,0.00048360956,0.000012961838,0.00027786248,0.0061313873],"genre_scores_gemma":[0.67768914,0.0000032769794,0.32066092,0.00095509074,0.00011666772,0.0005037517,0.000030194216,0.0000062509257,0.00003473416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874514,0.000020728088,0.00028559953,0.0002762786,0.0004146223,0.00025760266],"domain_scores_gemma":[0.998813,0.00022416173,0.000069146736,0.00047400303,0.00040033818,0.0000193767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043001206,0.00010364753,0.00009444423,0.0004787596,0.00023037016,0.00009089346,0.0006891677,0.00005805375,0.0000037336338],"category_scores_gemma":[0.000015003841,0.000097287986,0.00002748111,0.0048794267,0.000121951045,0.00026723644,0.0002810174,0.00044016074,0.00007840191],"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":[0.0000025960014,0.000021004349,0.0000025977015,0.000011002015,0.000003667521,6.589644e-7,0.000008775634,0.01936528,0.000005779626,0.59046704,0.0030126586,0.38709897],"study_design_scores_gemma":[0.00016211299,0.00015678085,0.0006272854,0.0000760129,0.0000012551222,8.507185e-7,0.00003953748,0.6832505,0.00092095416,0.20759472,0.107043654,0.00012631947],"about_ca_topic_score_codex":0.0000022076283,"about_ca_topic_score_gemma":1.2297222e-7,"teacher_disagreement_score":0.6774622,"about_ca_system_score_codex":0.000099205754,"about_ca_system_score_gemma":0.00008492183,"threshold_uncertainty_score":0.3967289},"labels":[],"label_agreement":null}]}