{"meta":{"query_hash":"a9b34c708942","filters":{"venue":"Chashm/andāz-i mudīriyyat-i mālī"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"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/a9b34c708942","api":"https://metacan.xera.ac/api/v1/cohort?venue=Chashm%2Fand%C4%81z-i+mud%C4%ABriyyat-i+m%C4%81l%C4%AB"},"results":[{"id":"W3044892214","doi":"10.52547/jfmp.10.29.147","title":"Comparing the Identifying Criteria for Financially Distressed Companies using Logistic Regression and Artificial Intelligence Methods","year":2020,"lang":"en","type":"article","venue":"Chashm/andāz-i mudīriyyat-i mālī","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Logistic regression; Artificial intelligence; Statistics; Psychology; Business; Computer science; Mathematics","score_opus":0.3412260141716453,"score_gpt":0.3978341070567644,"score_spread":0.05660809288511914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044892214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95908135,0.00092150364,0.03273186,0.0009962071,0.00007751861,0.0003822664,0.00090686156,0.00030792505,0.0045944974],"genre_scores_gemma":[0.9819313,0.0002189325,0.015779024,0.00004327212,0.000024783436,0.00015298871,0.0010450031,0.000024676343,0.0007799065],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9915936,0.003814159,0.0014038761,0.00054397184,0.0018722186,0.0007722416],"domain_scores_gemma":[0.9494929,0.036327682,0.0059273643,0.00094042555,0.005576651,0.001734984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010031625,0.0014738777,0.0012331946,0.013980766,0.0012685492,0.005278356,0.0012655226,0.0014423302,0.003047706],"category_scores_gemma":[0.057466798,0.00035307187,0.0024303636,0.0068065682,0.0008680302,0.0020898818,0.00238749,0.001471121,0.00076025754],"study_design_candidate":"bench_or_experimental","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":[0.0028116098,0.0008595685,0.7538376,0.0009545684,0.0008746623,0.001461849,0.0017149311,0.07994866,0.0016850275,0.006585404,0.0046266722,0.1446394],"study_design_scores_gemma":[0.00027989945,0.0011898058,0.29084045,0.00041276214,0.0005043308,0.00078736775,0.014440812,0.67148274,0.0042564743,0.011655862,0.003914349,0.00023525208],"about_ca_topic_score_codex":0.013249316,"about_ca_topic_score_gemma":0.008873334,"teacher_disagreement_score":0.013980766,"about_ca_system_score_codex":0.002554583,"about_ca_system_score_gemma":0.0032237694,"threshold_uncertainty_score":0.05305296},"labels":[],"label_agreement":null},{"id":"W4367547514","doi":"10.52547/jfmp.12.40.9","title":"The Effects of Order Flow Imbalance on Stock Prices in Tehran Stock Exchange","year":2022,"lang":"en","type":"article","venue":"Chashm/andāz-i mudīriyyat-i mālī","topic":"Islamic Finance and Banking Studies","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Stock exchange; Stock (firearms); Econometrics; Economics; Financial economics; Monetary economics; Mathematics; Finance; Geography","score_opus":0.00895856259592697,"score_gpt":0.2134415174624467,"score_spread":0.2044829548665197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367547514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99763334,0.00018602618,0.00012988108,0.00027913458,0.00003382591,0.0000063199905,0.0002568741,0.000016125416,0.0014585421],"genre_scores_gemma":[0.99873346,0.00013453966,0.00008671735,0.00002501841,0.000030691834,0.0000036641945,0.0003660957,0.000003860698,0.0006159499],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997018,0.000041734296,0.0000372305,0.000039516177,0.00012527771,0.000054437958],"domain_scores_gemma":[0.99847776,0.00044243963,0.0004841461,0.00006864013,0.00031892039,0.00020809106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004971618,0.00027399478,0.000311333,0.0009706185,0.00036889425,0.0012138272,0.00019861154,0.00043333773,0.0030908275],"category_scores_gemma":[0.0038057559,0.00013541641,0.00033961426,0.00083222456,0.00022297501,0.00073713175,0.0003926707,0.0006882511,0.0003401453],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021053466,0.0004631043,0.9370399,0.00008678259,0.00027038384,0.0022376468,0.00074819045,0.012759355,0.00550434,0.0021318009,0.005514399,0.031138746],"study_design_scores_gemma":[0.000056607092,0.0001858773,0.97049195,0.000016279413,0.00009239202,0.0002057641,0.0007817715,0.024917185,0.0009158533,0.001159509,0.0011548392,0.000022125794],"about_ca_topic_score_codex":0.015240368,"about_ca_topic_score_gemma":0.013384818,"teacher_disagreement_score":0.015240368,"about_ca_system_score_codex":0.00082717696,"about_ca_system_score_gemma":0.0005771833,"threshold_uncertainty_score":0.0303033},"labels":[],"label_agreement":null}]}