{"id":"W4399361581","doi":"10.1038/d41586-024-01596-2","title":"China’s research clout leads to growth in homegrown science publishing","year":2024,"lang":"en","type":"article","venue":"Nature","topic":"Science, Research, and Medicine","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"CARE Canada","funders":"","keywords":"China; Wish; Publishing; Set (abstract data type); Political science; Sociology; Computer science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009029509,0.0001236486,0.0002206552,0.002674147,0.0001988952,0.0005962013,0.0007735228,0.0004715226,0.0001094031],"category_scores_gemma":[0.01041656,0.00008464605,0.00004607758,0.01003654,0.0006759196,0.0009655873,0.000306168,0.007789069,0.0001097089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004739681,"about_ca_system_score_gemma":0.001744356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00041113,"about_ca_topic_score_gemma":0.00008312744,"domain_scores_codex":[0.9948145,0.00006762259,0.0001902803,0.0007209787,0.003194908,0.001011669],"domain_scores_gemma":[0.9981707,0.0001559618,0.00001007931,0.0003951179,0.0005987408,0.0006694468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005418607,0.0004036773,0.06398197,0.00135276,0.00002790014,0.004096839,0.02155877,0.000006236553,0.262317,0.07605187,0.4027656,0.1668955],"study_design_scores_gemma":[0.001124038,0.0007527531,0.8980414,0.001985763,0.00001184729,0.0002373056,0.001418634,0.000806023,0.01933861,0.002237163,0.07379221,0.0002543124],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8422624,0.00325315,0.00001036326,0.09118906,0.0009965118,0.0004536384,0.000003869753,0.00009659461,0.06173436],"genre_scores_gemma":[0.9903482,0.0001265959,0.000267267,0.002245544,0.0008826937,0.00002311562,0.000006013686,0.00001796945,0.006082658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8340594,"threshold_uncertainty_score":0.9979191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05022794720746734,"score_gpt":0.4396495511153763,"score_spread":0.389421603907909,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}