{"id":"W4296912948","doi":"10.1109/tcss.2022.3204052","title":"Adaptive Collaboration With Training Plan Considering Role Correlation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Process (computing); Plan (archaeology); Machine learning; Correlation; Knowledge management; Process management; Engineering; Mathematics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003245913,0.0001656707,0.0002119901,0.000198386,0.001731924,0.0001940899,0.0002000511,0.000056181,0.00002493956],"category_scores_gemma":[0.000001921204,0.0001785557,0.00006223243,0.0006599604,0.00002823001,0.0004692579,0.000003608436,0.0002370265,0.00001967055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003938701,"about_ca_system_score_gemma":0.0002653117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002642887,"about_ca_topic_score_gemma":0.00005268537,"domain_scores_codex":[0.9979246,0.0003379218,0.0003631176,0.0003682139,0.0007989029,0.0002072348],"domain_scores_gemma":[0.9991932,0.0001731862,0.0002757096,0.000116427,0.00017616,0.00006529753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004329952,0.00007385637,0.00002348328,0.000007439312,0.00005642708,0.000004811013,0.01021226,0.9753269,0.00008026714,0.01069827,0.00009758474,0.003375376],"study_design_scores_gemma":[0.0008117002,0.0002406102,0.0005857523,0.00002011965,0.00001494222,0.0000489769,0.006484745,0.9905077,0.00004066931,0.0003864753,0.0006182158,0.0002400518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01826207,0.00002706325,0.9786558,0.0002528612,0.001458117,0.0005880266,0.00006991059,0.0002249092,0.0004612459],"genre_scores_gemma":[0.9960734,4.025337e-7,0.003242488,0.00007931547,0.0001230791,0.0002854671,0.0000280087,0.00001828183,0.0001495406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9778113,"threshold_uncertainty_score":0.9995677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03787212153739823,"score_gpt":0.2415006091697866,"score_spread":0.2036284876323884,"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."}}