{"id":"W2396664359","doi":"","title":"Supplemental Case Acquisition Using Mixed-Initiative Control","year":2011,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Task (project management); Computer science; Control (management); Expert system; Knowledge acquisition; Software; Multi-agent system; Artificial intelligence; Machine learning; Human–computer interaction; Engineering; Systems engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.003245173,0.0001320841,0.0001442126,0.00005277935,0.001061323,0.0001831197,0.0006427989,0.00007398328,0.0002102412],"category_scores_gemma":[0.00003306576,0.00009424397,0.0001674185,0.0004592977,0.000159435,0.0007268531,0.0003097621,0.0003803362,0.0001100807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002308109,"about_ca_system_score_gemma":0.0001267798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001204745,"about_ca_topic_score_gemma":0.00004313302,"domain_scores_codex":[0.9973761,0.0007217123,0.0002498628,0.0003455955,0.0007478316,0.0005588665],"domain_scores_gemma":[0.9986145,0.0002837261,0.00008391055,0.000550965,0.0003461307,0.000120773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003194973,0.001266887,0.01050012,0.0004075608,0.001630799,0.002558595,0.3979181,0.0005139659,0.07024688,0.1648614,0.3394721,0.01030421],"study_design_scores_gemma":[0.003537561,0.0004208387,0.009988758,0.00008029011,0.00004040431,0.000903091,0.01075404,0.9444494,0.0225017,0.004393902,0.002374552,0.0005554494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4097511,0.0001155103,0.5850866,0.001849378,0.001018152,0.001226146,0.00008028904,0.0001422797,0.0007304827],"genre_scores_gemma":[0.9951121,0.0000123022,0.003900335,0.0004315329,0.0004050586,0.00005732281,0.000007688206,0.00001184709,0.00006182323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9439355,"threshold_uncertainty_score":0.8162949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2121267139488552,"score_gpt":0.3812041776550542,"score_spread":0.169077463706199,"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."}}