{"id":"W3002393529","doi":"10.1007/978-3-030-39512-4_28","title":"Improving Policy-Capturing with Active Learning for Real-Time Decision Support","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Thales (Canada)","funders":"","keywords":"Computer science; Annotation; Machine learning; Artificial intelligence; Context (archaeology); Decision support system; Active learning (machine learning); Dilemma; Sampling (signal processing); Mathematics; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001468661,0.00119458,0.001115297,0.00062779,0.0004247269,0.001865793,0.00193425,0.001489626,0.004115353],"category_scores_gemma":[0.006413367,0.0006044034,0.000785142,0.0009619513,0.0006881059,0.003157611,0.001468463,0.003355692,0.000941765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006636344,"about_ca_system_score_gemma":0.0006541378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002360594,"about_ca_topic_score_gemma":0.002240183,"domain_scores_codex":[0.9993796,0.0001939453,0.00005454059,0.0001427443,0.0001684717,0.00006067442],"domain_scores_gemma":[0.9968034,0.002421279,0.0001445755,0.0002881434,0.0002797948,0.00006277808],"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":[0.0001912444,0.0002999989,0.0004871933,0.0001949642,0.0001111825,0.00006103158,0.000118289,0.4915611,0.005572669,0.02315305,0.00397302,0.4742762],"study_design_scores_gemma":[0.000005167582,0.00001529253,0.00004260239,0.00000722464,0.000008233411,0.00001032274,0.000004111414,0.9907898,0.001442338,0.007146606,0.0005231995,0.000005161415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004589508,0.0005674795,0.992327,0.0001610976,0.0001113932,0.00001997699,0.00003685284,0.0005885566,0.001598012],"genre_scores_gemma":[0.5266084,0.001348367,0.4637333,0.0003425585,0.0003198639,0.0001510596,0.000301587,0.0002238046,0.006971044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004115353,"threshold_uncertainty_score":0.01376718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05004723113546603,"score_gpt":0.3527111450397733,"score_spread":0.3026639139043072,"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."}}