{"id":"W2163823146","doi":"10.7939/r3gh9bg82","title":"TDS+: Improving Temperature Discovery Search","year":2015,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Game theory; Algorithm; Mathematical optimization; Mathematics; Theoretical computer science; Mathematical economics","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":[0.0003799,0.0000939546,0.0000891183,0.00006256947,0.00006870905,0.0006162563,0.0008931271,0.00005730897,0.00001450426],"category_scores_gemma":[0.0001083127,0.0000718021,0.00003765486,0.000306997,0.00005184713,0.001510878,0.0004315061,0.0001633784,0.0003961071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004996907,"about_ca_system_score_gemma":0.0001895062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002388711,"about_ca_topic_score_gemma":0.00004014381,"domain_scores_codex":[0.998875,0.00004488074,0.0001463613,0.0003072916,0.0003451223,0.0002813548],"domain_scores_gemma":[0.9991243,0.00005729948,0.00002103452,0.0005192504,0.0001338623,0.0001442552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001794541,0.0001593424,0.004732053,0.00002064857,0.00002299884,0.00009949558,0.007025135,0.001771851,0.05423868,0.6622479,0.02347405,0.2461899],"study_design_scores_gemma":[0.0001812673,0.0003439391,0.0007471872,0.00002805905,0.000005036141,0.00005515119,0.002780808,0.2116485,0.7448388,0.03110993,0.007494332,0.0007669927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2095243,0.0001556848,0.7651244,0.00279809,0.0009866498,0.0001604923,9.7769e-7,0.0003926673,0.02085671],"genre_scores_gemma":[0.9647892,0.000001911015,0.02658283,0.0004868947,0.0001509676,0.000004574222,5.808834e-7,0.000006827143,0.007976212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7552649,"threshold_uncertainty_score":0.5942574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05432164067465754,"score_gpt":0.3042735190652889,"score_spread":0.2499518783906313,"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."}}