{"id":"W1548895693","doi":"10.1007/978-0-387-35706-5_1","title":"Evaluation Function Tuning via Ordinal Correlation","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Metric (unit); Correlation; Computer science; Function (biology); Feature (linguistics); Quality (philosophy); Evaluation function; Space (punctuation); Artificial intelligence; Ordinal optimization; Mathematics; Data mining; Machine learning; Ordinal data; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006208108,0.0007797338,0.00192749,0.001195706,0.0005907416,0.001973567,0.001628933,0.001082829,0.005944444],"category_scores_gemma":[0.02002043,0.0005069968,0.0006577799,0.001249157,0.001111801,0.002877781,0.002191294,0.002627597,0.001294226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009834071,"about_ca_system_score_gemma":0.0009013879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006688247,"about_ca_topic_score_gemma":0.0008589796,"domain_scores_codex":[0.994591,0.002657079,0.0003051827,0.0006534001,0.001497893,0.0002954123],"domain_scores_gemma":[0.9910101,0.005557403,0.0004320939,0.001461934,0.001310142,0.0002282526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005482199,0.0002555983,0.001617235,0.0002670575,0.0001447564,0.00004964773,0.0001139533,0.1674225,0.008956575,0.1208049,0.00817877,0.6916407],"study_design_scores_gemma":[0.00003003219,0.00009254484,0.0005301626,0.00002815726,0.00002257252,0.00003900155,0.00001272402,0.9472793,0.00325607,0.04681971,0.001868287,0.00002136134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008159295,0.0005479281,0.9863963,0.00014622,0.00008033995,0.00003905466,0.00002489114,0.0008980824,0.003707911],"genre_scores_gemma":[0.4762866,0.0003975271,0.5095438,0.0002551053,0.0002401117,0.0002513287,0.0001954651,0.0006863787,0.01214364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006208108,"threshold_uncertainty_score":0.03283203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03255499166891886,"score_gpt":0.2965026661006124,"score_spread":0.2639476744316935,"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."}}