{"id":"W192439236","doi":"10.1007/978-3-642-41888-4_16","title":"Fitness Morphs and Nonlinear Projections of Agent-Case Embeddings to Characterize Fitness Landscapes","year":2013,"lang":"en","type":"book-chapter","venue":"Emergence, complexity and computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Guelph","funders":"","keywords":"Fitness landscape; Cellular automaton; Set (abstract data type); Euclidean space; Computer science; Metric (unit); Fitness approximation; Representation (politics); Artificial intelligence; Theoretical computer science; Mathematics; Machine learning; Fitness function; Genetic algorithm; Combinatorics","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":[0.0007523836,0.0005547654,0.000388494,0.001121896,0.000375177,0.001471044,0.000600774,0.0008160064,0.003134796],"category_scores_gemma":[0.005160514,0.0002650192,0.0005520475,0.000771838,0.001516733,0.00287125,0.00129318,0.001451509,0.0003306763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007643109,"about_ca_system_score_gemma":0.00027103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008093158,"about_ca_topic_score_gemma":0.0009108503,"domain_scores_codex":[0.9997421,0.0000983942,0.00001688614,0.00005256416,0.00006240427,0.00002754807],"domain_scores_gemma":[0.9987306,0.0006778943,0.0001498317,0.0001972081,0.0001397844,0.0001047915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001106826,0.00009317514,0.004774803,0.0001131092,0.00005325603,0.000165229,0.00052281,0.2080861,0.00605528,0.7093641,0.002356145,0.06830545],"study_design_scores_gemma":[0.000004522295,0.00002334393,0.001316963,0.00001665063,0.000007048385,0.0001084108,0.00009201849,0.6975648,0.0004565122,0.2993459,0.001050123,0.00001374013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2228451,0.000821759,0.7576059,0.0006160815,0.000067333,0.00008150721,0.0002762681,0.0002079076,0.01747824],"genre_scores_gemma":[0.8823976,0.0004415776,0.1105254,0.0000735644,0.00005457793,0.00009861802,0.0003819584,0.0001434955,0.005883182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003134796,"threshold_uncertainty_score":0.0104869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05144254749948868,"score_gpt":0.2812642816896747,"score_spread":0.229821734190186,"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."}}