{"id":"W4382567046","doi":"10.1007/978-3-031-36336-8_84","title":"Towards Extracting Adaptation Rules from Neural Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Adaptation (eye); Computer science; Artificial neural network; Artificial intelligence; Decision tree; Machine learning; Tree (set theory); Adaptive system; Modal; Reading (process); Linguistics; Psychology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001015487,0.0002281525,0.0002336571,0.000756159,0.0005460982,0.001142632,0.003879525,0.0001566687,0.000008360642],"category_scores_gemma":[0.000120428,0.0002382601,0.00005306392,0.0004697705,0.0006963726,0.008932373,0.002773233,0.0005726182,0.0001544982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001259612,"about_ca_system_score_gemma":0.0001978922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001459386,"about_ca_topic_score_gemma":0.00006395756,"domain_scores_codex":[0.9979998,0.00004004047,0.0008248209,0.0003440088,0.0005160683,0.0002752515],"domain_scores_gemma":[0.9966071,0.000590101,0.0004437689,0.001874526,0.0003836692,0.0001008038],"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":[8.707406e-7,0.000003977208,0.00001304553,0.000003152882,0.00000284772,4.886538e-7,0.002193167,0.0108388,5.67414e-7,0.3399037,0.00005657866,0.6469828],"study_design_scores_gemma":[0.00004320934,0.00001927212,0.0006478509,0.0001072403,0.000003354745,0.000004652706,0.00006897144,0.9559556,0.000005611934,0.03303195,0.009876714,0.0002355947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009443218,0.0002632853,0.9323006,0.000985295,0.0008739263,0.0002593902,0.00001279227,0.0002544265,0.06495584],"genre_scores_gemma":[0.2854614,0.006720491,0.7025621,0.002182784,0.0003884243,0.00008795998,0.0003344103,0.00004997541,0.002212483],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9451168,"threshold_uncertainty_score":0.9998943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1160882043551873,"score_gpt":0.3293326420590116,"score_spread":0.2132444377038243,"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."}}