{"id":"W2009450360","doi":"10.1016/j.tcs.2010.07.004","title":"Parameterized pursuit-evasion games","year":2010,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Parameterized complexity; Pursuit-evasion; Computer science; Evasion (ethics); Mathematics; Theoretical computer science; Artificial intelligence; Algorithm; Biology; Genetics","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":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002191969,0.0002423084,0.0002504467,0.0002436567,0.0005019258,0.001186327,0.005822652,0.0001007466,0.0001896766],"category_scores_gemma":[0.0004708018,0.0001931847,0.000103383,0.001340713,0.005363877,0.001178736,0.001894239,0.0005090376,0.0005995719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003134452,"about_ca_system_score_gemma":0.0001684501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005892052,"about_ca_topic_score_gemma":0.000002263912,"domain_scores_codex":[0.9965655,0.00009450848,0.0003851141,0.001012051,0.001111304,0.0008314849],"domain_scores_gemma":[0.9970801,0.000486122,0.00009386421,0.00162761,0.0002772615,0.0004350158],"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":[0.000004628898,0.00004540955,0.00004978752,0.000001760305,0.00000154,0.00001031204,0.0002755755,0.00002011656,0.03799429,0.8062465,0.00005523736,0.1552949],"study_design_scores_gemma":[0.00007828381,0.0001727021,0.0006335452,0.00001267282,0.000003006874,0.00006639003,0.00000623288,0.4779967,0.1294366,0.3902664,0.00101998,0.0003075042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2218505,0.000007150343,0.7705491,0.001754374,0.002338905,0.0001655954,5.947678e-7,0.0003878453,0.002946006],"genre_scores_gemma":[0.6986277,0.000002155091,0.3005573,0.0006084262,0.0001724123,0.000008699515,2.367236e-7,0.000007770886,0.00001525406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4779766,"threshold_uncertainty_score":0.9998505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803399688126057,"score_gpt":0.2857333243694043,"score_spread":0.2676993274881437,"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."}}