{"id":"W2291805099","doi":"10.18438/b8ps5s","title":"Riding Into the Sunset","year":2016,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Sunset; Computer science; Astronomy; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007287532,0.0000897113,0.00006229456,0.00008704142,0.0002708452,0.0007531781,0.0006840682,0.0000392226,0.00009672983],"category_scores_gemma":[0.002633287,0.00004837576,0.00002515103,0.0003632926,0.00009949394,0.3682906,0.0002369133,0.0001016116,0.0003546592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008954948,"about_ca_system_score_gemma":0.0001260595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007118359,"about_ca_topic_score_gemma":4.445041e-8,"domain_scores_codex":[0.9989783,0.0002045666,0.0002936116,0.0001315212,0.000242998,0.0001490038],"domain_scores_gemma":[0.994513,0.004716381,0.0002244452,0.0004231236,0.0000534697,0.00006956078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003386515,0.000006183619,0.0001999912,0.000009412183,0.000003721595,0.000001517579,0.0008829398,0.00003197344,0.0001563865,0.7304302,0.002691447,0.2655524],"study_design_scores_gemma":[0.00006019002,0.00008196699,0.0007615115,0.0002002363,0.000005814005,0.00001838009,0.0004116659,0.01657674,0.03168178,0.004687413,0.9453676,0.0001466583],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002054229,0.000322917,0.5762249,0.418023,0.0002458604,0.0001440913,9.140196e-7,0.0001859471,0.002798147],"genre_scores_gemma":[0.8203484,0.002046356,0.04798298,0.129131,0.000126538,0.0000376467,0.000001861926,0.000006731324,0.0003184671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9426762,"threshold_uncertainty_score":0.7262914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02262247559244892,"score_gpt":0.2758814776506828,"score_spread":0.2532590020582339,"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."}}