{"id":"W1484372654","doi":"10.1007/978-3-540-87608-3_10","title":"An Improved Safety Solver in Go Using Partial Regions","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Solver; Computer science; Margin (machine learning); Problem solver; Algorithm; Computer graphics (images); Computational science; Programming language; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008669614,0.0006115891,0.0006348075,0.0009839566,0.0004040534,0.0004576787,0.0043946,0.0004730325,0.00002388327],"category_scores_gemma":[0.0001168184,0.0005997656,0.0001586534,0.0009371358,0.001353536,0.001412309,0.001147963,0.001115218,0.0000483113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006260583,"about_ca_system_score_gemma":0.001076564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002486199,"about_ca_topic_score_gemma":0.000772708,"domain_scores_codex":[0.9950699,0.00007771963,0.0009138316,0.002027195,0.0008816422,0.001029667],"domain_scores_gemma":[0.9966809,0.0003880733,0.0003465897,0.002081438,0.0002376692,0.00026535],"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":[0.00002903496,0.0001281004,0.000260906,0.00001968471,0.00001159817,0.0004295722,0.005376973,0.3409965,0.001562353,0.01454187,0.00003226794,0.6366112],"study_design_scores_gemma":[0.0001032024,0.0001491584,0.00006973227,0.0002071471,0.000004127133,0.0001095932,5.130137e-7,0.9544208,0.003141063,0.03972475,0.001368568,0.0007013513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006069707,0.0002273225,0.9951408,0.0004247484,0.002025382,0.0004643732,0.000004136672,0.0001779713,0.0009282872],"genre_scores_gemma":[0.3855796,0.0001303559,0.6113376,0.001650035,0.00101686,0.00001120041,0.000005445361,0.00007229385,0.0001965965],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6359099,"threshold_uncertainty_score":0.9996454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04546164751459495,"score_gpt":0.2925259192794561,"score_spread":0.2470642717648611,"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."}}