{"id":"W941090075","doi":"10.1609/socs.v6i1.18356","title":"The Spurious Path Problem in Abstraction","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spurious relationship; Abstraction; Computer science; Path (computing); Heuristic; State (computer science); Semaphore; Algorithm; Artificial intelligence; Programming language; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005117331,0.0009368588,0.001108401,0.001218814,0.002460405,0.002378489,0.002074148,0.001919993,0.00353981],"category_scores_gemma":[0.02495542,0.001134152,0.00186522,0.001997603,0.006646075,0.008555519,0.007604789,0.004025559,0.0005183892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001506367,"about_ca_system_score_gemma":0.003051677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002654304,"about_ca_topic_score_gemma":0.002948332,"domain_scores_codex":[0.9931124,0.002746789,0.0005001582,0.0009764757,0.00210784,0.0005563398],"domain_scores_gemma":[0.985499,0.008436821,0.000938358,0.003952662,0.0009208958,0.0002522244],"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":[0.0001793217,0.00004096539,0.002623923,0.0003574768,0.0000811679,0.0006488612,0.001386793,0.07127513,0.002798787,0.8441625,0.003005442,0.07343959],"study_design_scores_gemma":[0.00002321851,0.00005910897,0.0003285359,0.0001036285,0.00007299625,0.0003923213,0.000213535,0.1103446,0.004830011,0.870059,0.01353769,0.00003534975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01891345,0.0004075232,0.9739083,0.0006891894,0.00006141764,0.00006506866,0.0001013114,0.0006857095,0.005167979],"genre_scores_gemma":[0.4433299,0.0007370989,0.5486529,0.0006258941,0.00006139571,0.0003848709,0.0003559341,0.0005041203,0.005347719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005117331,"threshold_uncertainty_score":0.02706331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108065674391502,"score_gpt":0.2446828402938578,"score_spread":0.2338762728547076,"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."}}