{"id":"W2333336945","doi":"10.1037/a0038392","title":"Superset versus substitution-letter priming: An evaluation of open-bigram models.","year":2014,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Human Perception & Performance","topic":"Visual and Cognitive Learning Processes","field":"Psychology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bigram; Priming (agriculture); Computer science; Prime (order theory); Orthographic projection; Arithmetic; Natural language processing; Set (abstract data type); Speech recognition; Artificial intelligence; Mathematics; Programming language; Combinatorics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02358983,0.001454465,0.001260763,0.00137406,0.0005664201,0.001721408,0.003366131,0.001755211,0.008656404],"category_scores_gemma":[0.04510031,0.0006934891,0.001219746,0.0007439439,0.001811998,0.006419876,0.002003274,0.001522203,0.001430182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018911,"about_ca_system_score_gemma":0.0008943967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007107073,"about_ca_topic_score_gemma":0.0005275772,"domain_scores_codex":[0.9951559,0.002761402,0.0002500602,0.0007205351,0.001010505,0.000101594],"domain_scores_gemma":[0.9149242,0.07241499,0.004132019,0.005437685,0.00162396,0.001467182],"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":[0.1133767,0.005767072,0.02824081,0.007392479,0.002415069,0.001158454,0.005733961,0.0495371,0.09901464,0.2392967,0.004615976,0.4434511],"study_design_scores_gemma":[0.005882162,0.02157072,0.02412106,0.0005088691,0.002294485,0.001999333,0.0009961738,0.5315447,0.04183923,0.3627092,0.006108992,0.0004250025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8208571,0.005765127,0.1419888,0.0009895152,0.0006506124,0.001371609,0.0004045636,0.0006834773,0.02728922],"genre_scores_gemma":[0.9525771,0.001676822,0.04206821,0.0004378491,0.0002182784,0.0005622471,0.0002942409,0.0002223987,0.001942909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02358983,"threshold_uncertainty_score":0.1247565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2490963234838949,"score_gpt":0.4948971492684628,"score_spread":0.2458008257845679,"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."}}