{"id":"W4256072618","doi":"10.31234/osf.io/ytnjp","title":"Indirect associations in learning semantic and syntactic lexical relationships","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Carleton University","funders":"","keywords":"Natural language processing; Artificial intelligence; Computer science; Construct (python library); Word (group theory); Associative property; Semantics (computer science); Association (psychology); Word Association; Part of speech; Similarity (geometry); Meaning (existential); Linguistics; Psychology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001540819,0.0005006946,0.000671313,0.0008099164,0.0004872562,0.001281518,0.0009396685,0.000718104,0.001622045],"category_scores_gemma":[0.01253868,0.0005106027,0.000672249,0.0009074973,0.001105653,0.005056241,0.002150096,0.001362063,0.0002905595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006489992,"about_ca_system_score_gemma":0.0004946136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001427355,"about_ca_topic_score_gemma":0.001911961,"domain_scores_codex":[0.9993173,0.0003212003,0.00004799598,0.0001844475,0.0000784382,0.00005070507],"domain_scores_gemma":[0.994572,0.004149004,0.0004134498,0.0004624723,0.0002452146,0.0001578699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006916001,0.0004030905,0.03784062,0.0003755896,0.0004446233,0.0002926782,0.001628645,0.4958302,0.008934199,0.165841,0.001577992,0.2861398],"study_design_scores_gemma":[0.00002864001,0.0001039124,0.002537598,0.00001723267,0.00005020643,0.00008779494,0.00009979514,0.7402333,0.00233376,0.2538897,0.0005985037,0.00001954104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7010971,0.0003571777,0.2944158,0.0004716705,0.00002699265,0.00003433494,0.000104873,0.0003110163,0.003181043],"genre_scores_gemma":[0.9701488,0.000194834,0.02792133,0.00005430464,0.00002109111,0.0000658448,0.0002037081,0.00003309969,0.001357016],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001622045,"threshold_uncertainty_score":0.00814873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06851572527720474,"score_gpt":0.2848876306803419,"score_spread":0.2163719054031372,"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."}}