{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000819342,0.0001268262,0.0002376728,0.0002213881,0.00009637276,0.0002230415,0.0003529387,0.0002616034,0.000008223572],"category_scores_gemma":[0.0005752245,0.0001311077,0.00004111417,0.0001566349,0.00001000313,0.0002017598,0.0009267538,0.001485747,0.00004772446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001317958,"about_ca_system_score_gemma":0.0001393429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001775136,"about_ca_topic_score_gemma":0.0001465661,"domain_scores_codex":[0.9985479,0.0002915986,0.0002784991,0.0004882043,0.0002051373,0.0001886247],"domain_scores_gemma":[0.9988045,0.0005880901,0.0001343863,0.0003949761,0.00003228167,0.0000458413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[8.092733e-7,0.00003781881,0.8808282,0.0000860231,0.00003343149,0.000009324586,0.002863916,0.0612334,0.00001573232,0.05008317,0.00004700747,0.004761163],"study_design_scores_gemma":[0.0000929581,0.00000644059,0.1641301,0.00008495291,0.000007702258,0.000003678816,0.00004421518,0.8224434,0.00001084623,0.01295273,0.00005413009,0.0001688936],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2451826,0.0001472574,0.7421136,0.00182798,0.0004406493,0.0002673752,7.532603e-7,0.000215883,0.009803913],"genre_scores_gemma":[0.9655369,0.00002222071,0.0330684,0.00004087741,0.00003303423,0.00001125994,0.000005564901,0.000007603378,0.001274146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.76121,"threshold_uncertainty_score":0.6454913,"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."}}