{"id":"W1522404235","doi":"","title":"Near-synonym Lexical Choice in Latent Semantic Space","year":2010,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Latent semantic analysis; Computer science; Artificial intelligence; Natural language processing; Synonym (taxonomy); Probabilistic latent semantic analysis; Curse of dimensionality; Context (archaeology); Space (punctuation); Semantic space; Task (project management); Representation (politics)","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":[0.0001892934,0.00007420661,0.00009160172,0.00003759652,0.00003866944,0.0001270859,0.0005239946,0.00006140694,0.000108068],"category_scores_gemma":[0.00004826091,0.00006226323,0.00002750357,0.0001672133,0.00002272499,0.0002182773,0.0002155204,0.0002623617,0.0001764533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001121031,"about_ca_system_score_gemma":0.00004351732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006877854,"about_ca_topic_score_gemma":0.001486052,"domain_scores_codex":[0.9991798,0.00001732145,0.0001343212,0.0002778158,0.0001596789,0.0002310043],"domain_scores_gemma":[0.9993441,0.00005900811,0.00001897176,0.000483002,0.00001892242,0.00007599279],"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":[0.000004368423,0.0002822432,0.1908394,0.00003693892,0.0000128657,0.0001179922,0.001547335,0.003410602,0.01460188,0.6827518,0.001418721,0.1049759],"study_design_scores_gemma":[0.0002142209,0.00001333797,0.04779856,0.000007015776,9.061194e-7,0.00001297946,0.000002993865,0.9424189,0.001063015,0.00184899,0.006489974,0.0001290569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.653616,0.0000074995,0.3359083,0.005018448,0.0004295036,0.00006557692,5.273563e-8,0.0001117213,0.004842915],"genre_scores_gemma":[0.8981797,7.037241e-7,0.09973764,0.0003849289,0.00006337684,0.000003155925,1.23756e-7,0.000003788306,0.001626549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9390084,"threshold_uncertainty_score":0.2539021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02212281892441357,"score_gpt":0.2580109281140938,"score_spread":0.2358881091896803,"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."}}