{"id":"W2510135622","doi":"10.48550/arxiv.1608.07738","title":"Testing APSyn against Vector Cosine on Similarity Estimation","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Atomic Energy of Canada Limited; University of Oxford; University of Wisconsin-Madison","keywords":"Cosine similarity; Similarity (geometry); Computer science; Discrete cosine transform; Estimation; Artificial intelligence; Pattern recognition (psychology); Algorithm; Engineering","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":[0.03113014,0.002516489,0.002426974,0.005712111,0.001415948,0.003435964,0.003378553,0.00360436,0.004499046],"category_scores_gemma":[0.1298867,0.0004815042,0.001435266,0.005397126,0.002353868,0.01054272,0.00609797,0.002469845,0.002790793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158942,"about_ca_system_score_gemma":0.001709924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003987855,"about_ca_topic_score_gemma":0.002957754,"domain_scores_codex":[0.9599506,0.02359485,0.002872613,0.00553896,0.007086921,0.000955918],"domain_scores_gemma":[0.8933504,0.08412988,0.003265136,0.01167386,0.005799671,0.001781119],"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":[0.009833773,0.00221586,0.1425074,0.002805374,0.00396246,0.0004481412,0.0008289262,0.1387254,0.00650275,0.02127917,0.03666262,0.6342281],"study_design_scores_gemma":[0.0005303957,0.004126378,0.02409755,0.000225847,0.0004381413,0.0009513169,0.001248037,0.9313679,0.007305636,0.02222274,0.007339782,0.000146185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7846924,0.01270697,0.1656361,0.002057095,0.002052574,0.0007347712,0.006550268,0.005645688,0.01992407],"genre_scores_gemma":[0.925077,0.0007336935,0.06308024,0.0003759127,0.0003892992,0.0003130554,0.008040664,0.0003587904,0.001631456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03113014,"threshold_uncertainty_score":0.1646339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1181623652735462,"score_gpt":0.2010710518645653,"score_spread":0.08290868659101915,"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."}}