{"id":"W2250838119","doi":"","title":"Application of the Tightness Continuum Measure to Chinese Information Retrieval","year":2010,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Text segmentation; Word (group theory); Natural language processing; Measure (data warehouse); Pattern recognition (psychology); Information retrieval; Data mining; 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":[0.0002343237,0.00004173499,0.00005101545,0.0000292779,0.00003684603,0.00003614845,0.0005940656,0.0000348218,0.000004571067],"category_scores_gemma":[0.00007407457,0.00002433693,0.00002194089,0.000279683,0.000009496111,0.0004022902,0.0001352318,0.00007981445,0.00002417896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005438319,"about_ca_system_score_gemma":0.00002503334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003291667,"about_ca_topic_score_gemma":0.00006164038,"domain_scores_codex":[0.9995,0.000009781717,0.0001449614,0.0000762034,0.0002043244,0.00006476104],"domain_scores_gemma":[0.9992255,0.00001553898,0.00005892184,0.0005307039,0.0001416533,0.00002767229],"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":[0.00002240125,0.0000549935,0.04340399,0.00004925244,0.00001277071,7.808693e-8,0.005104445,0.000755,0.2998693,0.347489,0.001858305,0.3013805],"study_design_scores_gemma":[0.0004803175,0.00002592499,0.2061415,0.00001285711,0.000004026983,0.000007927883,0.00002653004,0.6557546,0.1044054,0.008898076,0.0239987,0.0002441261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2274756,7.563306e-7,0.767617,0.001796255,0.0002744274,0.0001495148,2.917171e-7,0.00003696485,0.002649113],"genre_scores_gemma":[0.9744447,6.176053e-8,0.02507243,0.0003268394,0.00003664277,0.000004107139,3.536324e-7,0.000001127858,0.0001137498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7469691,"threshold_uncertainty_score":0.1103932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005421990263527392,"score_gpt":0.2200545502786905,"score_spread":0.2146325600151631,"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."}}