{"id":"W4312783711","doi":"10.1007/978-3-031-08329-7_5","title":"An Entropy-Based Comment Ranking Method with Word Embedding Clustering","year":2012,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Cluster analysis; Zipf's law; Artificial intelligence; Natural language processing; Document clustering; Word2vec; Word embedding; Information retrieval; Intuition; Word (group theory); Embedding; Mathematics; Statistics; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007285122,0.0005765126,0.0007425375,0.0002987803,0.000172578,0.0003722066,0.0009699919,0.000266187,0.0001091346],"category_scores_gemma":[0.00001053588,0.0005429965,0.000064142,0.00006134364,0.00009634376,0.0008774399,0.0003182045,0.0005800973,0.000007773964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003303662,"about_ca_system_score_gemma":0.0001352223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008025362,"about_ca_topic_score_gemma":0.0002437896,"domain_scores_codex":[0.9974334,0.0001411202,0.0007246218,0.0006274041,0.0005165966,0.0005568567],"domain_scores_gemma":[0.997691,0.0003086698,0.0005414183,0.001147804,0.0001353838,0.000175704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004751533,0.00004047259,0.00006080989,0.000278828,0.00006646231,0.0001825263,0.001069365,0.0004493252,0.00001567075,0.9077023,0.002296065,0.08779065],"study_design_scores_gemma":[0.0006122529,0.0004466588,0.00001620905,0.001183343,0.00005630885,0.00009224103,0.00004328212,0.0652421,0.0002809807,0.02705274,0.9038119,0.001162027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[4.213511e-7,0.0005101345,0.969413,0.0002412447,0.0004993256,0.0005296604,0.0001191996,0.0003026763,0.02838427],"genre_scores_gemma":[0.0006984433,0.0002353154,0.9891191,0.001011431,0.0001811288,0.00006575736,0.00008675823,0.0001116016,0.008490491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9015158,"threshold_uncertainty_score":0.9997022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02778378140175008,"score_gpt":0.3081096424914891,"score_spread":0.280325861089739,"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."}}