{"id":"W1553770895","doi":"10.1007/11878773_83","title":"Using Various Indexing Schemes and Multiple Translations in the CL-SR Task at CLEF 2005","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Clef; Computer science; Search engine indexing; Information retrieval; Weighting; Task (project management); Natural language processing; Scheme (mathematics); Document retrieval; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007221941,0.002539,0.003125165,0.00311743,0.002992651,0.003599316,0.003115116,0.004039215,0.02904609],"category_scores_gemma":[0.01856536,0.001071027,0.001799775,0.004430941,0.0009644846,0.008324843,0.002888571,0.00312278,0.01457652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159034,"about_ca_system_score_gemma":0.003537891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007726159,"about_ca_topic_score_gemma":0.014959,"domain_scores_codex":[0.9925055,0.003293485,0.0008494948,0.00172791,0.001110885,0.0005127359],"domain_scores_gemma":[0.9869572,0.006880504,0.0004112174,0.002753757,0.002540012,0.00045729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002940103,0.001081334,0.001974334,0.002560669,0.0002358596,0.0007308002,0.0009742594,0.007413278,0.03076056,0.01015087,0.2976725,0.6435055],"study_design_scores_gemma":[0.006037851,0.003718138,0.008187696,0.0007127994,0.001094466,0.004048013,0.005768438,0.4569525,0.18643,0.08375112,0.2423134,0.0009854932],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4308045,0.009049674,0.3307664,0.0104095,0.003205752,0.002127767,0.04676786,0.1062197,0.06064889],"genre_scores_gemma":[0.3551042,0.001291587,0.492244,0.001573239,0.0006554332,0.000633928,0.1164871,0.009230588,0.02278007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02904609,"threshold_uncertainty_score":0.09716886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129079208859862,"score_gpt":0.2697184983472176,"score_spread":0.2484277062586189,"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."}}