{"id":"W4362598841","doi":"10.48550/arxiv.2304.01019","title":"Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ranking (information retrieval); Information retrieval; Pace; Context (archaeology); Task (project management); Simple (philosophy); Natural language processing; Artificial intelligence; Query expansion","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007563229,0.0002721471,0.0003070456,0.0003197233,0.0003192474,0.0004446638,0.0008314358,0.000257219,0.000002113194],"category_scores_gemma":[0.0004316886,0.0003207795,0.0001547104,0.000385284,0.00006499137,0.001156067,0.001557949,0.0004238276,0.000009582317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319825,"about_ca_system_score_gemma":0.0001049569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001479018,"about_ca_topic_score_gemma":0.00002617008,"domain_scores_codex":[0.9983517,0.00009581995,0.0002989889,0.0007743282,0.0001195982,0.0003595494],"domain_scores_gemma":[0.9980729,0.0005853272,0.0002857529,0.0006493757,0.0003123342,0.00009434295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002057417,0.00001686301,0.00968738,0.000474871,0.00009771714,0.00006476097,0.001723093,0.948373,0.00004347561,0.02943431,0.00009106186,0.009787753],"study_design_scores_gemma":[0.0008187781,0.00004228508,0.002219486,0.00008547634,0.00003604824,0.00000346781,0.00005053546,0.9547614,0.0001799414,0.04130999,0.0001742746,0.0003182615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.469335,0.0000177137,0.5291374,0.00003758672,0.0007044263,0.000459764,0.00002094649,0.0002523742,0.00003479773],"genre_scores_gemma":[0.996573,0.00002200158,0.002938871,0.0001072868,0.0001678073,0.000002675608,0.00005614881,0.00001721097,0.0001150135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.527238,"threshold_uncertainty_score":0.9999244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07533395319001727,"score_gpt":0.2354221850205383,"score_spread":0.160088231830521,"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."}}