{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01012831,0.002225603,0.001674693,0.003921855,0.001655168,0.002526734,0.00365482,0.00223123,0.00434698],"category_scores_gemma":[0.02103966,0.000772196,0.001179017,0.003606902,0.00078614,0.005970321,0.002029065,0.003377412,0.004310065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001919108,"about_ca_system_score_gemma":0.00220972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01557426,"about_ca_topic_score_gemma":0.03273068,"domain_scores_codex":[0.9941412,0.002443707,0.0004953362,0.001317531,0.001211591,0.0003906808],"domain_scores_gemma":[0.9927896,0.002381244,0.0002934737,0.002061321,0.002292455,0.0001819216],"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":[0.000858873,0.0009610637,0.003492545,0.0006435693,0.0005239364,0.00008664501,0.0002117977,0.1139778,0.01436313,0.008114314,0.03305379,0.8237126],"study_design_scores_gemma":[0.0001699462,0.0004782222,0.002952988,0.00008553397,0.0001810459,0.00009733396,0.0001117411,0.9567474,0.01567065,0.01453604,0.008869955,0.00009907864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1375102,0.01454907,0.7916204,0.0015392,0.001525598,0.0009409464,0.004798604,0.02950021,0.0180158],"genre_scores_gemma":[0.4599304,0.002035137,0.5125501,0.000408919,0.0005491122,0.000802209,0.01293683,0.001309168,0.009478154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01557426,"threshold_uncertainty_score":0.05356431,"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."}}