{"id":"W3153794000","doi":"10.48550/arxiv.2104.05740","title":"A Replication Study of Dense Passage Retriever","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Replication (statistics); Labrador Retriever; Business; Biology; Medicine; Virology; Surgery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003660127,0.0001762583,0.0003004417,0.0001660503,0.00006285818,0.00006849527,0.001533433,0.0001754541,0.00001272997],"category_scores_gemma":[0.00009381774,0.0002170899,0.0001217131,0.0005876402,0.00002724193,0.0002475416,0.002421275,0.0003679248,0.000006737736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001290562,"about_ca_system_score_gemma":0.0001737061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003467872,"about_ca_topic_score_gemma":0.00006915311,"domain_scores_codex":[0.9978821,0.0001829206,0.000254254,0.001381796,0.0001244751,0.0001744157],"domain_scores_gemma":[0.9955355,0.00006377859,0.0003155659,0.003752858,0.000254878,0.00007745102],"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":[0.0001146679,0.003219508,0.1243783,0.0004911622,0.0008057821,0.002800964,0.0152752,0.6904303,0.0009077864,0.14852,0.0003823842,0.01267388],"study_design_scores_gemma":[0.001087033,0.0001426768,0.01638544,0.0001747921,0.0001362322,0.000007344096,0.001476024,0.9666215,0.0006917156,0.01260172,0.00008730517,0.0005882234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.558734,0.00002693697,0.4403999,0.00002795653,0.0001647404,0.0001924166,0.000001050391,0.00007111334,0.0003819661],"genre_scores_gemma":[0.9962569,0.0000381479,0.002897183,0.00003057221,0.00002805221,9.273441e-7,0.000003752718,0.00000925214,0.0007351454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.437523,"threshold_uncertainty_score":0.885267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1032666392796188,"score_gpt":0.2051160122093425,"score_spread":0.1018493729297237,"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."}}