{"id":"W4403577522","doi":"10.1145/3627673.3679959","title":"Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval","year":2024,"lang":"en","type":"article","venue":"","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Labrador Retriever; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282829,0.001616352,0.001495148,0.001204998,0.0005323896,0.001099773,0.002921291,0.001446682,0.006511558],"category_scores_gemma":[0.003696901,0.0006196449,0.001172661,0.001074641,0.0006494567,0.003676957,0.001923194,0.001766635,0.005480259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008642413,"about_ca_system_score_gemma":0.001265825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006468774,"about_ca_topic_score_gemma":0.01031752,"domain_scores_codex":[0.9994505,0.0001272675,0.00004401015,0.0001652202,0.0001445029,0.00006856915],"domain_scores_gemma":[0.9992028,0.0002819333,0.00005849329,0.0002518616,0.00016295,0.00004198622],"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":[0.0008224212,0.0004163301,0.00198734,0.0009619851,0.000317366,0.0004312174,0.0003048995,0.1027327,0.08450892,0.01309864,0.03979206,0.7546261],"study_design_scores_gemma":[0.0001113434,0.000304259,0.0003827507,0.00002799232,0.00007601217,0.0002474608,0.00007532539,0.9483255,0.02882086,0.009117609,0.01245462,0.00005617677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03488304,0.003489823,0.9028447,0.0005124698,0.0002067767,0.0003206878,0.001373093,0.05198733,0.004382101],"genre_scores_gemma":[0.3588827,0.00170922,0.6130106,0.001424761,0.0001935735,0.0006249935,0.006032581,0.00200004,0.01612148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006511558,"threshold_uncertainty_score":0.02178335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406762784815016,"score_gpt":0.2747566818798541,"score_spread":0.2606890540317039,"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."}}