{"id":"W4384823501","doi":"10.1145/3539618.3591805","title":"Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tevatron; Computer science; Ranking (information retrieval); Pipeline (software); Flexibility (engineering); Implementation; Code (set theory); Generalization; Artificial intelligence; Information retrieval; Machine learning; Software engineering; Programming language; Large Hadron Collider; Particle physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001146659,0.001502817,0.001062724,0.001409492,0.0005746878,0.002328816,0.003826006,0.001245216,0.02095091],"category_scores_gemma":[0.006529558,0.0009652508,0.001552554,0.001551323,0.0005437515,0.004792212,0.003784375,0.002520899,0.01804593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153857,"about_ca_system_score_gemma":0.001596424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008665132,"about_ca_topic_score_gemma":0.01620508,"domain_scores_codex":[0.9991352,0.0001654384,0.0001021275,0.0001924293,0.0003128265,0.0000921126],"domain_scores_gemma":[0.9990116,0.0003976716,0.00005918467,0.0002929769,0.0001855403,0.0000529417],"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":[0.0008153313,0.0002874814,0.001240773,0.001750192,0.0003934464,0.0004997042,0.0004292605,0.08246176,0.0233911,0.03684234,0.2892091,0.5626795],"study_design_scores_gemma":[0.0002297985,0.0001804671,0.0005104317,0.0001282534,0.00007946383,0.0003959302,0.00009352694,0.8370114,0.02015532,0.04709082,0.09398954,0.000135056],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00713301,0.001544023,0.7240615,0.0004539232,0.0002817638,0.0003394629,0.005990424,0.2496673,0.01052858],"genre_scores_gemma":[0.1076073,0.00178794,0.8111088,0.0009402149,0.0001427614,0.001200556,0.02719743,0.02572235,0.02429264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02095091,"threshold_uncertainty_score":0.07008779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05170918939528313,"score_gpt":0.2977516510353155,"score_spread":0.2460424616400324,"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."}}