{"id":"W6891672098","doi":"10.48448/5f7b-mw39","title":"Precise Zero-Shot Dense Retrieval without Relevance Labels","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Relevance (law); Embedding; Encoding (memory); Relevance feedback; Document retrieval; Encoder; Vector space model; Bottleneck","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001512056,0.0008945705,0.001276112,0.0008640696,0.0006299404,0.001425012,0.002185001,0.001189879,0.005954571],"category_scores_gemma":[0.005463501,0.0005761983,0.0005800591,0.0008819397,0.001227329,0.00515508,0.003221827,0.00130765,0.004022088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000774782,"about_ca_system_score_gemma":0.001185895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004007144,"about_ca_topic_score_gemma":0.007417973,"domain_scores_codex":[0.9987223,0.0002481311,0.00008307601,0.0003657763,0.000417844,0.0001628337],"domain_scores_gemma":[0.9978095,0.0007242617,0.000135332,0.000882663,0.0003477373,0.0001005231],"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":[0.0009876072,0.0004769978,0.001437721,0.0007016112,0.0001183375,0.0003021971,0.0005913667,0.02901229,0.07718946,0.01669133,0.02145518,0.8510359],"study_design_scores_gemma":[0.0002474425,0.0009836609,0.002482571,0.00009399482,0.0001424709,0.001636913,0.0006653682,0.8103117,0.1024694,0.05178041,0.02901601,0.000170062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05030028,0.001383361,0.928224,0.0003472242,0.0001294835,0.000290935,0.0006405498,0.01266604,0.006018152],"genre_scores_gemma":[0.5229424,0.0007208749,0.4500341,0.0007583278,0.0001556614,0.0002616505,0.003430532,0.0008067447,0.02088968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005954571,"threshold_uncertainty_score":0.01991999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03680912418638017,"score_gpt":0.3184238519734348,"score_spread":0.2816147277870546,"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."}}