{"id":"W4385612789","doi":"10.1145/3539618.3591925","title":"SIGIR 2023 Workshop on Retrieval Enhanced Machine Learning (REML @ SIGIR 2023)","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada)","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; ENCODE; Question answering; Context (archaeology); Robustness (evolution); Information retrieval","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":[0.0376346,0.003924039,0.004492078,0.005369885,0.001560307,0.007474353,0.007600189,0.008031345,0.02370932],"category_scores_gemma":[0.02673007,0.001377559,0.002332305,0.003743034,0.002601418,0.01308002,0.006074001,0.009218978,0.02342568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003865713,"about_ca_system_score_gemma":0.004626456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01257276,"about_ca_topic_score_gemma":0.01563671,"domain_scores_codex":[0.986845,0.006354034,0.0006243329,0.00191802,0.003215963,0.001042604],"domain_scores_gemma":[0.9779232,0.01117929,0.0005581294,0.002804243,0.004708117,0.002827012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003867217,0.0004274769,0.0003256716,0.0005307544,0.0001667602,0.0001468561,0.0002420557,0.002250662,0.002460695,0.006646497,0.7612077,0.2252082],"study_design_scores_gemma":[0.0003473723,0.0006857322,0.002092734,0.0006065805,0.0002099536,0.0006995662,0.000506514,0.05151613,0.006658026,0.03978191,0.8966783,0.0002170535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02138869,0.2011082,0.5052229,0.1008572,0.06948334,0.002527021,0.0107082,0.02472866,0.06397573],"genre_scores_gemma":[0.07852136,0.05658193,0.5441977,0.03425077,0.03344235,0.003182943,0.03996053,0.005262069,0.2046003],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0376346,"threshold_uncertainty_score":0.1990331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03283412424060274,"score_gpt":0.2765557851906155,"score_spread":0.2437216609500127,"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."}}