{"id":"W2964507227","doi":"","title":"Entity Retrieval Docker Image for OSIRRC at SIGIR 2019.","year":2019,"lang":"en","type":"article","venue":"International ACM SIGIR Conference on Research and Development in Information Retrieval","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Information retrieval; Natural language processing; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008696611,0.0002010979,0.000279245,0.001294575,0.0002603116,0.00143344,0.001738282,0.0001311398,0.002892073],"category_scores_gemma":[0.01095064,0.0001665705,0.00005723457,0.000632729,0.0001791337,0.002628674,0.001467525,0.0003597496,0.005294649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004372649,"about_ca_system_score_gemma":0.0004266075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003533322,"about_ca_topic_score_gemma":0.00004367063,"domain_scores_codex":[0.9936718,0.0001724679,0.001187016,0.0005016521,0.003945496,0.000521538],"domain_scores_gemma":[0.994002,0.002736847,0.0002947728,0.0006245256,0.002144059,0.0001978105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01858358,0.0009422167,0.05272777,0.0003159462,0.0002905795,0.00002439203,0.007007212,0.0000990791,0.003380479,0.4564485,0.354348,0.1058322],"study_design_scores_gemma":[0.00337286,0.0004394439,0.05538226,0.0001191174,0.000002065671,0.000005007984,0.001489412,0.002605948,0.007639399,0.0531845,0.8753053,0.0004546955],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.91348,0.00002162676,0.006266566,0.009548864,0.001988938,0.002215516,0.0005028773,0.00006812875,0.06590741],"genre_scores_gemma":[0.9692096,0.000169241,0.006775473,0.001025787,0.00009143858,0.00006436439,0.001094124,0.00001331928,0.02155664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5209572,"threshold_uncertainty_score":0.9996032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2349553371768173,"score_gpt":0.4577698368885389,"score_spread":0.2228144997117216,"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."}}