{"id":"W1482214210","doi":"10.1002/meet.14505001042","title":"Closing the loop: Assisting archival appraisal and information retrieval in one sweep","year":2013,"lang":"en","type":"article","venue":"Proceedings of the American Society for Information Science and Technology","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"European Commission","keywords":"Relevance (law); Computer science; Judgement; Selection (genetic algorithm); Information retrieval; Closing (real estate); Process (computing); Sketch; Data science; Artificial intelligence; Epistemology; Political science; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.001543823,0.00009648435,0.00015396,0.0003240314,0.0005520448,0.0005013258,0.0009574132,0.00004199622,3.927481e-7],"category_scores_gemma":[0.001125675,0.00006186758,0.00005119789,0.003463638,0.002010129,0.01128859,0.0006238046,0.0002282194,0.000002860011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005767744,"about_ca_system_score_gemma":0.0001398924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004576844,"about_ca_topic_score_gemma":2.399265e-7,"domain_scores_codex":[0.9985403,0.000003093168,0.0004352738,0.000112119,0.0005689605,0.0003401876],"domain_scores_gemma":[0.9980696,0.0001124273,0.0005762785,0.0001377811,0.001054631,0.00004928797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002053021,0.00002285731,0.008684234,0.0001370951,0.00001378522,7.577251e-9,0.009450934,0.00000547361,0.01360387,0.2949423,0.0004759257,0.6726429],"study_design_scores_gemma":[0.002326594,0.0009745399,0.4227271,0.0002079839,0.00004275996,0.0001061026,0.05103693,0.4218056,0.06523898,0.01941982,0.01522339,0.0008901846],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831865,0.000006337219,0.005062067,0.0102512,0.0000565731,0.0006021174,0.000003281374,0.0000701498,0.0007617578],"genre_scores_gemma":[0.9835002,0.00003271422,0.01557734,0.0008337431,0.000008275758,0.00004096447,7.73317e-7,0.000001734952,0.000004273654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6717528,"threshold_uncertainty_score":0.8183951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507023193186833,"score_gpt":0.265205359100969,"score_spread":0.2501351271691007,"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."}}