{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06903158,0.0008100924,0.002177417,0.00883956,0.003907554,0.01867888,0.002606746,0.003356835,0.007043563],"category_scores_gemma":[0.2158812,0.0008032618,0.0007571703,0.005900286,0.007198346,0.02124047,0.01069274,0.003512515,0.001857684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002886353,"about_ca_system_score_gemma":0.007142898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00255288,"about_ca_topic_score_gemma":0.002792112,"domain_scores_codex":[0.9385129,0.04272131,0.00317939,0.002895519,0.01143023,0.001260662],"domain_scores_gemma":[0.8165158,0.1362313,0.009959647,0.01369614,0.02073008,0.002867098],"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":[0.001424434,0.0007635483,0.01407806,0.001651353,0.0001658606,0.0004781976,0.03579055,0.004107319,0.007795419,0.1587022,0.009546822,0.7654963],"study_design_scores_gemma":[0.0005100688,0.001362184,0.01830079,0.002858165,0.000523122,0.0009390803,0.03080954,0.09615479,0.01761836,0.6883192,0.1419237,0.0006809864],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2117344,0.007544592,0.6273538,0.03064141,0.0008739785,0.002316655,0.0002399301,0.003087414,0.1162078],"genre_scores_gemma":[0.6943596,0.001308567,0.2952532,0.001320927,0.0003624729,0.0006028507,0.0001336762,0.000343408,0.006315262],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06903158,"threshold_uncertainty_score":0.3650783,"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."}}