{"id":"W6911011387","doi":"10.5064/f6buax58/qafgeb","title":"Burke_EDI_NENA.search_selection.2017.09.24.xlsx","year":2018,"lang":"en","type":"dataset","venue":"Syracuse University Qualitative Data Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Selection (genetic algorithm); Identifier; Data source; Workbook; Search engine; State (computer science)","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":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts"],"category_scores_codex":[0.004416646,0.001464769,0.001525899,0.002008181,0.002254735,0.0004183718,0.008327537,0.001252037,0.0006810537],"category_scores_gemma":[0.002540875,0.001666158,0.0003979848,0.002053529,0.002936196,0.003381686,0.005406103,0.002838522,0.01435578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002598642,"about_ca_system_score_gemma":0.002204468,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01311589,"about_ca_topic_score_gemma":0.002153976,"domain_scores_codex":[0.9833046,0.007680994,0.001055088,0.003719122,0.002659479,0.001580762],"domain_scores_gemma":[0.9836699,0.002477972,0.00184767,0.009026182,0.002005135,0.0009731796],"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":[0.0008905192,0.0006419445,0.000005101631,0.000369147,0.001584294,0.00174665,0.0036492,0.00000149624,0.0005621402,0.00007760737,0.9904518,0.00002013567],"study_design_scores_gemma":[0.001431921,0.0004688511,0.00003083837,0.0003889576,0.001067159,0.0001775881,0.01327938,0.00003867433,0.0001626242,0.00005485006,0.9811193,0.001779804],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002728113,0.0002568364,0.0001014795,0.0001128914,0.004258897,0.001097335,0.9912348,0.0006185877,0.002046328],"genre_scores_gemma":[0.00002353613,0.0003196324,0.001210915,0.00008389598,0.002653214,0.000005654995,0.9839383,0.0001834791,0.01158133],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01367473,"threshold_uncertainty_score":0.9998102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1135563221052726,"score_gpt":0.3816580605555429,"score_spread":0.2681017384502703,"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."}}