{"id":"W3107632228","doi":"10.17504/protocols.io.spmedk6","title":"UK REF 2014 Analysis Data and R Script v1","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Data file; Raw data; Syntax; File format; Set (abstract data type); Programming language; Data set; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008416702,0.001984894,0.002191205,0.0035892,0.001275389,0.003892664,0.002990926,0.001395969,0.4733617],"category_scores_gemma":[0.05691046,0.001881293,0.001881003,0.004058902,0.001171523,0.002072805,0.003202282,0.002849626,0.3601372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001597196,"about_ca_system_score_gemma":0.005353916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006533369,"about_ca_topic_score_gemma":0.009140386,"domain_scores_codex":[0.9946144,0.001791647,0.0007651983,0.001383533,0.001103684,0.0003414456],"domain_scores_gemma":[0.9740893,0.01324197,0.001670128,0.005889902,0.004388176,0.0007204274],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000296979,0.00001992788,0.000798383,0.0009444069,0.0000696842,0.00005051785,0.00009483723,0.0003489181,0.0004481518,0.001809934,0.9845031,0.0106151],"study_design_scores_gemma":[0.0004463441,0.00009530979,0.004600397,0.0006254967,0.0001058069,0.0001648,0.0001294488,0.001149655,0.0023959,0.01166816,0.9784892,0.0001295252],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009533708,0.0001773354,0.02156761,0.0006326953,0.0003311387,0.001033468,0.9210848,0.04605905,0.008160486],"genre_scores_gemma":[0.0104292,0.00030647,0.09969307,0.002243854,0.0002611723,0.01718183,0.7280483,0.1025103,0.03932579],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9964108,"threshold_uncertainty_score":0.7511855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4184746936605389,"score_gpt":0.5652060883358335,"score_spread":0.1467313946752946,"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."}}