{"id":"W2903980694","doi":"10.1101/496927","title":"Using a diabetes discussion forum and Wikipedia to detect the alignment of public interests and the research literature","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Scopus; Latent Dirichlet allocation; Topic model; Original research; Public health; Political science; Library science; MEDLINE; Public relations; Medicine; Computer science; Information retrieval","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"],"consensus_categories":[],"category_scores_codex":[0.005531392,0.000478959,0.0004718897,0.0166472,0.001138276,0.002142933,0.0005112774,0.000991778,0.001111599],"category_scores_gemma":[0.03306333,0.0002296991,0.0003734234,0.007055232,0.0005199041,0.003292073,0.002107726,0.0005416392,0.0004341404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008662323,"about_ca_system_score_gemma":0.0007927789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003068827,"about_ca_topic_score_gemma":0.005324982,"domain_scores_codex":[0.996559,0.001237197,0.0004565567,0.0007036804,0.0007608306,0.0002828616],"domain_scores_gemma":[0.9487363,0.03226263,0.01031437,0.001502918,0.004569798,0.002613995],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001159184,0.0006114599,0.8526959,0.001790061,0.0001864741,0.001032102,0.02156189,0.0008579566,0.01175348,0.001371486,0.007780936,0.09919901],"study_design_scores_gemma":[0.00008252653,0.0005451086,0.9215022,0.0004521752,0.000184898,0.001127828,0.01947732,0.02235123,0.005564833,0.002527071,0.02602604,0.0001587602],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987118,0.0006513953,0.003125126,0.0002992267,0.0000847788,0.0002767606,0.005201661,0.0002331578,0.003009908],"genre_scores_gemma":[0.9755771,0.0002298268,0.01445055,0.0001261821,0.0001564809,0.000569185,0.007475266,0.00004227522,0.001373053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9944686,"threshold_uncertainty_score":0.02925313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05336882479122627,"score_gpt":0.3385828762751554,"score_spread":0.2852140514839291,"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."}}