{"id":"W2903220936","doi":"","title":"Research guides: Grey Literature and Statistics for Dentistry: Home","year":2011,"lang":"en","type":"libguides","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Grey literature; Oral health; Geography; Statistics; Dentistry; MEDLINE; Medicine; Political science; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007140767,0.001207532,0.001877425,0.0145714,0.001062452,0.00574292,0.002534195,0.002240512,0.3103951],"category_scores_gemma":[0.05801104,0.001594899,0.001151915,0.02263887,0.0007625667,0.006166358,0.003324868,0.002143088,0.1946115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002065377,"about_ca_system_score_gemma":0.009721655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008657836,"about_ca_topic_score_gemma":0.01678286,"domain_scores_codex":[0.9960786,0.001059633,0.001078378,0.0002285924,0.001432414,0.0001224067],"domain_scores_gemma":[0.9303417,0.04877528,0.003235438,0.002273514,0.01399722,0.001376827],"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":[0.00003565188,0.0000345513,0.000119146,0.002847072,0.00001142358,0.00007332362,0.0004443372,0.000202431,0.0001725607,0.005138944,0.8836846,0.107236],"study_design_scores_gemma":[0.00004331386,0.00001266857,0.0003732262,0.001852614,0.000009809591,0.00009538583,0.0002825865,0.0001916421,0.0001664333,0.006384598,0.9905618,0.0000259981],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.002128052,0.01064279,0.112115,0.01974679,0.002345923,0.005461326,0.497263,0.04920955,0.3010876],"genre_scores_gemma":[0.007201315,0.02203649,0.5071583,0.00657382,0.001353048,0.009404014,0.2375501,0.01939301,0.1893299],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3103951,"threshold_uncertainty_score":0.9836377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07723222167012465,"score_gpt":0.3789187941579624,"score_spread":0.3016865724878378,"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."}}