{"id":"W2657797703","doi":"10.3389/fmicb.2017.01068","title":"Context Is Everything: Harmonization of Critical Food Microbiology Descriptors and Metadata for Improved Food Safety and Surveillance","year":2017,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; Public Health Agency of Canada; University of Manitoba; University of British Columbia; Simon Fraser University","funders":"Genome British Columbia; Government of Canada; Genome Canada","keywords":"Metadata; Food microbiology; Harmonization; Food safety; Context (archaeology); Clinical microbiology; Business; Biotechnology; Biology; Microbiology; Food science; Computer science; World Wide Web; Bacteria","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0307296,0.0009715799,0.001023099,0.01006862,0.001871823,0.007506683,0.003560992,0.001879498,0.002573754],"category_scores_gemma":[0.03227357,0.000727765,0.001568901,0.01032156,0.002081983,0.01604411,0.01022003,0.002448977,0.001825312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003604451,"about_ca_system_score_gemma":0.01384966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01473396,"about_ca_topic_score_gemma":0.01175439,"domain_scores_codex":[0.987765,0.004442249,0.002758499,0.001798682,0.002531188,0.0007043589],"domain_scores_gemma":[0.9660375,0.00597549,0.003523544,0.01316737,0.009102072,0.002193989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005753668,0.0006141739,0.02193826,0.002117546,0.0002490218,0.0006186091,0.005825148,0.006813955,0.0173718,0.2649108,0.1213716,0.5575937],"study_design_scores_gemma":[0.0001157222,0.0002194571,0.01175965,0.002512471,0.0002720558,0.0004728899,0.004832868,0.01761379,0.01127361,0.1301236,0.8205497,0.0002541819],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03349728,0.004987264,0.8527026,0.01693189,0.001774396,0.004763293,0.02597582,0.01660361,0.04276385],"genre_scores_gemma":[0.1022033,0.003170235,0.8263146,0.003635357,0.0004082488,0.001696345,0.05601798,0.001778004,0.004775988],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0307296,"threshold_uncertainty_score":0.1625156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750996937636953,"score_gpt":0.2576362256928161,"score_spread":0.2401262563164465,"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."}}