{"id":"W4394325863","doi":"10.6084/m9.figshare.16775881","title":"Analysis code and dataset to accompany Salmonella Heidelberg analysis from Ontario 2013","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Salmonella; Code (set theory); Computer science; Programming language; Biology; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.001041133,0.0008319244,0.001050863,0.002587406,0.001092042,0.001267466,0.001655194,0.000702807,0.12148],"category_scores_gemma":[0.008959506,0.0006026832,0.0009670472,0.005577351,0.000385033,0.0004854468,0.00144808,0.0008321802,0.03786702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005762576,"about_ca_system_score_gemma":0.01363607,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6024148,"about_ca_topic_score_gemma":0.7757273,"domain_scores_codex":[0.9992494,0.0000817235,0.0001071683,0.0001977795,0.0002041238,0.0001596945],"domain_scores_gemma":[0.9952401,0.001035186,0.0004736392,0.0006862196,0.002064498,0.0005004292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001053221,0.000009651245,0.004026552,0.0006466215,0.00004910065,0.00002181448,0.00005166001,0.0003092506,0.00009843509,0.0003635609,0.9915636,0.002754391],"study_design_scores_gemma":[0.000361366,0.0000197624,0.0421484,0.0006615614,0.0001130192,0.00006692445,0.0001795405,0.0006154005,0.0003426723,0.001188139,0.9542548,0.00004832921],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001333086,0.00001494017,0.0000975643,0.0000348069,0.000006070179,0.00002159799,0.999161,0.0001116757,0.0004189952],"genre_scores_gemma":[0.001532074,0.00004822297,0.0006192448,0.00004804459,0.000006942204,0.000283243,0.9957426,0.0001618403,0.001557692],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3975852,"threshold_uncertainty_score":0.7998533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03764690934156056,"score_gpt":0.3046678786611539,"score_spread":0.2670209693195933,"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."}}