{"id":"W1123396362","doi":"","title":"Detecting pathogenic Yersinia enterocolitica in surface water from the Grand River watershed: An evaluation and comparison of methods","year":2008,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Yersinia bacterium, plague, ectoparasites research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Water Network; Ontario Ministry of Health and Long-Term Care; Public Health Agency; Public Health Agency of Canada","keywords":"Yersinia enterocolitica; Watershed; Surface water; Hydrology (agriculture); Environmental science; Biology; Geology; Computer science; Environmental engineering; Machine learning; Bacteria; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0009620856,0.000484712,0.0003086504,0.001202639,0.0004969264,0.0007805303,0.000369241,0.000444247,0.0005043049],"category_scores_gemma":[0.000973184,0.0002210808,0.0002772094,0.001281639,0.0004217369,0.0003598963,0.0003839372,0.000214874,0.0001636665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005808785,"about_ca_system_score_gemma":0.0005924846,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02372344,"about_ca_topic_score_gemma":0.04826324,"domain_scores_codex":[0.9989741,0.0002779608,0.00009253548,0.0002123,0.0003692982,0.00007391262],"domain_scores_gemma":[0.9992854,0.0001726409,0.0001526714,0.00002402932,0.0003191595,0.00004607212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005213958,0.000575661,0.7581322,0.0005884481,0.00008054711,0.0005953843,0.002972262,0.0003652005,0.1934746,0.00007885521,0.0002372849,0.04237806],"study_design_scores_gemma":[0.00002009504,0.0020779,0.9471173,0.00007772836,0.0001196086,0.0008267857,0.004807595,0.00269081,0.03925209,0.00003902189,0.002925559,0.00004530402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9964482,0.0005153983,0.001400383,0.0000707466,0.00001129946,0.0001693548,0.0003341458,0.00002234263,0.001028192],"genre_scores_gemma":[0.9784245,0.001723054,0.01658437,0.00008382557,0.00001693046,0.0002147841,0.0010189,0.000009872326,0.001923744],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9762766,"threshold_uncertainty_score":0.04717076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981866425974371,"score_gpt":0.3190340072081037,"score_spread":0.28921534294836,"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."}}