{"id":"W6925581355","doi":"10.18164/e1df5764-1eec-4a9f-9c03-f515b396b717","title":"Total Gaseous Mercury (TGM)","year":2014,"lang":"en","type":"dataset","venue":"ECCC Data Catalogue","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Quebec; Environment and Climate Change Canada; Government of Canada","funders":"","keywords":"Mercury (programming language); Cartridge; Thermal desorption; Spectrum analyzer; Quality standard; Inlet; MERCURE","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009797133,0.002142451,0.001685292,0.004990958,0.0005317882,0.001787827,0.002176573,0.001441505,0.01817794],"category_scores_gemma":[0.003684707,0.0004572527,0.001427176,0.009075255,0.0003745301,0.00112126,0.001272468,0.001435027,0.02237873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001728118,"about_ca_system_score_gemma":0.002461917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03059791,"about_ca_topic_score_gemma":0.027832,"domain_scores_codex":[0.9986754,0.0001316589,0.0002386813,0.0004618263,0.0003436048,0.0001488375],"domain_scores_gemma":[0.9986131,0.0002110393,0.0003148111,0.0003174157,0.0004263758,0.0001171941],"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":[0.0008023696,0.0001241574,0.02290842,0.009018041,0.0005244478,0.0001818656,0.000141666,0.002248594,0.002357075,0.001956464,0.9412595,0.01847739],"study_design_scores_gemma":[0.0002715354,0.00005458243,0.03001991,0.0005280646,0.0001269649,0.0001560919,0.0001193714,0.0005573411,0.002062213,0.0009224605,0.9651355,0.00004592709],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005134802,0.00009627901,0.00005834552,0.00002210385,0.00001184421,0.00001235474,0.9986595,0.0001599677,0.0004660694],"genre_scores_gemma":[0.001466089,0.0001263082,0.0003801709,0.00002969093,0.000007143309,0.00009783483,0.9973774,0.00004153731,0.0004739666],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03059791,"threshold_uncertainty_score":0.06083959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03037196534749633,"score_gpt":0.2798428022917202,"score_spread":0.2494708369442239,"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."}}