{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005446353,0.0005589309,0.0009598912,0.0002075777,0.0001241698,0.00008395659,0.001363801,0.0006179378,0.001527021],"category_scores_gemma":[0.00119631,0.0004241958,0.0001448565,0.0002165838,0.000192588,0.0001806288,0.001380205,0.001032722,0.00426309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007622992,"about_ca_system_score_gemma":0.0002569976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002701562,"about_ca_topic_score_gemma":0.0006763971,"domain_scores_codex":[0.9970262,0.00008242868,0.0005374765,0.001369144,0.0004339826,0.0005507659],"domain_scores_gemma":[0.9938159,0.0001162793,0.0002622636,0.005408028,0.000102999,0.0002945521],"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":[0.00009356345,0.0001610464,0.000001099354,0.0005981203,0.0001710721,0.0003168252,0.000003543843,8.050224e-8,0.0008594455,0.000001829508,0.9950538,0.002739572],"study_design_scores_gemma":[0.0004054768,0.0001476636,0.00003518417,0.000287474,0.0006954396,0.001217571,0.000004953721,0.000005276102,0.0008368199,0.000003857706,0.9958619,0.0004983889],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001390304,0.001517007,0.000002825326,0.001925088,0.001491853,0.0005346406,0.9942485,0.00008085455,0.00006019911],"genre_scores_gemma":[0.0004374932,0.000710486,0.0002167439,0.001740716,0.003502307,0.000002082284,0.9923559,0.00005141662,0.0009828737],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.004038883,"threshold_uncertainty_score":0.999821,"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."}}