{"id":"W6888678728","doi":"10.21963/12667","title":"Investigation of the Toxic Effects of Mercury in Landlocked Arctic Char","year":2019,"lang":"en","type":"dataset","venue":"Canadian Cryospheric Information Network","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Landlocked country; Char; Arctic char; Arctic; The arctic; Groenlandia","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006365262,0.0003905526,0.0006635002,0.0002564706,0.00009026522,0.0000395536,0.0008204788,0.0005380857,0.0002536099],"category_scores_gemma":[0.0004696757,0.000345003,0.0001503932,0.002302162,0.0002009712,0.0006265705,0.00008212752,0.0006335586,0.0008671258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042782,"about_ca_system_score_gemma":0.002291285,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09269594,"about_ca_topic_score_gemma":0.1937139,"domain_scores_codex":[0.9971752,0.0002765408,0.001183599,0.0001814257,0.000556498,0.0006267188],"domain_scores_gemma":[0.9965475,0.0002328217,0.001592045,0.001090452,0.0002603942,0.0002768067],"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":[0.00001688275,0.000003784415,0.001670747,0.001746668,0.00005244374,0.000001279364,0.0003562191,0.008797165,0.000005271946,0.00006300877,0.987137,0.000149561],"study_design_scores_gemma":[0.0008284066,0.00007061542,0.03873013,0.002053208,0.0001344734,0.000005647441,0.0000561398,0.001029638,0.00004540264,0.0001697238,0.9564115,0.0004650872],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0148651,0.0004323929,0.000006796101,0.00009393793,0.002537963,0.002591211,0.9791924,0.00002116365,0.0002590302],"genre_scores_gemma":[0.03661545,0.00006846964,0.0001006656,0.001159875,0.0002066668,0.00009955503,0.9616611,0.00004644803,0.00004176765],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.101018,"threshold_uncertainty_score":0.9999108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007327861880136491,"score_gpt":0.1904758265762776,"score_spread":0.1831479646961411,"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."}}