{"id":"W6908563941","doi":"10.26008/1912/bco-dmo.779779.1","title":"Mercury and methylmercury concentrations in surface snow samples from USCGC Healy HLY1502 in the Canada and Makarov Basins of the Arctic Ocean; Dutch Harbor to Dutch Harbor from August to October 2015","year":2021,"lang":"en","type":"dataset","venue":"Open Access Server of the Woods Hole Scientific Community (Woods Hole Scientific Community)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Ocean Sciences","keywords":"Mercury (programming language); Methylmercury; Snow; Arctic; Cruise; Geotraces","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","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","sts","open_science"],"category_scores_codex":[0.02299861,0.001643399,0.002722624,0.001374239,0.007732652,0.01212201,0.04286083,0.0005726772,0.000903202],"category_scores_gemma":[0.005398017,0.001246352,0.0004675868,0.01741455,0.005164062,0.002768853,0.04518972,0.00762256,0.00003859151],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001367734,"about_ca_system_score_gemma":0.006195884,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9683418,"about_ca_topic_score_gemma":0.994096,"domain_scores_codex":[0.9589195,0.03011705,0.003136587,0.002122626,0.003844839,0.001859353],"domain_scores_gemma":[0.9665673,0.007303691,0.002117971,0.02145163,0.001705644,0.000853801],"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":[0.0002332354,0.002964172,0.02777713,0.0004984536,0.0003863479,0.00001527157,0.01141099,0.002384762,0.004288602,0.00005433809,0.9498279,0.0001587904],"study_design_scores_gemma":[0.003804353,0.0001562907,0.360828,0.005920591,0.001133695,0.000009118387,0.03381112,0.0003904663,0.01259521,0.001131305,0.5774377,0.002782166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.526894,0.0005485305,0.000001606546,0.002916861,0.001984047,0.002984633,0.4645794,0.00001874703,0.00007221732],"genre_scores_gemma":[0.8060197,0.00006571422,0.0004255937,0.001280225,0.00004666044,0.0001420272,0.1911548,0.0001715162,0.0006938394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3723902,"threshold_uncertainty_score":0.9996313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08322409404005557,"score_gpt":0.3464680011646917,"score_spread":0.2632439071246361,"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."}}