{"id":"W6937005458","doi":"10.5879/31n7-ym68","title":"Expedition Arctic Ocean 2016 - Meteorological, Oceanographic and Ship Data Collected Onboard Icebreaker Oden during August to September 2016","year":2018,"lang":"en","type":"dataset","venue":"Swedish National Data Service","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Cruise; Arctic; Continental shelf; Sea ice; Research vessel; The arctic; Marine geology; Halocline; Observatory","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","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow","open_science","insufficient_payload"],"category_scores_codex":[0.003027309,0.001452424,0.001241511,0.001794799,0.0009641063,0.0009565396,0.008903601,0.001031259,0.006088069],"category_scores_gemma":[0.003187274,0.001404306,0.000109945,0.003880772,0.0004366397,0.004528189,0.01197932,0.001305791,0.01109668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005456888,"about_ca_system_score_gemma":0.00129362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001904645,"about_ca_topic_score_gemma":0.01069521,"domain_scores_codex":[0.9882125,0.000821241,0.001581143,0.004430139,0.003585342,0.001369652],"domain_scores_gemma":[0.9866297,0.001199299,0.001152903,0.007327082,0.002724967,0.0009660149],"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":[0.0006568163,0.0004344428,0.0001578826,0.0008145202,0.0007478694,0.0000589099,0.00002530087,0.000006896831,0.0001371046,0.000008876917,0.9969473,0.000004042024],"study_design_scores_gemma":[0.002161392,0.0001391962,0.01173448,0.000888782,0.0009917007,0.0002137053,0.00006179653,0.000338202,0.00001262951,0.0003538175,0.9814038,0.001700486],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008374977,0.0004062885,0.000005595615,0.00101032,0.001145847,0.001824421,0.986481,0.0004258308,0.0003257785],"genre_scores_gemma":[0.0004304912,0.0003504967,0.001342697,0.003654767,0.003851975,0.0001036333,0.9895352,0.0002795534,0.0004512286],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01554352,"threshold_uncertainty_score":0.9998226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07330842560541638,"score_gpt":0.3189880469861565,"score_spread":0.2456796213807401,"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."}}