{"id":"W2946056622","doi":"10.1016/j.polar.2019.05.008","title":"Getting necessary historical data out of deep freeze","year":2019,"lang":"en","type":"article","venue":"Polar Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dominion Astrophysical Observatory","funders":"National Research Council","keywords":"Environmental science; Physical geography; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.01004228,0.0007382582,0.0008780152,0.00440798,0.00201351,0.006103226,0.00142128,0.001160969,0.01588227],"category_scores_gemma":[0.0698873,0.001006436,0.0006861907,0.004630145,0.001888556,0.01431113,0.005682358,0.004073876,0.005058127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001563908,"about_ca_system_score_gemma":0.004161507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005390181,"about_ca_topic_score_gemma":0.00581222,"domain_scores_codex":[0.9960151,0.001189497,0.0004329614,0.000538016,0.001457486,0.0003669119],"domain_scores_gemma":[0.9656264,0.01026829,0.001695201,0.01295934,0.008036792,0.001413962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007431513,0.0001877433,0.04858929,0.001666638,0.000247293,0.001703043,0.008524014,0.01198309,0.008526413,0.2159947,0.1254146,0.5764201],"study_design_scores_gemma":[0.00006034401,0.0001255502,0.0163984,0.001793617,0.0001440047,0.0005556572,0.008913314,0.01309748,0.009159147,0.3582652,0.591345,0.0001423108],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1883588,0.01242044,0.5855506,0.04267975,0.01028929,0.000650786,0.02833387,0.006453836,0.1252626],"genre_scores_gemma":[0.6385255,0.008272425,0.2921224,0.004390616,0.001849365,0.0003639369,0.02718855,0.004965371,0.02232178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01588227,"threshold_uncertainty_score":0.05313152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1745864926481814,"score_gpt":0.3909494831375621,"score_spread":0.2163629904893807,"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."}}