{"id":"W2981677129","doi":"10.1029/2019jc015100","title":"Multi‐Model Intercomparison of the Pan‐Arctic Ice‐Algal Productivity on Seasonal, Interannual, and Decadal Timescales","year":2019,"lang":"en","type":"article","venue":"Journal of Geophysical Research Oceans","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; University of Victoria","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Japan Agency for Marine-Earth Science and Technology; Western Canada Research Grid; Compute Canada; Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology; National Aeronautics and Space Administration; Office of Polar Programs; University of Victoria; National Science Foundation","keywords":"Sea ice; Environmental science; Arctic ice pack; Climatology; Arctic; Oceanography; Bloom; Productivity; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002189578,0.0009876282,0.0007611184,0.0005570462,0.0006016002,0.0009376871,0.001164061,0.00102829,0.001068507],"category_scores_gemma":[0.002042108,0.0006031253,0.001997812,0.0007134084,0.00029215,0.0009719117,0.0007055525,0.0007182053,0.0001565653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001506014,"about_ca_system_score_gemma":0.001182296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04975711,"about_ca_topic_score_gemma":0.02966157,"domain_scores_codex":[0.9996251,0.0001484527,0.00003175273,0.0001070836,0.00003753602,0.00005017986],"domain_scores_gemma":[0.9991373,0.00039523,0.0001067515,0.0001257204,0.0001604637,0.00007463861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002563512,0.00009566692,0.02134586,0.00002684295,0.0004998244,0.00004944765,0.00004461752,0.9719868,0.001712498,0.0002839877,0.0004633705,0.00323474],"study_design_scores_gemma":[0.0001086208,0.0001424058,0.01821049,0.000008914932,0.0001555187,0.00001414365,0.00005157045,0.9788949,0.001592649,0.0003027823,0.000480897,0.00003700627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932017,0.0001260675,0.003634264,0.0001647277,0.00006005338,0.00002269573,0.001245727,0.0002350133,0.001309745],"genre_scores_gemma":[0.9964722,0.00003650389,0.001945505,0.00003357884,0.0000082847,0.00003566419,0.001153631,0.00003132586,0.0002833308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04975711,"threshold_uncertainty_score":0.09893501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03365996800386466,"score_gpt":0.3052444688116694,"score_spread":0.2715845008078048,"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."}}