{"id":"W2139791975","doi":"10.5194/gmd-9-1827-2016","title":"Inconsistent strategies to spin up models in CMIP5: implications for ocean biogeochemical model performance assessment","year":2016,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Seventh Framework Programme; Horizon 2020 Framework Programme; Norges Forskningsråd; European Commission; Grand Équipement National De Calcul Intensif; National Science Foundation","keywords":"Coupled model intercomparison project; Biogeochemical cycle; Earth system science; Environmental science; Climate model; Climatology; Biogeochemistry; Sensitivity (control systems); Spin-up; Climate change; Computer science; Oceanography; Geology; Ecology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007968887,0.0007659909,0.0006109073,0.0004362069,0.0007517608,0.001224559,0.001556127,0.0007738383,0.0008270348],"category_scores_gemma":[0.01881832,0.0004526816,0.0008856319,0.0004922741,0.000655408,0.001125871,0.0009470847,0.001469171,0.0001121424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144068,"about_ca_system_score_gemma":0.00107229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01595089,"about_ca_topic_score_gemma":0.01440769,"domain_scores_codex":[0.9989666,0.0006064501,0.00008790971,0.0001333492,0.0001136706,0.00009202339],"domain_scores_gemma":[0.9932754,0.003701118,0.0005771614,0.001365093,0.0007607513,0.0003205029],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004708188,0.0002834011,0.0994709,0.0001260357,0.0006436448,0.0001810293,0.0002412181,0.8701969,0.004138118,0.004269744,0.001705147,0.01827315],"study_design_scores_gemma":[0.0001249375,0.0001445601,0.01321994,0.00003722456,0.0001040049,0.00002787954,0.0001671308,0.9790211,0.003676505,0.002575211,0.0008513088,0.00005014838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9705547,0.0002937668,0.0249611,0.0007335236,0.0001309015,0.00008677861,0.0008275098,0.0003676904,0.002044016],"genre_scores_gemma":[0.9864963,0.00005988727,0.01249235,0.0001221661,0.00001191436,0.00006241703,0.000530436,0.00008096807,0.0001436957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9920311,"threshold_uncertainty_score":0.042144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972046445520269,"score_gpt":0.2548106824727345,"score_spread":0.2150902180175318,"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."}}