{"id":"W4244521331","doi":"10.5194/acp-2018-340","title":"The climate effects of increasing ocean albedo: An idealizedrepresentation of solar geoengineering","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Climate Change and Geoengineering","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Pacific Northwest National Laboratory; Battelle; U.S. Department of Energy","keywords":"Albedo (alchemy); Environmental science; Climatology; Atmospheric sciences; Climate model; Earth's energy budget; Atmosphere (unit); Radiative forcing; Climate change; Climate sensitivity; Solar constant; Forcing (mathematics); Cloud albedo; Cloud forcing; Radiative flux; Radiative transfer; Meteorology; Cloud cover; Solar irradiance; Geography; Oceanography; Geology; Cloud computing","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":[0.0005805624,0.0002995784,0.0002163501,0.0001620392,0.0002243826,0.0005950454,0.0002398638,0.0003345362,0.001911135],"category_scores_gemma":[0.001007528,0.0001132898,0.0003437343,0.0001421838,0.0006253622,0.0005377101,0.0006184268,0.0003669259,0.0000847441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004542735,"about_ca_system_score_gemma":0.0003961962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002244547,"about_ca_topic_score_gemma":0.001828731,"domain_scores_codex":[0.9997106,0.0001587616,0.00001127997,0.00004431351,0.00003437246,0.00004061274],"domain_scores_gemma":[0.999637,0.0001160743,0.00006310898,0.0001189122,0.00003311817,0.00003176184],"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":[0.001351437,0.0004375987,0.01777763,0.0001502659,0.0001548454,0.0002387959,0.0001033118,0.8735453,0.05828123,0.03562468,0.001267353,0.01106767],"study_design_scores_gemma":[0.0003940533,0.0009668546,0.03490756,0.00001823697,0.00007077144,0.0001105288,0.0002133087,0.9148311,0.0138208,0.03111703,0.003480534,0.00006925608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9632606,0.00007939971,0.02312941,0.000408743,0.00004913616,0.00006744461,0.0005113294,0.00009608446,0.01239777],"genre_scores_gemma":[0.9974789,0.00002108611,0.0020974,0.00003738188,0.000005323786,0.00003000806,0.00006056815,0.000004844031,0.0002644222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002244547,"threshold_uncertainty_score":0.006393373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005667547936127,"score_gpt":0.2490109946179753,"score_spread":0.238954319138614,"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."}}