{"id":"W6931995245","doi":"10.5285/a963d9415bb74247830f8704f825aa90","title":"ESA Sea Surface Temperature Climate Change Initiative (SST_cci): GHRSST Multi-Product ensemble (GMPE), v2.0","year":2020,"lang":"en","type":"dataset","venue":"NERC Environmental Data Service","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sea surface temperature; Advanced very-high-resolution radiometer; Satellite; Radiometer; Sea ice; Climate change; Downscaling; Climate model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.0006195121,0.001013662,0.0008411424,0.0001361872,0.0003527528,0.0004804653,0.007948685,0.0005295321,0.0005539127],"category_scores_gemma":[0.00008782637,0.001009958,0.0001071804,0.0007581598,0.0001777376,0.003208537,0.01054874,0.001630204,0.00459311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002838523,"about_ca_system_score_gemma":0.0001039447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00064337,"about_ca_topic_score_gemma":0.0003062875,"domain_scores_codex":[0.9933606,0.0005490831,0.000841365,0.002891425,0.001430546,0.0009269776],"domain_scores_gemma":[0.9931329,0.0001574271,0.0006022747,0.005472445,0.00003571254,0.0005992385],"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":[0.00001844338,0.0002998239,0.00002538964,0.0003939225,0.00008154222,0.0002329038,0.0004205577,0.000002252593,0.006714805,0.000002165334,0.9880455,0.003762766],"study_design_scores_gemma":[0.00127758,0.0002139193,0.0009909827,0.0003552623,0.0001773481,0.00008638797,0.0004144194,0.003486859,0.01345966,0.00001177972,0.9776558,0.001870035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006515776,0.0006527751,0.004101,0.002460688,0.0008123074,0.001498182,0.9899523,0.000431773,0.00002582424],"genre_scores_gemma":[0.00008714406,0.005455393,0.06035635,0.02607664,0.0006006482,0.0001156085,0.9072081,0.00007956273,0.00002058725],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08274423,"threshold_uncertainty_score":0.9992351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07833790267036615,"score_gpt":0.2997334305902876,"score_spread":0.2213955279199215,"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."}}