{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001719863,0.00169548,0.00153517,0.001130652,0.0004631951,0.001104207,0.001696211,0.0008374869,0.006935708],"category_scores_gemma":[0.002066458,0.0005670521,0.001195832,0.00476892,0.0003052168,0.001464189,0.0009232004,0.001547143,0.006288746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173933,"about_ca_system_score_gemma":0.003575938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1081728,"about_ca_topic_score_gemma":0.07621273,"domain_scores_codex":[0.9994599,0.0000971411,0.00003629094,0.0001392601,0.0002072037,0.0000601396],"domain_scores_gemma":[0.9990576,0.00006003357,0.00008476771,0.0002293826,0.0004958758,0.00007228101],"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":[0.0004214847,0.0001533928,0.02267254,0.0008385609,0.0008962616,0.000107893,0.0001380919,0.05879857,0.004150776,0.004300329,0.8478416,0.05968046],"study_design_scores_gemma":[0.0007301834,0.00009843952,0.07452203,0.0001979795,0.0003464979,0.00007798304,0.0001159777,0.1101606,0.00685173,0.004753119,0.8019153,0.0002300175],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0144628,0.0003106528,0.01202975,0.0003734989,0.0004066825,0.0002286577,0.9594209,0.004585896,0.008181253],"genre_scores_gemma":[0.02648255,0.0003085296,0.01962362,0.00009778066,0.00007589811,0.0005261484,0.9491783,0.001158343,0.002548861],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1081728,"threshold_uncertainty_score":0.2150864,"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."}}