{"id":"W4393723948","doi":"10.5281/zenodo.7255758","title":"Numerical Model Generated Hawaii Test Scenes for EarthCARE Pre-launch Studies - Part 2: Hydrometeor and Aerosol Properties","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aerosol; Remote sensing; Meteorology; Environmental science; Test (biology); Atmospheric sciences; Aerospace engineering; Computer science; Geography; Geology; Engineering","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":[0.0003067311,0.0009326654,0.0004696336,0.0006521415,0.0003663363,0.0005907547,0.001581634,0.0006411187,0.00987234],"category_scores_gemma":[0.0008796212,0.0002815497,0.0007635524,0.001067309,0.0002405981,0.0006352382,0.0004484874,0.0009456947,0.003338997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353286,"about_ca_system_score_gemma":0.001536274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1647591,"about_ca_topic_score_gemma":0.1980819,"domain_scores_codex":[0.9998251,0.0000204165,0.00001031037,0.0000544683,0.00004913701,0.00004055101],"domain_scores_gemma":[0.999542,0.00004982706,0.00002501005,0.00007659844,0.0002518286,0.00005483894],"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":[0.0007089215,0.0007069635,0.04367685,0.0006564848,0.00043288,0.000352769,0.0002051666,0.432959,0.008425559,0.002979654,0.4830883,0.0258074],"study_design_scores_gemma":[0.000585695,0.0001255506,0.04213691,0.00009199016,0.0001096725,0.00009810697,0.0003965165,0.8225823,0.008152247,0.002060958,0.1234894,0.0001706683],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1593679,0.0002747568,0.008657716,0.0005244117,0.0004468802,0.0003053851,0.8080916,0.007952279,0.01437891],"genre_scores_gemma":[0.1994224,0.0001094827,0.01190675,0.00009660555,0.00003843681,0.0002739072,0.7848773,0.0006209816,0.002654122],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1647591,"threshold_uncertainty_score":0.3276002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07890331464514341,"score_gpt":0.2620000496480012,"score_spread":0.1830967350028578,"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."}}