{"id":"W4382585254","doi":"10.1016/j.heliyon.2023.e17604","title":"Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Global Affairs Canada; African Institute for Mathematical Sciences; National Research Foundation; United States Agency for International Development; International Development Research Centre; Division of Mathematical Sciences; Ministry of Education, Science and Technology; Government of Canada; Defence Science Institute; Department of Science and Innovation, South Africa; Botswana International University of Science and Technology","keywords":"Seasonality; Environmental science; Rain gauge; Satellite; Climatology; Precipitation; Meteorology; Identification (biology); Statistics; Geography; Mathematics; Geology; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008964421,0.00004481592,0.00007942632,0.0000556721,0.00008791307,0.00001680447,0.00009356494,0.00002710339,0.00004775796],"category_scores_gemma":[0.0002982361,0.00003128453,0.00002866408,0.0002800092,0.00004451887,0.0001186384,0.000006278464,0.00002810558,0.000004333445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001551453,"about_ca_system_score_gemma":0.0000201693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001234953,"about_ca_topic_score_gemma":0.01243591,"domain_scores_codex":[0.9993897,0.00007226694,0.0001520678,0.0001199528,0.0001996752,0.00006634275],"domain_scores_gemma":[0.9990867,0.0005377848,0.000153737,0.0001435827,0.00005918527,0.00001900492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007929149,0.00000509372,0.6022962,0.0001135021,0.00002104701,3.680292e-8,0.0001219548,0.00101871,0.0009994617,0.000001031347,0.00001092225,0.3953328],"study_design_scores_gemma":[0.0001628453,0.00004031373,0.8044088,0.00003823434,0.00004734498,9.203467e-8,0.0001236578,0.1905637,0.003798401,0.00003805954,0.0007450728,0.00003343206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805326,0.00190985,0.01642536,0.0002279658,0.0003536189,0.0002107183,0.0002545217,0.00002538125,0.00005999869],"genre_scores_gemma":[0.9985572,0.000636962,0.00005462263,0.00001736554,0.00005067672,0.000001485509,0.0006503263,0.000001238095,0.00003017772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3952993,"threshold_uncertainty_score":0.6939532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0884609425732562,"score_gpt":0.2741101766156084,"score_spread":0.1856492340423522,"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."}}