{"id":"W4410996627","doi":"10.1175/mwr-d-24-0090.1","title":"A Feature-Based Framework to Investigate Atmospheric Predictability","year":2025,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Deutsche Forschungsgemeinschaft","keywords":"Predictability; Feature (linguistics); Environmental science; Climatology; Computer science; Atmospheric models; Meteorology; Geology; Mathematics; Atmosphere (unit); Statistics; Geography","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":[0.00133973,0.0003880526,0.0005799925,0.002172989,0.0004885824,0.001170425,0.0008192897,0.0006617123,0.001096439],"category_scores_gemma":[0.004711017,0.0001963477,0.0007808064,0.001775657,0.0007882359,0.001283202,0.0009690218,0.0008494051,0.0001113468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006228999,"about_ca_system_score_gemma":0.0005105413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004995975,"about_ca_topic_score_gemma":0.003061622,"domain_scores_codex":[0.9995176,0.0002123652,0.00002503366,0.00009365808,0.0001016186,0.00004978328],"domain_scores_gemma":[0.9972724,0.001739082,0.0002986684,0.0002778739,0.0003212024,0.00009075851],"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":[0.000226503,0.0002299216,0.02192749,0.0001352751,0.0003558923,0.000457122,0.0002425383,0.7308188,0.0134263,0.1104041,0.002191219,0.1195848],"study_design_scores_gemma":[0.000004009063,0.00002205704,0.001874097,0.000004846463,0.0000126859,0.00002273781,0.00001614993,0.9777218,0.0003660347,0.01955716,0.0003907404,0.0000077801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07441344,0.000421052,0.9229504,0.0002842887,0.00003129579,0.00003405332,0.0003578885,0.0003288642,0.001178687],"genre_scores_gemma":[0.8470069,0.0002102152,0.1516898,0.00005333158,0.00009356699,0.00006910378,0.0003697309,0.00005035675,0.0004570298],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004995975,"threshold_uncertainty_score":0.00993377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103953434552947,"score_gpt":0.2658798652350494,"score_spread":0.25484033088952,"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."}}