{"id":"W3035925203","doi":"10.1007/978-3-030-50433-5_17","title":"On the Complementary Role of Data Assimilation and Machine Learning: An Example Derived from Air Quality Analysis","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Computer science; Covariance; Kalman filter; Statistics; Algorithm; Interpolation (computer graphics); Data assimilation; Mathematical optimization; Mathematics; Artificial intelligence","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":[0.0006531862,0.0004411821,0.0006511782,0.0005895135,0.0008309579,0.002240105,0.0008037906,0.001728942,0.002762199],"category_scores_gemma":[0.001979576,0.0002552898,0.0007647859,0.001657232,0.001745584,0.00212754,0.001503158,0.002039224,0.0004717114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006908479,"about_ca_system_score_gemma":0.0006773043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01246419,"about_ca_topic_score_gemma":0.01250277,"domain_scores_codex":[0.9997326,0.0001056961,0.00001164335,0.00003558697,0.00009088839,0.00002354582],"domain_scores_gemma":[0.9991068,0.0007018564,0.00002269603,0.00004852161,0.00010136,0.0000186966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007507859,0.00009771235,0.001653249,0.0002420891,0.00004527772,0.0009995654,0.0007511286,0.0257867,0.002895162,0.8638297,0.01112088,0.09250336],"study_design_scores_gemma":[0.0000167661,0.00004820623,0.002295213,0.0001051211,0.00003244299,0.0004558143,0.000426522,0.1337139,0.001556976,0.8114321,0.04987425,0.00004268368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05800125,0.02082476,0.6142227,0.01609944,0.001102956,0.00007942054,0.0002997224,0.0003111196,0.2890587],"genre_scores_gemma":[0.6278454,0.01698187,0.2883519,0.002414417,0.001146392,0.00006896479,0.0002832747,0.000255118,0.0626526],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01246419,"threshold_uncertainty_score":0.02478331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1254289210993772,"score_gpt":0.28466955005173,"score_spread":0.1592406289523528,"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."}}