{"id":"W2113489110","doi":"10.1175/jamc-d-11-0155.1","title":"Regression-Guided Clustering: A Semisupervised Method for Circulation-to-Environment Synoptic Classification","year":2011,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Climate variability and models","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Cluster analysis; Weighting; Computer science; Regression; Multivariate statistics; Linear regression; Regression analysis; Atmospheric circulation; Scale (ratio); Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003015791,0.0009077127,0.0009268937,0.002460169,0.0008146558,0.001067548,0.002498949,0.001196244,0.001519339],"category_scores_gemma":[0.007224887,0.0005281599,0.0009462098,0.002199634,0.0007201593,0.0009979598,0.001146668,0.001124739,0.001187813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008499305,"about_ca_system_score_gemma":0.001486671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0063748,"about_ca_topic_score_gemma":0.008996815,"domain_scores_codex":[0.9980989,0.0008265778,0.0001187476,0.0004924933,0.0003745686,0.00008873143],"domain_scores_gemma":[0.9960567,0.001562211,0.000497323,0.0006555688,0.001133048,0.0000952489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000263479,0.0003131739,0.003990778,0.0002178155,0.0003747575,0.0001391494,0.0003887743,0.4130859,0.01128901,0.01820768,0.01615833,0.5355711],"study_design_scores_gemma":[0.000009032789,0.00001029138,0.0004029973,0.000007543566,0.000008139926,0.00001956773,0.00001610954,0.9910222,0.001066754,0.006270974,0.001155602,0.0000107882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00523009,0.00008647943,0.9927264,0.00007591401,0.0000178441,0.00007587058,0.0001553687,0.001285241,0.0003466611],"genre_scores_gemma":[0.1142897,0.0000882885,0.8818939,0.000102583,0.00008133518,0.0003927784,0.001260683,0.000448384,0.001442169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0063748,"threshold_uncertainty_score":0.01594919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06085980069558818,"score_gpt":0.2943756450360039,"score_spread":0.2335158443404158,"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."}}