{"id":"W2092322905","doi":"10.1023/b:enmo.0000049391.27372.99","title":"Monthly Mean Afternoon Mixing-Layer Depths “Tuned” to the Eco-Climatic Regions of the Canadian Prairie Provinces","year":2004,"lang":"en","type":"article","venue":"Environmental Modeling & Assessment","topic":"Climate variability and models","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Hydrographic Service","funders":"","keywords":"Radiosonde; Environmental science; Terrain; Vegetation (pathology); Boundary layer; Climatology; Mixing (physics); Atmospheric sciences; Planetary boundary layer; Humidity; Meteorology; Geography; Geology; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006361782,0.0002873976,0.0002297522,0.00004439694,0.0007234162,0.00007128553,0.000772528,0.0001002219,0.0003379562],"category_scores_gemma":[0.00001937306,0.0001910639,0.0001651252,0.000165361,0.0002677486,0.0002016303,0.0005273349,0.0002903764,0.0001593383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002517653,"about_ca_system_score_gemma":0.0001361092,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08174732,"about_ca_topic_score_gemma":0.4222653,"domain_scores_codex":[0.9974726,0.0001232669,0.0004927997,0.000554651,0.0007814391,0.0005751883],"domain_scores_gemma":[0.9986339,0.00004515101,0.0001119651,0.0009173569,0.000003957834,0.000287725],"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":[0.000009681107,0.0002133391,0.01467979,0.00001235998,0.00002377195,0.00000289405,0.002237171,0.9777403,0.004117702,0.0004560084,0.0001334342,0.0003734922],"study_design_scores_gemma":[0.002353575,0.0007751388,0.1318412,0.0004640272,0.0004307844,0.00003523034,0.005652951,0.8129475,0.007224962,0.02838876,0.007917284,0.001968519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785103,0.00002989918,0.005360165,0.009184078,0.0002055916,0.001294582,0.00008852719,0.00002655283,0.005300262],"genre_scores_gemma":[0.9948537,0.00001155782,0.003819465,0.0009247966,0.00003650939,0.0001647791,0.00001700681,0.00003388212,0.0001383167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3405179,"threshold_uncertainty_score":0.9243674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207374451233132,"score_gpt":0.2483844408172931,"score_spread":0.2276469956939799,"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."}}