{"id":"W33085455","doi":"10.1021/acs.analchem.0c03127","title":"Estimating Density Contrast From Global Geopotential Fields","year":2004,"lang":"en","type":"article","venue":"AGU Spring Meeting Abstracts","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Geopotential; Contrast (vision); Geopotential height; Density contrast; Geodesy; Geology; Mathematics; Econometrics; Meteorology; Computer science; Geography; Artificial intelligence; Precipitation; Physics","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":[0.0001775827,0.0002330549,0.0002288438,0.002359892,0.0001309197,0.0003955194,0.0002043666,0.0002136018,0.001292881],"category_scores_gemma":[0.0008260428,0.0001353383,0.0001434009,0.001242932,0.0001135822,0.0005219111,0.0003882787,0.0001494979,0.0003266204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002574786,"about_ca_system_score_gemma":0.0001602847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005475549,"about_ca_topic_score_gemma":0.009197504,"domain_scores_codex":[0.999926,0.00001007355,0.00000310758,0.00001998993,0.00002477621,0.00001603787],"domain_scores_gemma":[0.9998636,0.00005350342,0.00001976316,0.00001296592,0.0000351192,0.00001497678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006034328,0.0001305626,0.4518515,0.0002089561,0.0002434948,0.0004043569,0.0003574386,0.05647575,0.1303232,0.004078841,0.003497236,0.3518252],"study_design_scores_gemma":[0.00004316551,0.00007505804,0.6637402,0.000029129,0.00009700874,0.0005185207,0.0003706482,0.3065925,0.02149102,0.003025185,0.003963384,0.00005439092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9447646,0.0003628461,0.04607691,0.00006307583,0.00002068695,0.00003860996,0.002231588,0.0008480386,0.00559368],"genre_scores_gemma":[0.9867623,0.0001216317,0.01152228,0.00000723561,0.00001195162,0.00001081819,0.001172404,0.00004353685,0.0003477669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005475549,"threshold_uncertainty_score":0.01088738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005206838478956811,"score_gpt":0.1970382730464521,"score_spread":0.1918314345674952,"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."}}