Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Informed analysis of policies related to food security, global climate change, wetland ecology, environmental nutrient flux, element cycling, groundwater weathering, continental denudation, human health, and others depends to a large extent on quantitative estimates of solute mass fluxes into and out of all global element pools including the enigmatic global aquifer systems. Herein for the first time, we proffer the mean global solute concentration of all major and selected minor and trace solutes in the active groundwater that represents 99% of liquid fresh water on Earth. Concentrations in this significant element pool have yielded to a geospatial machine learning kNN-nearest neighbors' algorithm with numerous geospatial predictors utilizing a large new lithology/climate/aquifer age/elevation based solute database. The predicted concentrations are consistent with traditional solute ratios, concentrations, and thermodynamic saturation indices.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9796178 | PMC |
http://dx.doi.org/10.1111/gwat.13205 | DOI Listing |
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