Monitoring and assessing global water quality - the GEMS-Water experience
Bibliographic record
Abstract
Evaluation and assessment of fresh and inland water quality at the regional and global scales is not a simple task. UNEPs GEMS/Water has operated a comprehensive freshwater quality monitoring and assessment programme for over 20 years and is the only such global programme. GEMS/Water operates by inviting national governments to provide water quality data from their water quality monitoring programmes. The data is then compiled into a global database, GLOWDAT, which is a value-added process. GEMS/Water, United Nations agencies and other international organizations use the data to undertake global and regional scale water quality assessments. More than 100 countries participate in the programme that has a database of 1.6 million data entries. Participating countries control, for example, the type of data collected, the location of sampling sites, the frequency of monitoring, the analytical and field methods used and the frequency at which data is transferred to GEMS/Water. In order to make effective water quality assessments, identify emerging water quality issues and environmental 'hotspots', the data available must be of good quality, comparable between countries for a specific parameter, be geographically representative for a given region and be up-to-date. The only way for GEMS/Water to ensure that all these characteristics are satisfied in GLOWDAT would be for GEMS/Water to operate its own global water quality-monitoring programme. This is economically unfeasible. However, GEMS/Water has an operational manual, a modular training course and operates a QA/QC programme to help countries with data quality. Some countries have modernized their water quality programme, a complex and comprehensive activity that includes legal and institutional considerations, technical issues, and a strategic program of capacity building. Implementation of such comprehensive programmes in more countries will lead to better quality data for GEMS/Water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".