Progress on Ambient Water Quality Mid-term status of SDG Indicator 6.3.2 and acceleration needs, with a special focus on Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This report presents the latest results and findings from the 2023 data drive for SDG Indicator 6.3.2 on ambient water quality, and provides key messages on water quality monitoring and assessment and highlights acceleration needs. Indicator 6.3.2 is the proportion of water bodies with good ambient water quality, compared to national or subnational standards. The indicator is based on measurements of five water quality parameters that provide information on the most prevalent pressures on water quality at the global level and, over time, indicates whether efforts to “improve water quality” by 2030 are on track Water is vital to human and planetary health and the internationally agreed goals that back it, including the 2030 Agenda for Sustainable Development, the Kunming-Montreal Global Biodiversity Framework, the Sendai Framework and the Paris Agreement.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it