Achieving Sustainable Development Goals from a Water Perspective
Bibliographic record
Abstract
Efforts to meet human water needs only at local scales may cause negative environmental externality and stress on the water system at regional and global scales. Hence, assessing SDG targets requires a broad and in-depth knowledge of the global to local dynamics of water availability and use. Further, Interconnection and trade-offs between different SDG targets may lead to sub-optimal or even adverse outcome if the set of actions are not properly pre-designed considering such interlinkages. Thus scientific research and evidence have a role to play in facilitating the implementation of SDGs through assessments and policy engagement from global to local scales. The paper addresses some of these challenges related to implementation and monitoring the targets of the Sustainable Development Goals from a water perspective, based on the key findings of a conference organised in 2015 with the focus on three essential aspects of SDGs- indicators, interlinkages and implementation. The paper discusses that indicators should not be too simple but ultimately deliver sustainability measures. The paper finds that remote sensing and earth observation technologies can play a key role in supporting the monitoring of water targets. It also recognises that implementing SDGs is a societal process of development, and there is need to link how SDGs relate to public benefits and communicate this to the broader public.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".