Muddy Waters: Refining the Way forward for the “Sustainability Science” of Socio-Hydrogeology
Bibliographic record
Abstract
The trouble with groundwater is that despite its critical importance to global water supplies, it frequently attracts insufficient management attention relative to more visible surface water sources, irrespective of regional climate, socioeconomic profile, and regulatory environment. To this end, the recently defined sub-discipline of “socio-hydrogeology”, an extension of socio-hydrology, seeks to translate and exchange knowledge with and between non-expert end-users, in addition to involving non-expert opinion and experience in hydrogeological investigations, thus emphasising a “bottom-up” methodology. It is widely acknowledged that issues pertaining to groundwater quality, groundwater quantity, climate change, and a poor general awareness and understanding of groundwater occurrence and movement are global in their scope. Moreover, while effective communication and engagement represent the key tenet of socio-hydrogeology, the authors consider that multiple actors should be identified and incorporated using stakeholder network analysis and may include policymakers, media and communications experts, mobile technology developers, and social scientists, to appropriately convey demographically focused bi-directional information, with the hydrogeological community representing the communication keystone. Accordingly, this article aims to highlight past and current work, elucidate key areas of development within socio-hydrogeology, and offer recommendations to ensure global efficacy of this increasingly important and growing field going forward. The authors seek to assist in protecting our global groundwater resource for future generations via an improved framework for understanding the interaction between communities and hydrogeological systems.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.063 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".