A New Assessment Framework for Transience in Hydrogeological Systems
Bibliographic record
Abstract
The importance of transience in the management of hydrogeologic systems is often uncertain. We propose a clear framework for determining the likely importance of transient behavior in groundwater systems in a management context. The framework incorporates information about aquifer hydraulics, hydrological drivers, and time scale of management. It is widely recognized that aquifers respond on different timescales to hydrological change and that hydrological drivers themselves, such as climate, are not stationary in time. We propose that in order to assess whether transient behavior is likely to be of practical importance, three factors need to be examined simultaneously: (1) aquifer response time, which can be expressed in terms of the response to a step hydrological change (τstep ) or periodic change (τcycle ); (2) temporal variation of the dominant hydrological drivers, such as dominant climatic systems in a region; (3) the management timescale and spatial scale of interest. Graphical tools have been developed to examine these factors in conjunction, and assess how important transient behavior is likely to be in response to particular hydrological drivers, and thus which drivers are most likely to induce transience in a specified management timeframe. The method is demonstrated using two case studies; a local system that responds rapidly and is managed on yearly to decadal timeframes and a regional system that exhibits highly delayed responses and was until recently being assessed as a high level nuclear waste repository site. Any practical groundwater resource problem can easily be examined using the proposed framework.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".