Demonstration of a value of information metric to assess the use of geophysical data for a groundwater application
Bibliographic record
Abstract
ABSTRACT Effective groundwater management requires that decision makers choose strategies for the allocation and conservation of water resources that satisfy the objectives of, and draw support from, multiple stakeholders with complex and often contradictory value judgments. We demonstrate a value of information (VOI) approach to assess the benefits of acquiring geophysical data as part of a groundwater management strategy in light of data uncertainty. VOI is a method for determining if the acquisition of information improves a decision maker’s ability to select an optimal action given value judgments, risk tolerance, and anticipated consequences of the action. As a practical example we examine the uncertainty associated with time-domain electromagnetic (TDEM) data and evaluate the contribution of these data to management of desalination operations in a coastal aquifer where there is a risk of contaminating freshwater resources. The reliability of TDEM data is evaluated using a forward modeling approach to calculate data sensitivity to parameters of interest in the decision analysis. We evaluate the value added by acquiring uncertain data using a standard VOI approach. The analysis shows additional geophysical information could improve groundwater managers’ ability to make decisions about desalination operations and quantifies the contribution of geophysical data to the management decision. We note several measures that can be taken to improve estimates of the data reliability as well as the valuation of alternative actions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".