A coupled Bayesian and fault tree methodology to assess future groundwater conditions in light of climate change
Bibliographic record
Abstract
Abstract. Maintaining acceptable groundwater levels, particularly in arid areas, while protecting ecosystems, are key measures against desertification. Due to complicated hydrological processes and their inherent uncertainties, investigations of groundwater recharge conditions are challenging, particularly in arid areas under climate changing conditions. To assist planning to protect against desertification, a fault tree methodology, in conjunction with fuzzy logic and Bayesian data mining, are applied to Minqin Oasis, a highly vulnerable regime in northern China. A set of risk factors is employed within the fault tree framework, with fuzzy logic translating qualitative risk data into probabilities. Bayesian data mining is used to quantify the contribution of each risk factor to the final aggregated risk. The implications of both historical and future climate trends are employed for temperature, precipitation and potential evapotranspiration (PET) to assess water table changes under various future scenarios. The findings indicate that water table levels will continue to drop at the rate of 0.6 m yr−1 in the future when climatic effects alone are considered, if agricultural and industrial production capacity remain at 2004 levels.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".