The unbearable fuzziness of being sustainable: an integrated, fuzzy logic-based aquifer health index
Bibliographic record
Abstract
We introduce a groundwater sustainability index offering a novel combination of features. It is holistic in the sense that it incorporates both water quantity and water quality indicators. The former employs the signal-to-noise ratio of long-term trends estimated via robust regression; the latter uses concentration of the primary contaminant of concern. A fuzzy inference system integrates these unlike metrics. The system also explicitly encodes expert knowledge and stakeholder values, and directly acknowledges subjectivity in environmental condition “grading,” through the use of linguistic rules and fuzzy sets, respectively. The fuzzy rule base is constructed such that poor environmental conditions captured by one measure are not hidden by good performance in another. A standard Mamdani (max–min) inference engine is used with centroid defuzzification. The outcome is an intuitively accessible index ranging from 0 to 100. The method is demonstrated using examples from the Abbotsford-Sumas aquifer, an important and managerially challenging transboundary (Canada–US) water resource. Editor D. Koutsoyiannis; Associate editor E. RozosCitation Fleming, S.W., Wong, C., and Graham, G., 2014. The unbearable fuzziness of being sustainable: an integrated, fuzzy logic-based aquifer health index. Hydrological Sciences Journal, 59 (6), 1154–1166. http://dx.doi.org/10.1080/02626667.2014.907496
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".