The unbearable fuzziness of being sustainable: an integrated, fuzzy logic-based aquifer health index
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.008 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it