Bibliographic record
Abstract
We appreciate Lee Trotta’s thoughtful contribution to the discussion of ground water vulnerability (Trotta 2007, this issue). Discussions such as this can only help raise the awareness of hydrogeologists of the important role that concepts of well and aquifer vulnerability have to play in the protection of our ground water resources. We thank Trotta for bringing some interesting government publications to our attention. In our attempt to be as comprehensive as possible in our review of different published methodologies for assessing well and aquifer vulnerability, we did not encounter many of the references mentioned by Trotta. They were certainly not ignored; we were simply not aware of them. We agree that government publications generally adhere to the highest professional standards, but unfortunately, as Trotta points out, they may be hard to find (they are not available at our university library). In any case, these pioneering papers should form part of the history of ground water vulnerability. The Wisconsin project appears to be particularly relevant as the approach for determining aquifer vulnerability seems to be physically based, if we understand Trotta correctly. This would likely make it the first effort using this approach. Applying the concept to aquifer vulnerability, the quantity sought would be the travel time along a vertical path from a source to the water table. In our paper (Frind et al. 2006), we extend this basic concept to the total travel time from a contaminant source to a well along a three-dimensional travel path, taking into account all hydrogeological processes along the path, including advection and dispersion, and we express well vulnerability in terms of the time taken for the contaminant to reach the well, as well as the concentration of the arriving contaminant. The concept applies to both known and unknown sources. This approach has little in common with index-based methods such as DRASTIC, and it serves a different purpose. Our comparison between physically based methods and index methods (Frind et al. 2004) used a subwatershed containing a glacial moraine, of a size much smaller than the state of Wisconsin. The index method applied in that example is presently being promoted by the Ministry of the Environment of Ontario. Identical data were used in both methods. The dramatic differences showed that this particular index method is not well suited for representing highly complex systems such as glacial moraines. In less complex aquifer systems, physical- and index-based methods may give similar results.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.008 |
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".