Tracking of Karst Contamination Using Alternative Monitoring Technologies: Hidden River Cave Kentucky
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
Karst groundwater contamination presents great challenges for efficient monitoring because of rapid, discrete transport and the diversity of contaminants. Here a low cost approach is described and applied to Hidden River Cave, Kentucky, where a long history of contamination has been experienced. Local knowledge was acquired through informal interviews and coupled with observations of contaminant residues, faunal distributions and fluorescence spectra in the cave. The resulting patterns were interpreted using Google Earth and Street View to identify specific contaminant sources in the affected sub-catchment of the cave. Despite success in matching contaminant sources with the contamination history and pattern, the informal nature of the investigation renders it unacceptable as the basis for any intervention. But such low cost studies will be needed for the majority of contamination occurrences where financial resources are very limited. A radical revision of our adversarial approach to environmental management will be required for such a change to occur.
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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 | 0.000 |
| 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 | 0.000 |
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