Predicting Potential Sites of Covered Karstification
Bibliographic record
Abstract
Our aim was the prediction of karstification. We measured the karstic bedrock and the overlying superficial cover in four areas of Hungary and one area in Romania. One of our tools was the widely used geophysical techniques i.e. VES and multi-electrode method. We also made observations on mountainous, Mediterranean and tropical karsts. In these areas the occurrence of covered karsts, of either syngenetic or postgenetic type, is high. Based on the measured data we determined the conditions under which covered karst formation is possible. For example the conditions that induce syngenetic karstification are: cavities, caves, and shafts within the bedrock, places where the superficial cover is locally thinner, or places where the impermeable beds edge out. An indicator of postgenetic karstification is the presence of lenticular intercalations in the superficial cover (sites of former dolines). Knowing these conditions in any karst area we can readily identify the potential sites where covered karst formation is possible in the near future. If these sites are known, engineering structures can be planned so that potential dangers due to karstification are avoided.
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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 | 0.000 |
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 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".