Bibliographic record
Abstract
Abstract Karst aquifers are some of the most important and well-used sources of water worldwide. The tapping of karst waters for use as drinking water has been important in the historical and economic development of many karst regions. Recent studies have found that karstified rocks and aquifer systems cover c. 15% of the Earth's ice-free land. The greatest area of karst outcrops (>1 × 10 6 km 2 ) is in Russia, the USA, China and Canada. In the Mediterranean basin, groundwater is generally more abundant in karst than in other aquifers and has been extensively exploited. Karst groundwater is also widely used in the Middle East, China, North America, and northern and eastern Africa and is of crucial importance for the sustainable development of tourism and the economy. Karst aquifers currently supply c. 10% of the global population with drinking water and, in some zones, they are the only water resource available. However, the share of karst aquifers in the global supply of water will decrease with the predicted increase in population, concentrated in urban areas, and improvements in treatment technologies for water from other sources.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".