Bibliographic record
Abstract
Water, as vital natural resource, is indispensable for human activities, both directly and indirectly. It significantly contributes to a country’s economic development, encompassing above-ground and underground water resources. However, ongoing pollution from surface contaminants is causing concerning degradation in both confined and unconfined aquifers, warranting the need for addressing this issue. Water quality indices (WQIs) serve this purpose by simplifying complex water quality data, providing a single value for easier interpretation. Surface water quality indices have achieved global recognition, while the development of groundwater quality indices is an evolving field. WQIs are established based on specific water quality criteria set by national and international organizations, which consider various parameters based on the intended use of water bodies. Consequently, numerous WQI models exist, including National Sanitation Foundation (NSFWQI), Oregon (OWQI), British Columbia (BCWQI), Canadian Council of Ministers of the Environments (CCMEWQI), and country-specific variants tailored to the unique requirements of individual regions such as Vietnam, India, Indonesia, Spain, Canada, Malaysia, and others, all in accordance with the specific characteristics of the water system under assessment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".