The exploratory dataset of isotopic composition of different water sources across Kazakhstan
Bibliographic record
Abstract
This work presents the dataset of stable water isotopes of oxygen and hydrogen measured in water samples from different sources (precipitation, surface water, groundwater, tap water) across Kazakhstan from 2017 to 2018 and from 2020 to 2023. The dataset includes results on isotopic composition of 399 water samples, namely precipitation: event-based (n=108), cumulative monthly (n=22); surface water: lakes, reservoirs, brooks, rivers, channels (n=175), groundwater: shallow and artesian groundwater, spring (n=85), tapwater (n=9). For each sample name of the source, location, latitude, longitude and date of sampling, measurement uncertainty (one standard deviation) are available. The samples were assessed by plotting the data in dual δ18O vs. δ2H isotope space with reference to values found in the published literature and fitting a linear regression equation for Astana (event) precipitation. Overall, this is the first dataset covering wide range of sources across Kazakhstan, which could be used by global and regional water resource assessments and studies such as tracing water sources, hydrograph separation and end-member analyses, isotope mass balance, evapotranspiration partitioning, residence time analysis and groundwater recharge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".