Application of stable water isotopes to quantify the water balance of Delta Marsh
Bibliographic record
Abstract
This thesis as a part of the rehabilitation project Restoring the Tradition at Delta Marsh, details stable water isotope research. Stable water isotopes are applied as a tool to quantify the water balance of the Marsh. A four-year sampling campaign for stable water isotopes was launched. Coupled with the hydraulic and hydrologic modelling, stable water isotopes assist in the understanding of contemporary water balance and the relative contribution of inflows to evaporative losses. Two hydrologically different years (2013 and 2014) are compared to offer insight into the Marsh functioning under differing climatic conditions. A contemporary isotopic framework was developed to determine correlation between end-members influencing Marsh hydrologic change. The framework has shown that the Marsh is not in hydrologic steady-state, which was previously confirmed by 2D hydraulic modelling. An isotope mass balance mixing model was established to evaluate evaporative loss (relative to inflows), and to determine residence time and water yield including marsh-lake dynamic interactions. Time series modelling was performed and confirmed that a time-dependent isotopic model is more suitable than a fraction-dependent model for modelling Marsh isotope composition. Evaporation is the most significant component in the water balance. The isotope mass balance model demonstrated that evaporation to inflow ratio was 22% in 2013 and 24% in 2014. Water residence time was found to be 99 and 140 days in 2013 and 2014, respectively. Water yield for both years was approximately 280 mm/year.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".