Monitoring the water balance of seepage lakes to track regional responses to an evolving climate
Bibliographic record
Abstract
Understanding the causes of large fluctuations in lake water levels is important for adaptive resource management. The relatively simple water budgets of small seepage lakes make them potentially useful model systems, provided that key water balance components can be well constrained. Here, spatial variability in measured rates of evaporation (E) and precipitation (P) at the whole lake scale was investigated, and the effect on daily and seasonal water balance estimates was quantified. To estimate spatial variability, triplicate sensor platforms were deployed on and near an 18 ha seepage lake. Lake stage (S) was monitored at a single node in the lake. The water balance was closed by estimating net groundwater seepage (Gnet) analytically as Gnet = ΔS – (P – E). Instrumentation on a second seepage lake was maintained by citizen scientists to assess the potential for more widespread sensor deployments. Data were collected at 30-minute time steps for six months. The results indicate that low-cost sensor networks with single nodes to measure E, P, and ΔS provide well-constrained water budgets at daily and seasonal time scales.
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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 | 0.000 | 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".