Data for: Stable isotope-inferred hydrology of ponds created by the Mount St. Helens eruption
Bibliographic record
Abstract
Freshwater ponds are prevalent globally and provide critical ecosystem functions (e.g., water storage and groundwater recharge), yet little is known of their hydrological features immediately following formation. We analysed stable isotopes of water (δ2H, δ18O) to characterize spatio-temporal hydrologic variation in ponds created by the Mount St. Helens eruption. We also examined how climate and landscape features interact to regulate local hydrology. Ponds were sampled for isotopic analysis in spring (2015, 2017, 2018) and summer (2018). Mass balance models characterized the water balance of ponds (evaporation: inflow; E:I), as well as water sources (rain, snow). Other variables were measured in situ (temperature, conductance), collected from data sources (meteorology), or quantified with remote sensing (vegetation). Bayesian estimates of standard ellipse areas (SEAB) were used to compare isotopic values among years, whereas linear models were used to examine local and regional drivers of E:I, as well as intra-annual isotopic shifts. We observed high inter-annual variability (as SEAB), suggesting that snow was the main water source in wet years, but that the proportion of rain and snow varied among sites in dry years. Spring E:I was negatively correlated with total precipitation, whereas the importance of evaporation in summer varied with pond morphology, with large shallow ponds exhibiting the greatest evaporation. Evaporation regulated the hydrology of ponds with higher dissolved organic carbon (DOC; as residuals of DOC and chlorophyll). We show that simple metrics of basin morphometry can predict seasonal variability in pond hydrology, allowing managers to better estimate pond sensitivity to future climate conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".