Global prediction of <i>δ</i><sub>A</sub> and <i>δ</i><sup>2</sup>H‐<i>δ</i><sup>18</sup>O evaporation slopes for lakes and soil water accounting for seasonality
Bibliographic record
Abstract
Global trends in the δ 2 H‐ δ 18 O enrichment slope of continental lakes and shallow soil water undergoing natural evaporation are predicted on the basis of a steady state isotope balance model using basic monthly climate data (i.e., temperature and humidity), isotopes in precipitation data, and a simple equilibrium liquid‐vapor model to estimate isotopes in atmospheric moisture. The approach, which demonstrates the extension of well‐known conceptual models in stable isotope hydrology to the global scale, is intended to serve as a baseline reference for evaluating field‐based isotope measurements of vapor, surface water, and soil water and as a diagnostic tool for more complex ecosystem models, including isotope‐equipped climate models. Our simulations reproduce the observed local evaporation line slopes (4–5 range for lakes and 2–3 range for soil water) for South America, Africa, Australia, and Europe. A systematic increase in slopes (5–8 range for lakes) toward the high latitudes is also predicted for lakes and soil water in northern North America, Asia, and Antarctica illustrating a latitudinal (mainly seasonality‐related) control on the evaporation signals that has not been widely reported. The over‐riding control on the poleward steepening of the local evaporation lines is found to be the isotopic separation between evaporation‐flux‐weighted atmospheric moisture and annual precipitation, and to lesser extents temperature and humidity, all of which are influenced by enhanced seasonality in cold regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".