Using Stable Water Isotope Composition (δ<sup>18</sup>O and δ<sup>2</sup>H) to Track the Interannual Responses of Arctic and Tropical Andean Water Bodies to Rising Air Temperatures
Bibliographic record
Abstract
Abstract Lakes in the Arctic and tropical Andes are experiencing some of the largest temperature increases on the planet with coeval marked limnological changes, but little data exist on water balance parameters from these regions. Here, we present a unique data set of water stable isotope composition (δ18O and δ2H) from a suite of 49 water bodies in the Canadian Arctic (Resolute Bay, Cornwallis Island, and Cape Herschel, Ellesmere Island) and the tropical Andes (Cajas National Park, Ecuador) spanning various years from 2009 to 2016. We show that an increase in air temperature over the study period resulted in evaporative enrichment of water isotopes in most Arctic sites highlighting the significance of evaporative losses to small Arctic ponds during the prolonged ice‐free summers now experienced in this part of the world. Exceptions include some Arctic waterbodies that received abundant snowmelt and large, ice‐covered lakes less prone to evaporation. Data from the Andean lakes indicated evaporative effects were minimal due to abundant precipitation. These data, in combination with limnological records and paleolimnological research from each region, provide a holistic view on how freshwater ecosystems are responding to recent warming in climatically sensitive Arctic and Andean environments.
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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 | 0.001 | 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".