Warmer–wetter climate drives shift in δ<i>D</i>–δ<sup>18</sup>O composition of precipitation across the Queen Elizabeth Islands, Arctic Canada
Bibliographic record
Abstract
We examine how recent increases in air temperature and precipitation, together with reductions in sea ice extent, may have affected the regional δD–δ18O composition of precipitation. In spring 2014, 80 snow samples were collected from six glaciers and ice caps across the Queen Elizabeth Islands, and in 2009 and 2014, two shallow ice cores were collected from Agassiz Ice Cap and White Glacier, respectively. The snow samples showed average δ18O values from 2013 to 2014 to be approximately 2‰–3‰ higher than those recorded in 1973–1974 in nearby locations, with the ice cores showing similar trends in δ18O values. A zonal average water isotope model was used to help understand the causes of the increased δ18O values, using inputs calibrated for observed changes in temperature, vapour flux, and sea ice extent. Model results indicate that atmospheric temperature changes account for <1‰ of the observed change in δ18O values, and that changes in local water input and precipitation driven by changes in sea ice extent only have an effect in coastal regions. Enhanced meridional vapour flux to the Queen Elizabeth Islands is, therefore, also required to explain the observed increases in δ18O values, with fluxes ∼7% higher today than in the 1970s, consistent with the change in precipitation.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".