On the δ<sup>18</sup>O, δD and D‐excess relations in meteoric precipitation and during equilibrium freezing: theoretical approach and field examples
Bibliographic record
Abstract
Abstract Analysis of the δD and δ18O composition of ice is commonly used to provide insight into the origin of ice bodies. However, studies have questioned the use of the co‐isotope relationship to differentiate ground ice types. This study reviews the principles of fractionations affecting δD, δ18O and deuterium excess (d) in meteoric precipitation and during equilibrium freezing of water under changing freezing rates. Traditionally, regression slope values (S D‐18O) between δD and δ18O of less than 6 have been used to suggest that ground ice was formed by freezing of liquid water but here it is shown that S D‐18O values of less than 7.3 can be suggestive of freezing under equilibrium conditions. This maximum freezing S D‐18O value falls within the range of many local meteoric water lines at sites in the Arctic, which can complicate proper identification of subsurface ice types. Many studies are starting to use the calculation of d to infer the origin of subsurface ice. However, d values do not provide much information on the origin of subsurface ice, as d is dependent on freezing conditions. To make proper use of d, its relation with D needs to be investigated, with no relation reflecting meteoric precipitation and a negative relation indicative of freezing. In all cases, it is recommended that stable O‐H isotope measurements be supported by additional distinguishing tools (i.e. entrapped gases) when attempting to infer subsurface ice types. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".