Isotope thermometry in melt‐affected ice cores
Bibliographic record
Abstract
A statistically significant relationship is observed between stable water isotopes (δ18O) and melt amounts in a melt‐affected firn core (SSummit) taken from the Prince of Wales Icefield, Ellesmere Island, Canada. By contrast, a low‐melt firn core taken from a higher‐elevation, higher‐latitude location on the same icefield shows no relationship between these variables. We interpret this as evidence for meltwater‐induced isotopic enrichment at SSummit. A percent melt–based correction slope is applied to isotopic values from SSummit. Uncorrected and corrected temperature records derived from the raw and corrected δ18O values are compared to bias‐corrected temperature data from the NCEP Reanalysis. Improvements are observed in the isotopic reconstruction of SSummit annual precipitation‐weighted temperatures when we correct for meltwater enrichment, with a reduction from +0.6°C to 0.0°C in the mean annual error and a decrease in root‐mean‐square error from 1.8°C to 1.6°C. The correction factor appears to overcorrect isotopic modification during high melt years such as 1999, during which SSummit experienced nearly 70% more melt than the average from 1975 to 2000. Excluding 1999 data from the correction analysis results in a slight reduction in mean absolute error from 1.4°C to 1.3°C. These results suggest that melt‐induced isotopic modification cannot be corrected in very high melt years.
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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 | 0.000 | 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".