Interpreting H<sub>2</sub>O isotope variations in high‐altitude ice cores using a cyclone model
Bibliographic record
Abstract
Vertical profiles of isotope (δ18O or δD) values versus altitude (z) from sea level to high altitude provide a link to cyclones, which impact most ice core sites. Cyclonic structure variations cause anomalous variations in ice core δ time series which may obscure the basic temperature signal. Only one site (Mount Logan, Yukon) provides a complete δ versus z profile generated solely from data. At other sites, such a profile has to be constructed by supplementing field data. This requires using the so‐called isotopic or δ thermometer which relates δ to a reference temperature (T). The construction of gapped sections of δ versus z curves requires assuming a typical atmospheric lapse rate (dT/dz), where T is air temperature, and using the slope (dδ/dT) of a site‐derived δ thermometer to calculate dδ/dz. Using a three‐layer model of a cyclone, examples are given to show geometrically how changes in the thickness of the middle, mixed layer leads to the appearance of anomalous δ values in time series (producing decalibration of the δ thermometer there). The results indicate that restrictions apply to the use of the δ thermometer in ice core paleothermometry, according to site altitude, regional meteorology, and climate state.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".