Bibliographic record
Abstract
The isotopic δ thermometer for snow is an equation that relates the isotopic δ value (derived from 18O/16O or D/H values) to some prescribed reference temperature. In contrast to the usual linear least squares fit of mean annual isotopic 〈δ〉 versus site mean annual surface temperature 〈Ts〉, a cubic equation is fitted to a composite, global, spatial (〈δ〉, 〈Ts〉) data set. A ±4‰ local variance along the curve indicates that δ values are influenced by factors other than the temperature signal, but in the long‐scale smoothing process these factors are filtered out. As 〈Ts〉 data over the complete range of raw temperatures are inhomogeneous, because of the existence of strong temperature inversions in the cold half, a transformation scheme is applied to homogenize the data. This produces a new series, 〈Tatm〉. These values are more likely to be near the mean condensation temperature (〈Tc〉) of the snow. The slope, d〈δ〉/d〈Tatm〉, as a quadratic function of 〈Tatm〉, best fits many published spatial and temporal slope determinations. The results have obvious application to the theory of isotopic processes not covered here. However, in an important practical application, the new equation can be utilized in the construction of diagrams of δ versus altitude where a δ value needs to be obtained from a known or calculated air temperature and where field data are gapped, especially at high altitude. Such diagrams can be used to demonstrate cases where site‐specific δ thermometers may become invalid because of atmospheric structural changes, as shown in an associated paper that will be briefly outlined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".