Temporal-Spatial Distribution of Stable Isotopes in Precipitation and Its Relationship with ENSO over the North America
Bibliographic record
Abstract
The characteristics of temporal-spatial distribution of δ~(18)O in precipitation and its relationships with temperature,precipitation amount and ENSO were analyzed in this study.The analyses show that the latitudinal distribution of the mean δ~(18)O in precipitation is very remarkable in the North America,regardless over lands or oceans.The mean δ~(18)O differences between continent and ocean are less besides high latitudes.With increasing latitude,the δ~(18)O in precipitation decreases quickly.It is found that the temperature effect appears over the whole North America continent,and becomes much more marked with increasing latitude.The variations of extension and intensity characterize a distributional difference of temperature effect on different seasons.The amount effect happens mainly in low-latitude oceans,east coast of the low-middle-latitude Pacific Ocean and northwest coast of Gulf Stream.However,no amount effect in inland.Similarly,the variations of extension and intensity characterize also a distributional difference of amount effect on different seasons.In inland and high latitudes of the North America,the Δδ~(18)O displays greater positive value corresponding to the distinct temperature effect;in low-latitude oceans,it does smaller or a negative value;and in the same latitudes,Δδ~(18)O in land is markedly greater than that in ocean.Furthermore,there are distinct positive correlations between SST in Nino-4 and δ~(18)O in precipitation at Ottawa and Midway Island that stand for continental and oceanic situations respectively,in which,the continuous correlations between the precipitation δ~(18)O in May and SST in Nioo-4 are remarkable,showing that the strong signal form ENSO has important impact on the variations of stable isotopes in precipitation of the land and the ocean in this period.
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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 | 0.001 |
| 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 | 0.000 |
| 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".