Water Stable Isotope Composition of Precipitations at Two Stations in Antananarivo-Madagascar: A Comparative Study
Bibliographic record
Abstract
In the “Global Network of Isotopes in Precipitation” database, Antananarivo has two distinct datasets from two stations. Thirty-four years separate the two datasets. This study aims on the one hand to depict the variations of the water stable isotopes composition of precipitations from the two stations and understand their origins, mainly in relation to meteorological factors. On the other hand, the Antananarivo data are compared with regional and international data to identify other sources of isotope composition variability in precipitation. Isotope records showed that after thirty-four-year gap, summer and winter (the two main seasons) precipitations are more enriched in heavy isotopes. The precipitation amount fluctuation would mostly contribute to this temporal variation. Opposite to summer and winter precipitations, inter-season rainfalls have similar isotope values after thirty-four years. The two stations are geographically close and the spatial aspect is therefore negligible since there are no latitude nor altitude effects on the isotope composition of precipitations. Regarding the second order parameter d-excess, the monthly mean values from both stations are higher than 100/00 and could indicate moisture recycling. The comparison with regional/international data showed that the isotope variability in precipitation is primarily due to precipitation amount effect, different moisture source, the stations distance from it and the change of meteorological factors along the moisture trajectory.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".