Are precipitation characteristics and patterns impacting oak trees decline in the Zagros region of western Iran?
Bibliographic record
Abstract
The objective was to investigate if changes in annual, monthly, and seasonal precipitation are associated with emergence of declining oak trees in Iran. Daily precipitation data were obtained from 20 synoptic stations distributed over the Zagros area from 1988-2019. Non-parametric Mann-Kendall (MK) test and Sen's Slope estimator (Qmed value) were applied to identify significant trends in the precipitation data. ‘‘De Martonne’’ climate classification (i.e., De Martonne aridity index (IDM) was used for climate classification. Although most stations showed decreasing trends in annual precipitation during the studied period (1988-2019), these trends were statistically significant at only two stations. The mean number of events per year pre- and post- oak decline was not significantly different (68 events before against 71 events after decline). Most of the annual precipitation in the Zagros region falls in winter and spring (80% in total). However, this ratio decreased after the year 2000 by 6% (not significant) compared with before. The difference between the average annual precipitation, before (1988-2000) and after (2000-2019) the emergence of the oak decline phenomenon, were not statistically significant in any of the climate types (semi-arid: 406 mm vs. 378 mm), Mediterranean (530 mm vs. 489 mm), and humid (924 mm vs. 912 mm) as well as in whole Zagros region (537 mm vs. 508 mm). Although our data suggested insignificant trends in precipitation for most stations, future research should investigate if rising temperature in the Zagros area has resulted in higher evaporation and drier soil thereby accelerating the oak tree decline.
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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".