Bibliographic record
Abstract
Climate change in Jordan is investigated in this study. Daily data from 1990–2020 for the Amman area is used to monitor climate change in Jordan. The Expert Team Climate Change Detection and Indices (ETCCDI) software is utilized to test the extreme indices. The study shows general warming through the summer days (SU); number of summer days; the annual count of days when (TX); daily maximum temperature > 25° C, which increased by 3 days per decade. The annual maximum of daily maximum temperature (TXx) increased by 0.66 per decade. Most temperature indices (annual minimum of daily maximum temperature (TXn), percentages of days when daily max. TX > 90 th percentiles (TX90p), percentages of days when daily MIN TN. > 90 th (TN90P), etc.) show increasing trends during the study period. The precipitation indices (R10), the number of heavy precipitation days, the annual count of days when prec ≥10 mm, show a decreasing trend, while (R20), the number of very heavy precipitation days, the annual count of days when prec ≥20 mm shows a slight increasing trend. A Maksim generator is used to detect the difference between the reference period 1990–2020 and the output of three climatic models. The study revealed that the difference between the reference period and the results of climate models was 1.5–3.5 degrees C., using RCP 8.5 scenario in the year 2050. The difference was less in Scenario 4.5 and Scenario 2.6.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".