The Impact of Geostrophic Transport on the Temporal and Spatial Structure of Wind-Driven Coastal Upwelling/Downwelling over the Persian Gulf
Bibliographic record
Abstract
Geostrophic transport can affect the structure of the wind-driven coastal upwelling/downwelling. Focusing on quantifying this impact is vital to understanding circulation dynamics in the Persian Gulf. To this end, in this study, after the investigation of wind patterns, the temporal and spatial structure of coastal upwelling/downwelling using the Ekman transport upwelling index, and the intra-annual vertical variability of temperature are investigated based on the daily wind, and monthly temperature data time series of 28 years (1993–2020). Then, the geostrophic transport using an improved methodology and the total cross-shore transport as a sum of Ekman and geostrophic transport are estimated based on the monthly SLA data time series. The results indicated that the region, located around 51.5 and 28 (48 and 29 and 50.5 and 25.5) experienced the most intense coastal upwelling (downwelling) at a peak in June with larger mixed and thermocline layers than other regions. The intensity of Ekman transport is higher than the geostrophic transport in the Persian Gulf due to the presence of the prevailing wind and the shallowness of the mixed layers’ depth. We found that the intensity of the coastal upwelling (downwelling) decreases (increases) under favorable spatial and temporal conditions by considering the geostrophic transport in the upwelling index.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".