Bibliographic record
Abstract
Abstract. This study presents an ensemble of atmosphere-ocean coupled Regional Climate Model (RCM) simulations for the Mid-Holocene (MH) climate across the Middle East, Mediterranean and North Africa (MENA) regions. This ensemble is generated using the Weather Research and Forecast (WRF) model with online coupling to the Regional Ocean Modelling System (ROMS) ocean model, which simulates the dynamics of the entire Mediterranean Sea, utilizing forcing derived from the Mid-Holocene (MH) climate generated from the UofT-CCSM4 General Circulation Model (GCM) with a prescribed Green Sahara (GS), and having a prescribed GS in the land surface in WRF, but allowing the land surface scheme in WRF to compute influence from GS. The results of this ensemble are characterized by an increased precipitation field, similar to previous results, and a 2 m surface temperature that is lower than those in results that have a fully prescribed GS in WRF. Further analysis determined that under the same GS prescription as in a previous study, the land surface scheme used in this study produces higher evaporation and a smaller change in albedo, jointly producing a lower 2 m surface temperature. This conclusion is supported by a new set of sensitivity experiments that modifies the prescribed land surface field, which also showed that the climate over the Middle East is sensitive to land surface states over northern Africa.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.310 | 0.192 |
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".