Seismic Settlement Evaluation of the Nihal Atakas Mosque after the 2023 Kahramanmaras, Turkiye, Earthquakes
Bibliographic record
Abstract
February 6, 2023, Kahramanmaras earthquake sequence caused significant damage in the southern and south-eastern parts of Turkey. Two subsequent reconnaissance campaigns, participated by the leading author, revealed evidence of seismic-induced liquefaction in the City of Iskenderun. Furthermore, widespread damage in terms of seismic-induced settlements and tilting of the buildings was observed. The recently built, iconic Nihal Atakas Mosque, which is founded on reclaimed land at the coast of Iskenderun, experienced some damage but was repaired and reopened to the community shortly after the earthquakes. The areas surrounding the mosque, however, settled up to 0.30 m, and lateral spreading-induced horizontal displacements were observed at a similar order of magnitude. In this study, empirical liquefaction-induced free-field reconsolidation settlement models are used to calculate the seismic-induced settlements, and the results are compared against the measured counterparts. The comparison indicates that the simplified models estimate settlements within a factor of 2 of the measured settlements.
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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 | 0.000 |
| 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".