Monitoring and Modeling Long-term Environmental Influences on a Shallow Excavation Using Machine Learning: Case Study of Tomb TT95 in the Theban Necropolis, Luxor, Egypt
Bibliographic record
Abstract
Exposure of rock in thermal cycling has been linked to reduction in intact rock sample strength at a laboratory scale and has been characterized as a potential triggering mechanism for rock falls. However, the long-term effect of climatic fluctuations on shallow rock excavations still remains unknown. This research investigates the long-term effects of climatic fluctuations on shallow rock excavations by monitoring a damaged pillar in TT95, an underground funerary chapel in Theban Necropolis, Luxor, Egypt. Using two orthogonally placed extensometers, relative displacements of the pillar were measured alongside temperature and humidity. Findings indicate that pillar displacements correlate with seasonal temperature changes, showing a 0.02 mm/year drift. Data-driven CNN models were developed for forecasting, with one extensometer model achieving high accuracy (R²: 0.98) and the other performing poorly (R²: -0.2). The study suggests that continued pillar drift could cause loose rock blocks on the chapel ceiling to detach over time.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".