Impact of climate on multi-wythe stone masonry walls
Bibliographic record
Abstract
The Hudson Bay Trading Company constructed the Prince of Wales Fort in the early eighteenth century with the goal of securing the fur trade in northern Canada. As a result of the fort's northern latitude the walls remained partially frozen throughout much of each year until recently. Warming in the climate has raised the average yearly temperature so freeze–thaw cycles have caused a breakdown of the mortar within the stone masonry, enabling washout from melting snow and rain. This has led to lateral deformation in some sections of the walls and collapse in others. Two-dimensional finite-element models have been formulated to represent the in situ conditions of a damaged wall section with varying strength mortar and bond conditions. The models were created in Abaqus and focus on the case of self-weight. Micro-modelling techniques were employed to model the stones and grout individually. Results indicate a significant reduction in either the strength or bonding capacity of the mortar will lead to instability in the wall sections studied. The research identifies a new general mechanism of failure for multi-wythe masonry walls and their susceptibility to environmental conditions, of which practising engineers should be aware when assessing heritage masonry structures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".