Thermally induced deterioration behaviour of two dolomitic marbles under heating–cooling cycles
Bibliographic record
Abstract
Thermally induced deterioration behaviour can cause severe weathering in marbles. Most previous studies focus on the deterioration behaviour of calcitic marbles. Relevant studies of dolomitic marbles are generally carried out under a 'high temperature and low cycling times' condition. Little attention is focused on the deterioration behaviour in dolomitic marbles when they are subjected to a large quantity of heating-cooling cycles under a 'low temperature and high cycling times' condition. This paper presents experimental investigations on the thermally induced deterioration behaviour of two Beijing dolomitic marbles (Qingbaishi Marble (QM) and Hanbaiyu Marble (HM)) under heating-cooling cycles up to 1000 cycling times. The applied temperature range is from -20°C to 60°C which is to simulate the seasonal temperature variations in Beijing city, China. Related properties such as weight loss, three-dimensional microtopography, elastic wave velocity and uniaxial compressive strength were measured at certain cycles. The results indicate that thermally induced deterioration behaviour will result in a continuous weight loss in dolomitic marble samples. Mechanical properties of those two marbles are strongly affected by heating and cooling treatments, which were reflected by the reductions of dynamic Young's modulus and uniaxial compressive strength with an increase of thermal cycles. Compared with QM, HM displays a higher level of thermally induced deterioration which should be due to the abundance of quartz mineral.
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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 | 0.000 | 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".