Analysis of the Microbiomes on Two Cultural Heritage Sites
Bibliographic record
Abstract
In the World Cultural Heritage of Leshan Giant Buddha and the West Lake area, the statues and monuments have been affected by the colonizing organisms. In the past three years, we used next-generation sequencing technology to analyze the biofilm of 14 sites in two places. The result showed that microbiomes were different on monuments, especially in terms of microbial composition, diversity, and potential functions. More phototrophic organisms and bacteria involved in the nitrogen-sulfur cycle were found in the West Lake area (WLA), while heterotrophs were discovered in the Leshan Giant Buddha area (LGBA). The biofilms on sandstone in LGBA possessed a higher alpha diversity of bacteria and fungi community than that on limestone in WLA. Biofilms in LGBA showed higher gene expression in metabolisms, such as the metabolism of carbohydrates, lipids, xenobiotics, and terpenoids. While biofilms in WLA exhibited higher levels of genetic information processing genes, which are associated with coping with harsh environments. Combine with XRD and SEM-EDS analysis, petrographic properties were considered the main factors that affect biological colonization. Environmental conditions (temperature, humidity, and precipitation) were similar in the two regions, which are suitable for the growth of organisms. Microbiome dissimilarities (beta diversity) indicated that the microbial compositions of biofilms were associated with the geographic features of the sampling sites. The stones in the two regions have their own petrographic properties that greatly influence microbiome colonization, such as microstructure, porosity, water permeability, and minerals composition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".