Bacterially-induced dissolution of calcite: the role of bacteria in limestone weathering
Bibliographic record
Abstract
The interaction between microorganisms and the calcite mineral surface in aqueous solutions, under earth surface conditions, was the focus of this study. More specifically, we investigated if bacterial attachment and metabolism increase the dissolution rates of calcite crystals and alter their surfaces in solution. A natural microbial consortium, rather than model organisms, was used in the experiments. Weathered samples from the Trenton carbonates were collected on the flanks of Mount Royal in Montréal (Québec, Canada). The associated bacteria were identified using molecular biology DNA fingerprinting techniques. This information was used to determine the nutrient requirements of suitable growth media. Samples contained typical soil dwelling organisms from the phylum Actinobacteria, gram-positive heterotrophs. Bacteria were combined with cleaved Iceland Spar calcite rhombohedra in a low-ionic strength (10−2 M) NaCl solution at ambient pCO2 , 25°C and 1 atm pressure. The effect of solution chemistry (e.g. the presence of phosphate) on the calcite dissolution kinetics was also investigated. The dissolution rates in the presence of bacteria, did not vary significantly from abiotic conditions, but decreased notably in the presence of phosphate.
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 | 0.000 |
| 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 | 0.001 | 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".