Bibliographic record
Abstract
Abstract Diverse biogenic and abiogenic processes produce calcite speleothems. From a biogenic perspective, cave microbes mediate a wide range of destructive and constructive processes that collectively influence the growth of calcite speleothems and their internal fabrics. Destructive processes include substrate breakdown by dissolution, boring and residue micrite production, whereas constructive processes include microbe calcification, trapping and binding of detrital particles to substrates, and microbial induced calcite precipitation. Biogenesis can be established from: (1) the presence of mineralized microbes; (2) fabrics, such as stromatolite-like structures, that can be attributed to microbial activity; and/or (3) geochemical proxies (carbon and oxygen isotopes, lipid biomarkers) considered indicative of microbe activity. Such criteria have, for example, been used to demonstrate microbial involvement in the formation of pool fingers, stalactites/stalagmites, cave pisoliths and moonmilk. Nevertheless, absolute proof of microbial biogenesis in calcitic speleothems is commonly difficult because taphonomic processes and/or diagenetic processes commonly mask evidence of microbial activity. The assumption that calcitic speleothems are abiogenic, which has been tacitly assumed in many studies, is dangerous as there is clear evidence that microbes thrive in most caves and can directly and indirectly influence calcite precipitation in many different ways.
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 | 0.001 |
| 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".