Environmental paleomicrobiology: using <scp>DNA</scp> preserved in aquatic sediments to its full potential
Bibliographic record
Abstract
In-depth knowledge about spatial and temporal variation in microbial diversity and function is needed for a better understanding of ecological and evolutionary responses to global change. In particular, the study of microbial ancient DNA preserved in sediment archives from lakes and oceans can help us to evaluate the responses of aquatic microbes in the past and make predictions about future biodiversity change in those ecosystems. Recent advances in molecular genetic methods applied to the analysis of historically deposited DNA in sediments have not only allowed the taxonomic identification of past aquatic microbial communities but also enabled tracing their evolution and adaptation to episodic disturbances and gradual environmental change. Nevertheless, some challenges remain for scientists to take full advantage of the rapidly developing field of paleo-genetics, including the limited ability to detect rare taxa and reconstruct complete genomes for evolutionary studies. Here, we provide a brief review of some of the recent advances in the field of environmental paleomicrobiology and discuss remaining challenges related to the application of molecular genetic methods to study microbial diversity, ecology, and evolution in sediment archives. We anticipate that, in the near future, environmental paleomicrobiology will shed new light on the processes of microbial genome evolution and microbial ecosystem responses to quaternary environmental changes at an unprecedented level of detail. This information can, for example, aid geological reconstructions of biogeochemical cycles and predict ecosystem responses to environmental perturbations, including in the context of human-induced global changes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".