Environmental genomics, the big picture?
Bibliographic record
Abstract
The enormous sequencing capabilities of our times might be reaching the point of overflowing the possibilities to analyse data and allow for a feedback on where to focus the available resources. We have now a foreseeable future in which most bacterial species will have an annotated genome. However, we know also that most prokaryotic diversity would not be included there. On the one hand, there is the problem of many groups not being easily amenable to culture and hence not represented in culture-centred microbial taxonomy. On the other hand, the gene pools present in one species can be orders of magnitude larger than the genome of one strain (selected for genome sequencing). Contrasting with eukaryotic genomes, the repertoire of genes present in one prokaryotic cell genome does not correlate stringently with its taxonomic identity. Hence gene catalogues from one environment might provide more meaningful information than the classical species catalogues. Metagenomics or microbial environmental genomics provide a different tool that gravitates around the habitat rather than the species. Such a tool could be just the right way to complement "organismal genomics". Its potential to advance our understanding of microbial ecology and prokaryotic diversity and evolution is discussed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".