Peer Reviewed: Genomics technologies for environmental science
Bibliographic record
Abstract
The ongoing explosion of nucleotide sequence data from prokaryotic and eukaryotic organisms is creating a vast information resource. Potential applications range fromunderstanding and combating human disease to characterizing naturally occurring microbial communities, whose combined biomass is the singlemost important biological force influencing global elemental cycles and the balance of atmospheric gases. Although the health sector is moving rapidly into the postgenomics era, environmental genomics, the use of genomics to address environmental issues and problems, remains in its infancy. For example, although specific nucleotide sequence information has been available for some time, whole genome sequences for environmentally relevant microorganisms are only now beginning to appear in databases. Presently, ecological studies ofmicrobial communities remain largely process-oriented. Measurable metabolic parameters, like nitrogen fixation and substrate biodegradation, are determined as a function of the whole community. The contribution of individual bacterial species to these parameters is nearly impossible to determine becausemany organisms cannot be cultivated in vitro. The use of genomics-based tools to augment these traditional methods promises to accelerate our understanding of the complexities of species diversity, population dynamics, andmetabolic pathways withinmicrobial communities in soil and water. Their use should markedly improve the reliability and accuracy of remediation activities and of predictions of adverse environmental impacts before they happen.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.044 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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; both teacher heads agree on what is shown here.
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".