Bibliographic record
Abstract
This page describes a project to use metagenomics for monitoring ecosystem and environmental health with respect to watersheds. This database contains metagenome data sets and annotations allowing various applications of the data. This paper describes a study to identify novel cellulases that are active in ionic liquids. This article describes an oil spill in the Canadian arctic and a metagenomic study coincident with the ongoing hydrocarbon bioremediation project. This article deals with the recovery of microbial populations in two sites in the Gulf of Mexico prior to and after impact from the Deepwater Horizon oil spill. Metabolic reconstruction is important in the use of genomic and metagenomic data. This blog to discusses differences in annotation tools and offers information on new tools. This set of PowerPoint slides gives some examples of application of metagenomic studies of microbes. This study describes mining of metagenomic data for novel glycohydrolases that might be useful to deconstruct plant biomass and produce biofuels. This company offers services using metagenomics to monitor microbial populations with respect to biofouling, biocide effectiveness and hydrogen sulfide production; support services for drilling industries. Potential applications of the hydrocarbon metagenomics project are in oil sands remediation and stimulating coal bed methane generation. This article highlights microbiology relevant to the oil industry and includes such methods as metagenomic analysis. Microbes in the gut influence the efficacy or activation of oral drugs. This page links to an article discussing how metagenomics could help in this assessment. This project was initiated to analyse metabolic pathways reconstructed from metagenomic data. This article reviews literature on our current understanding of how complex microbial consortia degrade xenobiotic compounds in soil, water and the human intestine and the application of metagenomics to aid in these studies.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.025 |
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".