Methods, FASTA files and relevant statistics for draft MAGs generated from an anaerobic nitrate-reducing benzene-degrading enrichment culture
Bibliographic record
Abstract
Presented within are draft metagenome assembled genomes (MAGs) created from multiple genome assemblies of nitrate-reducing benzene-degrading enrichment cultures (NRBC) derived from soil samples collected from a decommissioned gas station located in Toronto, Canada. The primary objective was to obtain closed genomes of organisms from NRBC in order to understand each organism's role in the enrichment culture. Two Microbial Resource Announcements link to this data.Associated data and methods are provided in the following 5 items:1. A readme file describing all of the associated datasets and methods for assembly and binning, as well as references.2. An excel file (with multiple worksheets) with links to NCBI-deposited raw reads and metagenome assemblies as well as quality statistics for draft metagenome assembled genomes (MAGs).3. A metagenome assembly created using assembler IDBA-UD v. 1.1.1 from the 2018 Illumina paired end sequencing dataset that was not deposited to NCBI as it was too similar to the assembly created with Spades v. 3.10.1 (deposited as JAUJDY010000000) but did yield a good starting bin for Thermincola.4. FASTA files for the 79 dereplicated MAGs created from the multiple genome assemblies in different years of various subcultures of an anaerobic nitrate-reducing benzene-degrading enrichment culture (NRBC).5. A gap-filling perl script used to assemble contigs.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.231 | 0.120 |
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".