Bibliographic record
Abstract
This data archive contains results generated using a high resolution shotgun metagenomics (HRSM) bioinformatic pipeline (ShotgunMG - https://jtremblay.github.io/shotgunmg.html) for the following projects: Human gut microbiome dataset: PRJNA588513 Antarctic soil dataset: PRJNA513362 Agricultural soil dataset: PRJNA513362 Mock communities: PRJNA873699 These analyses were performed in the context of evaluating if shallow shotgun metagenomic sequencing is an adequate approach to analyze SM sequencing data using a HRSM pipeline. Briefly, these PRJNA projects were analyzed using identical bioinformatic procedures, but using various initial raw sequencing data loads. Notable end results in each archive includes: de novo co-assembly (fasta files), contigs and genes abubance matrices. Beta-diversity (Bray-Curtis dissimilarity matrices), alpha diversity (richness, chao1, Simpson and Shannon indexes matrices), taxonomic summaries from the kingdom to up to the species level and Metagenome Assembled Genomes (MAGs).
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.061 |
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".