Long-read metagenomic dataset from domestic rabbit manure and domestic rabbit manure-derived vermicompost
Bibliographic record
Abstract
This dataset describes samples collected from two Domestic Rabbit manure sources and three Domestic Rabbit manure-derived vermicompost bins. Three samples were taken from each and total DNA was isolated. Nanopore sequencing was used to collect data from all isolated DNA samples. After length and quality filtering, 181.5 gigabases (Gb) of sequencing data was collected across 15 samples. Streptomyces, Bradyrhizobium, Mesorhizobium , and Microbacterium were in the top 5 genera for all vermicompost samples, but two vermicompost samples had very high proportions of Escherichia and Mycobacterium . Vermicomposting can enable the development of beneficial microbial communities, but often lacking a thermophilic phase, may also allow for the growth of potentially pathogenic microbes. The vermicomposts described by this dataset contains both beneficial and potentially harmful microbial communities and may be used to support comparisons between composts and vermicomposts of different backgrounds for safety and utility.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".