A dynamic web resource for robust and reproducible genomics in nonmodel species: marineomics.io
Bibliographic record
Abstract
Abstract Genomic methods are becoming increasingly valuable and established in ecological research, particularly in nonmodel species. Supporting their progress and adoption requires investment in resources that promote (i) reproducibility of genomic analyses, (ii) accessibility of learning tools and (iii) keeping pace with rapidly developing methods and principles. We introduce marineomics.io, an open‐source, living document to disseminate tutorials, reproducibility tools and best principles for ecological genomic research in marine and nonmodel systems. The website's existing content spans population and functional genomics, including current recommendations for whole‐genome sequencing, RAD‐seq, Pool‐seq and RNA‐seq. With the goal to facilitate the development of new, similar resources, we describe our process for aggregating and synthesizing methodological principles from the ecological genomics community to inform website content. We also detail steps for authorship and submission of new website content, as well as protocols for providing feedback and topic requests from the community. These web resources were constructed with guidance for doing rigorous, reproducible science. Collaboration and contributions to the website are encouraged from scientists of all skill sets and levels of expertise.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".