Mitogenome metadata: current trends and proposed standards
Bibliographic record
Abstract
Mitogenome metadata are descriptive terms about the sequence, and its specimen description that allow both to be digitally discoverable and interoperable. Here, we review a sampling of mitogenome metadata published in the journal Mitochondrial DNA between 2005 and 2014. Specifically, we have focused on a subset of metadata fields that are available for GenBank records, and specified by the Genomics Standards Consortium (GSC) and other biodiversity metadata standards; and we assessed their presence across three main categories: collection, biological and taxonomic information. To do this we reviewed 146 mitogenome manuscripts, and their associated GenBank records, and scored them for 13 metadata fields. We also explored the potential for mitogenome misidentification using their sequence diversity, and taxonomic metadata on the Barcode of Life Datasystems (BOLD). For this, we focused on all Lepidoptera and Perciformes mitogenomes included in the review, along with additional mitogenome sequence data mined from Genbank. Overall, we found that none of 146 mitogenome projects provided all the metadata we looked for; and only 17 projects provided at least one category of metadata across the three main categories. Comparisons using mtDNA sequences from BOLD, suggest that some mitogenomes may be misidentified. Lastly, we appreciate the research potential of mitogenomes announced through this journal; and we conclude with a suggestion of 13 metadata fields, available on GenBank, that if provided in a mitogenomes's GenBank record, would increase their research value.
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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 | 0.056 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".