Protocol for creating a gene dictionary for organelle genomes using the Gene Dictionary Tool
Bibliographic record
Abstract
Here, we present a protocol for creating a gene dictionary for fungal mitochondrial genomes using the Gene Dictionary Tool. Through a Python Command Line Interface (CLI), the user identifies what annotations are missing in the inputted dictionary. Via two Jupyter Notebooks, the user builds a gene dictionary based on attributes retrieved from inputted GFF3 files. The final output, a .gdict file, is findable, accessible, interoperable, and reusable (FAIR). This protocol can be adapted to create a gene dictionary for other genomes. • Protocol for creating a comprehensive and versionable gene dictionary across genomes • Guidance on how to use and implement the gdt Python library • Steps for the iterative creation of gene dictionaries for organelle genomes • Instructions on how to process genome features with poor identifying information Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Here, we present a protocol for creating a gene dictionary for fungal mitochondrial genomes using the Gene Dictionary Tool. Through a Python command line interface, the user identifies what annotations are missing in the inputted dictionary. Via two Jupyter Notebooks, the user builds a gene dictionary based on attributes retrieved from inputted GFF3 files. The final output, a .gdict file, is findable, accessible, interoperable, and reusable (FAIR). This protocol can be adapted to create a gene dictionary for other genomes.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.125 | 0.105 |
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".