Protocol for creating a gene dictionary for organelle genomes using the Gene Dictionary Tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.001 | 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 it