Imageomics / Imageomics/bioclip-vector-db
Incorporate per-rank scores with taxonomic predictions in the db metadata
Open
- Dominant language
- Python
- Stars
- 1
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Description
Extending #6, including the score on a per-rank basis by rolling up the species‑level probabilities in a single model pass should help the usability of this taxonomic info.
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by reading issue #6 and tracing how taxonomic predictions are currently represented in the database metadata. Define the per-rank score behavior from the species-level probabilities and verify that the scores are produced in one model pass and stored with the taxonomic information.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- databases, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100