TeaPGDB: Tea Plant Genome Database
Bibliographic record
Abstract
As the most widely consumed beverage in the world, tea has various nutritional, economic, and global cultural values. With the development of the third-generation sequencing technology, several genome sequences of tea plants have been published. These genomic data have pivotal information that is of benefit to tea plant breeders and biologists in advancing tea plant improvement and the final quality of tea products. We hereby present the integrative online database, Tea Plant Genome Database (TeaPGDB; http://eplant.njau.edu.cn/tea), which incorporates the published genome sequences of tea plants. The current release of TeaPGDB hosts published tea plant genome data with various online tools, including JBrowse, gene search, SSR search, BLAST. TeaPGDB also contains a download server, which provides access for the download of genome-related data and rich annotation files. TeaPGDB is committed to collecting, integrating, and annotating published tea plant genome data, providing data support for research on tea plant heredity, evolution, breeding for resistance, plant improvement, and facilitating the characterization of important traits or flavor related genes in the community. Compared with other tea plant databases, this database not only contains more complete genome data and gene annotation information, but also has a user-friendly interface for researchers in the field.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.053 |
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".