WormBase 2014: new views of curated biology
Bibliographic record
Abstract
WormBase (http://www.wormbase.org/) is a highly curated resource dedicated to supporting research using the model organism Caenorhabditis elegans. With an electronic history predating the World Wide Web, WormBase contains information ranging from the sequence and phenotype of individual alleles to genome-wide studies generated using next-generation sequencing technologies. In recent years, we have expanded the contents to include data on additional nematodes of agricultural and medical significance, bringing the knowledge of C. elegans to bear on these systems and providing support for underserved research communities. Manual curation of the primary literature remains a central focus of the WormBase project, providing users with reliable, up-to-date and highly cross-linked information. In this update, we describe efforts to organize the original atomized and highly contextualized curated data into integrated syntheses of discrete biological topics. Next, we discuss our experiences coping with the vast increase in available genome sequences made possible through next-generation sequencing platforms. Finally, we describe some of the features and tools of the new WormBase Web site that help users better find and explore data of interest.
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 | 0.029 | 0.056 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.017 | 0.022 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.046 | 0.048 |
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".