Capturing scientific knowledge in computable form
Bibliographic record
Abstract
Technological advances in computing provide major opportunities to complement human reasoning and to dramatically speed up science - but only if structured knowledge is available to enable efficient communication between humans and computers. Traditionally, biological knowledge is captured in publications and knowledge bases. Knowledge in papers is not directly in a computable, structured form; curated structured knowledge bases are limited by manual curation processes. To accelerate knowledge capture and communication and keep pace with the rapid growth of scientific reports, we developed the Biofactoid (biofactoid.org) software suite. Biofactoid accelerates the transfer of knowledge from the minds of authors into computable, widely shared, structured knowledge and can be used as part of the standard publication process. Biofactoid is a web-based system for scientists to compose a structured representation of networks of interactions between genes, their products, and chemical compounds, represented using the expressive power of a formal ontology (BioPAX). The resulting knowledge items are shared via public information resources and can be discovered and analyzed in the context of all existing computable knowledge. We envision adoption of software technology for knowledge capture by scientists and publishers as part of an ecosystem of tools, in which scientific reasoning is supported by efficient knowledge computation.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".