Bibliographic record
Abstract
Elicit is an online tool developed by Ought, a nonprofit machine learning (ML) research lab based in the United States.It is a free artificial intelligence (AI) research assistant that "uses language models to automate part of researchers' workflows" [1].Ideal for evidence synthesis and text extraction, Elicit pulls publications from Semantic Scholar and expedites the literature review process.Users enter a research question into the search box and the AI attempts to identify the top papers in the field.The AI can find relevant papers without perfect keyword matching, summarize takeaways from the paper, and extract key information into a research matrix.Taking inspiration from the systematic review process, the language model retrieves and condenses the information into component parts, thus allowing users to filter topics from a paper's abstract including a shortened version of the abstract, intervention, outcomes, number of participants, population summary, and more.Elicit is ideal for questions that have empirical research (e.g., research in biomedicine) with interventions, randomized controlled trials, and questions generally structured as "What are the effects of x on y?" or "Does x affect y?" [2].
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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.400 | 0.136 |
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".