Bibliographic record
Abstract
Dear Reader, Preclinical tests are crucial for assessing the toxicity and safety of new drugs before moving on to evaluating their efficacy and immunogenicity. Animal models, as well as alternatives, have long been used to study human biology and pathology. However, animal experimentation has been restricted in recent years due to increasing complexities in animal usage and growing concerns about animal welfare groups. There have been ethical concerns raised about the unnecessary or excessive use of experimental animals. As a result, many jurisdictions, including the Indian government, the European Union (EU), the United States, Canada, and South Korea, have restricted usage of experimental animals. Another reason for the limitation of using animals in experiments is uncertainty of results. According to one study, 90% of experimental drugs fail clinical trials, implying that they do not accurately reflect human physiology. The Indian government recently amended the New Drugs and Clinical Trials 2023 (NDCT) Act aimed at fostering the replacement, reduction and refinement of animal testing and the use of non-animal and human-relevant methods to assess the safety and efficacy of new drugs.
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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".