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 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".