Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humans are exposed to a variety of chemicals in their everyday lives through interactions with the environment and through the use of consumer products. It is a basic requirement that these products are tested to assure they are safe under normal and reasonably foreseeable conditions of use. Within the European Union, the majority of tests used for generating toxicological data rely on animals. However recent changes in legislation (e.g., 7(th) amendment of the Cosmetics Directive and REACH) are driving researchers to develop and adopt non-animal alternative methods with which to assure human safety. Great strides have been made to this effect, but what other opportunities/technologies exist that could expedite this? Tissue engineering has increasing scope to contribute to replacing animals with scientifically robust alternatives in basic research and safety testing, but is this application of the technology being fully exploited? This review highlights how the consumer products industry is applying tissue engineering to ensure chemicals are safe for human use without using animals, and identifies areas for future development and application of the technology.
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.
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) | 0.001 | 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 it