Bibliographic record
Abstract
Dr. Chandrasekera is the founder and director of the Canadian Center for Alternatives to Animal Medicine, the first research center in Canada dedicated to the cultivation of non-animal based research methods in the bio-medical sciences. She holds a doctorate in Biochemistry and Molecular Biology from the University of Calgary and is the director of laboratory science with the Physicians Committee for Responsible Medicine.\nBeyond Animal Testing: Working Towards a Paradigm Shift\nDespite the wealth of knowledge obtained over a century of extensive animal research, effective treatments remain elusive and a failure-prone endeavour for most diseases prevalent today—many breakthroughs in research labs do not make it into our clinics. Similarly, for chemical risk assessment, the legacy animal-based methods do not reliably predict adverse outcomes on human health and the environment. From the Americas to the Far East, countries across the globe have already established national centres dedicated to the development and validation of non-animal alternative methods, and Canada joined this league last Fall with the Canadian Centre for Alternatives to Animal Methods (CCAAM), and its subsidiary, Canadian Centre for the Validation of Alternative Methods (CaCVAM) located at the University of Windsor. The overarching vision of CCAAM/CaCVAM is to reduce and replace the use of animals in Canadian biomedical research, education, and regulatory testing through 21st century science, innovation, and ethics. This presentation will provide an overview of the current state of affairs in animal testing and animal replacement efforts as well as future perspectives on the need to accept human biology as the gold standard.
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 | 0.088 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.021 | 0.051 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".