Animal research is saving lives, but funding is needed to improvewelfare: Submission to the New South Wales parliamentary inquiry
Bibliographic record
Abstract
Many kinds of animal research are occurring in New South Wales (NSW), with biomedical research among the most prominent. As behavioural neuroscientists, we study the neural mechanisms of motivation and cognition in rodents, which is important for developing new treatments for a range of psychological disorders, such as substance use disorder, as well as neurodegenerative diseases such as Alzheimer’s. The welfare and wellbeing of the animals we study is of critical importance, not only to ensure the quality of our data but to our sense of morality as compassionate human beings. Biomedical animal research is highly regulated and the pharmacological and biological tools we use pose negligible risks to the public. Meanwhile, our research brings enormous benefits to NSW by building expertise and supporting biotechnology companies. Although research on complex behaviours cannot be replaced by non-animal procedures, we believe that there is much scope for refinement and improvement in animal welfare in NSW. For example, investing in a local breeding facility to produce animals used in NSW research projects would significantly reduce the stress associated with importing animals from interstate or overseas. Additionally, standard animal housing could be improved through targeted and ongoing investment to refit animal facilities and support additional caretaker and veterinary staff to provide higher degrees of welfare.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".