Lennie: a smartphone application with novel implications for the management of animal colonies
Bibliographic record
Abstract
Researchers rely on animals for their clinical applicability and ease of monitoring. However, careful management is required to ensure the animal and financial costs are minimized. The incorporation of 'smartphone' technology in research has increased exponentially, with a focus on the development of innovative research-based applications. We have developed a smartphone application designed to address the needs of modern researchers in the management of their colonies. 'Lennie' introduces a new method for the management of small to medium-sized animal colonies. Lennie allows users wireless access to their colonies with the ability to create and edit from virtually anywhere. Lennie also offers the ability to manage colonies based on experiments by assigning animals based on priority. Experimental time-points are also recorded with integrated scheduling options using the calendar function. Lennie represents an alternative to current large-scale software options, as the application design is simple, and requires no training or manuals. As the technological landscape is constantly evolving, we must continue to find ways to improve upon current practices to ensure that research is completed with efficiency and efficacy. With this new method of animal management, researchers are able to spend less time record keeping and can focus their efforts on continued innovation.
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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 | 0.001 | 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 | 0.000 |
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".