The application of citizen science to an undergraduate research project on canine cognition
Bibliographic record
Abstract
Animal research provides meaningful insight into animals' skills and abilities, further enhancing our care for and understanding of them. However, performing authentic animal research in an undergraduate class is difficult because of cost and limited resources. One solution to this challenge is citizen science. Citizen science is a form of research conducted by members of the public who perform experiments and gather information for researchers, allowing for wide-scale data collection with minimal cost associations. Thus, an experiment using the citizen science approach was performed in Animal Bioscience 360 at the University of Saskatchewan to determine if there were cognitive differences in groups of dogs. Teams of two students performed cognition tests on their own dogs and tested four aspects of cognitive ability: memory, object permanence, perspective-taking, and response to human cues. Together, the class tested 42 dogs and uploaded the experimental data to Excel. Students developed hypotheses to test whether dogs differing in age, gender, breed, obedience training, or household status had different cognitive profiles. There were no significant differences in cognition except that dogs living in single-dog households yawned significantly more often in response to human yawning than multi-dog households (P ≤ 0.05). The citizen science approach provided 61 students with an authentic research experience and improved their writing and numeracy skills. Undergraduate research experience assists in practical skill development, improved academic performance, and degree completion. Citizen science enhances participants' knowledge of the research area and provides a level of transparency toward scientific research.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".