We’re Ruã Daros, João Costa, Marina von Keyserlingk, Maria Hötzel, Heather Neave and Daniel Weary. We recently published a study in PLOS ONE that found dairy calves experience emotional effects when undergoing routine procedures, such dehorning – AUA!
Bibliographic record
Abstract
Hi Reddit, Our names are Ruã Daros, João Costa, Marina von Keyserlingk, Maria Hötzel, Heather Neave and Daniel Weary. We are researchers from the University of British Columbia in Canada and the Universidade Federal de Santa Catarina in Brazil. Our research focuses on animal welfare, how to use changes in behaviour to make inferences about the quality of life that animal’s experience. We recently published a study entitled “Separation from the Dam Causes Negative Judgement Bias in Dairy Calves” in PLOS ONE. Young farm animals, including dairy calves, are often separated from the dam far earlier than what occurs under natural conditions. Farms animals are also sometimes subjected to painful procedures like hot-iron dehorning. The aim of this study was to better understand the effects of these routine procedures on the emotions of animals. One way to investigate mood states is to look for evidence of judgement biases. We tested for cognitive biases in calves before and after separation from the cow and dehorning, and found diminished responding to intermediate, ambiguous stimuli (evidence of a pessimistic response) following both physical pain and social loss. This paper illustrates one approach to investigating emotional states in animals, and draws parallels in the emotional experience of physical and social pain. We will be online at 1pm EST (10am PST), and we look forward to hearing your questions about our work! Please also follow us in Twitter @ubcAWP.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".