Bibliographic record
Abstract
With an increase in demand for rewarding horse-human relationships and training programs focused on equine welfare, positive reinforcement is posed as a suitable addition to equine training regimes. Positive reinforcement is not without challenges, and suggestions to overcome problems associated with hand feeding, as used in positive reinforcement, have been explored. Positive reinforcement was shown to reduce signs of body tension during training, which could be used to improve horse welfare and potentially reduce the time needed when training basic grooming and veterinary techniques, making these procedures safer for both the horses and people involved. Primary reinforcers are beneficial in helping horses to learn to perform new and potentially fear-inducing tasks, though the introduction of a secondary reinforcer was not shown to prolong extinction of these learned behaviours. Applications of positive reinforcement include training foals, reducing stress in rehabilitated horses, and reducing potentially dangerous behaviours associated with trailer loading and hoof handling. Suggestions for how positive reinforcement can be used while riding are also presented. Overall, positive reinforcement has been found to be a useful tool in training horses and an effective method for gaining a more willing equine partner. Despite this, more research needs to be done due to small experimental group sizes and other applications of positive reinforcement, including work under saddle, are sorely lacking in current literature. This lack of research should not discourage trainers and owners from integrating positivereinforcement into their routines.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.153 | 0.081 |
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".