Bibliographic record
Abstract
Abstract Nutraceutical supplements have become requisite fare in equine stables across North America and Europe, and a robust marketing engine has propagated the notion that every horse owner has the ability to contribute to the management – and even treatment – of some of the most important health and performance issues facing the modern horse. The voracious appetite of horse owners and managers for these supplements has vastly outpaced research into equine-specific efficacy, safety or toxicity of the majority of available products. Indeed, even government regulators have been left scrambling to accommodate the unique characteristics of nutraceuticals for horses within existing feed and drug guidelines, whilst the groundswell of consumer demand creates a fertile and attractive venue for a myriad of equine nutraceutical products. This presentation will identify peculiarities of horses and horse enthusiasts which define the opportunities and challenges associated with equine nutraceutical products. The current state of scientific inquiry will be explored, focusing on supplements targeting common equine health issues including arthritis, laminitis and gastrointestinal disorders. This critical mass of scientific evidence is then compared with popular marketing of equine nutraceuticals, in order to caliper the distance between science and fiction.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".