Taking the Bitter with the Sweet - A Preliminary Study of the Short-Term Response of Horses to Various Tastants in Solutions
Bibliographic record
Abstract
Horses can distinguish sweet, salty, sour, and bitter tastes, but little is known about their preferences for various tastants. Understanding horse taste preferences can aid in increasing water intake by adding a preferred tastant or by masking an unpleasant taste to encourage administration of medications, for example. The quantity of water intake by horses was examined over five separate trials involving a two-choice preference test between tap water and water containing varying concentrations of sucrose (0-50g/100ml), citric acid (0-2.43mg/100ml), quinine (0-30mg/100ml) or a mix of sucrose (10mg/100ml)/citric acid (1.31mg/100ml) and sucrose (10mg/100ml)/quinine (20mg/100ml). Horses (n = 5) showed a weak preference for sweetened water up to 10mg/100ml (p < .001), with a rejection at higher concentrations. Horses rejected all concentrations of both sour (n = 12 horses; p < .001) and bitter (n = 6 horses; p < .001) solutions. In the mixed tastant trials, sucrose mixed with citric acid was only weakly rejected compared to the sucrose solution alone, which was moderately rejected (n = 5 horses; p < .001). Similarly, mixed sucrose/quinine solution intake increased over the quinine solution alone (n = 9 horses; p < .001). There was a large variation among individual horses within each trial, with some horses strongly rejecting sucrose solutions and others strongly preferring citric acid solutions. No horse indicated a preference for bitter solution in any trial. Age (p < .001), breed (p < .001), and exercise (p = .004) all influenced total fluid intake in the sour trial, not dependent on treatment (p = .063). These preliminary results show that some horses appear to prefer sweet and a preferred tastant can mask a less preferred tastant.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".