Nutrient and plant secondary compound composition and iron‐binding capacity in leaves and green stems of commonly used plant browse (Carolina willow; <i>Salix caroliniana</i>) fed to zoo‐managed browsing herbivores
Bibliographic record
Abstract
Plant secondary compounds are diverse structurally, and associated biological effects can vary depending on multiple factors including chemical structure and reaction conditions. Phenolic compounds such as tannins can chelate dietary iron, and supplementation of animal species sensitive to iron overload with tannins may prevent/treat iron overload disorder. We assessed the nutrient and phenolic composition and iron-binding capacity of Carolina willow (Salix caroliniana), a plant fed to zoo-managed browsing herbivores. Based on studies in other plant species and the chemical structures of phenolic compounds, we hypothesized that the concentration of condensed tannins in willow would be inversely related to the concentration of phenolic glycosides and directly related to iron-binding capacity. Our results indicated that willow nutrient composition varied by year, season, and plant part, which could be taken into consideration when formulating animal diets. We also found that the predominant plant secondary compounds were condensed tannins with minimal phenolic glycosides. Instead of binding to iron, the willow leaf extracts reduced iron from the ferric to ferrous form, which may have prooxidative effects and increase the bioavailability of iron depending on animal species, gastrointestinal conditions, and whole animal processes. We recommend identifying alternative compounds that effectively chelate iron in vitro and conducting chelation therapy trials in vivo to assess potential effects on iron balance and overall animal health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".