Near infrared reflectance spectroscopy (NIRS) analyses of nutrient composition and condensed tannin concentrations in carolina willow (<i>Salix caroliniana</i>)
Bibliographic record
Abstract
Iron overload disorder has been described in a number of zoo-managed species, and it has been recommended to increase the tannin composition of the diet as a safe way to minimize iron absorption in these iron-sensitive species. The goal of this study was to examine the potential of near infrared reflectance spectroscopy (NIRS) as a rapid and simple screening tool to assess willow (Salix caroliniana) nutrient composition (crude protein: CP; acid detergent fiber: ADF; neutral detergent fiber: NDF; lignin, gross energy: GE) and condensed tannin (CT) concentrations. Calibration equations were developed by regression of the lab values from 2 years using partial least squares on n = 144 NIRS spectra to predict n = 20 independent validation samples. Using the full 2-year dataset, good prediction statistics were obtained for CP, ADF, NDF, and GE in plant leaves and stems (r(2 ) > 0.75). NIRS did not predict lignin concentrations reliably (leaves r(2) = 0.52, stems r(2) = 0.33); however, CTs were predicted moderately well (leaves r(2) = 0.72, stems r(2) = 0.67). These data indicate that NIRS can be used to quantify several key nutrients in willow leaves and stems including concentrations of plant secondary compounds which, depending on the bioactivity of the compound, may be targeted to feed iron-sensitive browsing animals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".