PSVI-6 Predicting Fecal Composition Using Near Infrared Spectroscopy (Nirs): Expanding the Calibration to Include Grazing Beef Samples
Bibliographic record
Abstract
Abstract A near-infrared spectroscopy (NIRS) calibration was previously developed to predict fecal composition using samples from beef heifers fed high forage diets ( > 95% forage dry matter basis) during total collection digestibility studies. The objective of the current study was to expand the fecal composition calibration with samples from grazing beef cattle. Fecal samples were collected from beef steers grazing two annual and two perennial forage mixtures over 2 growing seasons. Individual samples (n = 12/paddock) were composited by paddock resulting in 30 samples from year 1, and 24 from year two. Fecal samples were oven dried at 55°C for 48 hours and ground through a 1.0 mm screen prior to scanning on a FOSS DS2500 scanning monochromator (FOSS, Eden Prairie, MN). The grazing fecal spectra (n = 54) was added to the existing library and then mathematically treated for scatter correction. Modified partial least squares (MPLS) regression was performed to develop equations to predict fecal composition [organic matter (OM), nitrogen (N), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), undigestible NDF (uNDF), calcium (Ca), and phosphorus (P)]. The calibrations for fecal OM, N, NDF, ADF, ADL, uNDF, Ca, P resulted in R2CV between 0.86 and 0.96 and SECV of 1.73, 0.07, 1.65, 1.20, 0.63, 1.91, 0.21, and 0.07, respectively. This study confirms the potential of NIRS to predict fecal chemical composition of beef cattle fed high forage or grazing forage diets. Future steps include expansion and further validation of the calibration equations to include digestibility and intake predictions by estimating the internal markers lignin and uNDF.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".