112 Evaluation of fecal near-infrared reflectance spectroscopy profiling technology to predict forage intake estimated using n-alkane markers in grazing cattle
Bibliographic record
Abstract
Improved methodology to estimate intake of grazing animals is needed for better-informed management strategies, as current techniques are limited. The objective of this study was to evaluate the accuracy of fecal near-infrared reflectance spectroscopy (NIRS) to predict forage intake estimated using n-alkane markers in grazing animals. Fecal samples were collected from individual animals across 11 trials (n = 260) in which forage DMI was predicted using the alkane-ratio technique. For each trial, fecal samples were collected 2 times daily for 5 consecutive days and composite samples were subjected to NIRS analysis by a Foss NIRS 6500 scanning monochromator (Foss, Eden, Prairie, MN). Fecal spectra were used to develop equations to predict fecal alkane concentration (8 trials; n = 212) and n-alkane predicted DMI (11 trials; n = 260). For the prediction of fecal alkane concentrations, coefficients of determination for calibration (R2c) and cross-validation (R2cv) were 0.90 and 0.87, respectively, for fecal C32 concentration and 0.99 and 0.98, respectively, for fecal C31 concentration. Calibration and cross-validation accuracies (R2c and R2cv) for the prediction of forage DMI estimated using the n-alkane method were 0.90 and 0.87, respectively. These results indicate the presence of strong associations between fecal NIRS spectra and fecal alkane concentrations but do not provide information regarding the robustness of these equations, which is necessary for industry application. To evaluate the robustness of the equations in this study, independent-trial validation was performed. This type of validation was accomplished by removing a single trial from the database and using the remaining 10 trials to develop the calibration equation to predict the independent trial. For this study, independent-trial validation results for the prediction of fecal alkane concentrations and forage DMI estimated using the n-alkane method were poor (R2v < 0.15). Although cross-validation results indicate the potential of this technology to predict forage intake of grazing animals, the independent-trial validation results suggest that a larger database will be needed to enhance robustness of predictive equations across diverse production systems. 1 1SEL = standard laboratory error; SEC = SE of calibration; SECV = SE of cross-validation.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".