Evaluating old and novel proxies for in vitro digestion assays in wild ruminants
Bibliographic record
Abstract
ABSTRACT In vitro digestion assays provide useful data for wild ruminant ecologists. It remains highly constraining, however, to conduct these assays in the field. We evaluated various proxies for in vitro digestion assays that use ruminal liquor from wild ruminants. The ruminal liquor of freshly killed white‐tailed deer ( Odocoileus virginianus ) was used to measure in vitro true digestibility (IVTD), total gas, and volatile fatty acids (VFA) production of dominant summer and winter forage. We evaluated the ability of 4 proxies to predict these digestibility variables: 1) IVTD, total gas, and VFA produced with cow ruminal liquor; 2) acid detergent fiber (ADF) concentration; 3) CO 2 production from soil–forage mixtures; and 4) predictive models based on near infrared spectra (NIRS) of forages. Predictions using NIRS were best correlated with all deer digestibility variables (each r 2 > 0.93) with limited bias across forage species. Forage ADF and CO 2 production from soil–forage mixtures were related with all digestibility variables ( r 2 = 0.62–0.95). Digestion assays using cow ruminal liquor was relatively good for predicting IVTD ( r 2 = 0.94) from deer ruminal liquor, but the weakest for predicting total gas ( r 2 = 0.63) and VFA production ( r 2 = 0.44). Calibrated NIRS models could substantially improve the prediction of in vitro digestibility variables. If access to NIRS technology or the ability to collect reference data with deer inocula for NIRS calibrations is impractical, forage ADF or CO 2 production from soil–forage mixtures remain acceptable alternatives. Comparing the digestion kinetics and VFA production of wild and domestic ruminants is an innovative avenue for better understanding the adaptive ecophysiology of digestion by different herbivores. © 2016 The Wildlife Society.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".