Models to predict dry feed intake in Holstein calves to 4 months of age
Bibliographic record
Abstract
Voluntary daily dry feed intake (DFI) in Holstein calves was predicted using 60,761 individual daily observations collected from 1,235 Holstein calves in 30 experiments from 4 research stations in the United States and Europe. Consumption of dry feed (calf starter and hay, kg/d or percent of body weight) was measured from 3 to 114 d of age. Linear models and 2- and 3-parameter nonlinear models were evaluated to predict DFI using age of calf, intake of milk replacer, ambient temperature, percent forage, and neutral detergent fiber concentration in ration dry matter (DM) as independent variables. The initial data set was randomly divided within study location into development (80% of all observations) and validation data sets, and initial screening was conducted using the development data set. Five nonlinear models and 3 linear models (candidate models) were identified and used in further model evaluation. Cross-validation studies (n = 20) with the validation data set were conducted by linear regression of DFI with predicted DFI as independent variable. Candidate models were subsequently evaluated with data from 12 published studies in 2 analyses. The exponential model that best predicted daily DFI in Holstein calves in original and external data sets was DFI (kg/d) = 1.3207 × e [(−5.3892 + 0.6376 × MEgap) × EXP(−0.0392 × Age)] – 0.0013 × Temp + 0.0032 × NDFDM + 0.0026 × Age × MEgap – 0.3646 × PctForage [coefficient of determination (R 2 ) = 0.92, concordance correlation coefficient (CCC) = 0.96, and mean square error of prediction (MSEP) = 0.10 kg]; where MEgap (Mcal/d) = difference of daily metabolizable energy (ME) requirement and ME intake from milk replacer; Age=age of calf (d) from 3 to 114, Temp=mean daily ambient temperature (°C), NDFDM=ration neutral detergent fiber (% DM); PctForage=percent forage in ration DM. The linear model that best predicted DFI was DFI (kg/d = −0.1349 + 0.0106 × Age + 0.1808 × MEgap + 0.0013 × Age × MEgap + 0.0001 × Temp + 0.00002 × Age × Temp (R 2 = 0.93, CCC=0.96, and MSEP=0.10 kg). When Temp and ration characteristics were not included, optimal models were 1.4362 × e [(−4.6646 + 0.5234 × MEgap) × EXP(−0.0361 × Age)] + 0.0025 × Age × MEgap (R 2 = 0.92, CCC=0.96, and MSEP=0.11 kg) and −0.1344 + 0.0102 × Age + 0.1810 × MEgap + 0.0013 × Age × MEgap [R 2 = 0.93, CCC=0.96, and MSEP=0.10 kg]. Models of daily DFI may improve prediction of nutrient supply to young Holstein calves to approximately 4 mo of age, thereby increasing prediction of growth performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".