Incorporation of Milk Yield, Dry Matter Intake and Phosphorous Excretion Predictive Functions in the Development of a Multi-Objective Dairy Feed Formulation Software Program
Bibliographic record
Abstract
Predictive functions for milk yield, dry matter intake, and phosphorous-manure derived from the National Research Council 2001 and the present study were incorporated in the development of a multiple objective dairy feed formulation software program (MoF-Dairy Edition-2010); that attempted to optimise feed cost, milk yield and profits while minimising Phosphorous-excretion in manure. Important objects in the feed milling industry considered in the program development were feed millers, dairy farmers, and government feed policy regulatory guidelines. The multi-objective formulation approach comprises hierarchical design levels which include data, model, tools, and output layers. Program database objects are manipulated using VB.NET programming language within a Microsoft .NET Framework Environment. Users interact with the program by providing individual details after which a customer system instance is created. Program formulation inputs are entered through VB forms linked to the core simulation model layer (Microsoft SQL Server Database) to automatically calculate and generate nutrient requirements in accordance with NRC, 2001 for the particular cow or cow production groups under specified production performance parameters. The final solution is obtained by allowing the program to solve for the most feasible combination of available ingredients under the imposed formulation, ingredient as well as nutrient constraints. Program outputs include tailor-made reports on feed formulae; and the accompanying physical nutrient compositions and nutrient deviation analysis; potential unit and gross P-manure environmental pollution, and business economic analysis; detailing concentrate supplementation rates per cow per milking as well as the corresponding projected daily milk profit margins.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".