Fitness of four different mathematical models to the lactation curve of Brown Swiss cows in Konya Province of Turkey
Bibliographic record
Abstract
The aim of this study was to investigate the fitness of Incomplete Gamma (WD), Exponential (WIL), Mixed Log (MIL) and Polynomial Regression (AS) models to the lactation curve of Brown Swiss Cows. Data were collected from 143 Brown Swiss cows raised on the Alt?nova State Farm in Konya Province, Turkey. Milk yield was recorded monthly, and milk records were started at the third week of lactation (mean = 16.9 day, SD = 0.7). Total milk yields estimated by the four models were very close to real total milk yield. The models were found to be adequate for estimation of milk yield. The MIL model underestimated the peak yield significantly. The differences between peak yields of the models and real peak yields were not significant and ranged from 27.70 to 29.01 L. All models forecasted peak time earlier than real peak time. The differences for the persistency values of the four models were significant. The AS model's persistency value was nearly equal to the real persistency value (77.56 vs. 77.59%). R2 values of the models changed from 86.05 to 97.95%. The AS model gave the best R2 and the least MSPE values. Consequently, the AS model showed the best fit to the lactation data of Brown Swiss cows and allowed a suitable definition of the lactation curve.Key words: Brown Swiss, cows, lactation curve, milk yield, mathematical model
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".