Prediction of growth and feed efficiency in mink using machine learning algorithms
Bibliographic record
Abstract
• Machine learning algorithms in predicting growth and feed efficiency in mink were tested. • The extreme gradient boosting performed the most accurate in predicting feed efficiency in mink. • The feed efficiency measures in mink were predicted accurately without costly daily feed Intake. • Body length, BW, sex, color and age were employed to predict growth and feed efficiency. • The most significant feature in predicting growth and feed efficiency in mink was sex. The feed efficiency ( FE ) expresses as the amount of feed required per unit of BW gain. Since feed cost is the major input cost in the mink industry, evaluating of FE is a crucial step for competitiveness of the mink industry. However, the FE measures have not been widely adopted for the mink due to the high cost of periodically measuring BW and daily feed intake. Measuring individual daily feed intake and BW is time-consuming, labor-intensive, and stressful for the animals and mink producers. The main objectives of this study were to (1) evaluate the application of machine learning ( ML ) algorithms to predict the average daily gain ( ADG ), feed conversion ratio ( FCR ), and residual feed intake ( RFI ) values during the whole growing and furring period (15 weeks from August 1st to November 14th) using less expensive features such as sex, color type, age, BW and length; (2) find the most significant contributing feature within the growth and furring period to predict the ADG, FCR and RFI. The color and sex features were recorded on 1 088 mink and mink’s age, BW and length were measured every 3 weeks from August 1st to November 14th which is called P1–P5. The ADG, FCR, and RFI were then predicted by the selected ML algorithms using multiple combinations of the observed and measured features from P1 to P5. By comparing the calculated ADG, FCR, and RFI values with the predicted values, it was determined that the most accurate combination of features was to include all features such as sex, color, age, BW and body length on August 1st (at the beginning of the P1). Among selected ML algorithms, the extreme gradient boosting ( XGB ) algorithm provided the most accurate and reliable prediction for the ADG ( R 2 = 0.71, RMSE = 0.10), FCR ( R 2 = 0.74, RMSE = 0.14), and RFI ( R 2 = 0.76, RMSE = 0.10). The XGB algorithm can be an accurate algorithm to predict the ADG, FCR, and RFI values without measuring costly daily feed intake. In addition, sex was identified as the most significant feature to predict the ADG, FCR, and RFI values with the importance scores of 0.85, 0.67, and 0.79, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".