Measurement and Prediction of Rectal Temperature of Chicken Based on Genetic Programming
Bibliographic record
Abstract
The rectal temperature is traditionally measured and taken as the body temperature of chickens.However, there is not yet a correlation model between body temperature and rectal temperature to reflect the dynamic change law of chicken body temperature in real time.To solve the problem, this paper establishes a correlation model between the wing temperature and rectal temperature of chickens based on genetic programming (GP).Then, the competitive selection method was applied to optimize the data on wing temperature and rectal temperature of chickens under room temperature.The competitive size was set to 5, and the optimization was terminated when the individual fitness remained unchanged in 3 consecutive generations.The model was run independently 40 times, and verified with the data on wing temperature and rectal temperature of chickens under cold stress.The results show that the mean absolute value of error was 0.025 for the fitting between wing and rectal temperatures, and the prediction error of the model was 0.17 %.Therefore, our model can automatically search for optimal relation function of rectal temperature and wing temperature and achieve a high fitting accuracy.The research provides a novel and rapid temperature measurement method for chickens.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".