Data Driven Enhancements to the Intestinal Integrity (I2) Index: A Novel Approach to Support Poultry Sustainability
Bibliographic record
Abstract
Background: Optimizing the intestinal integrity of poultry flocks through a comprehensive index measure, such as the intestinal integrity (I2) index, could help to promote sustainable production in the poultry industry. The I2 index is a tool for assessing the intestinal health of flocks based on flock level health and performance data, captured by Elanco Animal Health’s global surveillance system, i.e., the Health Tracking System (HTSi). The objectives of this study were to evaluate the relationships between the proposed I2 index and each of the following four performance parameters: average daily gain (ADG), feed conversion ratio (FCR), European production efficiency factor (EPEF), and percent livability; and compare the ability of the proposed I2 index to predict these performance parameters with the current I2 index. Results: The proposed I2 index was found to produce a greater range and increased variation in flock level I2 index scores as compared with the current I2 index. The proposed I2 index was found to predict the four performance measures at least as well as the current I2 index, and the results suggested that the proposed I2 index could be superior at predicting ADG, EPEF, and percent livability. Conclusion: Our results highlight the strength of data-driven approaches in the development and improvement of comprehensive health metrics.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".