458 Advanced computing and information technology to address challenges in livestock production
Bibliographic record
Abstract
Abstract Advanced computing and information technology have a crucial role in addressing challenges in livestock production by offering innovative solutions to improve efficiency, productivity, and animal welfare. These technologies rely on intelligent combinations of data analytics, Machine and Deep Learning (ML, DL), Internet of Things (IoT), and robotics solutions. In livestock production, advanced computing facilitates the collection, management, and analysis of vast amounts of data generated from various sources such as sensors, wearable devices, and monitoring systems. This data can provide insights into animal behavior, health status, production, and environmental conditions, enabling farmers to make informed decisions in real-time. Machine learning algorithms can build data-driven models able to predict disease outbreaks, optimize feed formulations, and identify patterns for better breeding selection. IoT devices can monitor environmental parameters like temperature, humidity, and air quality, ensuring optimal conditions for animal comfort and health. Robotics technologies can automate tasks such as feeding, milking, and cleaning, reducing labor costs and improving efficiency. Additionally, advanced computing enables the development of virtual modelling and simulation tools to test different scenarios and optimize production processes without the need for extensive physical experimentation. This presentation will focus on various technical aspects and examples related to leveraging advanced computing and information technology such that livestock producers can enhance productivity, minimize resource wastage, and promote sustainable practices, ultimately leading to improved profitability and animal welfare in the industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".