The potential of RFID technology for tracking Mangalica pigs in the extensive farming system–a research from Hungary
Bibliographic record
Abstract
Context In recent years, the focus on sustainable meat production in large-scale pig farming has increased. An essential element is acquiring and evaluating digital data on ecological, outdoor pig housing environments and individuals. Objective Some of the precision farming Internet of Things (IoT) tools that can be used in pasture-based, extensive pig production include those that have been proven in pasture-based ruminants and have been used in confined pig production for many years. These include Radio Frequency IDentification (RFID) technology, in which passive RFID tags attached to pigs’ ears capture signals from a reader equipped with an antenna. The reader can also be used to collect weather parameters. Methods In our on-farm research, which lasted 7 months and covered three seasons, we used RFID technology to investigate the effects of ambient temperature, humidity, air pressure, and hour of day on the appearance of breeding sows kept on pasture at the wallowing site. Results and conclusion We found that all three weather parameters and hour-of-day had a significant effect ( p < 0.001) on the appearance of sows at the wallow. We calculated the THI (Temperature Humidity Index) and found days in summer and early autumn when there was a risk of heat stress. This is essential information for the pig farmer because heat stress has a strong negative effect on sow fertility. Our on-farm study found that RFID technology is suitable for detecting the presence of pigs in the pasture. However, the limitations of the technology, both in terms of the housing environment and the species specificity of the pigs, need to be taken into account. Significance In pasture-based pig production systems, the meat production process is performed under extensive conditions of natural animal-environment interaction. The negative impact on the natural environment is much lower, but animal welfare is higher than in intensive pig farming. It is also a profit-oriented activity, where the production of traceable and reliable animal products is a social and economic requirement. Collecting information from trusted sources using precision livestock farming (PLF) techniques is also feasible in this type of farming, providing significant added value for pig farmers and consumers.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".