Perception of laying hen farmers, poultry veterinarians, and poultry experts regarding sensor-based continuous monitoring of laying hen health and welfare
Bibliographic record
Abstract
Daily farm management practices play an essential role in determining and steering health, welfare and productivity of laying hen flocks. Optimal management requires expertise of farmers and coworkers, especially when hens are kept in complex, large-scale aviary systems. Relatively little sensor-based support is available to farmers, even though numerous research groups are working on developing technologies to continuously detect deviations in layer health and welfare. A survey with laying hen farmers, poultry veterinarians and poultry experts from Western Europe and Canada was conducted to identify and prioritize indicators of optimal and suboptimal laying hen health and welfare in commercial farms. The status-quo of sensor technology and the advantages, wishes, and concerns regarding sensors were additionally assessed to contribute to the future development of a predictive monitoring tool that continuously monitors laying hen health and welfare. A total of 45 stakeholders were interviewed, of which 41 filled in an online questionnaire. Although the prioritization of indicators differed between stakeholders, the majority identified the use of feed and water intake, egg production and quality, sound, activity, and movement of hens as important indicators to assess health and welfare. Currently collected (sensor) data were not used to their full potential, and stakeholders missed the integration and storage of data into one monitoring system with easy visualization tools. Most interest was observed in the use of cameras and microphones to detect deviations in health and welfare at an early stage, to reduce subjectivity of the assessment and to gain more knowledge on layer behavior. It can be concluded that these results could steer research efforts towards the development of continuous monitoring techniques, and enhance their adaptability and acceptability by stakeholders.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".