Perception of laying hen farmers, poultry veterinarians and poultry experts on sensor-based continuous monitoring of health and welfare of laying hens
Bibliographic record
Abstract
Traditionally, laying hen farmers monitor health, welfare and productivity of their flockbased on feed and water intake of the birds, flock productive output, climatefactors, and behavioural observations. Due to the growing number of birds per layer farmand the decreased availability of personnel with sufficient knowledge on poultry, itbecomes increasingly difficult to safeguard and control bird health andwelfare. Concurrently, there is a global trend towards more sustainable livestock farmingwith amongst others profitable and efficient animal production with a low ecologicalfootprint. To keep up with these developments, farmers can benefit from state-of-the-artsensor technology, serving as artificial nose, ears and eyes that gather 24/7 data on flockhealth, welfare and productivity. This project aims to improve laying hen welfare byearly stress detection based on continuous assessment of reliable, predictive (animalbased)indicators. As a first step, a qualitative, multi-stakeholder survey was preparedto determine current and future sensor use and automation in aviaries to support on-farmhealth and welfare assessment. Knowledgeable laying hen farmers, practicing poultryveterinarians and experienced poultry experts specialised in e.g. nutrition, genetics andwelfare, all working in West-Europe and Canada, were selected for participation. Using apurposive heterogenous sampling approach, maximum diversity was createdamong our homogenous candidate group. Participants completed an online questionnaireand participated in a semi-structured interview consisting of narrative questions andfollow-up probing questions. The questionnaire aimed to identify several variables thatcould underly the answers given during the interview, such as sociodemographiccharacteristics. Laying hen farmers were additionally asked about farm management andhousing characteristics, while poultry veterinarians and experts were asked about detailson their profession and frequency of contact with the commercial poultry sector. Duringthe interview, participants were encouraged to identify relevant health and welfare issues,including their causal stressors and predictive indicators, to describe currentuse of sensor (data) during health and welfare assessment and to describe their interest infuture technologies. Qualitative content of the interviews is analysed, using an inductivecoding approach and summarized per stakeholder. Quantitative analysis includes variableranking and comparison between stakeholders, a binary logistic regression and a Fisherstest. Preliminary results will be shown during the WIAS Annual Conference. Final results will be used during consecutive steps of the project.
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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 | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".