Resilience as Welfare: Quantifying Adaptive Capacity in Farm Animals with Sensor-Enabled Phenotyping and Machine Learning
Bibliographic record
Abstract
Resilience, the capacity of an animal to recover rapidly and completely after a perturbation, has long been recognised but seldom quantified with precision. What has changed is the computational machinery now capable of measuring it. Climate variability, emerging disease pressures and increasingly complex production environments require systems that prioritise adaptive capacity rather than simple output stability. Here we show that resilience can be operationalised through the integration of multimodal sensing, state space modelling and machine learning. Continuous data streams from accelerometers, thermal and RGB imaging, milk meters, rumen boluses and behavioural trackers capture fine scale signatures of disturbance and recovery. Advanced filtering architectures reconstruct latent physiological states, while neural, hybrid and mechanistic statistical models extract recovery dynamics with accuracies between 80 and 99 percent and provide early warning of health compromise several days in advance. Across species, indicators based on variance, autocorrelation and area under the curve reliably distinguish resilient from fragile phenotypes, with heritabilities ranging from 0.026 to 0.432, allowing incorporation into genomic selection. Composite indices that combine production, behaviour, physiology and environmental context provide interpretable and challenge specific scoring frameworks for breeding and real time management. Despite these advances, governance mechanisms for responsible AI deployment remain incomplete, increasing the risk of misclassification, opacity and productivity centred optimisation. Resilience phenotyping is therefore both a technical and ethical opportunity. When paired with transparent data governance and welfare focused design, digital systems can shift livestock management from reactive oversight to anticipatory care. Together, sensor enabled phenotyping and computational modelling provide a scalable and welfare centred route for advancing adaptive livestock systems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".