Evaluating Pig Ear Skin Temperature : Intercontinental Transport vs Housing Conditions
Bibliographic record
Abstract
Variations in the skin temperature are often used as indicators of stress in animals. With advances in technology, it is now feasible to record high frequency livestock temperatures continuously for extended periods. In this study the objective was to analyse the response of pigs to different environmental conditions. The ear skin temperature of two batches of animals in intercontinental transport and housing was registered and characterized by the use of phase space diagram methodology. Eleven pigs were monitored during transport from a farm located in Reston (Canada), until their arrival at a farm in Montiel (Spain) during May 2016. After the itinerary by airplane and trucks separated with rest housing, pigs arrived to the farm after 94 hours. Fourteen finishers were monitored in one pen on a breeding farm in Villatobas (Spain) during one week in June 2017. To measure ear skin pig temperature, one logger with temperature sensor was glued to the RFID tag of each pig and placed on the inside half of the left ear. The areas and centroids of the phase space diagrams of ear skin temperatures were used to quantify the variability of the time temperature series. It was considered analysis of full times series and time series by periods (transport modality, transport transfers and day and night). The time series pattern for each individual was analysed independently in order to identify differences between animals of the same batch. The thermal variability between animals of the same batch was higher during transport than those identified in housed animals. The maximum and minimum areas for an animal during transport were 60-77% higher than the areas for housed animals, indicating more exposure to thermal stress during transport management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".