Landscape as a Shared Space for Badgers and Cattle: Insights Into Indirect Contact and Bovine Tuberculosis Transmission Risk
Bibliographic record
Abstract
ABSTRACT Though the magnitude of effect is uncertain, badger–cattle indirect contact has been implicated in bovine tuberculosis (bTB) transmission risk to cattle despite a paucity of data on badger space use. This study tracked field use by 35 GPS‐collared bTB test‐negative badgers (n = 3738 locational fixes, average fixes/badger = 107) and cattle grazing regimes at 446 fields over one grazing season (May–November 2016) on 18 farms (n = 56,202 field‐days). Individual badger visits spanned on average 3 farms (max. 9 farms). Badgers entered fields when occupied by grazing cattle on 20% of field‐days (nights). Most individual badgers (n = 25; 71%) were recorded in the same field as cattle on multiple occasions (up to 124 field‐days each). There was substantial interindividual variation, with 29% of badgers (n = 10) never co‐occurring with cattle. Badger field use was positively associated with dairy (rather than beef) production (especially when grazing cattle were present) and with fodder and rough grazing fields (compared with improved pasture and ‘other’ cattle‐related land use). Badgers were recorded in larger fields (range 0.06 to 10.9 ha) more frequently, especially when not actively grazed. They were significantly less likely to use fields with calves compared to fields containing cattle of other age groups. The presence of a badger sett in a field increased the likelihood of field use by tracked badgers. Farm management that minimises cattle–badger indirect contact in fields with setts may reduce bTB transmission risk to cattle. Delaying grazing of fodder fields after (silage) harvest until sward length has increased, restricting grazing to improved pastures, keeping calves with cows longer, or ensuring all batches of cattle have at least some calves present and not grazing fields with badger setts (or fencing around setts to prevent cattle access) may provide simple, cost‐effective strategies to reduce indirect badger–cattle contact, thus potentially lowering bTB transmission risk.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".