Bibliographic record
Abstract
Abstract Evidence climate change is impacting ticks and tick-borne infections is generally lacking. This is primarily because, in most parts of the world, there are no long-term and replicated data on the distribution and abundance of tick populations, and the prevalence and incidence of tick-borne infections. Notable exceptions exist, as in Canada where the northeastern advance of Ixodes scapularis and Lyme borreliosis in the USA prompted the establishment of tick and associated disease surveillance. As a result, the past 30 years recorded the encroachment and spread of I. scapularis and Lyme borreliosis across much of Canada concomitant with a 2-3 °C increase in land surface temperature. A similar northerly advance of I. ricinus [and associated Lyme borreliosis and tick-borne encephalitis (TBE)] has been recorded in northern Europe together with expansion of this species’ range to higher altitudes in Central Europe and the Greater Alpine Region, again concomitant with rising temperatures. Changes in tick species composition are being recorded, with increases in more heat tolerant phenotypes (such as Rhipicephalus microplus in Africa), while exotic species, such as Haemaphysalis longicornis and Hyalomma marginatum , are becoming established in the USA and Southern Europe, respectively. In the next 50 years these trends are likely to continue, whereas, at the southern extremities of temperate species’ ranges, diseases such as Lyme borreliosis and TBE may become less prevalent. Where socioeconomic conditions link livestock with livelihoods, as in Pakistan and much of Africa, a One Health approach is needed to tackling ticks and tick-borne infections under the increasing challenges presented by climate change.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".