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Record W2009608774 · doi:10.1016/j.ijppaw.2014.01.001

Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants

2014· review· en· W2009608774 on OpenAlexaff
Hannah Rose, Bryanne M. Hoar, Susan Kutz, Eric R. Morgan

Bibliographic record

VenueInternational Journal for Parasitology Parasites and Wildlife · 2014
Typereview
Languageen
FieldVeterinary
TopicHelminth infection and control
Canadian institutionsCanadian Science Centre for Human and Animal HealthUniversity of Calgary
FundersFP7 Food, Agriculture and Fisheries, Biotechnology
KeywordsWildlifeLivestockWildlife diseaseWildlife conservationWildlife managementBiologyEnvironmental resource managementEcologyClimate changeGeographyAgroforestryEnvironmental science

Abstract

fetched live from OpenAlex

Global change, including climate, policy, land use and other associated environmental changes, is likely to have a major impact on parasitic disease in wildlife, altering the spatio-temporal patterns of transmission, with wide-ranging implications for wildlife, domestic animals, humans and ecosystem health. Predicting the potential impact of climate change on parasites infecting wildlife will become increasingly important in the management of species of conservation concern and control of disease at the wildlife-livestock and wildlife-human interface, but is confounded by incomplete knowledge of host-parasite interactions, logistical difficulties, small sample sizes and limited opportunities to manipulate the system. By exploiting parallels between livestock and wildlife, existing theoretical frameworks and research on livestock and their gastrointestinal nematodes can be adapted to wildlife systems. Similarities in the gastrointestinal nematodes and the life-histories of wild and domestic ruminants, coupled with a detailed knowledge of the ecology and life-cycle of the parasites, render the ruminant-GIN host-parasite system particularly amenable to a cross-disciplinary approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.124
GPT teacher head0.466
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations45
Published2014
Admission routes1
Has abstractyes

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