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Record W3025450619

Tick Talk: Tick-borne Diseases of South Dakota.

2017· article· en· W3025450619 on OpenAlexaff
Mark K Huntington, Jay Allison

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsCentre for Family Medicine
Fundersnot available
KeywordsLyme diseaseTickRocky Mountain spotted feverTularemiaTick-borne diseaseDiseaseEnvironmental healthMedicineGeographyImmunologyVeterinary medicineVirologyPathology
DOInot available

Abstract

fetched live from OpenAlex

In addition to being a nuisance, ticks can carry disease. This article presents a brief review of ticks and associated tick-borne disease relevant to South Dakota and surrounding regions. Tick-borne diseases of special relevance in South Dakota include tularemia, Rocky Mountain spotted fever, and Lyme disease. A number of others may also be encountered in the state as well. Prompt treatment of suspected cases is important to ensure a successful recovery, and tick-avoidance measures can reduce the risks of acquiring them. Most of these conditions are nationally reportable infectious diseases.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.105
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1050.019

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 head0.035
GPT teacher head0.271
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2017
Admission routes1
Has abstractyes

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