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Record W2975434529 · doi:10.29169/1927-5129.2019.15.03

Anti Microbial Resistance in Salmonella

2019· article· en· W2975434529 on OpenAlexvenueno aff
Yashpal Singh, Anjani Saxena, Jeetendrasingh Bohra, Rajesh Kumar, M. Saxena

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsTyphoid feverAntibioticsPublic healthOutbreakMedicineSalmonellaSanitationAntibiotic resistanceEnvironmental healthDrug resistanceSalmonella typhiIntensive care medicineBiologyMicrobiologyVirology

Abstract

fetched live from OpenAlex

Antibiotics are one of the major drugs to eradicate microbial infection. Many types of antibiotics have been used as therapeutics in several fields such as medical, agriculture, animal husbandry for human beings as well as animals. In past few years microbes have become resistant to some common antibiotics. We found that drug resistance is escalating at an alarming rate. Some of the infections like typhoid, pneumonia, tuberculosis, and gonorrhea are becoming difficult to treat while antibiotics are becoming less effective. Typhoid fever is one of the most common foodborne illnesses leading to many deaths annually worldwide. The emergence of multi-drug resistant Salmonella enterica serovar Typhi strains (S. Typhi) has resulted in several large outbreaks of enteric fever in many developing countries of the world leading to increased morbidity and mortality. Multi-drug resistance remains a major public health problem, particularly in developing countries of Asia and Africa. Some important measures like rational use of antibiotics, improvement in public sanitation facilities, availability of clean drinking water, promotion of safe food handling practices and public health education can play a crucial role in the prevention of multiple drug resistant typhoid fever.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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.

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

Citations6
Published2019
Admission routes1
Has abstractyes

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