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Record W2143180312 · doi:10.1111/zph.12206

<i>Escherichia coli</i> O157:H7 – Discerning Facts from Fiction: An Integrated Research and Extension Project for Multiple Audiences

2015· article· en· W2143180312 on OpenAlexaboutno aff
David Moore, David R. Smith, W. M. Sischo, Kyle Heaton, Thomas E. Besser

Bibliographic record

VenueZoonoses and Public Health · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureAcademy of Nutrition and DieteticsAmerican Association of Bovine Practitioners
KeywordsMisinformationThe InternetGovernment (linguistics)Public relationsControl (management)Political scienceWorld Wide WebManagementComputer scienceLaw

Abstract

fetched live from OpenAlex

The O157:H7 (EcO157) epidemiology of Shiga-toxin-producing Escherichia coli (STEC) in cattle is complex, and myths about pre-harvest control are perpetuated. The objectives of this project were to identify perpetuated misinformation and inform four audiences about evidence-based risks and pre-harvest control of EcO157 by addressing: (i) EcO157 epidemiology and pre-harvest control; (ii) how food safety policy is created; and (iii) how to present accurate information about EcO157. An environmental scan using a daily Internet search helped identify themes for education. A literature review of pre-harvest control measures contributed to the development of educational materials (fact sheets, website, web presentations and conferences). Conference 1 was a webinar with 315 registrants, 10 countries including 41 US states and four Canadian provinces. Most participants felt confident in using their new knowledge, more than half felt confident enough to answer EcO157 questions from the public and many would recommend the recorded version of the webinar to colleagues. Conference 2 was live in the Washington, DC, area with most participants employed by the US government. All agreed that they better understood pre-harvest control, how food safety policy was made, and were confident they could create an effective message about STEC pre-harvest control. Videos were posted and received 348 Internet visitors within 2 months. Conference 3 was a webinar with a live audience and Twitter feeds, targeting people who give nutrition advice. Almost all ranked the programme good to excellent and relevant to their work. About 25% indicated that they would share: 'grass-fed beef is not safer than grain-fed', 25% would share information on effectiveness of cattle vaccines, and 14% would share information on message mapping. Across all conferences, major changes in knowledge included the following: there is no additional risk of EcO157 shedding from grain-fed versus grass-fed cattle, pre-harvest vaccination is efficacious, and production systems (pasture versus confinement) do not affect EcO157 shedding rates.

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)0.038
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.004
Scholarly communication0.0080.005
Open science0.0020.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.313
GPT teacher head0.368
Teacher spread0.055 · 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 designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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