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Record W7099809656

FOOD IRRADIATION BACKGROUNDER

2000· article· en· W7099809656 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBig Data and Digital Economy
Canadian institutionsnot available
Fundersnot available
KeywordsFood irradiationTrichinellaRaw meatBacteriaRipeningShelf lifeFood safetyFood processing
DOInot available

Abstract

fetched live from OpenAlex

Food irradiation is the process of exposing food to radiant energy in order to reduce or eliminate insect pests, bacteria and other microorganisms, therefore making it safer and more resistant to spoilage. In Canada, for example, spices are irradiated to kill insects. Irradiation may also be used to increase shelf life, by slowing the ripening or sprouting in fresh fruits and vegetables. (Health Canada, 2002) Beef, pork, and other meats sometimes are contaminated by parasitic organisms that may cause disease in humans when ingested. Among these are the parasitic nematode Trichinella spiralis, the bovine and pork tapeworms Cysticercus bovis and Cysticercus cellulosae, and the protozoan parasite Toxoplasma gondii. Irradiation is currently the only known method to eliminate E. coli O157:H7 bacteria in raw meat. The technology also significantly reduces levels of Listeria, Salmonella, and Campylobacter on raw product but does not eliminate all bacteria. Irradiated meat or poultry, for example, still requires refrigeration, but would be safe longer than if untreated. (Henkel, 1998; Solomon, 1999)

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.027
GPT teacher head0.210
Teacher spread0.183 · 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 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

Citations0
Published2000
Admission routes1
Has abstractyes

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