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Record W2504488390

[Australia]Food Safety.

2015· article· en· W2504488390 on OpenAlexaff
Brian Owler

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsAlberta Medical Association
Fundersnot available
KeywordsFood safetyBusinessGovernment (linguistics)LabellingProduct (mathematics)MarketingFood processingFood safety risk analysisConsumption (sociology)Public healthEnvironmental healthMedicinePolitical sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

Australia faces some serious challenges if we are to ensure the safety and supply of quality food and water. When it comes to food and food safety, one of the problems for the vast majority of Australians is knowing which foods and drinks, and in what amounts, are appropriate and which are not. This is especially so in today’s world of myriad food choices and confusing messages and marketing. That this is why simple, informative food labelling, such as the Health Star Rating, is crucially important to people’s health. The HSR system provides simple but prominent, information about how healthy the product is. It allows for quick and easy comparisons, and ideally assist people to make healthier choices. Food labelling is about promoting health and health awareness, as well as protecting public safety. Despite having a fairly robust system in place, Australia has experienced problems with food safety. Following an outbreak of hepatitis A that was linked to frozen berries imported from China, the Australian Government announced plans for clearer food country of origin labelling. Previous attempts to tighten food labelling standards had met with strong resistance from Australian food manufacturers, who complained that making changes would add significantly to production costs. Despite this apparent burden on food manufacturers, Australian consumers have come to expect strong food safety measures. Food labelling and country of origin labelling will make it easier for people to make healthy and informed choices about their food and drink consumption. The AMA has also been outspoken about the health impacts of climate change and in particular, the consequences on Australia’s food and water resources. There is considerable evidence that governments must plan for the major impacts of climate change, especially for extreme weather events, the spread of diseases and the possible disruption to supplies of food and water. The health effects of climate change will include increased heat-related illness and deaths, increased food and water borne diseases, and changing patterns of diseases. The incidence of conditions such as malaria, diarrhea, and cardio-respiratory problems is likely to rise. We also know that local changes in temperature and rainfall have altered distribution of some water-borne illnesses and disease vectors, and reduced food production for some vulnerable populations. Food insecurity and the threat to water supply must be addressed as a changing climate in Australia is likely to reduce local food yields and quality and increase food prices. This could lead to major health issue, especially for lower-income families and remote communities where food choices are often limited. Dietary insufficiencies, nutritional imbalances and health impairments, especially in young children, is a possible consequence of reduced food yields and increased prices. The AMA has called on our government to show leadership in addressing climate change and the effects it is having, and will have, on human health. This must include waste management plans and water conservation. Australia’s food and water sustainability are also at risk from fracking and the mining on prime agricultural land. There is mounting concern in Australia that fracking and coal seam gas mining will erode agricultural land production and potentially contaminate some water suppliers.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.325
Threshold uncertainty score0.963

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.3250.190

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 head0.128
GPT teacher head0.221
Teacher spread0.093 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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