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[Australia]Food Safety.

2015· article· en· W2504488390 sur OpenAlexaff
Brian Owler

Notice bibliographique

RevuePubMed · 2015
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueFood Safety and Hygiene
Établissements canadiensAlberta Medical Association
Organismes subventionnairesnon disponible
Mots-clésFood safetyBusinessGovernment (linguistics)LabellingProduct (mathematics)MarketingFood processingFood safety risk analysisConsumption (sociology)Public healthEnvironmental healthMedicinePolitical sciencePsychology
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,325
Score d'incertitude au seuil0,963

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0030,002
Science ouverte0,0010,002
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,3250,190

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,128
Tête enseignante GPT0,221
Écart entre enseignants0,093 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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