MétaCan
Menu
Back to cohort
Record W2778323077

Sodium reduction challenges and facilitators in breads and processed poultry products: an industry perspective

2016· dissertation· en· W2778323077 on OpenAlexaboutno aff
Brenda Carr

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2016
Typedissertation
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Reduction (mathematics)BusinessFood scienceChemistryComputer scienceMathematicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Excessive consumption of sodium has been shown to cause high blood pressure (Garriguet, 2007). The breads and processed meat categories were identified as the highest contributors to sodium consumption within the Canadian population. The goal of this research is to identify the challenges associated with reducing sodium in bread and processed poultry products within the food industry in Canada. Results are based on 10 interviews with industry experts as well as a review of relevant industry documents related to industry???s sodium reduction policies. Reaching Health Canada???s target of 25 percent sodium reduction has been challenging for industry. Reducing sodium is only one of industry???s priorities, which also include producing a product that is marketable to the Canadian public in terms of taste, shelf life, and aesthetics. Sodium reduction labeling policies set out by Health Canada have further restricted industries ability to communicate to the public products where sodium reduction has been achieved but fall short of the 25 percent target set by Health Canada. More time is required to drive public desire for sodium reduced products and for the industry to reduce sodium. More research is also required for consumer friendly, cost effective sodium replacement alternatives.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0020.002
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

Study designOther design
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuee-scholar@UOIT (University of Ontario Institute of Technology)Same topicSodium Intake and HealthFrench-language works237,207