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Record W35544945 · doi:10.1139/apnm-2021-0770

Performance and Decoder Complexity Estimates for Families of Low-Density Parity-Check Codes

2007· article· en· W35544945 on OpenAlexfundaboutno aff
S. Dolinar, K. Andrews

Bibliographic record

VenueIPNPR · 2007
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsnot available
FundersHeart and Stroke Foundation of Canada
KeywordsLow-density parity-check codeAlgorithmSoft-decision decoderDecoding methodsCode rateDecibelComputer scienceComputational complexity theoryCode (set theory)StatisticsMathematicsArithmeticTheoretical computer scienceTelecommunications

Abstract

fetched live from OpenAlex

Public recreation facilities are preferred gathering places for families to participate in physical, social, intellectual, and creative pursuits, and the importance of food environments in these facilities is gaining recognition. Evidence from other Canadian jurisdictions describes such food environments as unsupportive of health, which contradicts national recreation priorities to have healthy choices as the easy choices. This study aimed to characterize food environments in a convenient sample of Saskatchewan public recreation facilities. A convergent/parallel mixed methods study design used quantitative methods to determine the healthfulness of concession stands and vending machines and qualitative methods to examine barriers and facilitators to healthy eating in facilities. The results found that 5% of concession main dishes were defined as healthy and packaged foods/beverages in concession stands and vending machines were defined as <i>Offer Most Often</i> 6% and 8% of the time, respectively, according to Saskatchewan Nutrition Standards. Reported barriers to healthy eating were more than twice as prevalent as facilitators. To align with population health recommendations in Saskatchewan, food environments in public recreation facilities require immediate attention. The results and recommendations can be used to build collective action to address the problem and as a benchmark to measure change. <b>Novelty:</b> Only 5% of concession main dishes were defined as healthy. Only 6% of packaged foods and beverages in concessions, and 8% in vending, were defined as <i>Offer Most Often</i>. Reported barriers to healthy eating were more than twice as prevalent as facilitators, resulting in a current state that is difficult to change.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.299
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2007
Admission routes2
Has abstractyes

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