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Record W2565121766 · doi:10.5539/jfr.v6n1p59

Quality Evaluation of Flaxseed for Food Use Specifications

2016· article· en· W2565121766 on OpenAlexvenueaboutno aff
Anuradha Vegi, Charlene E. Wolf‐Hall, Clifford Hall

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsFood sciencePlate countAnimal scienceBiologyBacteria

Abstract

fetched live from OpenAlex

A Northern Great Plains regional survey of microbiological loads in flaxseed was completed for years 2008 and 2009. Effects of cleaning flaxseed on microbial loads including aerobic plate counts (APCs), mold counts (MCs) yeast counts (YCs), coliform counts (CCs), Escherichia coli counts, and Enterobacteriaceae counts (ECs) were determined. Chemical analyses including oil and linolenic acid -ALA indicated that all flaxseed had near normal oil content. This was the first reported survey for flaxseed. The pre-cleaned flaxseed had an average of 5.7 ± 0.1, 4.1 ± 0.2, 4.5 ± 0.2, 3.6 ± 0.1, and 3.0 ± 0.1 log colony forming units (CFU) g-1 of APC, CC, EC, YC and MC respectively. All counts were higher than those for cleaned seed. No E. coli was detected. The North Dakota-West (ND-W) region flaxseed had higher MC when compared to Canada, ND-North East (ND-NE) and ND-South East (ND-SE) region flaxseed. For APC, the counts were higher in flaxseed from Canada when compared to North Dakota. Cleaning the flaxseed should be considered an important step in reducing the microbial counts and also for maintaining high quality flaxseed.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.733
GPT teacher head0.570
Teacher spread0.163 · 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.

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

Citations1
Published2016
Admission routes2
Has abstractyes

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