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Record W1980056023 · doi:10.1007/s11746-003-0662-8

How green is green? Long‐term relationships between green seeds and chlorophyll in canola grading

2003· article· en· W1980056023 on OpenAlexaffabout
J. K. Daun

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaChlorophyllChlorophyll aMathematicsAgronomyChlorophyll bEnvironmental scienceHorticultureBotanyBiology

Abstract

fetched live from OpenAlex

Abstract Chlorophyll is undesirable in canola seeds because it is extracted into the oil resulting in problems during processing and utilization. In the Canadian grain grading system, and in similar systems in use in the United States and Australia, chlorophyll is estimated in canola seeds subjectively by crushing and counting the number of distinctly green seeds in a sample while simultaneously assessing the overall natural color of the crushed seeds. Chlorophyll contents of canola may be determined by extraction with solvent followed by spectrophotometric analysis or by using NIR instrumentation, capable of operating in the visible region and calibrated against samples with known amounts of chlorophyll. The relationship between the green seeds and chlorophyll content in canola export shipments from 1988 to 2001 was found to be linear. The intercept, referred to as the background chlorophyll, ranged from 6 to 16 mg/kg, and the slope ranged from 300 to 1000 mg/kg per green seed. In recent years, both the background chlorophyll and the slope have been increasing, resulting in an increase in the chlorophyll levels in top‐grade canola exported from Canada. The increase may be partly a result of the change in proportion of species of canola grown in Canada, and also may result from changes in perception of what constitutes a green seed. The use of an objective measurement of chlorophyll is recommended to improve the consistency of the grading system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.528

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 head0.027
GPT teacher head0.263
Teacher spread0.236 · 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

Citations10
Published2003
Admission routes2
Has abstractyes

Explore more

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