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Record W2092083834 · doi:10.1081/css-120014510

CADMIUM, COPPER, IRON, MANGANESE, SELENIUM, AND ZINC IN CANADIAN SPRING WHEAT

2002· article· en· W2092083834 on OpenAlexaffabout
Eugene J. Gawalko, R G Garrett, Thomas W Nowicki

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsCadmiumSeleniumZincManganeseTrace elementCropCopperAgronomyEnvironmental scienceBiofortificationGrowing seasonGrain qualityChemistryEnvironmental chemistryBiology

Abstract

fetched live from OpenAlex

Surveys of the average quality of different grades of the various classes of Canadian grains and oilseeds harvested each growing season are a major endeavor of the Grain Research Laboratory. With increasing interest in trace element levels in various cereal and oilseed crops, as part of the 1996, 1997 and 1998 Harvest Surveys, Western Canadian hard red spring wheat crops were characterized for cadmium, copper, iron, manganese, selenium and zinc. Samples were selected from producer submitted harvest survey samples received from crop districts in Manitoba, Saskatchewan and Alberta for the 1996, 1997 and 1998 crops. The methodology, including sample preparation, instrumentation and quality assurance, is described for each element. Data generated from the three crop years was evaluated with temporal and spatial variability studies. Year-to-year variations in grain chemistry are small for Cd, Mn, Se and Zn, but Cu and Fe contents show 12% and 9% decreases respectively over the three years. The overall variability for the plant-essential trace elements, Cu, Fe, Mn and Zn, is low in comparison with Cd and Se. It is demonstrated that the spatial variation in crop chemistry across the Canadian Prairie wheat producing region is greater than the year to year variations, and that soil properties are major factors controlling Cd and Se levels in grain.

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)0.002
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 score0.540
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.251
Teacher spread0.224 · 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

Citations24
Published2002
Admission routes2
Has abstractyes

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