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Record W3192502829 · doi:10.3390/plants10081589

Agronomic Performance in Low Phytic Acid Field Peas

2021· article· en· W3192502829 on OpenAlexafffundabout
Donna Lindsay, Ambuj Bhushan Jha, Gene Arganosa, Raymond P. Glahn, Thomas D. Warkentin

Bibliographic record

VenuePlants · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsPhytic acidCultivarBioavailabilityBiologyDry weightFood scienceHorticultureField peaAgronomyCrop

Abstract

fetched live from OpenAlex

Field pea is a pulse that delivers high protein content, slowly digestible starch and fiber, and many vitamins and minerals, including iron. Naturally occurring plant phytic acid molecules bind iron, lowering its availability for absorption during digestion. Two low phytic acid (lpa) pea lines, 1-2347-144 and 1-150-81, developed by our group had 15% lower yield and 6% lower seed weight relative to their progenitor cultivar. Subsequently, we crossed the two lpa lines and two cultivars, and derived 19 promising lpa pea breeding lines; here we document their agronomic performance based on 10 replicated field trials in Saskatchewan. Seventeen of these lpa lines yielded greater than 95% of the check mean (associated cultivars) and 16 were above 98% of the check mean for 1000 seed weight. The 19 lpa lines showed 27 to 55% lower phytic acid concentration than the check mean. Iron concentrations were similar in all the lpa lines and cultivars, yet the Caco-2 human cell culture assay revealed 14 of the 19 lpa lines had 11 to 55% greater iron bioavailability than check means. Thus, a single round of plant breeding has allowed for closing the gap in performance of low phytic acid pea.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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

Citations5
Published2021
Admission routes3
Has abstractyes

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