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Record W4388971491 · doi:10.3390/agronomy13122894

Agronomic Approach to Iron Biofortification in Chickpea

2023· article· en· W4388971491 on OpenAlexafffundabout
Tamanna Jahan, Bunyamin Tar’an

Bibliographic record

VenueAgronomy · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsBiofortificationCultivarAgronomyRandomized block designFertilizerCropBiologyHorticultureChemistryMicronutrient

Abstract

fetched live from OpenAlex

Chickpea (Cicer arietinum L.) is a staple crop in many developing countries where iron (Fe) deficiency is severe. The biofortification of chickpea is a possible solution to address the Fe deficiency problem. A chickpea biofortification experiment was conducted under field conditions to evaluate the effects of different doses of Fe fertilizer (0 kg ha−1,10 kg ha−1 and 30 kg ha−1 of Fe-EDDHA) on the Fe content in seeds from 18 chickpea cultivars. The experiment was designed as a factorial combination of 18 chickpea cultivars and three Fe fertilizer doses in a randomized complete block design with four replications at two locations in Saskatchewan in 2015 and 2016. The Fe concentration in seeds across 18 different chickpea cultivars increased with Fe fertilization. Fe concentration in X05TH20-2 and CDC Frontier cultivars increased from 57 µg g−1 to 59 µg g−1 and from 56 µg g−1 to 58 µg g−1, respectively, after adding Fe fertilizer in both locations in 2015 and 2016. The biofortified seeds of these two cultivars can provide approximately 6 mg Fe 100−1 g seeds. Thus, 67 and 150 g of Fe biofortified chickpea seeds can provide 50% of the recommended dietary allowance of Fe for men and women.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score1.000

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)0.0000.001

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 head0.022
GPT teacher head0.215
Teacher spread0.193 · 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.

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
Published2023
Admission routes3
Has abstractyes

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