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Record W2025137373 · doi:10.5539/jas.v6n2p160

Screening Maize (Zea mays) Genotypes for Tolerance to Witchweed (Striga asiatica L. Kuntze) Infection

2014· article· en· W2025137373 on OpenAlexvenueno aff
Moreblessing Chitagu, Joyful Tatenda Rugare, Stanford Mabasa

Bibliographic record

VenueJournal of Agricultural Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsStrigaBiologyCultivarAgronomyWeedZea maysShootBiomass (ecology)PoaceaeHorticultureGermination

Abstract

fetched live from OpenAlex

Striga asiatica (witchweed) is a parasitic weed that is a serious threat to maize (Zea mays L.) production in semi-arid environments in Africa. A pot experiment was conducted under glasshouse conditions to screen nine maize cultivars for their tolerance / tolerance to Striga infection at the University of Zimbabwe in the 2012 / 2013 rainfall season. Striga did not significantly (P > 0.05) reduce the final maize height of the maize cultivars, with the exception of three maize cultivars, namely, PHB3253, PHB30G19 and PHB30B50. Furthermore Striga did not significantly (P > 0.05) reduce the stem biomass, leaf biomass and cob biomass of all the maize cultivars evaluated. Generally the maize cultivars had significantly (P < 0.05) higher root / shoot ratios in the Striga infected plants compared to the uninfected plants. It was concluded that all the maize genotypes used in this study could possibly be tolerant to S. asiatica.

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 score0.965
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.226
Teacher spread0.213 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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