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Record W2068141607 · doi:10.1186/s13104-015-1108-x

Growth in Turface® clay permits root hair phenotyping along the entire crown root in cereal crops and demonstrates that root hair growth can extend well beyond the root hair zone

2015· letter· en· W2068141607 on OpenAlexafffund
Travis L. Goron, Sophia Watts, Charles Shearer, Manish N. Raizada

Bibliographic record

VenueBMC Research Notes · 2015
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversity of Guelph
FundersInternational Development Research CentreGovernment of Ontario
KeywordsRoot hairCrown (dentistry)BiologyFertigationRoot (linguistics)Root systemCropAgronomyFibrous root systemBotanyIrrigationDentistryMedicine

Abstract

fetched live from OpenAlex

In cereal crops, root hairs are reported to function within the root hair zone to carry out important roles in nutrient and water absorption. Nevertheless, these single cells remain understudied due to the practical challenges of phenotyping these delicate structures in large cereal crops growing on soil or other growth systems. Here we present an alternative growth system for examining the root hairs of cereal crops: the use of coarse Turface® clay alongside fertigation. This system allowed for root hairs to be easily visualized along the entire lengths of crown roots in three different cereal crops (maize, wheat, and finger millet). Surprisingly, we observed that the root hairs in these crops continued to grow beyond the canonical root hair zone, with the most root hair growth occurring on older crown root segments. We suggest that the Turface® fertigation system may permit a better understanding of the changing dynamics of root hairs as they age in large plants, and may facilitate new avenues for crop improvement below ground. However, the relevance of this system to field conditions must be further evaluated in other crops.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.005
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 head0.086
GPT teacher head0.293
Teacher spread0.206 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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