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Record W2740793222 · doi:10.3390/biology6030033

Seed Coating with Hydro-Absorbers as Potential Mitigation of Early Season Drought in Sorghum (Sorghum bicolor L. Moench)

2017· article· en· W2740793222 on OpenAlexaff
Linda Yuya Gorim, Folkard Asch

Bibliographic record

VenueBiology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsUniversity of Saskatchewan
FundersBundesministerium für Bildung und Forschung
KeywordsSorghumSeedlingBiologyAgronomySorghum bicolorCropBiomass (ecology)Growing seasonMoistureWater useDrought toleranceSoil waterWater contentEcology

Abstract

fetched live from OpenAlex

Climate change poses a threat to sorghum production systems by shifting the onset of the rainy season to a later date, increasing the risk of crop failure during crop establishment. The effects of drought on sorghum during seedling establishment have not been determined. Coating seeds with a water absorbing substance offers a way to buffer the seed against insufficient moisture in the surrounding soil. Seeds of two different sorghum varieties were coated with one of two commercially available hydro-absorbers: Stokosorb® and Geohumus®. These hydro-absorbers have the capacity to store water several times their own weight. The aim of this study was to compare the effects of the cited hydro-absorbers on early seedling growth of two sorghum landraces under different levels of soil water deficit. Seedlings were grown for 12 days under three water availability levels (Field capacity (FC), 50% of FC, and 25% of FC). The seedlings under water limited treatments were subsequently re-watered. Biomass, root length, plant height, leaf area, and leaf extension rate were monitored in two-day intervals for 24 days. Coating strongly affected seedling growth both under fully watered and water deficit conditions. Sorghum varieties differed in their responses to both soil water deficit and coating materials. In general, Stockosorb improved seedling performance under water limited conditions particularly by promoting root growth, whereas Geohumus did not.

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 score0.650
Threshold uncertainty score0.680

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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