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Record W2909740174 · doi:10.5539/jas.v11n2p90

Favourable and Unfavourable Effect of Homogeneous Static Magnetic Field on Germination of Zea mays L (Maize) Seeds

2019· article· en· W2909740174 on OpenAlexvenueno aff
J. Torres, Jainer E. Aranzazu-Osorio, E. Restrepo-Parra

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsnot available
Fundersnot available
KeywordsGerminationZea maysHomogeneousAgronomyHomogeneity (statistics)Magnetic fieldBiologyPhysicsMathematics

Abstract

fetched live from OpenAlex

The effect of homogeneous static magnetic stimulation on Zea mays L. (maize) seeds and its potential utility as a tool in biotechnological development for the improvement of maize seeds was studied. The values of magnetic flux density that influenced the biological development of some plant species of the Poaceae family were determined from a literature review. ICA V-305 variety corn seeds were exposed to seven values of magnetic flux density between 50.0 mT and 250.0 mT, with homogeneity of 98.4% and at (1.0, 3.0, 5.0 and 7.0) min exposure times. The mean germination time (MGT), index of germination speed (VGer) and germination rate (Gmax) were evaluated as responses. The magnetic flux density of 50.0 mT with a one-minute exposure time recorded the largest reduction (12.4%) in the MGT while the germination rate for the same treatment increased by 17.4% with respect to the control. No significant effects of the magnetic treatment were recorded for the Gmáx . The magnetic treatment of seeds with homogeneous static fields does not have as favourable a response as the treatments with fields with magnetic gradients, that is to say, using toroidal magnets.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.002
GPT teacher head0.206
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2019
Admission routes1
Has abstractyes

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