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Record W4375845955 · doi:10.24996/ijs.2023.64.4.7

Effect of Fusaric Acid (FA) Bio-stress on Certain Morphological Parameters for Potato (Solanum tuberosum. L) Cultivars in Vitro

2023· article· en· W4375845955 on OpenAlexaboutno aff
E. A. Altinawi, F. Al-Biski, Jaoudat Faddoul

Bibliographic record

VenueIraqi Journal of Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsSolanum tuberosumFusaric acidCultivarHorticulturePlant growthBiologyStress (linguistics)In vitroBotanyChemistryFusarium

Abstract

fetched live from OpenAlex

The experiment was carried out with the aim of studying the effect of biological stress on some morphological parameters of ten varieties of potatoes grown in vitro. Biological stress was applied by adding different concentrations of fusaric acid (0, 0.0125, 0.025, 0.05, 0.1, 0.2 mM), to the growth medium MS, and some growth parameters were measured, such as plant height (cm), number of leaves (leaf/plant¹), leaf area (mm²), number of roots (root.plant¹) and length it (cm), wet and dry weight of the plant (g). The results showed that the studied varieties were different in the response to biological stress according to the studied parameters. The addition of fusaric acid led to reduce all growth parameters compared with the control. The cluster analysis showed that based on the sum of the relative values ​​of the studied growth parameters, the studied varieties were distributed in three different groups: The first group includes three tolerant varieties to biological stress, and these are Toronto, Barcelona, and Suria). The second group includes four Moderate varieties of bio-stress, and these are Fabulla, Nectare, Spunta, and Ardappel. The third group included the following sensitive varieties, 7-four-7, Farida, and Joly. The results indicate that the in vitro screening technology can be used as a fast and efficient method to investigate the genetic variation of biological stress tolerance in potatoes.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.308
Teacher spread0.271 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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