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Record W4390570938 · doi:10.53555/sfs.v10i3.1908

Assessing The Response Of Spinacia Oleracea Cultivar Bloomsdale To Cadmium Metal Stress On Growth And Development

2023· article· en· W4390570938 on OpenAlexvenueno aff
Fawad Ali, Sahar Javed, Hammad Ali Munam, Muhammad Sajid, Arshad Rasool, Muhammad Adnan Hussain, Obaid Muhammad Abdullah

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsSpinaciaCadmiumSpinachCultivarPhotosynthesisHorticultureChemistryBotanyBiologyBiochemistry

Abstract

fetched live from OpenAlex

In certain areas of Pakistan, industrial effluent is used to irrigate vegetables. Metals are among the many pollutants found in industrial and municipal wastewater. The purpose of this study is to evaluate how Spinacia oleracea cultivar Bloomsdale responds to cadmium copper stress in terms of growth and development. The study's findings indicate that, compared to control conditions, the photosynthetic area is 56% smaller in high cadmium environments after 30 days of growth. Data were examined using t-tests and one-way ANOVA to determine how the cultivar Bloomsdale of Spinacia oleracea responded to cadmium. Tissue Cd concentrations rose in tandem with rising Cd stress. The structural, biochemical, emotional, tangible, and cellular processes of plants are altered by cadmium stress, which has an impact on photosynthesis, agricultural yield, and the growth and development of plants. After 30 days of growth, the photosynthetic area in high cadmium settings is 56% smaller than in control circumstances. The average yields fell to just 41 and 35 seeds after 30 days as a result of the reproductive suppression caused by the further intensification of cadmium to 5 and 7 ppm. Lowering the source of spinach's elevated resistance to cadmium can reduce the vegetable's cadmium levels and enhance food safety.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.182
GPT teacher head0.295
Teacher spread0.113 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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