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Record W4399461541 · doi:10.53555/sfs.v10i3.2777

Influence Of Moisture Content, Storage Condition And Seed Dimension On Seed Germination Of Solanum Nigrum Linn

2023· article· en· W4399461541 on OpenAlexvenueno aff
Pawan Kumar Modi, Shabnam Bee

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth and Agriculture Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGerminationSolanum nigrumWater contentDimension (graph theory)HorticultureMathematicsEnvironmental scienceBotanyBiologyEngineering

Abstract

fetched live from OpenAlex

 Medicinal  plants  cure  many  common  diseases  and  are  considered  essential  home  treatments  in  various  regions.  Solanum  nigrum,  known  for  its  medicinal  properties,  necessitates  the  development  of  effective  seed  germination  methods  to  ensure  consistent  and  improved  yields.  The  objective  of  this  study  was  to  evaluate  the  effects  of  moisture  content,  storage  conditions,  and  seed  dimensions  on  the  germination  of  Solanum  nigrum  over  4,  8,  12,  18,  and  24  months.  The  seed  germination  percentage  of  Solanum  nigrum  Linn.  gradually  declined  as  storage  time  increased  from  4  to  24  months.  At  10°C  storage,  45%  of  seeds  germinated  after  4  months,  dropping  to  22.2%  beyond  that  period.  At  40  (±2)  °C,  the  germination  rate  was  8.9%.  Notably,  at  10°C,  seed  germination  decreased  from  45%  to  27.2%  over  24  months.  Moisture  content  was  maintained  for  up  to  18  months  using  three  storage  methods:  polythene  bags,  gunny  bags,  and  cloth  bags.  Polythene  bags  preserved  maximum  moisture  for  up  to  24  months,  followed  by  gunny  and  cloth  bags.  A  positive  correlation  was  observed  between  seed  weight  and  germination  percentage.  This  study  proposes  a  seed  germination  method  to  produce  numerous  Solanum  nigrum  plants  quickly,  aiding  in  the  conservation  and  utilization  of  this  medicinally  valuable  species.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.085
GPT teacher head0.246
Teacher spread0.161 · 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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