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Record W4391763262 · doi:10.53555/sfs.v10i1s.2297

Efficiency Assessment Of PTF1 For Phosphorous Deficiency Tolerance In Rice

2023· article· en· W4391763262 on OpenAlexvenueno aff
Poulomi Sen, Amrita Chakraborty, Sutanu Sarkar, Ananti Pathak, Arijit Mukherjee, Somnath Bhattacharyya

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

Phosphorous (P) deficiency tolerant rice genotypes can accumulate higher amount of P even though plant-available P in soil is low. Gobindabhog, an aromatic Bengal landrace was registered as one of the P-deficiency tolerant genotypes which can accumulate P almost 27mg/plant when grown in P-deficient field. Popular rice cultivar, Shatabdi accumulates approximately 6mg/plant P when grown in same soil. Relative abundance of transcript of Phosphate Starvation Sensing Transcription Factor 1 (PTF1) gene was estimated from the roots of 15- days old seedlings, grown both in P-deficient and sufficient conditions. Expression of PTF1 showed up-regulation in Gobindabhog under P-deficient condition whereas it was down-regulated in Shatabdi. RIL and NIL population were developed by crossing P deficiency tolerant genotype Gobindabhog with susceptible Shatabdi. SSR marker linked with PTF1 gene was used for genotyping the RIL and NIL population. P-accumulation in shoot at the pre-flowering stage and yield attributing parameters were estimated from the two groups of RILs and NILs grown in P-deficient soil. Single Factor analysis ANOVA revealed that PTF1 allele from Gobindabhog significantly increases P-accumulation as well as yield. Consequently, SSR marker RM 8258, located 24.06 KB away from PTF1 gene, can be recommended as a functional marker for introgression of superior allele of PTF1 from Gobindabhog to other non-tolerant genotypes.

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)0.006
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 score0.011
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.159
GPT teacher head0.304
Teacher spread0.145 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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