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Record W4391746661 · doi:10.53555/sfs.v10i1s.2284

Prevalence Of Nematode Population In Two Different Rice Cultivation Systems And Its Impact On Crop Growth

2023· article· en· W4391746661 on OpenAlexvenueno aff
Arun Rathod, Shimpy Sarkar, Jayita Hore, Kriti Singh, Tamoghno Majumder, Kusal Roy

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsNematodeCropBiologyAgronomyPopulation growthPopulationBiotechnologyEcologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

An experiment was carried out to study the prevalence of nematode population in two different cultivation systems (SRI and conventional) on rice cv. IET- 4094 (Khitish). Total twenty-eight plots were laid out in paired plot technique. Irrespective of rice cultivation systems, there had been a steady hike in population of Meloidogyne graminicola and Tylenchorhynchus mashhoodi with the maximum being recorded near harvest and a sudden fall during fallow period was observed. The study indicated that SRI cultivation provided comparatively more favourable ecological condition for steady buildup of M. graminicola and T. mashhoodi population than conventional system. Between two systems, SRI method encountered comparatively more population of Hoplolaimus indicus. The spiral nematode, Helicotylenchus dihystera was also found associated with the rice crop but statistical analysis did not reveal any stable result to come into conclusion about their ecological preference. The study revealed that continuous submergence of rice field as in conventional system was detrimental to growth and reproduction of R. reniformis. The rice crop grown under SRI method acquired more vigorous growth as compared to conventional system. Profuse root and shoot growth as evidenced by root length, root biomass yield and straw yield in SRI might have limit the yield reduction in spite of huge infestation of nematodes.

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)0.001
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 score0.006
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.144
GPT teacher head0.309
Teacher spread0.165 · 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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