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Record W4225925325 · doi:10.37867/te130476

PHYTOCHEMICAL SCREENING OF HALOPHYTIC PLANT SUAEDA NUDIFLORA (WILLD.)

2021· article· en· W4225925325 on OpenAlex
Rachana Suthar, Hiteshkumar Solanki

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTowards Excellence · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsImpact
Fundersnot available
KeywordsPhytochemicalHalophyteBiologyTraditional medicineBotanySalinityEcologyMedicine

Abstract

fetched live from OpenAlex

The world is blessed with a great variety of natural vegetation, some of which are used as traditional medicine to cure various sicknesses and diseases. Phytochemicals are naturally occuring, biologically active chemical compounds in plants. Phytochemicals like carotenoids, flavanoids and polyphenols, they posses antioxidant activity and protect our cells against oxidative damage and reduce the risk of developing certain types of cancer. The majority of the active compounds are phenolic, vitamin C, vitamin E, tannins and carotenes. Coastal medicinal plants also engage in recreation to treat diabetes, particularly where most people have limited resources and do not have access to modern treatment. Halophytes are salt tolerant plant. Halophyte species which grow in a wide variety of saline habitats, from coastal sand dunes, salt marshes and mudflat to inland deserts, salt flats and steppes. Salt tolerance plants have more phytochemical and more antioxidant effect indicated that lot of literature. So in this way in my present study I have done phytochemical screening or preliminary phytochemical screening of halophyte Suaeda nudiflora.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.208
Teacher spread0.187 · 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