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Record W4395465643 · doi:10.18280/ijdne.190227

Domestication of Ciplukan (Physalis angulata L.) from Three Altitudes Using Watering Treatment

2024· article· en· W4395465643 on OpenAlexvenueno aff
Desy Setyaningrum, Maria Theresia Sri Budiastuti, Supriyono Supriyono, Bambang Pujiasmanto

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2024
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Medicinal Plants
Canadian institutionsnot available
Fundersnot available
KeywordsDomesticationPhysalisBiologyGeographyEcologyHorticulture

Abstract

fetched live from OpenAlex

Ciplukan (Physalis angulata L.) is a plant that is often considered a weed but contains secondary metabolites that can be used as traditional medicine.This supports the domestication of ciplukan at various altitudes using watering treatments.The research aims to examine the growth and yield of ciplukan from various heights for domestication using watering treatments.The research used a complete factorial randomized block design with 2 factors, namely the height of the seed origin and the watering volume.The altitude factor consists of three levels, namely lowland, medium land and highland.Watering volume factor with four levels, namely 100%, 75%, 50%, and 25% field capacity.The treatment of the height of the seed origin has an effect on plant biomass, namely that the highland seed origin shows the highest plant biomass.Watering volume affects plant height, number of leaves, root length, fresh weight and plant biomass.The growth and yield of ciplukan decreases as the watering volume decreases.The combination of treatment from midland seeds with a watering volume of 25% showed the lowest plant height.Domestication of ciplukan shows that the height of the seed origin and the volume of watering have a significant influence on the growth and production of ciplukan.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.283

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)0.0000.000

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 head0.027
GPT teacher head0.322
Teacher spread0.295 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicPhytochemicals and Medicinal PlantsFrench-language works237,207