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Record W2797218867 · doi:10.5539/ijb.v10n3p1

The Effect Pineaple Rind Extract (Ananas comosus L.) (Merr var. cayenne) Attack Intensity Cauliflower’s Pests (Brassica oleracea var. botrytis L. subvar. Cauliflora DC.)

2018· article· en· W2797218867 on OpenAlexvenueno aff
Sonya Lumowa, Sri Purwati, Syamsuryanto

Bibliographic record

VenueInternational Journal of Biology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth and Agriculture Techniques
Canadian institutionsnot available
FundersDirecció General de Recerca, Generalitat de CatalunyaDirektorat Riset dan Pengabdian Masyarakat
KeywordsAnanasBrassica oleraceaBotrytisHorticultureBiologyBotanyBotrytis cinerea

Abstract

fetched live from OpenAlex

The effect of pineaple rind extract (Ananas comosus (L.) Merr var. Cayenne) towards the intensity of pests attack from cauliflower (Brassica oleracea var. botrytis L. subvar. cauliflora DC.). The purpose of this research is to find out the effect of pineaple rind extract (Ananas comosus (L.) Merr var. Cayenne) towards the intensity of pests attack from cauliflower (Brassica oleracea var. botrytis L. subvar. cauliflora DC.). This research has been held for 2 months. The field reseach was held in the field in Loa Ipuh Laut Kecamatan Tenggarong Kota. This research used Random group design (RAK) with five treatments (control included) that has been repeated for twenty five times. Every treatment was 25 %, 50 %, 75 % and the control (without treatment) then was analyzed by using Anaysis of Variance (ANOVA) and was continued with BNJ test 5 %. The result of the research showed that every value was Farithmetic (46,79) (212,3) (66,14) (194,96) (82,11) > Ftable (3,01) it can be concluded that the allotment of vegetal pesticide from pineaple rind (Ananas comosus (L.) Merr var. Cayenne) can decrease the intensity of pests attack from cauliflower (Brassica oleracea var. botrytis L. subvar. cauliflora DC.).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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