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FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN CABAI RAWIT DI KOTA TOMOHON

2018· article· en· W2891838859 on OpenAlex
Ratag Yosi ., Paulus Adrian Pangemanan, Lorraine W. Th. Sondak

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAGRI-SOSIOEKONOMI · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployee Performance and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsPepperQuarter (Canadian coin)Agricultural scienceAgricultural economicsEconomicsToxicologyChemistryEnvironmental scienceFood scienceGeographyBiologyArchaeology

Abstract

fetched live from OpenAlex

The study aims to analyze the factors that affected demand of cayenne pepper in Tomohon City. This study conducted for 3 months from September to November 2017. Data used secondary data was time series data per quarter from year 2012 to 2016. Analysis used is multiple regression analysis. The result showed that factor of, cayenne pepper price, substitution goods (curly pepper) and complementary goods (shallots) significantly affected on cayenne pepper demand.*lrr*.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.017
GPT teacher head0.219
Teacher spread0.202 · 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