MétaCan
Menu
Back to cohort
Record W2970214503 · doi:10.22219/avicennia.v2i1.8315

TINGKAT PENDAPATAN ANGGOTA LMDH “LANCAR JAYA” DARI SEKTOR PERTANIAN HORTIKULTURA DI DESA NGANCAR KECAMATAN NGANCAR KABUPATEN KEDIRI

2019· article· en· W2970214503 on OpenAlex

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

VenueJournal of Forest Science Avicennia · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsHectareNonprobability samplingAgricultural scienceReceiptData collectionLikert scaleBusinessSocioeconomicsMarketingAgricultural economicsGeographyOperations managementMathematicsEngineeringAgricultureEconomicsStatisticsMedicineAccountingEnvironmental health

Abstract

fetched live from OpenAlex

Abstraction This research was conducted on 10 November 2018 - 31 January 2019 in Ngancar Village, Ngancar District, Kediri Regency. The study was intended to determine the level of income of LMDH "Current Jaya" members in Ngancar Village. In addition, to find out the factors that influence the success of the Forest Village Society Institute (LMDH) program that has been carried out in increasing the income of members of farmer groups. The location of Ngancar Village is due to the fact that the area is one of the tourist areas which has a relatively large number of poor people. Methods of data collection in the form of primary data collected by direct observation techniques in the field with interviews, questionnaires, and documentation studies of respondents (farmer group members) obtained by purposive sampling method. Secondary data is collected by the technique of recording data that already exists in related institutions. The data obtained will be processed by calculation and tabulation. While in the method of data analysis, researchers used two ways, namely an analysis of economic success (income), and the success factor of LMDH. For data processing methods, the income questionnaire uses the farm income formula (π) which is the difference between total receipt (TR) and total cost (TC), while the questionnaire success factor LMDH uses a Likert Scale. Based on the results of the research conducted, Chili (Capsicum annum L) commodity was obtained 68 respondents with a total income of Rp. 3,337,850,000, with an average income per hectare of Rp. 49,086,030 in one planting season. While the commodity Tomato (Solanum lycopersicum L) obtained 47 respondents with a total income of Rp. 1,368,899,000, with an average income per hectare of Rp. 29,125,510 in one planting season.Keywords: income, factor, LMDH

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)0.001
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 score0.422
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.000
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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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