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Record W2969824606 · doi:10.32938/jpm.v1i1.185

PENGGUNAAN REGRESI LINEAR MULTIPEL DAN METODE KUADRAT TERKECIL UNTUK MENGANALISISFAKTOR-FAKTOR YANG MEMPENGARUHI HASIL PRODUKSI JAGUNG DI KABUPATEN BELU

2019· article· id· W2969824606 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

VenueRANGE Jurnal Pendidikan Matematika · 2019
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Pangan merupakan kebutuhan pokok primer yang sangat vital dan bersifat hakiki. Salah satu bahan pangan yang sangat dibutuhan masyarakatpropinsi Nusa Tenggara Timur adalah jagung. Produksi jagung untuk penyediaan pangan sebagai pengganti beras telah berlangsung sejak dahulu dan menjadi prioritas mayoritas masyarakat di Kabupaten Belu.Penelitian ini dilakukan untuk mengetahui faktor-faktor yang mempengaruhi hasil produksi jagung di kabupatenBeluyaitu luas panen, curah hujan, dan hari hujan. Berdasarkan hasil penelitian menggunakan regresi linear multipel dan metode kuadrat terkecil diperoleh persamaan: Y^=3,1679+2,3216X1+0,6819X2-10,4391X3
 Setiap variabel bebas baikluas panen (X1), curah hujan (X2), maupun hari hujan (X3) berperan mempengaruhi hasil produksi jagung.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.020
GPT teacher head0.226
Teacher spread0.207 · 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