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Record W2491724522 · doi:10.29244/jitl.15.1.12-19

PENENTUAN INDEKS BAHAYA KEKERINGAN AGRO-HIDROLOGI: STUDI KASUS WILAYAH SUNGAI KARIANGO SULAWESI SELATAN

2013· article· id· W2491724522 on OpenAlex
Muhammad Munawir Syarif, Baba Barus, Sabri Effendy

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

VenueJurnal Ilmu Tanah dan Lingkungan · 2013
Typearticle
Languageid
FieldEnvironmental Science
TopicWater and Land Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsForestryGeography

Abstract

fetched live from OpenAlex

Kekeringan agro-hidrologi dapat diartikan sebagai kekurangan air permukaan, air tanah dan mencukupi untuk tanaman dan kebutuhan masyarakat untuk jangka waktu tertentu. Sejauh ini belum ada indeks kekerigan agro-hidrologi yang menggabungkan faktor iklim, air permukaan, dan air bawah permukaan tanah. Penelitian ini merumuskan sebuah indeks bahaya kekeringan (Ibk) sebagai indikator kekeringan agro-hidrologi. Model yang dikembangkan dari kombinasi curah hujan musim kering, kedalaman air tanah, jarak sumber air, tekstur tanah dan indeks ketersediaan air bagi tanaman dengan menggunakan metode penginderaan jauh dan GIS. Indeks bahaya kekeringan agro-hidrologi yang telah dikembangkan adalah Ibk= (0.33CH) + (0.27KAT) + (0.20SA) + (0.13T) + (0.0WSVI) dengan hasil validasi model menunjukkan kemiripan yang tinggi kekeringan di lapangan.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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