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Record W4313046693 · doi:10.56189/jagris.v2i1.27546

KARATERISTIK MORFOLOGI TANAH DARI BERBAGAI BAHAN INDUK DI DESA WAKOILA KECAMATAN SAWERIGADI, KABUPATEN MUNA BARAT

2022· article· en· W4313046693 on OpenAlexaff
NETTI RIDHA, M. TUFAILA HEMON, Namriah Namriah, ZULFIKAR ZULFIKAR, La Ode Rustam, Syamsu Alam

Bibliographic record

VenueJurnal Berkala Ilmu-Ilmu Pertanian (Journal of Agricultural Sciences) · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsAlluviumSoil waterHorizonSoil scienceParent rockGeologySoil textureSoil horizonTexture (cosmology)MineralogySoil morphologySoil testSoil classificationGeochemistryGeomorphologyMathematics

Abstract

fetched live from OpenAlex

Characteristics of the soil formed are influenced by the parent rock as the original material. Different parent rocks have different physical, chemical, and mineral compositions that affect the different characteristics of the soil formed. This study aims to determine the soil morphology characteristics of various parent materials in Wakoila Village, Sawerigadi District, West Muna Regency. This research was conducted by survey method and field analysis of three parent materials. In the three parent materials, the external characteristics and internal characteristics of the profile were observed. Based on the results of the study showed that the morphological characteristics of the soil from the parent material of gravel (PI), alluvium deposits (PII) and limestone (PIII) have various soil morphological characteristics, both soil color, soil solum depth, horizon thickness, horizon symbol, soil texture, soil structure, soil consistency and soil pores. Soils made from gravel are characterized by a coarser soil texture, whereas soils from limestone tend to have a higher clay content. Meanwhile, soils made from alluvium deposits are characterized by darker soil colors and tend to have more pores.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.192
Teacher spread0.183 · 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.

Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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